class: title, self-paced Deploying and Scaling Microservices<br/>with Docker and Kubernetes<br/> .nav[*Self-paced version*] .debug[ ``` ``` These slides have been built from commit: 73b0676 [shared/title.md](https://github.com/barpilot/container.training/tree/master/slides/shared/title.md)] --- class: title, in-person Deploying and Scaling Microservices<br/>with Docker and Kubernetes<br/><br/></br> .footnote[ **Slides[:](https://www.youtube.com/watch?v=h16zyxiwDLY) https://container.training.barpilot.io/** ] <!-- WiFi: **Something**<br/> Password: **Something** **Be kind to the WiFi!**<br/> *Use the 5G network.* *Don't use your hotspot.*<br/> *Don't stream videos or download big files during the workshop*<br/> *Thank you!* --> .debug[[shared/title.md](https://github.com/barpilot/container.training/tree/master/slides/shared/title.md)] --- ## A brief introduction - This was initially written by [Jérôme Petazzoni](https://twitter.com/jpetazzo) to support in-person, instructor-led workshops and tutorials - Credit is also due to [multiple contributors](https://github.com/barpilot/container.training/graphs/contributors) — thank you! - You can also follow along on your own, at your own pace - We included as much information as possible in these slides - We recommend having a mentor to help you ... - ... Or be comfortable spending some time reading the Kubernetes [documentation](https://kubernetes.io/docs/) ... - ... And looking for answers on [StackOverflow](http://stackoverflow.com/questions/tagged/kubernetes) and other outlets .debug[[k8s/intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/intro.md)] --- class: self-paced ## Hands on, you shall practice - Nobody ever became a Jedi by spending their lives reading Wookiepedia - Likewise, it will take more than merely *reading* these slides to make you an expert - These slides include *tons* of exercises and examples - They assume that you have access to a Kubernetes cluster - If you are attending a workshop or tutorial: <br/>you will be given specific instructions to access your cluster - If you are doing this on your own: <br/>the first chapter will give you various options to get your own cluster .debug[[k8s/intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/intro.md)] --- ## Accessing these slides now - We recommend that you open these slides in your browser: https://container.training.barpilot.io/ - Use arrows to move to next/previous slide (up, down, left, right, page up, page down) - Type a slide number + ENTER to go to that slide - The slide number is also visible in the URL bar (e.g. .../#123 for slide 123) .debug[[shared/about-slides.md](https://github.com/barpilot/container.training/tree/master/slides/shared/about-slides.md)] --- ## Accessing these slides later - Slides will remain online so you can review them later if needed (let's say we'll keep them online at least 1 year, how about that?) - You can download the slides using that URL: https://container.training.barpilot.io/slides.zip (then open the file `kube-full.yml.html`) - You will find new versions of these slides on: https://container.training/ .debug[[shared/about-slides.md](https://github.com/barpilot/container.training/tree/master/slides/shared/about-slides.md)] --- ## These slides are open source - You are welcome to use, re-use, share these slides - These slides are written in markdown - The sources of these slides are available in a public GitHub repository: https://github.com/barpilot/container.training - Typos? Mistakes? Questions? Feel free to hover over the bottom of the slide ... .footnote[👇 Try it! The source file will be shown and you can view it on GitHub and fork and edit it.] <!-- .exercise[ ```open https://github.com/barpilot/container.training/tree/master/slides/common/about-slides.md``` ] --> .debug[[shared/about-slides.md](https://github.com/barpilot/container.training/tree/master/slides/shared/about-slides.md)] --- class: extra-details ## Extra details - This slide has a little magnifying glass in the top left corner - This magnifying glass indicates slides that provide extra details - Feel free to skip them if: - you are in a hurry - you are new to this and want to avoid cognitive overload - you want only the most essential information - You can review these slides another time if you want, they'll be waiting for you ☺ .debug[[shared/about-slides.md](https://github.com/barpilot/container.training/tree/master/slides/shared/about-slides.md)] --- name: toc-part-1 ## Part 1 - [Pre-requirements](#toc-pre-requirements) - [Our sample application](#toc-our-sample-application) - [Kubernetes concepts](#toc-kubernetes-concepts) .debug[(auto-generated TOC)] --- name: toc-part-2 ## Part 2 - [First contact with `kubectl`](#toc-first-contact-with-kubectl) - [Running our first containers on Kubernetes](#toc-running-our-first-containers-on-kubernetes) - [Executing batch jobs](#toc-executing-batch-jobs) - [Labels and annotations](#toc-labels-and-annotations) - [Revisiting `kubectl logs`](#toc-revisiting-kubectl-logs) - [Accessing logs from the CLI](#toc-accessing-logs-from-the-cli) - [Declarative vs imperative](#toc-declarative-vs-imperative) .debug[(auto-generated TOC)] --- name: toc-part-3 ## Part 3 - [Kubernetes network model](#toc-kubernetes-network-model) - [Exposing containers](#toc-exposing-containers) - [Shipping images with a registry](#toc-shipping-images-with-a-registry) - [Running our application on Kubernetes](#toc-running-our-application-on-kubernetes) - [Exercise — deploying on Kubernetes](#toc-exercise--deploying-on-kubernetes) - [Deploying with YAML](#toc-deploying-with-yaml) .debug[(auto-generated TOC)] --- name: toc-part-4 ## Part 4 - [Setting up Kubernetes](#toc-setting-up-kubernetes) - [Running a local development cluster](#toc-running-a-local-development-cluster) - [Deploying a managed cluster](#toc-deploying-a-managed-cluster) - [Kubernetes distributions and installers](#toc-kubernetes-distributions-and-installers) - [The Kubernetes dashboard](#toc-the-kubernetes-dashboard) - [Security implications of `kubectl apply`](#toc-security-implications-of-kubectl-apply) - [k9s](#toc-ks) - [Tilt](#toc-tilt) - [Scaling a deployment](#toc-scaling-a-deployment) - [Scaling our demo app](#toc-scaling-our-demo-app) - [Daemon sets](#toc-daemon-sets) - [Labels and selectors](#toc-labels-and-selectors) - [Authoring YAML](#toc-authoring-yaml) - [Exercise — writing YAML](#toc-exercise--writing-yaml) .debug[(auto-generated TOC)] --- name: toc-part-5 ## Part 5 - [Controlling a Kubernetes cluster remotely](#toc-controlling-a-kubernetes-cluster-remotely) - [Accessing internal services](#toc-accessing-internal-services) - [Accessing the API with `kubectl proxy`](#toc-accessing-the-api-with-kubectl-proxy) - [Rolling updates](#toc-rolling-updates) - [Healthchecks](#toc-healthchecks) - [Recording deployment actions](#toc-recording-deployment-actions) .debug[(auto-generated TOC)] --- name: toc-part-6 ## Part 6 - [Namespaces](#toc-namespaces) - [Exposing HTTP services with Ingress resources](#toc-exposing-http-services-with-ingress-resources) - [Ingress and TLS certificates](#toc-ingress-and-tls-certificates) - [cert-manager](#toc-cert-manager) - [Kustomize](#toc-kustomize) - [Managing stacks with Helm](#toc-managing-stacks-with-helm) - [Helm chart format](#toc-helm-chart-format) - [Creating a basic chart](#toc-creating-a-basic-chart) - [Creating better Helm charts](#toc-creating-better-helm-charts) - [Helm secrets](#toc-helm-secrets) - [Exercise — Helm charts](#toc-exercise--helm-charts) - [CI/CD with GitLab](#toc-cicd-with-gitlab) .debug[(auto-generated TOC)] --- name: toc-part-7 ## Part 7 - [Network policies](#toc-network-policies) - [Authentication and authorization](#toc-authentication-and-authorization) - [The CSR API](#toc-the-csr-api) - [OpenID Connect](#toc-openid-connect) - [Pod Security Policies](#toc-pod-security-policies) - [Securing the control plane](#toc-securing-the-control-plane) .debug[(auto-generated TOC)] --- name: toc-part-8 ## Part 8 - [Volumes](#toc-volumes) - [Exercise — ConfigMaps](#toc-exercise--configmaps) - [Building images with the Docker Engine](#toc-building-images-with-the-docker-engine) - [Building images with Kaniko](#toc-building-images-with-kaniko) .debug[(auto-generated TOC)] --- name: toc-part-9 ## Part 9 - [Managing configuration](#toc-managing-configuration) - [Managing secrets](#toc-managing-secrets) - [Stateful sets](#toc-stateful-sets) - [Running a Consul cluster](#toc-running-a-consul-cluster) - [Persistent Volumes Claims](#toc-persistent-volumes-claims) - [Local Persistent Volumes](#toc-local-persistent-volumes) - [OpenEBS ](#toc-openebs-) .debug[(auto-generated TOC)] --- name: toc-part-10 ## Part 10 - [Centralized logging](#toc-centralized-logging) - [Collecting metrics with Prometheus](#toc-collecting-metrics-with-prometheus) - [Prometheus and Grafana](#toc-prometheus-and-grafana) - [Resource Limits](#toc-resource-limits) - [Defining min, max, and default resources](#toc-defining-min-max-and-default-resources) - [Namespace quotas](#toc-namespace-quotas) - [Limiting resources in practice](#toc-limiting-resources-in-practice) - [Checking Node and Pod resource usage](#toc-checking-node-and-pod-resource-usage) - [Cluster sizing](#toc-cluster-sizing) - [The Horizontal Pod Autoscaler](#toc-the-horizontal-pod-autoscaler) .debug[(auto-generated TOC)] --- name: toc-part-11 ## Part 11 - [Extending the Kubernetes API](#toc-extending-the-kubernetes-api) - [API server internals](#toc-api-server-internals) - [Custom Resource Definitions](#toc-custom-resource-definitions) - [The Aggregation Layer](#toc-the-aggregation-layer) - [Dynamic Admission Control](#toc-dynamic-admission-control) - [Operators](#toc-operators) - [Designing an operator](#toc-designing-an-operator) - [Kubebuilder](#toc-kubebuilder) - [Sealed Secrets](#toc-sealed-secrets) - [Exercise — sealed secrets](#toc-exercise--sealed-secrets) - [Policy Management with Kyverno](#toc-policy-management-with-kyverno) - [An ElasticSearch Operator](#toc-an-elasticsearch-operator) - [Finalizers](#toc-finalizers) - [Owners and dependents](#toc-owners-and-dependents) .debug[(auto-generated TOC)] --- name: toc-part-12 ## Part 12 - [Building our own cluster](#toc-building-our-own-cluster) - [Adding nodes to the cluster](#toc-adding-nodes-to-the-cluster) - [The Container Network Interface](#toc-the-container-network-interface) - [CNI internals](#toc-cni-internals) - [API server availability](#toc-api-server-availability) - [Static pods](#toc-static-pods) .debug[(auto-generated TOC)] --- name: toc-part-13 ## Part 13 - [Upgrading clusters](#toc-upgrading-clusters) - [Backing up clusters](#toc-backing-up-clusters) - [The Cloud Controller Manager](#toc-the-cloud-controller-manager) - [Git-based workflows](#toc-git-based-workflows) .debug[(auto-generated TOC)] --- name: toc-part-14 ## Part 14 - [Next steps](#toc-next-steps) - [Last words](#toc-last-words) - [Links and resources](#toc-links-and-resources) .debug[(auto-generated TOC)] .debug[[shared/toc.md](https://github.com/barpilot/container.training/tree/master/slides/shared/toc.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/Container-Ship-Freighter-Navigation-Elbe-Romance-1782991.jpg)] --- name: toc-pre-requirements class: title Pre-requirements .nav[ [Previous part](#toc-) | [Back to table of contents](#toc-part-1) | [Next part](#toc-our-sample-application) ] .debug[(automatically generated title slide)] --- # Pre-requirements - Be comfortable with the UNIX command line - navigating directories - editing files - a little bit of bash-fu (environment variables, loops) - Some Docker knowledge - `docker run`, `docker ps`, `docker build` - ideally, you know how to write a Dockerfile and build it <br/> (even if it's a `FROM` line and a couple of `RUN` commands) - It's totally OK if you are not a Docker expert! .debug[[shared/prereqs.md](https://github.com/barpilot/container.training/tree/master/slides/shared/prereqs.md)] --- class: title *Tell me and I forget.* <br/> *Teach me and I remember.* <br/> *Involve me and I learn.* Misattributed to Benjamin Franklin [(Probably inspired by Chinese Confucian philosopher Xunzi)](https://www.barrypopik.com/index.php/new_york_city/entry/tell_me_and_i_forget_teach_me_and_i_may_remember_involve_me_and_i_will_lear/) .debug[[shared/prereqs.md](https://github.com/barpilot/container.training/tree/master/slides/shared/prereqs.md)] --- ## Hands-on sections - The whole workshop is hands-on - We are going to build, ship, and run containers! - You are invited to reproduce all the demos - All hands-on sections are clearly identified, like the gray rectangle below .exercise[ - This is the stuff you're supposed to do! - Go to https://container.training.barpilot.io/ to view these slides <!-- ```open https://container.training.barpilot.io/``` --> ] .debug[[shared/prereqs.md](https://github.com/barpilot/container.training/tree/master/slides/shared/prereqs.md)] --- class: in-person ## Where are we going to run our containers? .debug[[shared/prereqs.md](https://github.com/barpilot/container.training/tree/master/slides/shared/prereqs.md)] --- class: in-person, pic ![You get a cluster](images/you-get-a-cluster.jpg) .debug[[shared/prereqs.md](https://github.com/barpilot/container.training/tree/master/slides/shared/prereqs.md)] --- class: in-person ## You get a cluster of cloud VMs - Each person gets a private cluster of cloud VMs (not shared with anybody else) - They'll remain up for the duration of the workshop - You should have a little card with login+password+IP addresses - You can automatically SSH from one VM to another - The nodes have aliases: `node1`, `node2`, etc. .debug[[shared/prereqs.md](https://github.com/barpilot/container.training/tree/master/slides/shared/prereqs.md)] --- class: in-person ## Why don't we run containers locally? - Installing this stuff can be hard on some machines (32 bits CPU or OS... Laptops without administrator access... etc.) - *"The whole team downloaded all these container images from the WiFi! <br/>... and it went great!"* (Literally no-one ever) - All you need is a computer (or even a phone or tablet!), with: - an internet connection - a web browser - an SSH client .debug[[shared/prereqs.md](https://github.com/barpilot/container.training/tree/master/slides/shared/prereqs.md)] --- class: in-person ## SSH clients - On Linux, OS X, FreeBSD... you are probably all set - On Windows, get one of these: - [putty](http://www.putty.org/) - Microsoft [Win32 OpenSSH](https://github.com/PowerShell/Win32-OpenSSH/wiki/Install-Win32-OpenSSH) - [Git BASH](https://git-for-windows.github.io/) - [MobaXterm](http://mobaxterm.mobatek.net/) - On Android, [JuiceSSH](https://juicessh.com/) ([Play Store](https://play.google.com/store/apps/details?id=com.sonelli.juicessh)) works pretty well - Nice-to-have: [Mosh](https://mosh.org/) instead of SSH, if your internet connection tends to lose packets .debug[[shared/prereqs.md](https://github.com/barpilot/container.training/tree/master/slides/shared/prereqs.md)] --- class: in-person, extra-details ## What is this Mosh thing? *You don't have to use Mosh or even know about it to follow along. <br/> We're just telling you about it because some of us think it's cool!* - Mosh is "the mobile shell" - It is essentially SSH over UDP, with roaming features - It retransmits packets quickly, so it works great even on lossy connections (Like hotel or conference WiFi) - It has intelligent local echo, so it works great even in high-latency connections (Like hotel or conference WiFi) - It supports transparent roaming when your client IP address changes (Like when you hop from hotel to conference WiFi) .debug[[shared/prereqs.md](https://github.com/barpilot/container.training/tree/master/slides/shared/prereqs.md)] --- class: in-person, extra-details ## Using Mosh - To install it: `(apt|yum|brew) install mosh` - It has been pre-installed on the VMs that we are using - To connect to a remote machine: `mosh user@host` (It is going to establish an SSH connection, then hand off to UDP) - It requires UDP ports to be open (By default, it uses a UDP port between 60000 and 61000) .debug[[shared/prereqs.md](https://github.com/barpilot/container.training/tree/master/slides/shared/prereqs.md)] --- class: in-person ## Connecting to our lab environment .exercise[ - Log into the first VM (`node1`) with your SSH client: ```bash ssh `user`@`A.B.C.D` ``` (Replace `user` and `A.B.C.D` with the user and IP address provided to you) <!-- ```bash for N in $(awk '/\Wnode/{print $2}' /etc/hosts); do ssh -o StrictHostKeyChecking=no $N true done ``` ```bash ### FIXME find a way to reset the cluster, maybe? ``` --> ] You should see a prompt looking like this: ``` [A.B.C.D] (...) user@node1 ~ $ ``` If anything goes wrong — ask for help! .debug[[shared/connecting.md](https://github.com/barpilot/container.training/tree/master/slides/shared/connecting.md)] --- class: in-person ## `tailhist` - The shell history of the instructor is available online in real time - Note the IP address of the instructor's virtual machine (A.B.C.D) - Open http://A.B.C.D:1088 in your browser and you should see the history - The history is updated in real time (using a WebSocket connection) - It should be green when the WebSocket is connected (if it turns red, reloading the page should fix it) .debug[[shared/connecting.md](https://github.com/barpilot/container.training/tree/master/slides/shared/connecting.md)] --- ## Doing or re-doing the workshop on your own? - Use something like [Play-With-Docker](http://play-with-docker.com/) or [Play-With-Kubernetes](https://training.play-with-kubernetes.com/) Zero setup effort; but environment are short-lived and might have limited resources - Create your own cluster (local or cloud VMs) Small setup effort; small cost; flexible environments - Create a bunch of clusters for you and your friends ([instructions](https://github.com/barpilot/container.training/tree/master/prepare-vms)) Bigger setup effort; ideal for group training .debug[[shared/connecting.md](https://github.com/barpilot/container.training/tree/master/slides/shared/connecting.md)] --- ## For a consistent Kubernetes experience ... - If you are using your own Kubernetes cluster, you can use [shpod](https://github.com/jpetazzo/shpod) - `shpod` provides a shell running in a pod on your own cluster - It comes with many tools pre-installed (helm, stern...) - These tools are used in many exercises in these slides - `shpod` also gives you completion and a fancy prompt .debug[[shared/connecting.md](https://github.com/barpilot/container.training/tree/master/slides/shared/connecting.md)] --- class: self-paced ## Get your own Docker nodes - If you already have some Docker nodes: great! - If not: let's get some thanks to Play-With-Docker .exercise[ - Go to http://www.play-with-docker.com/ - Log in - Create your first node <!-- ```open http://www.play-with-docker.com/``` --> ] You will need a Docker ID to use Play-With-Docker. (Creating a Docker ID is free.) .debug[[shared/connecting.md](https://github.com/barpilot/container.training/tree/master/slides/shared/connecting.md)] --- ## We will (mostly) interact with node1 only *These remarks apply only when using multiple nodes, of course.* - Unless instructed, **all commands must be run from the first VM, `node1`** - We will only check out/copy the code on `node1` - During normal operations, we do not need access to the other nodes - If we had to troubleshoot issues, we would use a combination of: - SSH (to access system logs, daemon status...) - Docker API (to check running containers and container engine status) .debug[[shared/connecting.md](https://github.com/barpilot/container.training/tree/master/slides/shared/connecting.md)] --- ## Terminals Once in a while, the instructions will say: <br/>"Open a new terminal." There are multiple ways to do this: - create a new window or tab on your machine, and SSH into the VM; - use screen or tmux on the VM and open a new window from there. You are welcome to use the method that you feel the most comfortable with. .debug[[shared/connecting.md](https://github.com/barpilot/container.training/tree/master/slides/shared/connecting.md)] --- ## Tmux cheatsheet [Tmux](https://en.wikipedia.org/wiki/Tmux) is a terminal multiplexer like `screen`. *You don't have to use it or even know about it to follow along. <br/> But some of us like to use it to switch between terminals. <br/> It has been preinstalled on your workshop nodes.* - Ctrl-b c → creates a new window - Ctrl-b n → go to next window - Ctrl-b p → go to previous window - Ctrl-b " → split window top/bottom - Ctrl-b % → split window left/right - Ctrl-b Alt-1 → rearrange windows in columns - Ctrl-b Alt-2 → rearrange windows in rows - Ctrl-b arrows → navigate to other windows - Ctrl-b d → detach session - tmux attach → reattach to session .debug[[shared/connecting.md](https://github.com/barpilot/container.training/tree/master/slides/shared/connecting.md)] --- ## Versions installed - Kubernetes 1.19.2 - Docker Engine 19.03.13 - Docker Compose 1.25.4 <!-- ##VERSION## --> .exercise[ - Check all installed versions: ```bash kubectl version docker version docker-compose -v ``` ] .debug[[k8s/versions-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/versions-k8s.md)] --- class: extra-details ## Kubernetes and Docker compatibility - Kubernetes 1.17 validates Docker Engine version [up to 19.03](https://github.com/kubernetes/kubernetes/pull/84476) *however ...* - Kubernetes 1.15 validates Docker Engine versions [up to 18.09](https://github.com/kubernetes/kubernetes/blob/master/CHANGELOG/CHANGELOG-1.15.md#dependencies) <br/> (the latest version when Kubernetes 1.14 was released) - Kubernetes 1.13 only validates Docker Engine versions [up to 18.06](https://github.com/kubernetes/kubernetes/blob/master/CHANGELOG/CHANGELOG-1.13.md#external-dependencies) - Is it a problem if I use Kubernetes with a "too recent" Docker Engine? -- class: extra-details - No! - "Validates" = continuous integration builds with very extensive (and expensive) testing - The Docker API is versioned, and offers strong backward-compatibility <br/> (if a client uses e.g. API v1.25, the Docker Engine will keep behaving the same way) .debug[[k8s/versions-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/versions-k8s.md)] --- ## Kubernetes versioning and cadence - Kubernetes versions are expressed using *semantic versioning* (a Kubernetes version is expressed as MAJOR.MINOR.PATCH) - There is a new *patch* release whenever needed (generally, there is about [2 to 4 weeks](https://github.com/kubernetes/sig-release/blob/master/release-engineering/role-handbooks/patch-release-team.md#release-timing) between patch releases, except when a critical bug or vulnerability is found: in that case, a patch release will follow as fast as possible) - There is a new *minor* release approximately every 3 months - At any given time, 3 *minor* releases are maintained (in other words, a given *minor* release is maintained about 9 months) .debug[[k8s/versions-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/versions-k8s.md)] --- ## Kubernetes version compatibility *Should my version of `kubectl` match exactly my cluster version?* - `kubectl` can be up to one minor version older or newer than the cluster (if cluster version is 1.15.X, `kubectl` can be 1.14.Y, 1.15.Y, or 1.16.Y) - Things *might* work with larger version differences (but they will probably fail randomly, so be careful) - This is an example of an error indicating version compability issues: ``` error: SchemaError(io.k8s.api.autoscaling.v2beta1.ExternalMetricStatus): invalid object doesn't have additional properties ``` - Check [the documentation](https://kubernetes.io/docs/setup/release/version-skew-policy/#kubectl) for the whole story about compatibility ??? :EN:- Kubernetes versioning and compatibility :FR:- Les versions de Kubernetes et leur compatibilité .debug[[k8s/versions-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/versions-k8s.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)] --- name: toc-our-sample-application class: title Our sample application .nav[ [Previous part](#toc-pre-requirements) | [Back to table of contents](#toc-part-1) | [Next part](#toc-kubernetes-concepts) ] .debug[(automatically generated title slide)] --- # Our sample application - We will clone the GitHub repository onto our `node1` - The repository also contains scripts and tools that we will use through the workshop .exercise[ <!-- ```bash cd ~ if [ -d container.training ]; then mv container.training container.training.$RANDOM fi ``` --> - Clone the repository on `node1`: ```bash git clone https://github.com/barpilot/container.training ``` ] (You can also fork the repository on GitHub and clone your fork if you prefer that.) .debug[[shared/sampleapp.md](https://github.com/barpilot/container.training/tree/master/slides/shared/sampleapp.md)] --- ## Downloading and running the application Let's start this before we look around, as downloading will take a little time... .exercise[ - Go to the `dockercoins` directory, in the cloned repo: ```bash cd ~/container.training/dockercoins ``` - Use Compose to build and run all containers: ```bash docker-compose up ``` <!-- ```longwait units of work done``` --> ] Compose tells Docker to build all container images (pulling the corresponding base images), then starts all containers, and displays aggregated logs. .debug[[shared/sampleapp.md](https://github.com/barpilot/container.training/tree/master/slides/shared/sampleapp.md)] --- ## What's this application? -- - It is a DockerCoin miner! 💰🐳📦🚢 -- - No, you can't buy coffee with DockerCoins -- - How DockerCoins works: - generate a few random bytes - hash these bytes - increment a counter (to keep track of speed) - repeat forever! -- - DockerCoins is *not* a cryptocurrency (the only common points are "randomness," "hashing," and "coins" in the name) .debug[[shared/sampleapp.md](https://github.com/barpilot/container.training/tree/master/slides/shared/sampleapp.md)] --- ## DockerCoins in the microservices era - DockerCoins is made of 5 services: - `rng` = web service generating random bytes - `hasher` = web service computing hash of POSTed data - `worker` = background process calling `rng` and `hasher` - `webui` = web interface to watch progress - `redis` = data store (holds a counter updated by `worker`) - These 5 services are visible in the application's Compose file, [docker-compose.yml]( https://github.com/barpilot/container.training/blob/master/dockercoins/docker-compose.yml) .debug[[shared/sampleapp.md](https://github.com/barpilot/container.training/tree/master/slides/shared/sampleapp.md)] --- ## How DockerCoins works - `worker` invokes web service `rng` to generate random bytes - `worker` invokes web service `hasher` to hash these bytes - `worker` does this in an infinite loop - every second, `worker` updates `redis` to indicate how many loops were done - `webui` queries `redis`, and computes and exposes "hashing speed" in our browser *(See diagram on next slide!)* .debug[[shared/sampleapp.md](https://github.com/barpilot/container.training/tree/master/slides/shared/sampleapp.md)] --- class: pic ![Diagram showing the 5 containers of the applications](images/dockercoins-diagram.svg) .debug[[shared/sampleapp.md](https://github.com/barpilot/container.training/tree/master/slides/shared/sampleapp.md)] --- ## Service discovery in container-land How does each service find out the address of the other ones? -- - We do not hard-code IP addresses in the code - We do not hard-code FQDNs in the code, either - We just connect to a service name, and container-magic does the rest (And by container-magic, we mean "a crafty, dynamic, embedded DNS server") .debug[[shared/sampleapp.md](https://github.com/barpilot/container.training/tree/master/slides/shared/sampleapp.md)] --- ## Example in `worker/worker.py` ```python redis = Redis("`redis`") def get_random_bytes(): r = requests.get("http://`rng`/32") return r.content def hash_bytes(data): r = requests.post("http://`hasher`/", data=data, headers={"Content-Type": "application/octet-stream"}) ``` (Full source code available [here]( https://github.com/barpilot/container.training/blob/8279a3bce9398f7c1a53bdd95187c53eda4e6435/dockercoins/worker/worker.py#L17 )) .debug[[shared/sampleapp.md](https://github.com/barpilot/container.training/tree/master/slides/shared/sampleapp.md)] --- class: extra-details ## Links, naming, and service discovery - Containers can have network aliases (resolvable through DNS) - Compose file version 2+ makes each container reachable through its service name - Compose file version 1 required "links" sections to accomplish this - Network aliases are automatically namespaced - you can have multiple apps declaring and using a service named `database` - containers in the blue app will resolve `database` to the IP of the blue database - containers in the green app will resolve `database` to the IP of the green database .debug[[shared/sampleapp.md](https://github.com/barpilot/container.training/tree/master/slides/shared/sampleapp.md)] --- ## Show me the code! - You can check the GitHub repository with all the materials of this workshop: <br/>https://github.com/barpilot/container.training - The application is in the [dockercoins]( https://github.com/barpilot/container.training/tree/master/dockercoins) subdirectory - The Compose file ([docker-compose.yml]( https://github.com/barpilot/container.training/blob/master/dockercoins/docker-compose.yml)) lists all 5 services - `redis` is using an official image from the Docker Hub - `hasher`, `rng`, `worker`, `webui` are each built from a Dockerfile - Each service's Dockerfile and source code is in its own directory (`hasher` is in the [hasher](https://github.com/barpilot/container.training/blob/master/dockercoins/hasher/) directory, `rng` is in the [rng](https://github.com/barpilot/container.training/blob/master/dockercoins/rng/) directory, etc.) .debug[[shared/sampleapp.md](https://github.com/barpilot/container.training/tree/master/slides/shared/sampleapp.md)] --- class: extra-details ## Compose file format version *This is relevant only if you have used Compose before 2016...* - Compose 1.6 introduced support for a new Compose file format (aka "v2") - Services are no longer at the top level, but under a `services` section - There has to be a `version` key at the top level, with value `"2"` (as a string, not an integer) - Containers are placed on a dedicated network, making links unnecessary - There are other minor differences, but upgrade is easy and straightforward .debug[[shared/sampleapp.md](https://github.com/barpilot/container.training/tree/master/slides/shared/sampleapp.md)] --- ## Our application at work - On the left-hand side, the "rainbow strip" shows the container names - On the right-hand side, we see the output of our containers - We can see the `worker` service making requests to `rng` and `hasher` - For `rng` and `hasher`, we see HTTP access logs .debug[[shared/sampleapp.md](https://github.com/barpilot/container.training/tree/master/slides/shared/sampleapp.md)] --- ## Connecting to the web UI - "Logs are exciting and fun!" (No-one, ever) - The `webui` container exposes a web dashboard; let's view it .exercise[ - With a web browser, connect to `node1` on port 8000 - Remember: the `nodeX` aliases are valid only on the nodes themselves - In your browser, you need to enter the IP address of your node <!-- ```open http://node1:8000``` --> ] A drawing area should show up, and after a few seconds, a blue graph will appear. .debug[[shared/sampleapp.md](https://github.com/barpilot/container.training/tree/master/slides/shared/sampleapp.md)] --- class: self-paced, extra-details ## If the graph doesn't load If you just see a `Page not found` error, it might be because your Docker Engine is running on a different machine. This can be the case if: - you are using the Docker Toolbox - you are using a VM (local or remote) created with Docker Machine - you are controlling a remote Docker Engine When you run DockerCoins in development mode, the web UI static files are mapped to the container using a volume. Alas, volumes can only work on a local environment, or when using Docker Desktop for Mac or Windows. How to fix this? Stop the app with `^C`, edit `dockercoins.yml`, comment out the `volumes` section, and try again. .debug[[shared/sampleapp.md](https://github.com/barpilot/container.training/tree/master/slides/shared/sampleapp.md)] --- class: extra-details ## Why does the speed seem irregular? - It *looks like* the speed is approximately 4 hashes/second - Or more precisely: 4 hashes/second, with regular dips down to zero - Why? -- class: extra-details - The app actually has a constant, steady speed: 3.33 hashes/second <br/> (which corresponds to 1 hash every 0.3 seconds, for *reasons*) - Yes, and? .debug[[shared/sampleapp.md](https://github.com/barpilot/container.training/tree/master/slides/shared/sampleapp.md)] --- class: extra-details ## The reason why this graph is *not awesome* - The worker doesn't update the counter after every loop, but up to once per second - The speed is computed by the browser, checking the counter about once per second - Between two consecutive updates, the counter will increase either by 4, or by 0 - The perceived speed will therefore be 4 - 4 - 4 - 0 - 4 - 4 - 0 etc. - What can we conclude from this? -- class: extra-details - "I'm clearly incapable of writing good frontend code!" 😀 — Jérôme .debug[[shared/sampleapp.md](https://github.com/barpilot/container.training/tree/master/slides/shared/sampleapp.md)] --- ## Stopping the application - If we interrupt Compose (with `^C`), it will politely ask the Docker Engine to stop the app - The Docker Engine will send a `TERM` signal to the containers - If the containers do not exit in a timely manner, the Engine sends a `KILL` signal .exercise[ - Stop the application by hitting `^C` <!-- ```key ^C``` --> ] -- Some containers exit immediately, others take longer. The containers that do not handle `SIGTERM` end up being killed after a 10s timeout. If we are very impatient, we can hit `^C` a second time! .debug[[shared/sampleapp.md](https://github.com/barpilot/container.training/tree/master/slides/shared/sampleapp.md)] --- ## Clean up - Before moving on, let's remove those containers .exercise[ - Tell Compose to remove everything: ```bash docker-compose down ``` ] .debug[[shared/composedown.md](https://github.com/barpilot/container.training/tree/master/slides/shared/composedown.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/aerial-view-of-containers.jpg)] --- name: toc-kubernetes-concepts class: title Kubernetes concepts .nav[ [Previous part](#toc-our-sample-application) | [Back to table of contents](#toc-part-1) | [Next part](#toc-first-contact-with-kubectl) ] .debug[(automatically generated title slide)] --- # Kubernetes concepts - Kubernetes is a container management system - It runs and manages containerized applications on a cluster -- - What does that really mean? .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- ## What can we do with Kubernetes? - Let's imagine that we have a 3-tier e-commerce app: - web frontend - API backend - database (that we will keep out of Kubernetes for now) - We have built images for our frontend and backend components (e.g. with Dockerfiles and `docker build`) - We are running them successfully with a local environment (e.g. with Docker Compose) - Let's see how we would deploy our app on Kubernetes! .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- ## Basic things we can ask Kubernetes to do -- - Start 5 containers using image `atseashop/api:v1.3` -- - Place an internal load balancer in front of these containers -- - Start 10 containers using image `atseashop/webfront:v1.3` -- - Place a public load balancer in front of these containers -- - It's Black Friday (or Christmas), traffic spikes, grow our cluster and add containers -- - New release! Replace my containers with the new image `atseashop/webfront:v1.4` -- - Keep processing requests during the upgrade; update my containers one at a time .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- ## Other things that Kubernetes can do for us - Autoscaling (straightforward on CPU; more complex on other metrics) - Resource management and scheduling (reserve CPU/RAM for containers; placement constraints) - Advanced rollout patterns (blue/green deployment, canary deployment) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- ## More things that Kubernetes can do for us - Batch jobs (one-off; parallel; also cron-style periodic execution) - Fine-grained access control (defining *what* can be done by *whom* on *which* resources) - Stateful services (databases, message queues, etc.) - Automating complex tasks with *operators* (e.g. database replication, failover, etc.) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- ## Kubernetes architecture .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- class: pic ![haha only kidding](images/k8s-arch1.png) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- ## Kubernetes architecture - Ha ha ha ha - OK, I was trying to scare you, it's much simpler than that ❤️ .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- class: pic ![that one is more like the real thing](images/k8s-arch2.png) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- ## Credits - The first schema is a Kubernetes cluster with storage backed by multi-path iSCSI (Courtesy of [Yongbok Kim](https://www.yongbok.net/blog/)) - The second one is a simplified representation of a Kubernetes cluster (Courtesy of [Imesh Gunaratne](https://medium.com/containermind/a-reference-architecture-for-deploying-wso2-middleware-on-kubernetes-d4dee7601e8e)) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- ## Kubernetes architecture: the nodes - The nodes executing our containers run a collection of services: - a container Engine (typically Docker) - kubelet (the "node agent") - kube-proxy (a necessary but not sufficient network component) - Nodes were formerly called "minions" (You might see that word in older articles or documentation) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- ## Kubernetes architecture: the control plane - The Kubernetes logic (its "brains") is a collection of services: - the API server (our point of entry to everything!) - core services like the scheduler and controller manager - `etcd` (a highly available key/value store; the "database" of Kubernetes) - Together, these services form the control plane of our cluster - The control plane is also called the "master" .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- class: pic ![One of the best Kubernetes architecture diagrams available](images/k8s-arch4-thanks-luxas.png) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- class: extra-details ## Running the control plane on special nodes - It is common to reserve a dedicated node for the control plane (Except for single-node development clusters, like when using minikube) - This node is then called a "master" (Yes, this is ambiguous: is the "master" a node, or the whole control plane?) - Normal applications are restricted from running on this node (By using a mechanism called ["taints"](https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/)) - When high availability is required, each service of the control plane must be resilient - The control plane is then replicated on multiple nodes (This is sometimes called a "multi-master" setup) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- class: extra-details ## Running the control plane outside containers - The services of the control plane can run in or out of containers - For instance: since `etcd` is a critical service, some people deploy it directly on a dedicated cluster (without containers) (This is illustrated on the first "super complicated" schema) - In some hosted Kubernetes offerings (e.g. AKS, GKE, EKS), the control plane is invisible (We only "see" a Kubernetes API endpoint) - In that case, there is no "master node" *For this reason, it is more accurate to say "control plane" rather than "master."* .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- class: pic ![](images/control-planes/single-node-dev.svg) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- class: pic ![](images/control-planes/managed-kubernetes.svg) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- class: pic ![](images/control-planes/single-control-and-workers.svg) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- class: pic ![](images/control-planes/stacked-control-plane.svg) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- class: pic ![](images/control-planes/non-dedicated-stacked-nodes.svg) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- class: pic ![](images/control-planes/advanced-control-plane.svg) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- class: pic ![](images/control-planes/advanced-control-plane-split-events.svg) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- class: extra-details ## How many nodes should a cluster have? - There is no particular constraint (no need to have an odd number of nodes for quorum) - A cluster can have zero node (but then it won't be able to start any pods) - For testing and development, having a single node is fine - For production, make sure that you have extra capacity (so that your workload still fits if you lose a node or a group of nodes) - Kubernetes is tested with [up to 5000 nodes](https://kubernetes.io/docs/setup/best-practices/cluster-large/) (however, running a cluster of that size requires a lot of tuning) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- class: extra-details ## Do we need to run Docker at all? No! -- - By default, Kubernetes uses the Docker Engine to run containers - We can leverage other pluggable runtimes through the *Container Runtime Interface* - <del>We could also use `rkt` ("Rocket") from CoreOS</del> (deprecated) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- class: extra-details ## Some runtimes available through CRI - [containerd](https://github.com/containerd/containerd/blob/master/README.md) - maintained by Docker, IBM, and community - used by Docker Engine, microk8s, k3s, GKE; also standalone - comes with its own CLI, `ctr` - [CRI-O](https://github.com/cri-o/cri-o/blob/master/README.md): - maintained by Red Hat, SUSE, and community - used by OpenShift and Kubic - designed specifically as a minimal runtime for Kubernetes - [And more](https://kubernetes.io/docs/setup/production-environment/container-runtimes/) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- class: extra-details ## Do we need to run Docker at all? Yes! -- - In this workshop, we run our app on a single node first - We will need to build images and ship them around - We can do these things without Docker <br/> (and get diagnosed with NIH¹ syndrome) - Docker is still the most stable container engine today <br/> (but other options are maturing very quickly) .footnote[¹[Not Invented Here](https://en.wikipedia.org/wiki/Not_invented_here)] .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- class: extra-details ## Do we need to run Docker at all? - On our development environments, CI pipelines ... : *Yes, almost certainly* - On our production servers: *Yes (today)* *Probably not (in the future)* .footnote[More information about CRI [on the Kubernetes blog](https://kubernetes.io/blog/2016/12/container-runtime-interface-cri-in-kubernetes)] .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- ## Interacting with Kubernetes - We will interact with our Kubernetes cluster through the Kubernetes API - The Kubernetes API is (mostly) RESTful - It allows us to create, read, update, delete *resources* - A few common resource types are: - node (a machine — physical or virtual — in our cluster) - pod (group of containers running together on a node) - service (stable network endpoint to connect to one or multiple containers) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- class: pic ![Node, pod, container](images/k8s-arch3-thanks-weave.png) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- ## Scaling - How would we scale the pod shown on the previous slide? - **Do** create additional pods - each pod can be on a different node - each pod will have its own IP address - **Do not** add more NGINX containers in the pod - all the NGINX containers would be on the same node - they would all have the same IP address <br/>(resulting in `Address alreading in use` errors) .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- ## Together or separate - Should we put e.g. a web application server and a cache together? <br/> ("cache" being something like e.g. Memcached or Redis) - Putting them **in the same pod** means: - they have to be scaled together - they can communicate very efficiently over `localhost` - Putting them **in different pods** means: - they can be scaled separately - they must communicate over remote IP addresses <br/>(incurring more latency, lower performance) - Both scenarios can make sense, depending on our goals .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- ## Credits - The first diagram is courtesy of Lucas Käldström, in [this presentation](https://speakerdeck.com/luxas/kubeadm-cluster-creation-internals-from-self-hosting-to-upgradability-and-ha) - it's one of the best Kubernetes architecture diagrams available! - The second diagram is courtesy of Weave Works - a *pod* can have multiple containers working together - IP addresses are associated with *pods*, not with individual containers Both diagrams used with permission. ??? :EN:- Kubernetes concepts :FR:- Kubernetes en théorie .debug[[k8s/concepts-k8s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/concepts-k8s.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/blue-containers.jpg)] --- name: toc-first-contact-with-kubectl class: title First contact with `kubectl` .nav[ [Previous part](#toc-kubernetes-concepts) | [Back to table of contents](#toc-part-2) | [Next part](#toc-running-our-first-containers-on-kubernetes) ] .debug[(automatically generated title slide)] --- # First contact with `kubectl` - `kubectl` is (almost) the only tool we'll need to talk to Kubernetes - It is a rich CLI tool around the Kubernetes API (Everything you can do with `kubectl`, you can do directly with the API) - On our machines, there is a `~/.kube/config` file with: - the Kubernetes API address - the path to our TLS certificates used to authenticate - You can also use the `--kubeconfig` flag to pass a config file - Or directly `--server`, `--user`, etc. - `kubectl` can be pronounced "Cube C T L", "Cube cuttle", "Cube cuddle"... .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- class: extra-details ## `kubectl` is the new SSH - We often start managing servers with SSH (installing packages, troubleshooting ...) - At scale, it becomes tedious, repetitive, error-prone - Instead, we use config management, central logging, etc. - In many cases, we still need SSH: - as the underlying access method (e.g. Ansible) - to debug tricky scenarios - to inspect and poke at things .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- class: extra-details ## The parallel with `kubectl` - We often start managing Kubernetes clusters with `kubectl` (deploying applications, troubleshooting ...) - At scale (with many applications or clusters), it becomes tedious, repetitive, error-prone - Instead, we use automated pipelines, observability tooling, etc. - In many cases, we still need `kubectl`: - to debug tricky scenarios - to inspect and poke at things - The Kubernetes API is always the underlying access method .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- ## `kubectl get` - Let's look at our `Node` resources with `kubectl get`! .exercise[ - Look at the composition of our cluster: ```bash kubectl get node ``` - These commands are equivalent: ```bash kubectl get no kubectl get node kubectl get nodes ``` ] .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- ## Obtaining machine-readable output - `kubectl get` can output JSON, YAML, or be directly formatted .exercise[ - Give us more info about the nodes: ```bash kubectl get nodes -o wide ``` - Let's have some YAML: ```bash kubectl get no -o yaml ``` See that `kind: List` at the end? It's the type of our result! ] .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- ## (Ab)using `kubectl` and `jq` - It's super easy to build custom reports .exercise[ - Show the capacity of all our nodes as a stream of JSON objects: ```bash kubectl get nodes -o json | jq ".items[] | {name:.metadata.name} + .status.capacity" ``` ] .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- class: extra-details ## Exploring types and definitions - We can list all available resource types by running `kubectl api-resources` <br/> (In Kubernetes 1.10 and prior, this command used to be `kubectl get`) - We can view the definition for a resource type with: ```bash kubectl explain type ``` - We can view the definition of a field in a resource, for instance: ```bash kubectl explain node.spec ``` - Or get the full definition of all fields and sub-fields: ```bash kubectl explain node --recursive ``` .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- class: extra-details ## Introspection vs. documentation - We can access the same information by reading the [API documentation](https://kubernetes.io/docs/reference/#api-reference) - The API documentation is usually easier to read, but: - it won't show custom types (like Custom Resource Definitions) - we need to make sure that we look at the correct version - `kubectl api-resources` and `kubectl explain` perform *introspection* (they communicate with the API server and obtain the exact type definitions) .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- ## Type names - The most common resource names have three forms: - singular (e.g. `node`, `service`, `deployment`) - plural (e.g. `nodes`, `services`, `deployments`) - short (e.g. `no`, `svc`, `deploy`) - Some resources do not have a short name - `Endpoints` only have a plural form (because even a single `Endpoints` resource is actually a list of endpoints) .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- ## Viewing details - We can use `kubectl get -o yaml` to see all available details - However, YAML output is often simultaneously too much and not enough - For instance, `kubectl get node node1 -o yaml` is: - too much information (e.g.: list of images available on this node) - not enough information (e.g.: doesn't show pods running on this node) - difficult to read for a human operator - For a comprehensive overview, we can use `kubectl describe` instead .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- ## `kubectl describe` - `kubectl describe` needs a resource type and (optionally) a resource name - It is possible to provide a resource name *prefix* (all matching objects will be displayed) - `kubectl describe` will retrieve some extra information about the resource .exercise[ - Look at the information available for `node1` with one of the following commands: ```bash kubectl describe node/node1 kubectl describe node node1 ``` ] (We should notice a bunch of control plane pods.) .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- ## Listing running containers - Containers are manipulated through *pods* - A pod is a group of containers: - running together (on the same node) - sharing resources (RAM, CPU; but also network, volumes) .exercise[ - List pods on our cluster: ```bash kubectl get pods ``` ] -- *Where are the pods that we saw just a moment earlier?!?* .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- ## Namespaces - Namespaces allow us to segregate resources .exercise[ - List the namespaces on our cluster with one of these commands: ```bash kubectl get namespaces kubectl get namespace kubectl get ns ``` ] -- *You know what ... This `kube-system` thing looks suspicious.* *In fact, I'm pretty sure it showed up earlier, when we did:* `kubectl describe node node1` .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- ## Accessing namespaces - By default, `kubectl` uses the `default` namespace - We can see resources in all namespaces with `--all-namespaces` .exercise[ - List the pods in all namespaces: ```bash kubectl get pods --all-namespaces ``` - Since Kubernetes 1.14, we can also use `-A` as a shorter version: ```bash kubectl get pods -A ``` ] *Here are our system pods!* .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- ## What are all these control plane pods? - `etcd` is our etcd server - `kube-apiserver` is the API server - `kube-controller-manager` and `kube-scheduler` are other control plane components - `coredns` provides DNS-based service discovery ([replacing kube-dns as of 1.11](https://kubernetes.io/blog/2018/07/10/coredns-ga-for-kubernetes-cluster-dns/)) - `kube-proxy` is the (per-node) component managing port mappings and such - `weave` is the (per-node) component managing the network overlay - the `READY` column indicates the number of containers in each pod (1 for most pods, but `weave` has 2, for instance) .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- ## Scoping another namespace - We can also look at a different namespace (other than `default`) .exercise[ - List only the pods in the `kube-system` namespace: ```bash kubectl get pods --namespace=kube-system kubectl get pods -n kube-system ``` ] .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- ## Namespaces and other `kubectl` commands - We can use `-n`/`--namespace` with almost every `kubectl` command - Example: - `kubectl create --namespace=X` to create something in namespace X - We can use `-A`/`--all-namespaces` with most commands that manipulate multiple objects - Examples: - `kubectl delete` can delete resources across multiple namespaces - `kubectl label` can add/remove/update labels across multiple namespaces .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- class: extra-details ## What about `kube-public`? .exercise[ - List the pods in the `kube-public` namespace: ```bash kubectl -n kube-public get pods ``` ] Nothing! `kube-public` is created by kubeadm & [used for security bootstrapping](https://kubernetes.io/blog/2017/01/stronger-foundation-for-creating-and-managing-kubernetes-clusters). .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- class: extra-details ## Exploring `kube-public` - The only interesting object in `kube-public` is a ConfigMap named `cluster-info` .exercise[ - List ConfigMap objects: ```bash kubectl -n kube-public get configmaps ``` - Inspect `cluster-info`: ```bash kubectl -n kube-public get configmap cluster-info -o yaml ``` ] Note the `selfLink` URI: `/api/v1/namespaces/kube-public/configmaps/cluster-info` We can use that! .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- class: extra-details ## Accessing `cluster-info` - Earlier, when trying to access the API server, we got a `Forbidden` message - But `cluster-info` is readable by everyone (even without authentication) .exercise[ - Retrieve `cluster-info`: ```bash curl -k https://10.96.0.1/api/v1/namespaces/kube-public/configmaps/cluster-info ``` ] - We were able to access `cluster-info` (without auth) - It contains a `kubeconfig` file .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- class: extra-details ## Retrieving `kubeconfig` - We can easily extract the `kubeconfig` file from this ConfigMap .exercise[ - Display the content of `kubeconfig`: ```bash curl -sk https://10.96.0.1/api/v1/namespaces/kube-public/configmaps/cluster-info \ | jq -r .data.kubeconfig ``` ] - This file holds the canonical address of the API server, and the public key of the CA - This file *does not* hold client keys or tokens - This is not sensitive information, but allows us to establish trust .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- class: extra-details ## What about `kube-node-lease`? - Starting with Kubernetes 1.14, there is a `kube-node-lease` namespace (or in Kubernetes 1.13 if the NodeLease feature gate is enabled) - That namespace contains one Lease object per node - *Node leases* are a new way to implement node heartbeats (i.e. node regularly pinging the control plane to say "I'm alive!") - For more details, see [KEP-0009] or the [node controller documentation] [KEP-0009]: https://github.com/kubernetes/enhancements/blob/master/keps/sig-node/0009-node-heartbeat.md [node controller documentation]: https://kubernetes.io/docs/concepts/architecture/nodes/#node-controller .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- ## Services - A *service* is a stable endpoint to connect to "something" (In the initial proposal, they were called "portals") .exercise[ - List the services on our cluster with one of these commands: ```bash kubectl get services kubectl get svc ``` ] -- There is already one service on our cluster: the Kubernetes API itself. .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- ## ClusterIP services - A `ClusterIP` service is internal, available from the cluster only - This is useful for introspection from within containers .exercise[ - Try to connect to the API: ```bash curl -k https://`10.96.0.1` ``` - `-k` is used to skip certificate verification - Make sure to replace 10.96.0.1 with the CLUSTER-IP shown by `kubectl get svc` ] The command above should either time out, or show an authentication error. Why? .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- ## Time out - Connections to ClusterIP services only work *from within the cluster* - If we are outside the cluster, the `curl` command will probably time out (Because the IP address, e.g. 10.96.0.1, isn't routed properly outside the cluster) - This is the case with most "real" Kubernetes clusters - To try the connection from within the cluster, we can use [shpod](https://github.com/jpetazzo/shpod) .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- ## Authentication error This is what we should see when connecting from within the cluster: ```json $ curl -k https://10.96.0.1 { "kind": "Status", "apiVersion": "v1", "metadata": { }, "status": "Failure", "message": "forbidden: User \"system:anonymous\" cannot get path \"/\"", "reason": "Forbidden", "details": { }, "code": 403 } ``` .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- ## Explanations - We can see `kind`, `apiVersion`, `metadata` - These are typical of a Kubernetes API reply - Because we *are* talking to the Kubernetes API - The Kubernetes API tells us "Forbidden" (because it requires authentication) - The Kubernetes API is reachable from within the cluster (many apps integrating with Kubernetes will use this) .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- ## DNS integration - Each service also gets a DNS record - The Kubernetes DNS resolver is available *from within pods* (and sometimes, from within nodes, depending on configuration) - Code running in pods can connect to services using their name (e.g. https://kubernetes/...) ??? :EN:- Getting started with kubectl :FR:- Se familiariser avec kubectl .debug[[k8s/kubectlget.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlget.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/chinook-helicopter-container.jpg)] --- name: toc-running-our-first-containers-on-kubernetes class: title Running our first containers on Kubernetes .nav[ [Previous part](#toc-first-contact-with-kubectl) | [Back to table of contents](#toc-part-2) | [Next part](#toc-executing-batch-jobs) ] .debug[(automatically generated title slide)] --- # Running our first containers on Kubernetes - First things first: we cannot run a container -- - We are going to run a pod, and in that pod there will be a single container -- - In that container in the pod, we are going to run a simple `ping` command -- - Sounds simple enough, right? -- - Except ... that the `kubectl run` command changed in Kubernetes 1.18! - We'll explain what has changed, and why .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- ## Choose your own adventure - First, let's check which version of Kubernetes we're running .exercise[ - Check our API server version: ```bash kubectl version ``` - Look at the **Server Version** in the second part of the output ] - In the following slides, we will talk about 1.17- or 1.18+ (to indicate "up to Kubernetes 1.17" and "from Kubernetes 1.18") .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- ## Starting a simple pod with `kubectl run` - `kubectl run` is convenient to start a single pod - We need to specify at least a *name* and the image we want to use - Optionally, we can specify the command to run in the pod .exercise[ - Let's ping the address of `localhost`, the loopback interface: ```bash kubectl run pingpong --image alpine ping 127.0.0.1 ``` <!-- ```hide kubectl wait pod --selector=run=pingpong --for condition=ready``` --> ] .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- ## What do we see? - In Kubernetes 1.18+, the output tells us that a Pod is created: ``` pod/pingpong created ``` - In Kubernetes 1.17-, the output is much more verbose: ``` kubectl run --generator=deployment/apps.v1 is DEPRECATED and will be removed in a future version. Use kubectl run --generator=run-pod/v1 or kubectl create instead. deployment.apps/pingpong created ``` - There is a deprecation warning ... - ... And a Deployment was created instead of a Pod 🤔 What does that mean? .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- ## Show me all you got! - What resources were created by `kubectl run`? .exercise[ - Let's ask Kubernetes to show us *all* the resources: ```bash kubectl get all ``` ] Note: `kubectl get all` is a lie. It doesn't show everything. (But it shows a lot of "usual suspects", i.e. commonly used resources.) .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- ## The situation with Kubernetes 1.18+ ``` NAME READY STATUS RESTARTS AGE pod/pingpong 1/1 Running 0 9s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/kubernetes ClusterIP 10.96.0.1 <none> 443/TCP 3h30m ``` We wanted a pod, we got a pod, named `pingpong`. Great! (We can ignore `service/kubernetes`, it was already there before.) .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- ## The situation with Kubernetes 1.17- ``` NAME READY STATUS RESTARTS AGE pod/pingpong-6ccbc77f68-kmgfn 1/1 Running 0 11s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/kubernetes ClusterIP 10.96.0.1 <none> 443/TCP 3h45 NAME READY UP-TO-DATE AVAILABLE AGE deployment.apps/pingpong 1/1 1 1 11s NAME DESIRED CURRENT READY AGE replicaset.apps/pingpong-6ccbc77f68 1 1 1 11s ``` Our pod is not named `pingpong`, but `pingpong-xxxxxxxxxxx-yyyyy`. We have a Deployment named `pingpong`, and an extra Replica Set, too. What's going on? .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- ## From Deployment to Pod We have the following resources: - `deployment.apps/pingpong` This is the Deployment that we just created. - `replicaset.apps/pingpong-xxxxxxxxxx` This is a Replica Set created by this Deployment. - `pod/pingpong-xxxxxxxxxx-yyyyy` This is a *pod* created by the Replica Set. Let's explain what these things are. .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- ## Pod - Can have one or multiple containers - Runs on a single node (Pod cannot "straddle" multiple nodes) - Pods cannot be moved (e.g. in case of node outage) - Pods cannot be scaled (except by manually creating more Pods) .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- class: extra-details ## Pod details - A Pod is not a process; it's an environment for containers - it cannot be "restarted" - it cannot "crash" - The containers in a Pod can crash - They may or may not get restarted (depending on Pod's restart policy) - If all containers exit successfully, the Pod ends in "Succeeded" phase - If some containers fail and don't get restarted, the Pod ends in "Failed" phase .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- ## Replica Set - Set of identical (replicated) Pods - Defined by a pod template + number of desired replicas - If there are not enough Pods, the Replica Set creates more (e.g. in case of node outage; or simply when scaling up) - If there are too many Pods, the Replica Set deletes some (e.g. if a node was disconnected and comes back; or when scaling down) - We can scale up/down a Replica Set - we update the manifest of the Replica Set - as a consequence, the Replica Set controller creates/deletes Pods .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- ## Deployment - Replica Sets control *identical* Pods - Deployments are used to roll out different Pods (different image, command, environment variables, ...) - When we update a Deployment with a new Pod definition: - a new Replica Set is created with the new Pod definition - that new Replica Set is progressively scaled up - meanwhile, the old Replica Set(s) is(are) scaled down - This is a *rolling update*, minimizing application downtime - When we scale up/down a Deployment, it scales up/down its Replica Set .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- ## `kubectl run` through the ages - When we want to run an app on Kubernetes, we *generally* want a Deployment - Up to Kubernetes 1.17, `kubectl run` created a Deployment - it could also create other things, by using special flags - this was powerful, but potentially confusing - creating a single Pod was done with `kubectl run --restart=Never` - other resources could also be created with `kubectl create ...` - From Kubernetes 1.18, `kubectl run` creates a Pod - other kinds of resources can still be created with `kubectl create` .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- ## Creating a Deployment the proper way - Let's destroy that `pingpong` app that we created - Then we will use `kubectl create deployment` to re-create it .exercise[ - On Kubernetes 1.18+, delete the Pod named `pingpong`: ```bash kubectl delete pod pingpong ``` - On Kubernetes 1.17-, delete the Deployment named `pingpong`: ```bash kubectl delete deployment pingpong ``` ] .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- ## Running `ping` in a Deployment <!-- ##VERSION## --> - When using `kubectl create deployment`, we cannot indicate the command to execute (at least, not in Kubernetes 1.18; but that changed in Kubernetes 1.19) - We can: - write a custom YAML manifest for our Deployment -- - (yeah right ... too soon!) -- - use an image that has the command to execute baked in - (much easier!) -- - We will use the image `jpetazzo/ping` (it has a default command of `ping 127.0.0.1`) .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- ## Creating a Deployment running `ping` - Let's create a Deployment named `pingpong` - It will use the image `jpetazzo/ping` .exercise[ - Create the Deployment: ```bash kubectl create deployment pingpong --image=jpetazzo/ping ``` - Check the resources that were created: ```bash kubectl get all ``` <!-- ```hide kubectl wait pod --selector=app=pingpong --for condition=ready ``` --> ] .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- class: extra-details ## In Kubernetes 1.19 - Since Kubernetes 1.19, we can specify the command to run - The command must be passed after two dashes: ```bash kubectl create deployment pingpong --image=alpine -- ping 127.1 ``` .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- ## Viewing container output - Let's use the `kubectl logs` command - We will pass either a *pod name*, or a *type/name* (E.g. if we specify a deployment or replica set, it will get the first pod in it) - Unless specified otherwise, it will only show logs of the first container in the pod (Good thing there's only one in ours!) .exercise[ - View the result of our `ping` command: ```bash kubectl logs deploy/pingpong ``` ] .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- ## Streaming logs in real time - Just like `docker logs`, `kubectl logs` supports convenient options: - `-f`/`--follow` to stream logs in real time (à la `tail -f`) - `--tail` to indicate how many lines you want to see (from the end) - `--since` to get logs only after a given timestamp .exercise[ - View the latest logs of our `ping` command: ```bash kubectl logs deploy/pingpong --tail 1 --follow ``` - Stop it with Ctrl-C <!-- ```wait seq=3``` ```keys ^C``` --> ] .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- ## Scaling our application - We can create additional copies of our container (I mean, our pod) with `kubectl scale` .exercise[ - Scale our `pingpong` deployment: ```bash kubectl scale deploy/pingpong --replicas 3 ``` - Note that this command does exactly the same thing: ```bash kubectl scale deployment pingpong --replicas 3 ``` - Check that we now have multiple pods: ```bash kubectl get pods ``` ] .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- class: extra-details ## Scaling a Replica Set - What if we scale the Replica Set instead of the Deployment? - The Deployment would notice it right away and scale back to the initial level - The Replica Set makes sure that we have the right numbers of Pods - The Deployment makes sure that the Replica Set has the right size (conceptually, it delegates the management of the Pods to the Replica Set) - This might seem weird (why this extra layer?) but will soon make sense (when we will look at how rolling updates work!) .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- ## Streaming logs of multiple pods - What happens if we try `kubectl logs` now that we have multiple pods? .exercise[ ```bash kubectl logs deploy/pingpong --tail 3 ``` ] `kubectl logs` will warn us that multiple pods were found. It is showing us only one of them. We'll see later how to address that shortcoming. .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- ## Resilience - The *deployment* `pingpong` watches its *replica set* - The *replica set* ensures that the right number of *pods* are running - What happens if pods disappear? .exercise[ - In a separate window, watch the list of pods: ```bash watch kubectl get pods ``` <!-- ```wait Every 2.0s``` ```tmux split-pane -v``` --> - Destroy the pod currently shown by `kubectl logs`: ``` kubectl delete pod pingpong-xxxxxxxxxx-yyyyy ``` <!-- ```tmux select-pane -t 0``` ```copy pingpong-[^-]*-.....``` ```tmux last-pane``` ```keys kubectl delete pod ``` ```paste``` ```key ^J``` ```check``` ```key ^D``` ```key ^C``` --> ] .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- ## What happened? - `kubectl delete pod` terminates the pod gracefully (sending it the TERM signal and waiting for it to shutdown) - As soon as the pod is in "Terminating" state, the Replica Set replaces it - But we can still see the output of the "Terminating" pod in `kubectl logs` - Until 30 seconds later, when the grace period expires - The pod is then killed, and `kubectl logs` exits ??? :EN:- Running pods and deployments :FR:- Créer un pod et un déploiement .debug[[k8s/kubectl-run.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-run.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/container-cranes.jpg)] --- name: toc-executing-batch-jobs class: title Executing batch jobs .nav[ [Previous part](#toc-running-our-first-containers-on-kubernetes) | [Back to table of contents](#toc-part-2) | [Next part](#toc-labels-and-annotations) ] .debug[(automatically generated title slide)] --- # Executing batch jobs - Deployments are great for stateless web apps (as well as workers that keep running forever) - Pods are great for one-off execution that we don't care about (because they don't get automatically restarted if something goes wrong) - Jobs are great for "long" background work ("long" being at least minutes or hours) - CronJobs are great to schedule Jobs at regular intervals (just like the classic UNIX `cron` daemon with its `crontab` files) .debug[[k8s/batch-jobs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/batch-jobs.md)] --- ## Creating a Job - A Job will create a Pod - If the Pod fails, the Job will create another one - The Job will keep trying until: - either a Pod succeeds, - or we hit the *backoff limit* of the Job (default=6) .exercise[ - Create a Job that has a 50% chance of success: ```bash kubectl create job flipcoin --image=alpine -- sh -c 'exit $(($RANDOM%2))' ``` ] .debug[[k8s/batch-jobs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/batch-jobs.md)] --- ## Our Job in action - Our Job will create a Pod named `flipcoin-xxxxx` - If the Pod succeeds, the Job stops - If the Pod fails, the Job creates another Pod .exercise[ - Check the status of the Pod(s) created by the Job: ```bash kubectl get pods --selector=job-name=flipcoin ``` ] .debug[[k8s/batch-jobs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/batch-jobs.md)] --- class: extra-details ## More advanced jobs - We can specify a number of "completions" (default=1) - This indicates how many times the Job must be executed - We can specify the "parallelism" (default=1) - This indicates how many Pods should be running in parallel - These options cannot be specified with `kubectl create job` (we have to write our own YAML manifest to use them) .debug[[k8s/batch-jobs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/batch-jobs.md)] --- ## Scheduling periodic background work - A Cron Job is a Job that will be executed at specific intervals (the name comes from the traditional cronjobs executed by the UNIX crond) - It requires a *schedule*, represented as five space-separated fields: - minute [0,59] - hour [0,23] - day of the month [1,31] - month of the year [1,12] - day of the week ([0,6] with 0=Sunday) - `*` means "all valid values"; `/N` means "every N" - Example: `*/3 * * * *` means "every three minutes" .debug[[k8s/batch-jobs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/batch-jobs.md)] --- ## Creating a Cron Job - Let's create a simple job to be executed every three minutes - Careful: make sure that the job terminates! (The Cron Job will not hold if a previous job is still running) .exercise[ - Create the Cron Job: ```bash kubectl create cronjob every3mins --schedule="*/3 * * * *" \ --image=alpine -- sleep 10 ``` - Check the resource that was created: ```bash kubectl get cronjobs ``` ] .debug[[k8s/batch-jobs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/batch-jobs.md)] --- ## Cron Jobs in action - At the specified schedule, the Cron Job will create a Job - The Job will create a Pod - The Job will make sure that the Pod completes (re-creating another one if it fails, for instance if its node fails) .exercise[ - Check the Jobs that are created: ```bash kubectl get jobs ``` ] (It will take a few minutes before the first job is scheduled.) .debug[[k8s/batch-jobs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/batch-jobs.md)] --- class: extra-details ## Setting a time limit - It is possible to set a time limit (or deadline) for a job - This is done with the field `spec.activeDeadlineSeconds` (by default, it is unlimited) - When the job is older than this time limit, all its pods are terminated - Note that there can also be a `spec.activeDeadlineSeconds` field in pods! - They can be set independently, and have different effects: - the deadline of the job will stop the entire job - the deadline of the pod will only stop an individual pod .debug[[k8s/batch-jobs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/batch-jobs.md)] --- class: extra-details ## What about `kubectl run` before v1.18? - Creating a Deployment: `kubectl run` - Creating a Pod: `kubectl run --restart=Never` - Creating a Job: `kubectl run --restart=OnFailure` - Creating a Cron Job: `kubectl run --restart=OnFailure --schedule=...` *Avoid using these forms, as they are deprecated since Kubernetes 1.18!* .debug[[k8s/batch-jobs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/batch-jobs.md)] --- ## Beyond `kubectl create` - As hinted earlier, `kubectl create` doesn't always expose all options - can't express parallelism or completions of Jobs - can't express healthchecks, resource limits - `kubectl create` and `kubectl run` are *helpers* that generate YAML manifests - If we write these manifests ourselves, we can use all features and options - We'll see later how to do that! ??? :EN:- Running batch and cron jobs :FR:- Tâches périodiques *(cron)* et traitement par lots *(batch)* .debug[[k8s/batch-jobs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/batch-jobs.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/container-housing.jpg)] --- name: toc-labels-and-annotations class: title Labels and annotations .nav[ [Previous part](#toc-executing-batch-jobs) | [Back to table of contents](#toc-part-2) | [Next part](#toc-revisiting-kubectl-logs) ] .debug[(automatically generated title slide)] --- # Labels and annotations - Most Kubernetes resources can have *labels* and *annotations* - Both labels and annotations are arbitrary strings (with some limitations that we'll explain in a minute) - Both labels and annotations can be added, removed, changed, dynamically - This can be done with: - the `kubectl edit` command - the `kubectl label` and `kubectl annotate` - ... many other ways! (`kubectl apply -f`, `kubectl patch`, ...) .debug[[k8s/labels-annotations.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/labels-annotations.md)] --- ## Viewing labels and annotations - Let's see what we get when we create a Deployment .exercise[ - Create a Deployment: ```bash kubectl create deployment clock --image=jpetazzo/clock ``` - Look at its annotations and labels: ```bash kubectl describe deployment clock ``` ] So, what do we get? .debug[[k8s/labels-annotations.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/labels-annotations.md)] --- ## Labels and annotations for our Deployment - We see one label: ``` Labels: app=clock ``` - This is added by `kubectl create deployment` - And one annotation: ``` Annotations: deployment.kubernetes.io/revision: 1 ``` - This is to keep track of successive versions when doing rolling updates .debug[[k8s/labels-annotations.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/labels-annotations.md)] --- ## And for the related Pod? - Let's look up the Pod that was created and check it too .exercise[ - Find the name of the Pod: ```bash kubectl get pods ``` - Display its information: ```bash kubectl describe pod clock-xxxxxxxxxx-yyyyy ``` ] So, what do we get? .debug[[k8s/labels-annotations.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/labels-annotations.md)] --- ## Labels and annotations for our Pod - We see two labels: ``` Labels: app=clock pod-template-hash=xxxxxxxxxx ``` - `app=clock` comes from `kubectl create deployment` too - `pod-template-hash` was assigned by the Replica Set (when we will do rolling updates, each set of Pods will have a different hash) - There are no annotations: ``` Annotations: <none> ``` .debug[[k8s/labels-annotations.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/labels-annotations.md)] --- ## Selectors - A *selector* is an expression matching labels - It will restrict a command to the objects matching *at least* all these labels .exercise[ - List all the pods with at least `app=clock`: ```bash kubectl get pods --selector=app=clock ``` - List all the pods with a label `app`, regardless of its value: ```bash kubectl get pods --selector=app ``` ] .debug[[k8s/labels-annotations.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/labels-annotations.md)] --- ## Settings labels and annotations - The easiest method is to use `kubectl label` and `kubectl annotate` .exercise[ - Set a label on the `clock` Deployment: ```bash kubectl label deployment clock color=blue ``` - Check it out: ```bash kubectl describe deployment clock ``` ] .debug[[k8s/labels-annotations.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/labels-annotations.md)] --- ## Other ways to view labels - `kubectl get` gives us a couple of useful flags to check labels - `kubectl get --show-labels` shows all labels - `kubectl get -L xyz` shows the value of label `xyz` .exercise[ - List all the labels that we have on pods: ```bash kubectl get pods --show-labels ``` - List the value of label `app` on these pods: ```bash kubectl get pods -L app ``` ] .debug[[k8s/labels-annotations.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/labels-annotations.md)] --- class: extra-details ## More on selectors - If a selector has multiple labels, it means "match at least these labels" Example: `--selector=app=frontend,release=prod` - `--selector` can be abbreviated as `-l` (for **l**abels) We can also use negative selectors Example: `--selector=app!=clock` - Selectors can be used with most `kubectl` commands Examples: `kubectl delete`, `kubectl label`, ... .debug[[k8s/labels-annotations.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/labels-annotations.md)] --- ## Other ways to view labels - We can use the `--show-labels` flag with `kubectl get` .exercise[ - Show labels for a bunch of objects: ```bash kubectl get --show-labels po,rs,deploy,svc,no ``` ] .debug[[k8s/labels-annotations.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/labels-annotations.md)] --- ## Differences between labels and annotations - The *key* for both labels and annotations: - must start and end with a letter or digit - can also have `.` `-` `_` (but not in first or last position) - can be up to 63 characters, or 253 + `/` + 63 - Label *values* are up to 63 characters, with the same restrictions - Annotations *values* can have arbitrary characters (yes, even binary) - Maximum length isn't defined (dozens of kilobytes is fine, hundreds maybe not so much) ??? :EN:- Labels and annotations :FR:- *Labels* et annotations .debug[[k8s/labels-annotations.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/labels-annotations.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/containers-by-the-water.jpg)] --- name: toc-revisiting-kubectl-logs class: title Revisiting `kubectl logs` .nav[ [Previous part](#toc-labels-and-annotations) | [Back to table of contents](#toc-part-2) | [Next part](#toc-accessing-logs-from-the-cli) ] .debug[(automatically generated title slide)] --- # Revisiting `kubectl logs` - In this section, we assume that we have a Deployment with multiple Pods (e.g. `pingpong` that we scaled to at least 3 pods) - We will highlights some of the limitations of `kubectl logs` .debug[[k8s/kubectl-logs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-logs.md)] --- ## Streaming logs of multiple pods - By default, `kubectl logs` shows us the output of a single Pod .exercise[ - Try to check the output of the Pods related to a Deployment: ```bash kubectl logs deploy/pingpong --tail 1 --follow ``` <!-- ```wait using pod/pingpong-``` ```keys ^C``` --> ] `kubectl logs` only shows us the logs of one of the Pods. .debug[[k8s/kubectl-logs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-logs.md)] --- ## Viewing logs of multiple pods - When we specify a deployment name, only one single pod's logs are shown - We can view the logs of multiple pods by specifying a *selector* - If we check the pods created by the deployment, they all have the label `app=pingpong` (this is just a default label that gets added when using `kubectl create deployment`) .exercise[ - View the last line of log from all pods with the `app=pingpong` label: ```bash kubectl logs -l app=pingpong --tail 1 ``` ] .debug[[k8s/kubectl-logs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-logs.md)] --- ## Streaming logs of multiple pods - Can we stream the logs of all our `pingpong` pods? .exercise[ - Combine `-l` and `-f` flags: ```bash kubectl logs -l app=pingpong --tail 1 -f ``` <!-- ```wait seq=``` ```key ^C``` --> ] *Note: combining `-l` and `-f` is only possible since Kubernetes 1.14!* *Let's try to understand why ...* .debug[[k8s/kubectl-logs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-logs.md)] --- class: extra-details ## Streaming logs of many pods - Let's see what happens if we try to stream the logs for more than 5 pods .exercise[ - Scale up our deployment: ```bash kubectl scale deployment pingpong --replicas=8 ``` - Stream the logs: ```bash kubectl logs -l app=pingpong --tail 1 -f ``` <!-- ```wait error:``` --> ] We see a message like the following one: ``` error: you are attempting to follow 8 log streams, but maximum allowed concurency is 5, use --max-log-requests to increase the limit ``` .debug[[k8s/kubectl-logs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-logs.md)] --- class: extra-details ## Why can't we stream the logs of many pods? - `kubectl` opens one connection to the API server per pod - For each pod, the API server opens one extra connection to the corresponding kubelet - If there are 1000 pods in our deployment, that's 1000 inbound + 1000 outbound connections on the API server - This could easily put a lot of stress on the API server - Prior Kubernetes 1.14, it was decided to *not* allow multiple connections - From Kubernetes 1.14, it is allowed, but limited to 5 connections (this can be changed with `--max-log-requests`) - For more details about the rationale, see [PR #67573](https://github.com/kubernetes/kubernetes/pull/67573) .debug[[k8s/kubectl-logs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-logs.md)] --- ## Shortcomings of `kubectl logs` - We don't see which pod sent which log line - If pods are restarted / replaced, the log stream stops - If new pods are added, we don't see their logs - To stream the logs of multiple pods, we need to write a selector - There are external tools to address these shortcomings (e.g.: [Stern](https://github.com/wercker/stern)) .debug[[k8s/kubectl-logs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-logs.md)] --- class: extra-details ## `kubectl logs -l ... --tail N` - If we run this with Kubernetes 1.12, the last command shows multiple lines - This is a regression when `--tail` is used together with `-l`/`--selector` - It always shows the last 10 lines of output for each container (instead of the number of lines specified on the command line) - The problem was fixed in Kubernetes 1.13 *See [#70554](https://github.com/kubernetes/kubernetes/issues/70554) for details.* ??? :EN:- Viewing logs with "kubectl logs" :FR:- Consulter les logs avec "kubectl logs" .debug[[k8s/kubectl-logs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectl-logs.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/distillery-containers.jpg)] --- name: toc-accessing-logs-from-the-cli class: title Accessing logs from the CLI .nav[ [Previous part](#toc-revisiting-kubectl-logs) | [Back to table of contents](#toc-part-2) | [Next part](#toc-declarative-vs-imperative) ] .debug[(automatically generated title slide)] --- # Accessing logs from the CLI - The `kubectl logs` command has limitations: - it cannot stream logs from multiple pods at a time - when showing logs from multiple pods, it mixes them all together - We are going to see how to do it better .debug[[k8s/logs-cli.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/logs-cli.md)] --- ## Doing it manually - We *could* (if we were so inclined) write a program or script that would: - take a selector as an argument - enumerate all pods matching that selector (with `kubectl get -l ...`) - fork one `kubectl logs --follow ...` command per container - annotate the logs (the output of each `kubectl logs ...` process) with their origin - preserve ordering by using `kubectl logs --timestamps ...` and merge the output -- - We *could* do it, but thankfully, others did it for us already! .debug[[k8s/logs-cli.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/logs-cli.md)] --- ## Stern [Stern](https://github.com/wercker/stern) is an open source project by [Wercker](http://www.wercker.com/). From the README: *Stern allows you to tail multiple pods on Kubernetes and multiple containers within the pod. Each result is color coded for quicker debugging.* *The query is a regular expression so the pod name can easily be filtered and you don't need to specify the exact id (for instance omitting the deployment id). If a pod is deleted it gets removed from tail and if a new pod is added it automatically gets tailed.* Exactly what we need! .debug[[k8s/logs-cli.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/logs-cli.md)] --- ## Checking if Stern is installed - Run `stern` (without arguments) to check if it's installed: ``` $ stern Tail multiple pods and containers from Kubernetes Usage: stern pod-query [flags] ``` - If it's missing, let's see how to install it .debug[[k8s/logs-cli.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/logs-cli.md)] --- ## Installing Stern - Stern is written in Go, and Go programs are usually shipped as a single binary - We just need to download that binary and put it in our `PATH`! - Binary releases are available [here](https://github.com/wercker/stern/releases) on GitHub - The following commands will install Stern on a Linux Intel 64 bit machine: ```bash sudo curl -L -o /usr/local/bin/stern \ https://github.com/wercker/stern/releases/download/1.11.0/stern_linux_amd64 sudo chmod +x /usr/local/bin/stern ``` - On macOS, we can also `brew install stern` or `sudo port install stern` <!-- ##VERSION## --> .debug[[k8s/logs-cli.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/logs-cli.md)] --- ## Using Stern - There are two ways to specify the pods whose logs we want to see: - `-l` followed by a selector expression (like with many `kubectl` commands) - with a "pod query," i.e. a regex used to match pod names - These two ways can be combined if necessary .exercise[ - View the logs for all the pingpong containers: ```bash stern pingpong ``` <!-- ```wait seq=``` ```key ^C``` --> ] .debug[[k8s/logs-cli.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/logs-cli.md)] --- ## Stern convenient options - The `--tail N` flag shows the last `N` lines for each container (Instead of showing the logs since the creation of the container) - The `-t` / `--timestamps` flag shows timestamps - The `--all-namespaces` flag is self-explanatory .exercise[ - View what's up with the `weave` system containers: ```bash stern --tail 1 --timestamps --all-namespaces weave ``` <!-- ```wait weave-npc``` ```key ^C``` --> ] .debug[[k8s/logs-cli.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/logs-cli.md)] --- ## Using Stern with a selector - When specifying a selector, we can omit the value for a label - This will match all objects having that label (regardless of the value) - Everything created with `kubectl run` has a label `run` - Everything created with `kubectl create deployment` has a label `app` - We can use that property to view the logs of all the pods created with `kubectl create deployment` .exercise[ - View the logs for all the things started with `kubectl create deployment`: ```bash stern -l app ``` <!-- ```wait seq=``` ```key ^C``` --> ] ??? :EN:- Viewing pod logs from the CLI :FR:- Consulter les logs des pods depuis la CLI .debug[[k8s/logs-cli.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/logs-cli.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/lots-of-containers.jpg)] --- name: toc-declarative-vs-imperative class: title Declarative vs imperative .nav[ [Previous part](#toc-accessing-logs-from-the-cli) | [Back to table of contents](#toc-part-2) | [Next part](#toc-kubernetes-network-model) ] .debug[(automatically generated title slide)] --- # Declarative vs imperative - Our container orchestrator puts a very strong emphasis on being *declarative* - Declarative: *I would like a cup of tea.* - Imperative: *Boil some water. Pour it in a teapot. Add tea leaves. Steep for a while. Serve in a cup.* -- - Declarative seems simpler at first ... -- - ... As long as you know how to brew tea .debug[[shared/declarative.md](https://github.com/barpilot/container.training/tree/master/slides/shared/declarative.md)] --- ## Declarative vs imperative - What declarative would really be: *I want a cup of tea, obtained by pouring an infusion¹ of tea leaves in a cup.* -- *¹An infusion is obtained by letting the object steep a few minutes in hot² water.* -- *²Hot liquid is obtained by pouring it in an appropriate container³ and setting it on a stove.* -- *³Ah, finally, containers! Something we know about. Let's get to work, shall we?* -- .footnote[Did you know there was an [ISO standard](https://en.wikipedia.org/wiki/ISO_3103) specifying how to brew tea?] .debug[[shared/declarative.md](https://github.com/barpilot/container.training/tree/master/slides/shared/declarative.md)] --- ## Declarative vs imperative - Imperative systems: - simpler - if a task is interrupted, we have to restart from scratch - Declarative systems: - if a task is interrupted (or if we show up to the party half-way through), we can figure out what's missing and do only what's necessary - we need to be able to *observe* the system - ... and compute a "diff" between *what we have* and *what we want* .debug[[shared/declarative.md](https://github.com/barpilot/container.training/tree/master/slides/shared/declarative.md)] --- ## Declarative vs imperative in Kubernetes - With Kubernetes, we cannot say: "run this container" - All we can do is write a *spec* and push it to the API server (by creating a resource like e.g. a Pod or a Deployment) - The API server will validate that spec (and reject it if it's invalid) - Then it will store it in etcd - A *controller* will "notice" that spec and act upon it .debug[[k8s/declarative.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/declarative.md)] --- ## Reconciling state - Watch for the `spec` fields in the YAML files later! - The *spec* describes *how we want the thing to be* - Kubernetes will *reconcile* the current state with the spec <br/>(technically, this is done by a number of *controllers*) - When we want to change some resource, we update the *spec* - Kubernetes will then *converge* that resource ??? :EN:- Declarative vs imperative models :FR:- Modèles déclaratifs et impératifs .debug[[k8s/declarative.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/declarative.md)] --- ## 19,000 words They say, "a picture is worth one thousand words." The following 19 slides show what really happens when we run: ```bash kubectl create deployment web --image=nginx ``` .debug[[k8s/deploymentslideshow.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/deploymentslideshow.md)] --- class: pic ![](images/kubectl-create-deployment-slideshow/01.svg) .debug[[k8s/deploymentslideshow.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/deploymentslideshow.md)] --- class: pic ![](images/kubectl-create-deployment-slideshow/02.svg) .debug[[k8s/deploymentslideshow.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/deploymentslideshow.md)] --- class: pic ![](images/kubectl-create-deployment-slideshow/03.svg) .debug[[k8s/deploymentslideshow.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/deploymentslideshow.md)] --- class: pic ![](images/kubectl-create-deployment-slideshow/04.svg) .debug[[k8s/deploymentslideshow.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/deploymentslideshow.md)] --- class: pic ![](images/kubectl-create-deployment-slideshow/05.svg) .debug[[k8s/deploymentslideshow.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/deploymentslideshow.md)] --- class: pic ![](images/kubectl-create-deployment-slideshow/06.svg) .debug[[k8s/deploymentslideshow.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/deploymentslideshow.md)] --- class: pic ![](images/kubectl-create-deployment-slideshow/07.svg) .debug[[k8s/deploymentslideshow.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/deploymentslideshow.md)] --- class: pic ![](images/kubectl-create-deployment-slideshow/08.svg) .debug[[k8s/deploymentslideshow.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/deploymentslideshow.md)] --- class: pic ![](images/kubectl-create-deployment-slideshow/09.svg) .debug[[k8s/deploymentslideshow.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/deploymentslideshow.md)] --- class: pic ![](images/kubectl-create-deployment-slideshow/10.svg) .debug[[k8s/deploymentslideshow.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/deploymentslideshow.md)] --- class: pic ![](images/kubectl-create-deployment-slideshow/11.svg) .debug[[k8s/deploymentslideshow.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/deploymentslideshow.md)] --- class: pic ![](images/kubectl-create-deployment-slideshow/12.svg) .debug[[k8s/deploymentslideshow.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/deploymentslideshow.md)] --- class: pic ![](images/kubectl-create-deployment-slideshow/13.svg) .debug[[k8s/deploymentslideshow.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/deploymentslideshow.md)] --- class: pic ![](images/kubectl-create-deployment-slideshow/14.svg) .debug[[k8s/deploymentslideshow.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/deploymentslideshow.md)] --- class: pic ![](images/kubectl-create-deployment-slideshow/15.svg) .debug[[k8s/deploymentslideshow.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/deploymentslideshow.md)] --- class: pic ![](images/kubectl-create-deployment-slideshow/16.svg) .debug[[k8s/deploymentslideshow.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/deploymentslideshow.md)] --- class: pic ![](images/kubectl-create-deployment-slideshow/17.svg) .debug[[k8s/deploymentslideshow.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/deploymentslideshow.md)] --- class: pic ![](images/kubectl-create-deployment-slideshow/18.svg) .debug[[k8s/deploymentslideshow.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/deploymentslideshow.md)] --- class: pic ![](images/kubectl-create-deployment-slideshow/19.svg) .debug[[k8s/deploymentslideshow.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/deploymentslideshow.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/plastic-containers.JPG)] --- name: toc-kubernetes-network-model class: title Kubernetes network model .nav[ [Previous part](#toc-declarative-vs-imperative) | [Back to table of contents](#toc-part-3) | [Next part](#toc-exposing-containers) ] .debug[(automatically generated title slide)] --- # Kubernetes network model - TL,DR: *Our cluster (nodes and pods) is one big flat IP network.* -- - In detail: - all nodes must be able to reach each other, without NAT - all pods must be able to reach each other, without NAT - pods and nodes must be able to reach each other, without NAT - each pod is aware of its IP address (no NAT) - pod IP addresses are assigned by the network implementation - Kubernetes doesn't mandate any particular implementation .debug[[k8s/kubenet.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubenet.md)] --- ## Kubernetes network model: the good - Everything can reach everything - No address translation - No port translation - No new protocol - The network implementation can decide how to allocate addresses - IP addresses don't have to be "portable" from a node to another (We can use e.g. a subnet per node and use a simple routed topology) - The specification is simple enough to allow many various implementations .debug[[k8s/kubenet.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubenet.md)] --- ## Kubernetes network model: the less good - Everything can reach everything - if you want security, you need to add network policies - the network implementation that you use needs to support them - There are literally dozens of implementations out there (https://github.com/containernetworking/cni/ lists more than 25 plugins) - Pods have level 3 (IP) connectivity, but *services* are level 4 (TCP or UDP) (Services map to a single UDP or TCP port; no port ranges or arbitrary IP packets) - `kube-proxy` is on the data path when connecting to a pod or container, <br/>and it's not particularly fast (relies on userland proxying or iptables) .debug[[k8s/kubenet.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubenet.md)] --- ## Kubernetes network model: in practice - The nodes that we are using have been set up to use [Weave](https://github.com/weaveworks/weave) - We don't endorse Weave in a particular way, it just Works For Us - Don't worry about the warning about `kube-proxy` performance - Unless you: - routinely saturate 10G network interfaces - count packet rates in millions per second - run high-traffic VOIP or gaming platforms - do weird things that involve millions of simultaneous connections <br/>(in which case you're already familiar with kernel tuning) - If necessary, there are alternatives to `kube-proxy`; e.g. [`kube-router`](https://www.kube-router.io) .debug[[k8s/kubenet.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubenet.md)] --- class: extra-details ## The Container Network Interface (CNI) - Most Kubernetes clusters use CNI "plugins" to implement networking - When a pod is created, Kubernetes delegates the network setup to these plugins (it can be a single plugin, or a combination of plugins, each doing one task) - Typically, CNI plugins will: - allocate an IP address (by calling an IPAM plugin) - add a network interface into the pod's network namespace - configure the interface as well as required routes etc. .debug[[k8s/kubenet.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubenet.md)] --- class: extra-details ## Multiple moving parts - The "pod-to-pod network" or "pod network": - provides communication between pods and nodes - is generally implemented with CNI plugins - The "pod-to-service network": - provides internal communication and load balancing - is generally implemented with kube-proxy (or e.g. kube-router) - Network policies: - provide firewalling and isolation - can be bundled with the "pod network" or provided by another component .debug[[k8s/kubenet.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubenet.md)] --- class: pic ![Overview of the three Kubernetes network layers](images/k8s-net-0-overview.svg) .debug[[k8s/kubenet.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubenet.md)] --- class: pic ![Pod-to-pod network](images/k8s-net-1-pod-to-pod.svg) .debug[[k8s/kubenet.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubenet.md)] --- class: pic ![Pod-to-service network](images/k8s-net-2-pod-to-svc.svg) .debug[[k8s/kubenet.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubenet.md)] --- class: pic ![Network policies](images/k8s-net-3-netpol.svg) .debug[[k8s/kubenet.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubenet.md)] --- class: pic ![View with all the layers again](images/k8s-net-4-overview.svg) .debug[[k8s/kubenet.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubenet.md)] --- class: extra-details ## Even more moving parts - Inbound traffic can be handled by multiple components: - something like kube-proxy or kube-router (for NodePort services) - load balancers (ideally, connected to the pod network) - It is possible to use multiple pod networks in parallel (with "meta-plugins" like CNI-Genie or Multus) - Some solutions can fill multiple roles (e.g. kube-router can be set up to provide the pod network and/or network policies and/or replace kube-proxy) ??? :EN:- The Kubernetes network model :FR:- Le modèle réseau de Kubernetes .debug[[k8s/kubenet.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubenet.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/train-of-containers-1.jpg)] --- name: toc-exposing-containers class: title Exposing containers .nav[ [Previous part](#toc-kubernetes-network-model) | [Back to table of contents](#toc-part-3) | [Next part](#toc-shipping-images-with-a-registry) ] .debug[(automatically generated title slide)] --- # Exposing containers - We can connect to our pods using their IP address - Then we need to figure out a lot of things: - how do we look up the IP address of the pod(s)? - how do we connect from outside the cluster? - how do we load balance traffic? - what if a pod fails? - Kubernetes has a resource type named *Service* - Services address all these questions! .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- ## Services in a nutshell - Services give us a *stable endpoint* to connect to a pod or a group of pods - An easy way to create a service is to use `kubectl expose` - If we have a deployment named `my-little-deploy`, we can run: `kubectl expose deployment my-little-deploy --port=80` ... and this will create a service with the same name (`my-little-deploy`) - Services are automatically added to an internal DNS zone (in the example above, our code can now connect to http://my-little-deploy/) .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- ## Advantages of services - We don't need to look up the IP address of the pod(s) (we resolve the IP address of the service using DNS) - There are multiple service types; some of them allow external traffic (e.g. `LoadBalancer` and `NodePort`) - Services provide load balancing (for both internal and external traffic) - Service addresses are independent from pods' addresses (when a pod fails, the service seamlessly sends traffic to its replacement) .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- ## Many kinds and flavors of service - There are different types of services: `ClusterIP`, `NodePort`, `LoadBalancer`, `ExternalName` - There are also *headless services* - Services can also have optional *external IPs* - There is also another resource type called *Ingress* (specifically for HTTP services) - Wow, that's a lot! Let's start with the basics ... .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- ## `ClusterIP` - It's the default service type - A virtual IP address is allocated for the service (in an internal, private range; e.g. 10.96.0.0/12) - This IP address is reachable only from within the cluster (nodes and pods) - Our code can connect to the service using the original port number - Perfect for internal communication, within the cluster .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- ## `LoadBalancer` - An external load balancer is allocated for the service (typically a cloud load balancer, e.g. ELB on AWS, GLB on GCE ...) - This is available only when the underlying infrastructure provides some kind of "load balancer as a service" - Each service of that type will typically cost a little bit of money (e.g. a few cents per hour on AWS or GCE) - Ideally, traffic would flow directly from the load balancer to the pods - In practice, it will often flow through a `NodePort` first .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- ## `NodePort` - A port number is allocated for the service (by default, in the 30000-32767 range) - That port is made available *on all our nodes* and anybody can connect to it (we can connect to any node on that port to reach the service) - Our code needs to be changed to connect to that new port number - Under the hood: `kube-proxy` sets up a bunch of `iptables` rules on our nodes - Sometimes, it's the only available option for external traffic (e.g. most clusters deployed with kubeadm or on-premises) .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- ## Running containers with open ports - Since `ping` doesn't have anything to connect to, we'll have to run something else - We could use the `nginx` official image, but ... ... we wouldn't be able to tell the backends from each other! - We are going to use `jpetazzo/httpenv`, a tiny HTTP server written in Go - `jpetazzo/httpenv` listens on port 8888 - It serves its environment variables in JSON format - The environment variables will include `HOSTNAME`, which will be the pod name (and therefore, will be different on each backend) .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- class: extra-details ## Supporting other CPU architectures - The `jpetazzo/httpenv` image is currently only available for `x86_64` (the "classic" Intel 64 bits architecture found on most PCs and Macs) - That image won't work on other architectures (e.g. Raspberry Pi or other ARM-based machines) - Note that Docker supports [multi-arch](https://www.docker.com/blog/multi-arch-build-and-images-the-simple-way/) images (so *technically* we could make it work across multiple architectures) - If you want to build `httpenv` for your own platform, here is the source: https://github.com/jpetazzo/httpenv .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- ## Creating a deployment for our HTTP server - We will create a deployment with `kubectl create deployment` - Then we will scale it with `kubectl scale` .exercise[ - In another window, watch the pods (to see when they are created): ```bash kubectl get pods -w ``` <!-- ```wait NAME``` ```tmux split-pane -h``` --> - Create a deployment for this very lightweight HTTP server: ```bash kubectl create deployment httpenv --image=jpetazzo/httpenv ``` - Scale it to 10 replicas: ```bash kubectl scale deployment httpenv --replicas=10 ``` ] .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- ## Exposing our deployment - We'll create a default `ClusterIP` service .exercise[ - Expose the HTTP port of our server: ```bash kubectl expose deployment httpenv --port 8888 ``` - Look up which IP address was allocated: ```bash kubectl get service ``` ] .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- ## Services are layer 4 constructs - You can assign IP addresses to services, but they are still *layer 4* (i.e. a service is not an IP address; it's an IP address + protocol + port) - This is caused by the current implementation of `kube-proxy` (it relies on mechanisms that don't support layer 3) - As a result: you *have to* indicate the port number for your service (with some exceptions, like `ExternalName` or headless services, covered later) .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- ## Testing our service - We will now send a few HTTP requests to our pods .exercise[ - Let's obtain the IP address that was allocated for our service, *programmatically:* ```bash IP=$(kubectl get svc httpenv -o go-template --template '{{ .spec.clusterIP }}') ``` <!-- ```hide kubectl wait deploy httpenv --for condition=available``` ```key ^D``` ```key ^C``` --> - Send a few requests: ```bash curl http://$IP:8888/ ``` - Too much output? Filter it with `jq`: ```bash curl -s http://$IP:8888/ | jq .HOSTNAME ``` ] -- Try it a few times! Our requests are load balanced across multiple pods. .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- class: extra-details ## `ExternalName` - Services of type `ExternalName` are quite different - No load balancer (internal or external) is created - Only a DNS entry gets added to the DNS managed by Kubernetes - That DNS entry will just be a `CNAME` to a provided record Example: ```bash kubectl create service externalname k8s --external-name kubernetes.io ``` *Creates a CNAME `k8s` pointing to `kubernetes.io`* .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- class: extra-details ## External IPs - We can add an External IP to a service, e.g.: ```bash kubectl expose deploy my-little-deploy --port=80 --external-ip=1.2.3.4 ``` - `1.2.3.4` should be the address of one of our nodes (it could also be a virtual address, service address, or VIP, shared by multiple nodes) - Connections to `1.2.3.4:80` will be sent to our service - External IPs will also show up on services of type `LoadBalancer` (they will be added automatically by the process provisioning the load balancer) .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- class: extra-details ## Headless services - Sometimes, we want to access our scaled services directly: - if we want to save a tiny little bit of latency (typically less than 1ms) - if we need to connect over arbitrary ports (instead of a few fixed ones) - if we need to communicate over another protocol than UDP or TCP - if we want to decide how to balance the requests client-side - ... - In that case, we can use a "headless service" .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- class: extra-details ## Creating a headless services - A headless service is obtained by setting the `clusterIP` field to `None` (Either with `--cluster-ip=None`, or by providing a custom YAML) - As a result, the service doesn't have a virtual IP address - Since there is no virtual IP address, there is no load balancer either - CoreDNS will return the pods' IP addresses as multiple `A` records - This gives us an easy way to discover all the replicas for a deployment .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- class: extra-details ## Services and endpoints - A service has a number of "endpoints" - Each endpoint is a host + port where the service is available - The endpoints are maintained and updated automatically by Kubernetes .exercise[ - Check the endpoints that Kubernetes has associated with our `httpenv` service: ```bash kubectl describe service httpenv ``` ] In the output, there will be a line starting with `Endpoints:`. That line will list a bunch of addresses in `host:port` format. .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- class: extra-details ## Viewing endpoint details - When we have many endpoints, our display commands truncate the list ```bash kubectl get endpoints ``` - If we want to see the full list, we can use one of the following commands: ```bash kubectl describe endpoints httpenv kubectl get endpoints httpenv -o yaml ``` - These commands will show us a list of IP addresses - These IP addresses should match the addresses of the corresponding pods: ```bash kubectl get pods -l app=httpenv -o wide ``` .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- class: extra-details ## `endpoints` not `endpoint` - `endpoints` is the only resource that cannot be singular ```bash $ kubectl get endpoint error: the server doesn't have a resource type "endpoint" ``` - This is because the type itself is plural (unlike every other resource) - There is no `endpoint` object: `type Endpoints struct` - The type doesn't represent a single endpoint, but a list of endpoints .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- class: extra-details ## The DNS zone - In the `kube-system` namespace, there should be a service named `kube-dns` - This is the internal DNS server that can resolve service names - The default domain name for the service we created is `default.svc.cluster.local` .exercise[ - Get the IP address of the internal DNS server: ```bash IP=$(kubectl -n kube-system get svc kube-dns -o jsonpath={.spec.clusterIP}) ``` - Resolve the cluster IP for the `httpenv` service: ```bash host httpenv.default.svc.cluster.local $IP ``` ] .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- class: extra-details ## `Ingress` - Ingresses are another type (kind) of resource - They are specifically for HTTP services (not TCP or UDP) - They can also handle TLS certificates, URL rewriting ... - They require an *Ingress Controller* to function ??? :EN:- Service discovery and load balancing :EN:- Accessing pods through services :EN:- Service types: ClusterIP, NodePort, LoadBalancer :FR:- Exposer un service :FR:- Différents types de services : ClusterIP, NodePort, LoadBalancer :FR:- Utiliser CoreDNS pour la *service discovery* .debug[[k8s/kubectlexpose.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlexpose.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/train-of-containers-2.jpg)] --- name: toc-shipping-images-with-a-registry class: title Shipping images with a registry .nav[ [Previous part](#toc-exposing-containers) | [Back to table of contents](#toc-part-3) | [Next part](#toc-running-our-application-on-kubernetes) ] .debug[(automatically generated title slide)] --- # Shipping images with a registry - Initially, our app was running on a single node - We could *build* and *run* in the same place - Therefore, we did not need to *ship* anything - Now that we want to run on a cluster, things are different - The easiest way to ship container images is to use a registry .debug[[k8s/shippingimages.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/shippingimages.md)] --- ## How Docker registries work (a reminder) - What happens when we execute `docker run alpine` ? - If the Engine needs to pull the `alpine` image, it expands it into `library/alpine` - `library/alpine` is expanded into `index.docker.io/library/alpine` - The Engine communicates with `index.docker.io` to retrieve `library/alpine:latest` - To use something else than `index.docker.io`, we specify it in the image name - Examples: ```bash docker pull gcr.io/google-containers/alpine-with-bash:1.0 docker build -t registry.mycompany.io:5000/myimage:awesome . docker push registry.mycompany.io:5000/myimage:awesome ``` .debug[[k8s/shippingimages.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/shippingimages.md)] --- ## Running DockerCoins on Kubernetes - Create one deployment for each component (hasher, redis, rng, webui, worker) - Expose deployments that need to accept connections (hasher, redis, rng, webui) - For redis, we can use the official redis image - For the 4 others, we need to build images and push them to some registry .debug[[k8s/shippingimages.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/shippingimages.md)] --- ## Building and shipping images - There are *many* options! - Manually: - build locally (with `docker build` or otherwise) - push to the registry - Automatically: - build and test locally - when ready, commit and push a code repository - the code repository notifies an automated build system - that system gets the code, builds it, pushes the image to the registry .debug[[k8s/shippingimages.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/shippingimages.md)] --- ## Which registry do we want to use? - There are SAAS products like Docker Hub, Quay ... - Each major cloud provider has an option as well (ACR on Azure, ECR on AWS, GCR on Google Cloud...) - There are also commercial products to run our own registry (Docker EE, Quay...) - And open source options, too! - When picking a registry, pay attention to its build system (when it has one) .debug[[k8s/shippingimages.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/shippingimages.md)] --- ## Building on the fly - Conceptually, it is possible to build images on the fly from a repository - Example: [ctr.run](https://ctr.run/) (deprecated in August 2020, after being aquired by Datadog) - It did allow something like this: ```bash docker run ctr.run/github.com/jpetazzo/container.training/dockercoins/hasher ``` - No alternative yet (free startup idea, anyone?) ??? :EN:- Shipping images to Kubernetes :FR:- Déployer des images sur notre cluster .debug[[k8s/shippingimages.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/shippingimages.md)] --- ## Self-hosting our registry *Note: this section shows how to run the Docker open source registry and use it to ship images on our cluster. While this method works fine, we recommend that you consider using one of the hosted, free automated build services instead. It will be much easier!* *If you need to run a registry on premises, this section gives you a starting point, but you will need to make a lot of changes so that the registry is secured, highly available, and so that your build pipeline is automated.* .debug[[k8s/buildshiprun-selfhosted.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/buildshiprun-selfhosted.md)] --- ## Using the open source registry - We need to run a `registry` container - It will store images and layers to the local filesystem <br/>(but you can add a config file to use S3, Swift, etc.) - Docker *requires* TLS when communicating with the registry - unless for registries on `127.0.0.0/8` (i.e. `localhost`) - or with the Engine flag `--insecure-registry` - Our strategy: publish the registry container on a NodePort, <br/>so that it's available through `127.0.0.1:xxxxx` on each node .debug[[k8s/buildshiprun-selfhosted.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/buildshiprun-selfhosted.md)] --- ## Deploying a self-hosted registry - We will deploy a registry container, and expose it with a NodePort .exercise[ - Create the registry service: ```bash kubectl create deployment registry --image=registry ``` - Expose it on a NodePort: ```bash kubectl expose deploy/registry --port=5000 --type=NodePort ``` ] .debug[[k8s/buildshiprun-selfhosted.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/buildshiprun-selfhosted.md)] --- ## Connecting to our registry - We need to find out which port has been allocated .exercise[ - View the service details: ```bash kubectl describe svc/registry ``` - Get the port number programmatically: ```bash NODEPORT=$(kubectl get svc/registry -o json | jq .spec.ports[0].nodePort) REGISTRY=127.0.0.1:$NODEPORT ``` ] .debug[[k8s/buildshiprun-selfhosted.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/buildshiprun-selfhosted.md)] --- ## Testing our registry - A convenient Docker registry API route to remember is `/v2/_catalog` .exercise[ <!-- ```hide kubectl wait deploy/registry --for condition=available```--> - View the repositories currently held in our registry: ```bash curl $REGISTRY/v2/_catalog ``` ] -- We should see: ```json {"repositories":[]} ``` .debug[[k8s/buildshiprun-selfhosted.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/buildshiprun-selfhosted.md)] --- ## Testing our local registry - We can retag a small image, and push it to the registry .exercise[ - Make sure we have the busybox image, and retag it: ```bash docker pull busybox docker tag busybox $REGISTRY/busybox ``` - Push it: ```bash docker push $REGISTRY/busybox ``` ] .debug[[k8s/buildshiprun-selfhosted.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/buildshiprun-selfhosted.md)] --- ## Checking again what's on our local registry - Let's use the same endpoint as before .exercise[ - Ensure that our busybox image is now in the local registry: ```bash curl $REGISTRY/v2/_catalog ``` ] The curl command should now output: ```json {"repositories":["busybox"]} ``` .debug[[k8s/buildshiprun-selfhosted.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/buildshiprun-selfhosted.md)] --- ## Building and pushing our images - We are going to use a convenient feature of Docker Compose .exercise[ - Go to the `stacks` directory: ```bash cd ~/container.training/stacks ``` - Build and push the images: ```bash export REGISTRY export TAG=v0.1 docker-compose -f dockercoins.yml build docker-compose -f dockercoins.yml push ``` ] Let's have a look at the `dockercoins.yml` file while this is building and pushing. .debug[[k8s/buildshiprun-selfhosted.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/buildshiprun-selfhosted.md)] --- ```yaml version: "3" services: rng: build: dockercoins/rng image: ${REGISTRY-127.0.0.1:5000}/rng:${TAG-latest} deploy: mode: global ... redis: image: redis ... worker: build: dockercoins/worker image: ${REGISTRY-127.0.0.1:5000}/worker:${TAG-latest} ... deploy: replicas: 10 ``` .warning[Just in case you were wondering ... Docker "services" are not Kubernetes "services".] .debug[[k8s/buildshiprun-selfhosted.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/buildshiprun-selfhosted.md)] --- class: extra-details ## Avoiding the `latest` tag .warning[Make sure that you've set the `TAG` variable properly!] - If you don't, the tag will default to `latest` - The problem with `latest`: nobody knows what it points to! - the latest commit in the repo? - the latest commit in some branch? (Which one?) - the latest tag? - some random version pushed by a random team member? - If you keep pushing the `latest` tag, how do you roll back? - Image tags should be meaningful, i.e. correspond to code branches, tags, or hashes .debug[[k8s/buildshiprun-selfhosted.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/buildshiprun-selfhosted.md)] --- ## Checking the content of the registry - All our images should now be in the registry .exercise[ - Re-run the same `curl` command as earlier: ```bash curl $REGISTRY/v2/_catalog ``` ] *In these slides, all the commands to deploy DockerCoins will use a $REGISTRY environment variable, so that we can quickly switch from the self-hosted registry to pre-built images hosted on the Docker Hub. So make sure that this $REGISTRY variable is set correctly when running the exercises!* .debug[[k8s/buildshiprun-selfhosted.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/buildshiprun-selfhosted.md)] --- ## Using images from the Docker Hub - For everyone's convenience, we took care of building DockerCoins images - We pushed these images to the DockerHub, under the [dockercoins](https://hub.docker.com/u/dockercoins) user - These images are *tagged* with a version number, `v0.1` - The full image names are therefore: - `dockercoins/hasher:v0.1` - `dockercoins/rng:v0.1` - `dockercoins/webui:v0.1` - `dockercoins/worker:v0.1` .debug[[k8s/buildshiprun-dockerhub.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/buildshiprun-dockerhub.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/two-containers-on-a-truck.jpg)] --- name: toc-running-our-application-on-kubernetes class: title Running our application on Kubernetes .nav[ [Previous part](#toc-shipping-images-with-a-registry) | [Back to table of contents](#toc-part-3) | [Next part](#toc-exercise--deploying-on-kubernetes) ] .debug[(automatically generated title slide)] --- # Running our application on Kubernetes - We can now deploy our code (as well as a redis instance) .exercise[ - Deploy `redis`: ```bash kubectl create deployment redis --image=redis ``` - Deploy everything else: ```bash kubectl create deployment hasher --image=dockercoins/hasher:v0.1 kubectl create deployment rng --image=dockercoins/rng:v0.1 kubectl create deployment webui --image=dockercoins/webui:v0.1 kubectl create deployment worker --image=dockercoins/worker:v0.1 ``` ] .debug[[k8s/ourapponkube.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ourapponkube.md)] --- class: extra-details ## Deploying other images - If we wanted to deploy images from another registry ... - ... Or with a different tag ... - ... We could use the following snippet: ```bash REGISTRY=dockercoins TAG=v0.1 for SERVICE in hasher rng webui worker; do kubectl create deployment $SERVICE --image=$REGISTRY/$SERVICE:$TAG done ``` .debug[[k8s/ourapponkube.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ourapponkube.md)] --- ## Is this working? - After waiting for the deployment to complete, let's look at the logs! (Hint: use `kubectl get deploy -w` to watch deployment events) .exercise[ <!-- ```hide kubectl wait deploy/rng --for condition=available kubectl wait deploy/worker --for condition=available ``` --> - Look at some logs: ```bash kubectl logs deploy/rng kubectl logs deploy/worker ``` ] -- 🤔 `rng` is fine ... But not `worker`. -- 💡 Oh right! We forgot to `expose`. .debug[[k8s/ourapponkube.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ourapponkube.md)] --- ## Connecting containers together - Three deployments need to be reachable by others: `hasher`, `redis`, `rng` - `worker` doesn't need to be exposed - `webui` will be dealt with later .exercise[ - Expose each deployment, specifying the right port: ```bash kubectl expose deployment redis --port 6379 kubectl expose deployment rng --port 80 kubectl expose deployment hasher --port 80 ``` ] .debug[[k8s/ourapponkube.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ourapponkube.md)] --- ## Is this working yet? - The `worker` has an infinite loop, that retries 10 seconds after an error .exercise[ - Stream the worker's logs: ```bash kubectl logs deploy/worker --follow ``` (Give it about 10 seconds to recover) <!-- ```wait units of work done, updating hash counter``` ```key ^C``` --> ] -- We should now see the `worker`, well, working happily. .debug[[k8s/ourapponkube.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ourapponkube.md)] --- ## Exposing services for external access - Now we would like to access the Web UI - We will expose it with a `NodePort` (just like we did for the registry) .exercise[ - Create a `NodePort` service for the Web UI: ```bash kubectl expose deploy/webui --type=NodePort --port=80 ``` - Check the port that was allocated: ```bash kubectl get svc ``` ] .debug[[k8s/ourapponkube.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ourapponkube.md)] --- ## Accessing the web UI - We can now connect to *any node*, on the allocated node port, to view the web UI .exercise[ - Open the web UI in your browser (http://node-ip-address:3xxxx/) <!-- ```open http://node1:3xxxx/``` --> ] -- Yes, this may take a little while to update. *(Narrator: it was DNS.)* -- *Alright, we're back to where we started, when we were running on a single node!* ??? :EN:- Running our demo app on Kubernetes :FR:- Faire tourner l'application de démo sur Kubernetes .debug[[k8s/ourapponkube.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ourapponkube.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/wall-of-containers.jpeg)] --- name: toc-exercise--deploying-on-kubernetes class: title Exercise — deploying on Kubernetes .nav[ [Previous part](#toc-running-our-application-on-kubernetes) | [Back to table of contents](#toc-part-3) | [Next part](#toc-deploying-with-yaml) ] .debug[(automatically generated title slide)] --- # Exercise — deploying on Kubernetes Let's deploy the wordsmith app on Kubernetes! As a reminder, we have the following components: | Name | Image | Port | |-------|---------------------------------|------| | db | jpetazzo/wordsmith-db:latest | 5432 | | web | jpetazzo/wordsmith-web:latest | 80 | | words | jpetazzo/wordsmith-words:latest | 8080 | We need `web` to be available from outside the cluster. See next slide if you need hints! .debug[[k8s/exercise-wordsmith.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/exercise-wordsmith.md)] --- ## Hints *Scroll one slide at a time to see hints.* -- - For each component, we need to create a deployment and a service -- - Deployments can be created with `kubectl create deployment` -- - Services can be created with `kubectl expose` -- - Public services (like `web`) need to use a special type (e.g. `NodePort`) .debug[[k8s/exercise-wordsmith.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/exercise-wordsmith.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/Container-Ship-Freighter-Navigation-Elbe-Romance-1782991.jpg)] --- name: toc-deploying-with-yaml class: title Deploying with YAML .nav[ [Previous part](#toc-exercise--deploying-on-kubernetes) | [Back to table of contents](#toc-part-3) | [Next part](#toc-setting-up-kubernetes) ] .debug[(automatically generated title slide)] --- # Deploying with YAML - So far, we created resources with the following commands: - `kubectl run` - `kubectl create deployment` - `kubectl expose` - We can also create resources directly with YAML manifests .debug[[k8s/yamldeploy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/yamldeploy.md)] --- ## `kubectl apply` vs `create` - `kubectl create -f whatever.yaml` - creates resources if they don't exist - if resources already exist, don't alter them <br/>(and display error message) - `kubectl apply -f whatever.yaml` - creates resources if they don't exist - if resources already exist, update them <br/>(to match the definition provided by the YAML file) - stores the manifest as an *annotation* in the resource .debug[[k8s/yamldeploy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/yamldeploy.md)] --- ## Creating multiple resources - The manifest can contain multiple resources separated by `---` ```yaml kind: ... apiVersion: ... metadata: ... name: ... ... --- kind: ... apiVersion: ... metadata: ... name: ... ... ``` .debug[[k8s/yamldeploy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/yamldeploy.md)] --- ## Creating multiple resources - The manifest can also contain a list of resources ```yaml apiVersion: v1 kind: List items: - kind: ... apiVersion: ... ... - kind: ... apiVersion: ... ... ``` .debug[[k8s/yamldeploy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/yamldeploy.md)] --- ## Deploying dockercoins with YAML - We provide a YAML manifest with all the resources for Dockercoins (Deployments and Services) - We can use it if we need to deploy or redeploy Dockercoins .exercise[ - Deploy or redeploy Dockercoins: ```bash kubectl apply -f ~/container.training/k8s/dockercoins.yaml ``` ] (If we deployed Dockercoins earlier, we will see warning messages, because the resources that we created lack the necessary annotation. We can safely ignore them.) .debug[[k8s/yamldeploy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/yamldeploy.md)] --- ## Deleting resources - We can also use a YAML file to *delete* resources - `kubectl delete -f ...` will delete all the resources mentioned in a YAML file (useful to clean up everything that was created by `kubectl apply -f ...`) - The definitions of the resources don't matter (just their `kind`, `apiVersion`, and `name`) .debug[[k8s/yamldeploy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/yamldeploy.md)] --- ## Pruning¹ resources - We can also tell `kubectl` to remove old resources - This is done with `kubectl apply -f ... --prune` - It will remove resources that don't exist in the YAML file(s) - But only if they were created with `kubectl apply` in the first place (technically, if they have an annotation `kubectl.kubernetes.io/last-applied-configuration`) .footnote[¹If English is not your first language: *to prune* means to remove dead or overgrown branches in a tree, to help it to grow.] .debug[[k8s/yamldeploy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/yamldeploy.md)] --- ## YAML as source of truth - Imagine the following workflow: - do not use `kubectl run`, `kubectl create deployment`, `kubectl expose` ... - define everything with YAML - `kubectl apply -f ... --prune --all` that YAML - keep that YAML under version control - enforce all changes to go through that YAML (e.g. with pull requests) - Our version control system now has a full history of what we deploy - Compares to "Infrastructure-as-Code", but for app deployments .debug[[k8s/yamldeploy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/yamldeploy.md)] --- class: extra-details ## Specifying the namespace - When creating resources from YAML manifests, the namespace is optional - If we specify a namespace: - resources are created in the specified namespace - this is typical for things deployed only once per cluster - example: system components, cluster add-ons ... - If we don't specify a namespace: - resources are created in the current namespace - this is typical for things that may be deployed multiple times - example: applications (production, staging, feature branches ...) ??? :EN:- Deploying with YAML manifests :FR:- Déployer avec des *manifests* YAML .debug[[k8s/yamldeploy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/yamldeploy.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)] --- name: toc-setting-up-kubernetes class: title Setting up Kubernetes .nav[ [Previous part](#toc-deploying-with-yaml) | [Back to table of contents](#toc-part-4) | [Next part](#toc-running-a-local-development-cluster) ] .debug[(automatically generated title slide)] --- # Setting up Kubernetes - Kubernetes is made of many components that require careful configuration - Secure operation typically requires TLS certificates and a local CA (certificate authority) - Setting up everything manually is possible, but rarely done (except for learning purposes) - Let's do a quick overview of available options! .debug[[k8s/setup-overview.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-overview.md)] --- ## Local development - Are you writing code that will eventually run on Kubernetes? - Then it's a good idea to have a development cluster! - Development clusters only need one node - This simplifies their setup a lot: - pod networking doesn't even need CNI plugins, overlay networks, etc. - they can be fully contained (no pun intended) in an easy-to-ship VM image - some of the security aspects may be simplified (different threat model) - Examples: Docker Desktop, k3d, KinD, MicroK8s, Minikube (some of these also support clusters with multiple nodes) .debug[[k8s/setup-overview.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-overview.md)] --- ## Managed clusters - Many cloud providers and hosting providers offer "managed Kubernetes" - The deployment and maintenance of the cluster is entirely managed by the provider (ideally, clusters can be spun up automatically through an API, CLI, or web interface) - Given the complexity of Kubernetes, this approach is *strongly recommended* (at least for your first production clusters) - After working for a while with Kubernetes, you will be better equipped to decide: - whether to operate it yourself or use a managed offering - which offering or which distribution works best for you and your needs .debug[[k8s/setup-overview.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-overview.md)] --- ## Managed clusters details - Pricing models differ from one provider to another - nodes are generally charged at their usual price - control plane may be free or incur a small nominal fee - Beyond pricing, there are *huge* differences in features between providers - The "major" providers are not always the best ones! .debug[[k8s/setup-overview.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-overview.md)] --- ## Managed clusters differences - Most providers let you pick which Kubernetes version you want - some providers offer up-to-date versions - others lag significantly (sometimes by 2 or 3 minor versions) - Some providers offer multiple networking or storage options - Others will only support one, tied to their infrastructure (changing that is in theory possible, but might be complex or unsupported) - Some providers let you configure or customize the control plane (generally through Kubernetes "feature gates") .debug[[k8s/setup-overview.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-overview.md)] --- ## Kubernetes distributions and installers - If you want to run Kubernetes yourselves, there are many options (free, commercial, proprietary, open source ...) - Some of them are installers, while some are complete platforms - Some of them leverage other well-known deployment tools (like Puppet, Terraform ...) - A good starting point to explore these options is this [guide](https://v1-16.docs.kubernetes.io/docs/setup/#production-environment) (it defines categories like "managed", "turnkey" ...) .debug[[k8s/setup-overview.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-overview.md)] --- ## kubeadm - kubeadm is a tool part of Kubernetes to facilitate cluster setup - Many other installers and distributions use it (but not all of them) - It can also be used by itself - Excellent starting point to install Kubernetes on your own machines (virtual, physical, it doesn't matter) - It even supports highly available control planes, or "multi-master" (this is more complex, though, because it introduces the need for an API load balancer) .debug[[k8s/setup-overview.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-overview.md)] --- ## Manual setup - The resources below are mainly for educational purposes! - [Kubernetes The Hard Way](https://github.com/kelseyhightower/kubernetes-the-hard-way) by Kelsey Hightower - step by step guide to install Kubernetes on Google Cloud - covers certificates, high availability ... - *“Kubernetes The Hard Way is optimized for learning, which means taking the long route to ensure you understand each task required to bootstrap a Kubernetes cluster.”* - [Deep Dive into Kubernetes Internals for Builders and Operators](https://www.youtube.com/watch?v=3KtEAa7_duA) - conference presentation showing step-by-step control plane setup - emphasis on simplicity, not on security and availability .debug[[k8s/setup-overview.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-overview.md)] --- ## About our training clusters - How did we set up these Kubernetes clusters that we're using? -- - We used `kubeadm` on freshly installed VM instances running Ubuntu LTS 1. Install Docker 2. Install Kubernetes packages 3. Run `kubeadm init` on the first node (it deploys the control plane on that node) 4. Set up Weave (the overlay network) with a single `kubectl apply` command 5. Run `kubeadm join` on the other nodes (with the token produced by `kubeadm init`) 6. Copy the configuration file generated by `kubeadm init` - Check the [prepare VMs README](https://github.com/barpilot/container.training/blob/master/prepare-vms/README.md) for more details .debug[[k8s/setup-overview.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-overview.md)] --- ## `kubeadm` "drawbacks" - Doesn't set up Docker or any other container engine (this is by design, to give us choice) - Doesn't set up the overlay network (this is also by design, for the same reasons) - HA control plane requires [some extra steps](https://kubernetes.io/docs/setup/independent/high-availability/) - Note that HA control plane also requires setting up a specific API load balancer (which is beyond the scope of kubeadm) ??? :EN:- Various ways to install Kubernetes :FR:- Survol des techniques d'installation de Kubernetes .debug[[k8s/setup-overview.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-overview.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/aerial-view-of-containers.jpg)] --- name: toc-running-a-local-development-cluster class: title Running a local development cluster .nav[ [Previous part](#toc-setting-up-kubernetes) | [Back to table of contents](#toc-part-4) | [Next part](#toc-deploying-a-managed-cluster) ] .debug[(automatically generated title slide)] --- # Running a local development cluster - Let's review some options to run Kubernetes locally - There is no "best option", it depends what you value: - ability to run on all platforms (Linux, Mac, Windows, other?) - ability to run clusters with multiple nodes - ability to run multiple clusters side by side - ability to run recent (or even, unreleased) versions of Kubernetes - availability of plugins - etc. .debug[[k8s/setup-devel.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-devel.md)] --- ## Docker Desktop - Available on Mac and Windows - Gives you one cluster with one node - Very easy to use if you are already using Docker Desktop: go to Docker Desktop preferences and enable Kubernetes - Ideal for Docker users who need good integration between both platforms .debug[[k8s/setup-devel.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-devel.md)] --- ## [k3d](https://k3d.io/) - Based on [K3s](https://k3s.io/) by Rancher Labs - Requires Docker - Runs Kubernetes nodes in Docker containers - Can deploy multiple clusters, with multiple nodes, and multiple master nodes - As of June 2020, two versions co-exist: stable (1.7) and beta (3.0) - They have different syntax and options, this can be confusing (but don't let that stop you!) .debug[[k8s/setup-devel.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-devel.md)] --- ## k3d in action - Install `k3d` (e.g. get the binary from https://github.com/rancher/k3d/releases) - Create a simple cluster: ```bash k3d cluster create petitcluster ``` - Create a more complex cluster with a custom version: ```bash k3d cluster create groscluster \ --image rancher/k3s:v1.18.9-k3s1 --servers 3 --agents 5 ``` (3 nodes for the control plane + 5 worker nodes) - Clusters are automatically added to `.kube/config` file .debug[[k8s/setup-devel.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-devel.md)] --- ## [KinD](https://kind.sigs.k8s.io/) - Kubernetes-in-Docker - Requires Docker (obviously!) - Deploying a single node cluster using the latest version is simple: ```bash kind create cluster ``` - More advanced scenarios require writing a short [config file](https://kind.sigs.k8s.io/docs/user/quick-start#configuring-your-kind-cluster) (to define multiple nodes, multiple master nodes, set Kubernetes versions ...) - Can deploy multiple clusters .debug[[k8s/setup-devel.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-devel.md)] --- ## [Minikube](https://minikube.sigs.k8s.io/docs/) - The "legacy" option! (note: this is not a bad thing, it means that it's very stable, has lots of plugins, etc.) - Supports many [drivers](https://minikube.sigs.k8s.io/docs/drivers/) (HyperKit, Hyper-V, KVM, VirtualBox, but also Docker and many others) - Can deploy a single cluster; recent versions can deploy multiple nodes - Great option if you want a "Kubernetes first" experience (i.e. if you don't already have Docker and/or don't want/need it) .debug[[k8s/setup-devel.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-devel.md)] --- ## [MicroK8s](https://microk8s.io/) - Available on Linux, and since recently, on Mac and Windows as well - The Linux version is installed through Snap (which is pre-installed on all recent versions of Ubuntu) - Also supports clustering (as in, multiple machines running MicroK8s) - DNS is not enabled by default; enable it with `microk8s enable dns` .debug[[k8s/setup-devel.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-devel.md)] --- ## VM with custom install - Choose your own adventure! - Pick any Linux distribution! - Build your cluster from scratch or use a Kubernetes installer! - Discover exotic CNI plugins and container runtimes! - The only limit is yourself, and the time you are willing to sink in! ??? :EN:- Kubernetes options for local development :FR:- Installation de Kubernetes pour travailler en local .debug[[k8s/setup-devel.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-devel.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/blue-containers.jpg)] --- name: toc-deploying-a-managed-cluster class: title Deploying a managed cluster .nav[ [Previous part](#toc-running-a-local-development-cluster) | [Back to table of contents](#toc-part-4) | [Next part](#toc-kubernetes-distributions-and-installers) ] .debug[(automatically generated title slide)] --- # Deploying a managed cluster *"The easiest way to install Kubernetes is to get someone else to do it for you." <br/> ([Jérôme Petazzoni](https://twitter.com/jpetazzo))* - Let's see a few options to install managed clusters! - This is not an exhaustive list (the goal is to show the actual steps to get started) - The list is sorted alphabetically - All the options mentioned here require an account with a cloud provider - ... And a credit card .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- ## AKS (initial setup) - Install the Azure CLI - Login: ```bash az login ``` - Select a [region](https://azure.microsoft.com/en-us/global-infrastructure/services/?products=kubernetes-service®ions=all ) - Create a "resource group": ```bash az group create --name my-aks-group --location westeurope ``` .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- ## AKS (create cluster) - Create the cluster: ```bash az aks create --resource-group my-aks-group --name my-aks-cluster ``` - Wait about 5-10 minutes - Add credentials to `kubeconfig`: ```bash az aks get-credentials --resource-group my-aks-group --name my-aks-cluster ``` .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- ## AKS (cleanup) - Delete the cluster: ```bash az aks delete --resource-group my-aks-group --name my-aks-cluster ``` - Delete the resource group: ```bash az group delete --resource-group my-aks-group ``` - Note: delete actions can take a while too! (5-10 minutes as well) .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- ## AKS (notes) - The cluster has useful components pre-installed, such as the metrics server - There is also a product called [AKS Engine](https://github.com/Azure/aks-engine): - leverages ARM (Azure Resource Manager) templates to deploy Kubernetes - it's "the library used by AKS" - fully customizable - think of it as "half-managed" Kubernetes option .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- ## Amazon EKS (the old way) - [Read the doc](https://docs.aws.amazon.com/eks/latest/userguide/getting-started-console.html) - Create service roles, VPCs, and a bunch of other oddities - Try to figure out why it doesn't work - Start over, following an [official AWS blog post](https://aws.amazon.com/blogs/aws/amazon-eks-now-generally-available/) - Try to find the missing Cloud Formation template -- .footnote[(╯°□°)╯︵ ┻━┻] .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- ## Amazon EKS (the new way) - Install `eksctl` - Set the usual environment variables ([AWS_DEFAULT_REGION](https://docs.aws.amazon.com/general/latest/gr/rande.html#eks_region), AWS_ACCESS_KEY, AWS_SECRET_ACCESS_KEY) - Create the cluster: ```bash eksctl create cluster ``` - Cluster can take a long time to be ready (15-20 minutes is typical) - Add cluster add-ons (by default, it doesn't come with metrics-server, logging, etc.) .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- ## Amazon EKS (cleanup) - Delete the cluster: ```bash eksctl delete cluster <clustername> ``` - If you need to find the name of the cluster: ```bash eksctl get clusters ``` .footnote[Note: the AWS documentation has been updated and now includes [eksctl instructions](https://docs.aws.amazon.com/eks/latest/userguide/getting-started-eksctl.html).] .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- ## Amazon EKS (notes) - Convenient if you *have to* use AWS - Needs extra steps to be truly production-ready - [Versions tend to be outdated](https://twitter.com/jpetazzo/status/1252948707680686081) - The only officially supported pod network is the [Amazon VPC CNI plugin](https://docs.aws.amazon.com/eks/latest/userguide/pod-networking.html) - integrates tightly with security groups and VPC networking - not suitable for high density clusters (with many small pods on big nodes) - other plugins [should still work](https://docs.aws.amazon.com/eks/latest/userguide/alternate-cni-plugins.html) but will require extra work .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- ## Digital Ocean (initial setup) - Install `doctl` - Generate API token (in web console) - Set up the CLI authentication: ```bash doctl auth init ``` (It will ask you for the API token) - Check the list of regions and pick one: ```bash doctl compute region list ``` (If you don't specify the region later, it will use `nyc1`) .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- ## Digital Ocean (create cluster) - Create the cluster: ```bash doctl kubernetes cluster create my-do-cluster [--region xxx1] ``` - Wait 5 minutes - Update `kubeconfig`: ```bash kubectl config use-context do-xxx1-my-do-cluster ``` - The cluster comes with some components (like Cilium) but no metrics server .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- ## Digital Ocean (cleanup) - List clusters (if you forgot its name): ```bash doctl kubernetes cluster list ``` - Delete the cluster: ```bash doctl kubernetes cluster delete my-do-cluster ``` .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- ## GKE (initial setup) - Install `gcloud` - Login: ```bash gcloud auth init ``` - Create a "project": ```bash gcloud projects create my-gke-project gcloud config set project my-gke-project ``` - Pick a [region](https://cloud.google.com/compute/docs/regions-zones/) (example: `europe-west1`, `us-west1`, ...) .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- ## GKE (create cluster) - Create the cluster: ```bash gcloud container clusters create my-gke-cluster --region us-west1 --num-nodes=2 ``` (without `--num-nodes` you might exhaust your IP address quota!) - The first time you try to create a cluster in a given project, you get an error - you need to enable the Kubernetes Engine API - the error message gives you a link - follow the link and enable the API (and billing) <br/>(it's just a couple of clicks and it's instantaneous) - Clutser should be ready in a couple of minutes .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- ## GKE (cleanup) - List clusters (if you forgot its name): ```bash gcloud container clusters list ``` - Delete the cluster: ```bash gcloud container clusters delete my-gke-cluster --region us-west1 ``` - Delete the project (optional): ```bash gcloud projects delete my-gke-project ``` .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- ## GKE (notes) - Well-rounded product overall (it used to be one of the best managed Kubernetes offerings available; now that many other providers entered the game, that title is debatable) - The cluster comes with many add-ons - Versions lag a bit: - latest minor version (e.g. 1.18) tends to be unsupported - previous minor version (e.g. 1.17) supported through alpha channel - previous versions (e.g. 1.14-1.16) supported .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- ## Scaleway (initial setup) - After creating your account, make sure you set a password or get an API key (by default, it uses email "magic links" to sign in) - Install `scw` (you need [CLI v2](https://github.com/scaleway/scaleway-cli/tree/v2#Installation), which in beta as of May 2020) - Generate the CLI configuration with `scw init` (it will prompt for your API key, or email + password) .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- ## Scaleway (create cluster) - Create the cluster: ```bash k8s cluster create name=my-kapsule-cluster version=1.18.3 cni=cilium \ default-pool-config.node-type=DEV1-M default-pool-config.size=3 ``` - After less than 5 minutes, cluster state will be `ready` (check cluster status with e.g. `scw k8s cluster list` on a wide terminal ) - Add connection information to your `.kube/config` file: ```bash scw k8s kubeconfig install `CLUSTERID` ``` (the cluster ID is shown by `scw k8s cluster list`) .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- class: extra-details ## Scaleway (automation) - If you want to obtain the cluster ID programmatically, this will do it: ```bash scw k8s cluster list # or CLUSTERID=$(scw k8s cluster list -o json | \ jq -r '.[] | select(.name="my-kapsule-cluster") | .id') ``` .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- ## Scaleway (cleanup) - Get cluster ID (e.g. with `scw k8s cluster list`) - Delete the cluster: ```bash scw cluster delete cluster-id=$CLUSTERID ``` - Warning: as of May 2020, load balancers have to be deleted separately! .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- ## Scaleway (notes) - The `create` command is a bit more complex than with other providers (you must specify the Kubernetes version, CNI plugin, and node type) - To see available versions and CNI plugins, run `scw k8s version list` - As of May 2020, Kapsule supports: - multiple CNI plugins, including: cilium, calico, weave, flannel - Kubernetes versions 1.15 to 1.18 - multiple container runtimes, including: Docker, containerd, CRI-O - To see available node types and their price, check their [pricing page]( https://www.scaleway.com/en/pricing/) .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- ## More options - Alibaba Cloud - [IBM Cloud](https://console.bluemix.net/docs/containers/cs_cli_install.html#cs_cli_install) - [Linode Kubernetes Engine (LKE)](https://www.linode.com/products/kubernetes/) - OVHcloud [Managed Kubernetes Service](https://www.ovhcloud.com/en/public-cloud/kubernetes/) - ... ??? :EN:- Installing a managed cluster :FR:- Installer un cluster infogéré .debug[[k8s/setup-managed.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-managed.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/chinook-helicopter-container.jpg)] --- name: toc-kubernetes-distributions-and-installers class: title Kubernetes distributions and installers .nav[ [Previous part](#toc-deploying-a-managed-cluster) | [Back to table of contents](#toc-part-4) | [Next part](#toc-the-kubernetes-dashboard) ] .debug[(automatically generated title slide)] --- # Kubernetes distributions and installers - Sometimes, we need to run Kubernetes ourselves (as opposed to "use a managed offering") - Beware: it takes *a lot of work* to set up and maintain Kubernetes - It might be necessary if you have specific security or compliance requirements (e.g. national security for states that don't have a suitable domestic cloud) - There are [countless](https://kubernetes.io/docs/setup/pick-right-solution/) distributions available - We can't review them all - We're just going to explore a few options .debug[[k8s/setup-selfhosted.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-selfhosted.md)] --- ## [kops](https://github.com/kubernetes/kops) - Deploys Kubernetes using cloud infrastructure (supports AWS, GCE, Digital Ocean ...) - Leverages special cloud features when possible (e.g. Auto Scaling Groups ...) .debug[[k8s/setup-selfhosted.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-selfhosted.md)] --- ## kubeadm - Provisions Kubernetes nodes on top of existing machines - `kubeadm init` to provision a single-node control plane - `kubeadm join` to join a node to the cluster - Supports HA control plane [with some extra steps](https://kubernetes.io/docs/setup/independent/high-availability/) .debug[[k8s/setup-selfhosted.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-selfhosted.md)] --- ## [kubespray](https://github.com/kubernetes-incubator/kubespray) - Based on Ansible - Works on bare metal and cloud infrastructure (good for hybrid deployments) - The expert says: ultra flexible; slow; complex .debug[[k8s/setup-selfhosted.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-selfhosted.md)] --- ## RKE (Rancher Kubernetes Engine) - Opinionated installer with low requirements - Requires a set of machines with Docker + SSH access - Supports highly available etcd and control plane - The expert says: fast; maintenance can be tricky .debug[[k8s/setup-selfhosted.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-selfhosted.md)] --- ## Terraform + kubeadm - Sometimes it is necessary to build a custom solution - Example use case: - deploying Kubernetes on OpenStack - ... with highly available control plane - ... and Cloud Controller Manager integration - Solution: Terraform + kubeadm (kubeadm driven by remote-exec) - [GitHub repository](https://github.com/enix/terraform-openstack-kubernetes) - [Blog post (in French)](https://enix.io/fr/blog/deployer-kubernetes-1-13-sur-openstack-grace-a-terraform/) .debug[[k8s/setup-selfhosted.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-selfhosted.md)] --- ## And many more ... - [AKS Engine](https://github.com/Azure/aks-engine) - Docker Enterprise Edition - [Lokomotive](https://github.com/kinvolk/lokomotive), leveraging Terraform and [Flatcar Linux](https://www.flatcar-linux.org/) - Pivotal Container Service (PKS) - [Tarmak](https://github.com/jetstack/tarmak), leveraging Puppet and Terraform - Tectonic by CoreOS (now being integrated into Red Hat OpenShift) - [Typhoon](https://typhoon.psdn.io/), leveraging Terraform - VMware Tanzu Kubernetes Grid (TKG) .debug[[k8s/setup-selfhosted.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-selfhosted.md)] --- ## Bottom line - Each distribution / installer has pros and cons - Before picking one, we should sort out our priorities: - cloud, on-premises, hybrid? - integration with existing network/storage architecture or equipment? - are we storing very sensitive data, like finance, health, military? - how many clusters are we deploying (and maintaining): 2, 10, 50? - which team will be responsible for deployment and maintenance? <br/>(do they need training?) - etc. ??? :EN:- Kubernetes distributions and installers :FR:- L'offre Kubernetes "on premises" .debug[[k8s/setup-selfhosted.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/setup-selfhosted.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/container-cranes.jpg)] --- name: toc-the-kubernetes-dashboard class: title The Kubernetes dashboard .nav[ [Previous part](#toc-kubernetes-distributions-and-installers) | [Back to table of contents](#toc-part-4) | [Next part](#toc-security-implications-of-kubectl-apply) ] .debug[(automatically generated title slide)] --- # The Kubernetes dashboard - Kubernetes resources can also be viewed with a web dashboard - Dashboard users need to authenticate (typically with a token) - The dashboard should be exposed over HTTPS (to prevent interception of the aforementioned token) - Ideally, this requires obtaining a proper TLS certificate (for instance, with Let's Encrypt) .debug[[k8s/dashboard.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dashboard.md)] --- ## Three ways to install the dashboard - Our `k8s` directory has no less than three manifests! - `dashboard-recommended.yaml` (purely internal dashboard; user must be created manually) - `dashboard-with-token.yaml` (dashboard exposed with NodePort; creates an admin user for us) - `dashboard-insecure.yaml` aka *YOLO* (dashboard exposed over HTTP; gives root access to anonymous users) .debug[[k8s/dashboard.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dashboard.md)] --- ## `dashboard-insecure.yaml` - This will allow anyone to deploy anything on your cluster (without any authentication whatsoever) - **Do not** use this, except maybe on a local cluster (or a cluster that you will destroy a few minutes later) - On "normal" clusters, use `dashboard-with-token.yaml` instead! .debug[[k8s/dashboard.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dashboard.md)] --- ## What's in the manifest? - The dashboard itself - An HTTP/HTTPS unwrapper (using `socat`) - The guest/admin account .exercise[ - Create all the dashboard resources, with the following command: ```bash kubectl apply -f ~/container.training/k8s/dashboard-insecure.yaml ``` ] .debug[[k8s/dashboard.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dashboard.md)] --- ## Connecting to the dashboard .exercise[ - Check which port the dashboard is on: ```bash kubectl get svc dashboard ``` ] You'll want the `3xxxx` port. .exercise[ - Connect to http://oneofournodes:3xxxx/ <!-- ```open http://node1:3xxxx/``` --> ] The dashboard will then ask you which authentication you want to use. .debug[[k8s/dashboard.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dashboard.md)] --- ## Dashboard authentication - We have three authentication options at this point: - token (associated with a role that has appropriate permissions) - kubeconfig (e.g. using the `~/.kube/config` file from `node1`) - "skip" (use the dashboard "service account") - Let's use "skip": we're logged in! -- .warning[Remember, we just added a backdoor to our Kubernetes cluster!] .debug[[k8s/dashboard.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dashboard.md)] --- ## Closing the backdoor - Seriously, don't leave that thing running! .exercise[ - Remove what we just created: ```bash kubectl delete -f ~/container.training/k8s/dashboard-insecure.yaml ``` ] .debug[[k8s/dashboard.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dashboard.md)] --- ## The risks - The steps that we just showed you are *for educational purposes only!* - If you do that on your production cluster, people [can and will abuse it](https://redlock.io/blog/cryptojacking-tesla) - For an in-depth discussion about securing the dashboard, <br/> check [this excellent post on Heptio's blog](https://blog.heptio.com/on-securing-the-kubernetes-dashboard-16b09b1b7aca) .debug[[k8s/dashboard.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dashboard.md)] --- ## `dashboard-with-token.yaml` - This is a less risky way to deploy the dashboard - It's not completely secure, either: - we're using a self-signed certificate - this is subject to eavesdropping attacks - Using `kubectl port-forward` or `kubectl proxy` is even better .debug[[k8s/dashboard.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dashboard.md)] --- ## What's in the manifest? - The dashboard itself (but exposed with a `NodePort`) - A ServiceAccount with `cluster-admin` privileges (named `kubernetes-dashboard:cluster-admin`) .exercise[ - Create all the dashboard resources, with the following command: ```bash kubectl apply -f ~/container.training/k8s/dashboard-with-token.yaml ``` ] .debug[[k8s/dashboard.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dashboard.md)] --- ## Obtaining the token - The manifest creates a ServiceAccount - Kubernetes will automatically generate a token for that ServiceAccount .exercise[ - Display the token: ```bash kubectl --namespace=kubernetes-dashboard \ describe secret cluster-admin-token ``` ] The token should start with `eyJ...` (it's a JSON Web Token). Note that the secret name will actually be `cluster-admin-token-xxxxx`. <br/> (But `kubectl` prefix matches are great!) .debug[[k8s/dashboard.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dashboard.md)] --- ## Connecting to the dashboard .exercise[ - Check which port the dashboard is on: ```bash kubectl get svc --namespace=kubernetes-dashboard ``` ] You'll want the `3xxxx` port. .exercise[ - Connect to http://oneofournodes:3xxxx/ <!-- ```open http://node1:3xxxx/``` --> ] The dashboard will then ask you which authentication you want to use. .debug[[k8s/dashboard.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dashboard.md)] --- ## Dashboard authentication - Select "token" authentication - Copy paste the token (starting with `eyJ...`) obtained earlier - We're logged in! .debug[[k8s/dashboard.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dashboard.md)] --- ## Other dashboards - [Kube Web View](https://codeberg.org/hjacobs/kube-web-view) - read-only dashboard - optimized for "troubleshooting and incident response" - see [vision and goals](https://kube-web-view.readthedocs.io/en/latest/vision.html#vision) for details - [Kube Ops View](https://codeberg.org/hjacobs/kube-ops-view) - "provides a common operational picture for multiple Kubernetes clusters" .debug[[k8s/dashboard.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dashboard.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/container-housing.jpg)] --- name: toc-security-implications-of-kubectl-apply class: title Security implications of `kubectl apply` .nav[ [Previous part](#toc-the-kubernetes-dashboard) | [Back to table of contents](#toc-part-4) | [Next part](#toc-ks) ] .debug[(automatically generated title slide)] --- # Security implications of `kubectl apply` - When we do `kubectl apply -f <URL>`, we create arbitrary resources - Resources can be evil; imagine a `deployment` that ... -- - starts bitcoin miners on the whole cluster -- - hides in a non-default namespace -- - bind-mounts our nodes' filesystem -- - inserts SSH keys in the root account (on the node) -- - encrypts our data and ransoms it -- - ☠️☠️☠️ .debug[[k8s/dashboard.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dashboard.md)] --- ## `kubectl apply` is the new `curl | sh` - `curl | sh` is convenient - It's safe if you use HTTPS URLs from trusted sources -- - `kubectl apply -f` is convenient - It's safe if you use HTTPS URLs from trusted sources - Example: the official setup instructions for most pod networks -- - It introduces new failure modes (for instance, if you try to apply YAML from a link that's no longer valid) ??? :EN:- The Kubernetes dashboard :FR:- Le *dashboard* Kubernetes .debug[[k8s/dashboard.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dashboard.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/containers-by-the-water.jpg)] --- name: toc-ks class: title k9s .nav[ [Previous part](#toc-security-implications-of-kubectl-apply) | [Back to table of contents](#toc-part-4) | [Next part](#toc-tilt) ] .debug[(automatically generated title slide)] --- # k9s - Somewhere in between CLI and GUI (or web UI), we can find the magic land of TUI - [Text-based user interfaces](https://en.wikipedia.org/wiki/Text-based_user_interface) - often using libraries like [curses](https://en.wikipedia.org/wiki/Curses_%28programming_library%29) and its successors - Some folks love them, some folks hate them, some are indifferent ... - But it's nice to have different options! - Let's see one particular TUI for Kubernetes: [k9s](https://k9scli.io/) .debug[[k8s/k9s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/k9s.md)] --- ## Installing k9s - If you are using a training cluster or the [shpod](https://github.com/jpetazzo/shpod) image, k9s is pre-installed - Otherwise, it can be installed easily: - with [various package managers](https://k9scli.io/topics/install/) - or by fetching a [binary release](https://github.com/derailed/k9s/releases) - We don't need to set up or configure anything (it will use the same configuration as `kubectl` and other well-behaved clients) - Just run `k9s` to fire it up! .debug[[k8s/k9s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/k9s.md)] --- ## What kind to we want to see? - Press `:` to change the type of resource to view - Then type, for instance, `ns` or `namespace` or `nam[TAB]`, then `[ENTER]` - Use the arrows to move down to e.g. `kube-system`, and press `[ENTER]` - Or, type `/kub` or `/sys` to filter the output, and press `[ENTER]` twice (once to exit the filter, once to enter the namespace) - We now see the pods in `kube-system`! .debug[[k8s/k9s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/k9s.md)] --- ## Interacting with pods - `l` to view logs - `d` to describe - `s` to get a shell (won't work if `sh` isn't available in the container image) - `e` to edit - `shift-f` to define port forwarding - `ctrl-k` to kill - `[ESC]` to get out or get back .debug[[k8s/k9s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/k9s.md)] --- ## Quick navigation between namespaces - On top of the screen, we should see shortcuts like this: ``` <0> all <1> kube-system <2> default ``` - Pressing the corresponding number switches to that namespace (or shows resources across all namespaces with `0`) - Locate a namespace with a copy of DockerCoins, and go there! .debug[[k8s/k9s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/k9s.md)] --- ## Interacting with Deployments - View Deployments (type `:` `deploy` `[ENTER]`) - Select e.g. `worker` - Scale it with `s` - View its aggregated logs with `l` .debug[[k8s/k9s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/k9s.md)] --- ## Exit - Exit at any time with `Ctrl-C` - k9s will "remember" where you were (and go back there next time you run it) .debug[[k8s/k9s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/k9s.md)] --- ## Pros - Very convenient to navigate through resources (hopping from a deployment, to its pod, to another namespace, etc.) - Very convenient to quickly view logs of e.g. init containers - Very convenient to get a (quasi) realtime view of resources (if we use `watch kubectl get` a lot, we will probably like k9s) .debug[[k8s/k9s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/k9s.md)] --- ## Cons - Doesn't promote automation / scripting (if you repeat the same things over and over, there is a scripting opportunity) - Not all features are available (e.g. executing arbitrary commands in containers) .debug[[k8s/k9s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/k9s.md)] --- ## Conclusion Try it out, and see if it makes you more productive! ??? :EN:- The k9s TUI :FR:- L'interface texte k9s .debug[[k8s/k9s.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/k9s.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/distillery-containers.jpg)] --- name: toc-tilt class: title Tilt .nav[ [Previous part](#toc-ks) | [Back to table of contents](#toc-part-4) | [Next part](#toc-scaling-a-deployment) ] .debug[(automatically generated title slide)] --- # Tilt - What does a development workflow look like? - make changes - test / see these changes - repeat! - What does it look like, with containers? 🤔 .debug[[k8s/tilt.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/tilt.md)] --- ## Basic Docker workflow - Preparation - write Dockerfiles - Iteration - edit code - `docker build` - `docker run` - test - `docker stop` Straightforward when we have a single container. .debug[[k8s/tilt.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/tilt.md)] --- ## Docker workflow with volumes - Preparation - write Dockerfiles - `docker build` + `docker run` - Iteration - edit code - test Note: only works with interpreted languages. <br/> (Compiled languages require extra work.) .debug[[k8s/tilt.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/tilt.md)] --- ## Docker workflow with Compose - Preparation - write Dockerfiles + Compose file - `docker-compose up` - Iteration - edit code - test - `docker-compose up` (as needed) Simplifies complex scenarios (multiple containers). <br/> Facilitates updating images. .debug[[k8s/tilt.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/tilt.md)] --- ## Basic Kubernetes workflow - Preparation - write Dockerfiles - write Kubernetes YAML - set up container registry - Iteration - edit code - build images - push images - update Kubernetes resources Seems simple enough, right? .debug[[k8s/tilt.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/tilt.md)] --- ## Basic Kubernetes workflow - Preparation - write Dockerfiles - write Kubernetes YAML - **set up container registry** - Iteration - edit code - build images - **push images** - update Kubernetes resources Ah, right ... .debug[[k8s/tilt.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/tilt.md)] --- ## We need a registry - Remember "build, ship, and run" - Registries are involved in the "ship" phase - With Docker, we were building and running on the same node - We didn't need a registry! - With Kubernetes, though ... .debug[[k8s/tilt.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/tilt.md)] --- ## Special case of single node clusters - If our Kubernetes has only one node ... - ... We can build directly on that node ... - ... We don't need to push images ... - ... We don't need to run a registry! - Examples: Docker Desktop, Minikube ... .debug[[k8s/tilt.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/tilt.md)] --- ## When we have more than one node - Which registry should we use? (Docker Hub, Quay, cloud-based, self-hosted ...) - Should we use a single registry, or one per cluster or environment? - Which tags and credentials should we use? (in particular when using a shared registry!) - How do we provision that registry and its users? - How do we adjust our Kubernetes YAML manifests? (e.g. to inject image names and tags) .debug[[k8s/tilt.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/tilt.md)] --- ## More questions - The whole cycle (build+push+update) is expensive - If we have many services, how do we update only the ones we need? - Can we take shortcuts? (e.g. synchronized files without going through a whole build+push+update cycle) .debug[[k8s/tilt.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/tilt.md)] --- ## Tilt - Tilt is a tool to address all these questions - There are other similar tools (e.g. Skaffold) - We arbitrarily decided to focus on that one .debug[[k8s/tilt.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/tilt.md)] --- ## Tilt in practice - The `dockercoins` directory in our repository has a `Tiltfile` - Go to that directory and try `tilt up` - Tilt should refuse to start, but it will explain why - Edit the `Tiltfile` accordingly and try again - Open the Tilt web UI (if running Tilt on a remote machine, you will need `tilt up --host 0.0.0.0`) - Watch as the Dockercoins app is built, pushed, started .debug[[k8s/tilt.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/tilt.md)] --- ## What's in our Tiltfile? - Kubernetes manifests for a local registry - Kubernetes manifests for DockerCoins - Instructions indicating how to build DockerCoins' images - A tiny bit of sugar (telling Tilt which registry to use) .debug[[k8s/tilt.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/tilt.md)] --- ## How does it work? - Tilt keeps track of dependencies between files and resources (a bit like a `make` that would run continuously) - It automatically alters some resources (for instance, it updates the images used in our Kubernetes manifests) - That's it! (And of course, it provides a great web UI, lots of libraries, etc.) .debug[[k8s/tilt.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/tilt.md)] --- ## What happens when we edit a file (1/2) - Let's change e.g. `worker/worker.py` - Thanks to this line, ```python docker_build('dockercoins/worker', 'worker') ``` ... Tilt watches the `worker` directory and uses it to build `dockercoins/worker` - Thanks to this line, ```python default_registry('localhost:30555') ``` ... Tilt actually renames `dockercoins/worker` to `localhost:30555/dockercoins_worker` - Tilt will tag the image with something like `tilt-xxxxxxxxxx` .debug[[k8s/tilt.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/tilt.md)] --- ## What happens when we edit a file (2/2) - Thanks to this line, ```python k8s_yaml('../k8s/dockercoins.yaml') ``` ... Tilt is aware of our Kubernetes resources - The `worker` Deployment uses `dockercoins/worker`, so it must be updated - `dockercoins/worker` becomes `localhost:30555/dockercoins_worker:tilt-xxx` - The `worker` Deployment gets updated on the Kubernetes cluster - All these operations (and their log output) are visible in the Tilt UI .debug[[k8s/tilt.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/tilt.md)] --- ## Configuration file format - The Tiltfile is written in [Starlark](https://github.com/bazelbuild/starlark) (essentially a subset of Python) - Tilt monitors the Tiltfile too (so it reloads it immediately when we change it) .debug[[k8s/tilt.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/tilt.md)] --- ## Tilt "killer features" - Dependency engine (build or run only what's necessary) - Ability to watch resources (execute actions immediately, without explicitly running a command) - Rich library of function and helpers (build container images, manipulate YAML manifests...) - Convenient UI (web; TUI also available) (provides immediate feedback and logs) - Extensibility! ??? :EN:- Development workflow with Tilt :FR:- Développer avec Tilt .debug[[k8s/tilt.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/tilt.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/lots-of-containers.jpg)] --- name: toc-scaling-a-deployment class: title Scaling a deployment .nav[ [Previous part](#toc-tilt) | [Back to table of contents](#toc-part-4) | [Next part](#toc-scaling-our-demo-app) ] .debug[(automatically generated title slide)] --- # Scaling a deployment - We will start with an easy one: the `worker` deployment .exercise[ - Open two new terminals to check what's going on with pods and deployments: ```bash kubectl get pods -w kubectl get deployments -w ``` <!-- ```wait RESTARTS``` ```key ^C``` ```wait AVAILABLE``` ```key ^C``` --> - Now, create more `worker` replicas: ```bash kubectl scale deploy/worker --replicas=10 ``` ] After a few seconds, the graph in the web UI should show up. <br/> (And peak at 10 hashes/second, just like when we were running on a single one.) .debug[[k8s/kubectlscale.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlscale.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/plastic-containers.JPG)] --- name: toc-scaling-our-demo-app class: title Scaling our demo app .nav[ [Previous part](#toc-scaling-a-deployment) | [Back to table of contents](#toc-part-4) | [Next part](#toc-daemon-sets) ] .debug[(automatically generated title slide)] --- # Scaling our demo app - Our ultimate goal is to get more DockerCoins (i.e. increase the number of loops per second shown on the web UI) - Let's look at the architecture again: ![DockerCoins architecture](images/dockercoins-diagram.svg) - The loop is done in the worker; perhaps we could try adding more workers? .debug[[k8s/scalingdockercoins.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/scalingdockercoins.md)] --- ## Adding another worker - All we have to do is scale the `worker` Deployment .exercise[ - Open a new terminal to keep an eye on our pods: ```bash kubectl get pods -w ``` <!-- ```wait RESTARTS``` ```tmux split-pane -h``` --> - Now, create more `worker` replicas: ```bash kubectl scale deployment worker --replicas=2 ``` ] After a few seconds, the graph in the web UI should show up. .debug[[k8s/scalingdockercoins.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/scalingdockercoins.md)] --- ## Adding more workers - If 2 workers give us 2x speed, what about 3 workers? .exercise[ - Scale the `worker` Deployment further: ```bash kubectl scale deployment worker --replicas=3 ``` ] The graph in the web UI should go up again. (This is looking great! We're gonna be RICH!) .debug[[k8s/scalingdockercoins.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/scalingdockercoins.md)] --- ## Adding even more workers - Let's see if 10 workers give us 10x speed! .exercise[ - Scale the `worker` Deployment to a bigger number: ```bash kubectl scale deployment worker --replicas=10 ``` <!-- ```key ^D``` ```key ^C``` --> ] -- The graph will peak at 10 hashes/second. (We can add as many workers as we want: we will never go past 10 hashes/second.) .debug[[k8s/scalingdockercoins.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/scalingdockercoins.md)] --- class: extra-details ## Didn't we briefly exceed 10 hashes/second? - It may *look like it*, because the web UI shows instant speed - The instant speed can briefly exceed 10 hashes/second - The average speed cannot - The instant speed can be biased because of how it's computed .debug[[k8s/scalingdockercoins.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/scalingdockercoins.md)] --- class: extra-details ## Why instant speed is misleading - The instant speed is computed client-side by the web UI - The web UI checks the hash counter once per second <br/> (and does a classic (h2-h1)/(t2-t1) speed computation) - The counter is updated once per second by the workers - These timings are not exact <br/> (e.g. the web UI check interval is client-side JavaScript) - Sometimes, between two web UI counter measurements, <br/> the workers are able to update the counter *twice* - During that cycle, the instant speed will appear to be much bigger <br/> (but it will be compensated by lower instant speed before and after) .debug[[k8s/scalingdockercoins.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/scalingdockercoins.md)] --- ## Why are we stuck at 10 hashes per second? - If this was high-quality, production code, we would have instrumentation (Datadog, Honeycomb, New Relic, statsd, Sumologic, ...) - It's not! - Perhaps we could benchmark our web services? (with tools like `ab`, or even simpler, `httping`) .debug[[k8s/scalingdockercoins.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/scalingdockercoins.md)] --- ## Benchmarking our web services - We want to check `hasher` and `rng` - We are going to use `httping` - It's just like `ping`, but using HTTP `GET` requests (it measures how long it takes to perform one `GET` request) - It's used like this: ``` httping [-c count] http://host:port/path ``` - Or even simpler: ``` httping ip.ad.dr.ess ``` - We will use `httping` on the ClusterIP addresses of our services .debug[[k8s/scalingdockercoins.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/scalingdockercoins.md)] --- ## Obtaining ClusterIP addresses - We can simply check the output of `kubectl get services` - Or do it programmatically, as in the example below .exercise[ - Retrieve the IP addresses: ```bash HASHER=$(kubectl get svc hasher -o go-template={{.spec.clusterIP}}) RNG=$(kubectl get svc rng -o go-template={{.spec.clusterIP}}) ``` ] Now we can access the IP addresses of our services through `$HASHER` and `$RNG`. .debug[[k8s/scalingdockercoins.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/scalingdockercoins.md)] --- ## Checking `hasher` and `rng` response times .exercise[ - Check the response times for both services: ```bash httping -c 3 $HASHER httping -c 3 $RNG ``` ] - `hasher` is fine (it should take a few milliseconds to reply) - `rng` is not (it should take about 700 milliseconds if there are 10 workers) - Something is wrong with `rng`, but ... what? ??? :EN:- Scaling up our demo app :FR:- *Scale up* de l'application de démo .debug[[k8s/scalingdockercoins.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/scalingdockercoins.md)] --- ## Let's draw hasty conclusions - The bottleneck seems to be `rng` - *What if* we don't have enough entropy and can't generate enough random numbers? - We need to scale out the `rng` service on multiple machines! Note: this is a fiction! We have enough entropy. But we need a pretext to scale out. (In fact, the code of `rng` uses `/dev/urandom`, which never runs out of entropy... <br/> ...and is [just as good as `/dev/random`](http://www.slideshare.net/PacSecJP/filippo-plain-simple-reality-of-entropy).) .debug[[shared/hastyconclusions.md](https://github.com/barpilot/container.training/tree/master/slides/shared/hastyconclusions.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/train-of-containers-1.jpg)] --- name: toc-daemon-sets class: title Daemon sets .nav[ [Previous part](#toc-scaling-our-demo-app) | [Back to table of contents](#toc-part-4) | [Next part](#toc-labels-and-selectors) ] .debug[(automatically generated title slide)] --- # Daemon sets - We want to scale `rng` in a way that is different from how we scaled `worker` - We want one (and exactly one) instance of `rng` per node - We *do not want* two instances of `rng` on the same node - We will do that with a *daemon set* .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Why not a deployment? - Can't we just do `kubectl scale deployment rng --replicas=...`? -- - Nothing guarantees that the `rng` containers will be distributed evenly - If we add nodes later, they will not automatically run a copy of `rng` - If we remove (or reboot) a node, one `rng` container will restart elsewhere (and we will end up with two instances `rng` on the same node) - By contrast, a daemon set will start one pod per node and keep it that way (as nodes are added or removed) .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Daemon sets in practice - Daemon sets are great for cluster-wide, per-node processes: - `kube-proxy` - `weave` (our overlay network) - monitoring agents - hardware management tools (e.g. SCSI/FC HBA agents) - etc. - They can also be restricted to run [only on some nodes](https://kubernetes.io/docs/concepts/workloads/controllers/daemonset/#running-pods-on-only-some-nodes) .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Creating a daemon set <!-- ##VERSION## --> - Unfortunately, as of Kubernetes 1.19, the CLI cannot create daemon sets -- - More precisely: it doesn't have a subcommand to create a daemon set -- - But any kind of resource can always be created by providing a YAML description: ```bash kubectl apply -f foo.yaml ``` -- - How do we create the YAML file for our daemon set? -- - option 1: [read the docs](https://kubernetes.io/docs/concepts/workloads/controllers/daemonset/#create-a-daemonset) -- - option 2: `vi` our way out of it .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Creating the YAML file for our daemon set - Let's start with the YAML file for the current `rng` resource .exercise[ - Dump the `rng` resource in YAML: ```bash kubectl get deploy/rng -o yaml >rng.yml ``` - Edit `rng.yml` ] .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## "Casting" a resource to another - What if we just changed the `kind` field? (It can't be that easy, right?) .exercise[ - Change `kind: Deployment` to `kind: DaemonSet` <!-- ```bash vim rng.yml``` ```wait kind: Deployment``` ```keys /Deployment``` ```key ^J``` ```keys cwDaemonSet``` ```key ^[``` ] ```keys :wq``` ```key ^J``` --> - Save, quit - Try to create our new resource: ```bash kubectl apply -f rng.yml ``` <!-- ```wait error:``` --> ] -- We all knew this couldn't be that easy, right! .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Understanding the problem - The core of the error is: ``` error validating data: [ValidationError(DaemonSet.spec): unknown field "replicas" in io.k8s.api.extensions.v1beta1.DaemonSetSpec, ... ``` -- - *Obviously,* it doesn't make sense to specify a number of replicas for a daemon set -- - Workaround: fix the YAML - remove the `replicas` field - remove the `strategy` field (which defines the rollout mechanism for a deployment) - remove the `progressDeadlineSeconds` field (also used by the rollout mechanism) - remove the `status: {}` line at the end -- - Or, we could also ... .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Use the `--force`, Luke - We could also tell Kubernetes to ignore these errors and try anyway - The `--force` flag's actual name is `--validate=false` .exercise[ - Try to load our YAML file and ignore errors: ```bash kubectl apply -f rng.yml --validate=false ``` ] -- 🎩✨🐇 -- Wait ... Now, can it be *that* easy? .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Checking what we've done - Did we transform our `deployment` into a `daemonset`? .exercise[ - Look at the resources that we have now: ```bash kubectl get all ``` ] -- We have two resources called `rng`: - the *deployment* that was existing before - the *daemon set* that we just created We also have one too many pods. <br/> (The pod corresponding to the *deployment* still exists.) .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## `deploy/rng` and `ds/rng` - You can have different resource types with the same name (i.e. a *deployment* and a *daemon set* both named `rng`) - We still have the old `rng` *deployment* ``` NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE deployment.apps/rng 1 1 1 1 18m ``` - But now we have the new `rng` *daemon set* as well ``` NAME DESIRED CURRENT READY UP-TO-DATE AVAILABLE NODE SELECTOR AGE daemonset.apps/rng 2 2 2 2 2 <none> 9s ``` .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Too many pods - If we check with `kubectl get pods`, we see: - *one pod* for the deployment (named `rng-xxxxxxxxxx-yyyyy`) - *one pod per node* for the daemon set (named `rng-zzzzz`) ``` NAME READY STATUS RESTARTS AGE rng-54f57d4d49-7pt82 1/1 Running 0 11m rng-b85tm 1/1 Running 0 25s rng-hfbrr 1/1 Running 0 25s [...] ``` -- The daemon set created one pod per node, except on the master node. The master node has [taints](https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/) preventing pods from running there. (To schedule a pod on this node anyway, the pod will require appropriate [tolerations](https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/).) .footnote[(Off by one? We don't run these pods on the node hosting the control plane.)] .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Is this working? - Look at the web UI -- - The graph should now go above 10 hashes per second! -- - It looks like the newly created pods are serving traffic correctly - How and why did this happen? (We didn't do anything special to add them to the `rng` service load balancer!) .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/train-of-containers-2.jpg)] --- name: toc-labels-and-selectors class: title Labels and selectors .nav[ [Previous part](#toc-daemon-sets) | [Back to table of contents](#toc-part-4) | [Next part](#toc-authoring-yaml) ] .debug[(automatically generated title slide)] --- # Labels and selectors - The `rng` *service* is load balancing requests to a set of pods - That set of pods is defined by the *selector* of the `rng` service .exercise[ - Check the *selector* in the `rng` service definition: ```bash kubectl describe service rng ``` ] - The selector is `app=rng` - It means "all the pods having the label `app=rng`" (They can have additional labels as well, that's OK!) .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Selector evaluation - We can use selectors with many `kubectl` commands - For instance, with `kubectl get`, `kubectl logs`, `kubectl delete` ... and more .exercise[ - Get the list of pods matching selector `app=rng`: ```bash kubectl get pods -l app=rng kubectl get pods --selector app=rng ``` ] But ... why do these pods (in particular, the *new* ones) have this `app=rng` label? .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Where do labels come from? - When we create a deployment with `kubectl create deployment rng`, <br/>this deployment gets the label `app=rng` - The replica sets created by this deployment also get the label `app=rng` - The pods created by these replica sets also get the label `app=rng` - When we created the daemon set from the deployment, we re-used the same spec - Therefore, the pods created by the daemon set get the same labels .footnote[Note: when we use `kubectl run stuff`, the label is `run=stuff` instead.] .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Updating load balancer configuration - We would like to remove a pod from the load balancer - What would happen if we removed that pod, with `kubectl delete pod ...`? -- It would be re-created immediately (by the replica set or the daemon set) -- - What would happen if we removed the `app=rng` label from that pod? -- It would *also* be re-created immediately -- Why?!? .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Selectors for replica sets and daemon sets - The "mission" of a replica set is: "Make sure that there is the right number of pods matching this spec!" - The "mission" of a daemon set is: "Make sure that there is a pod matching this spec on each node!" -- - *In fact,* replica sets and daemon sets do not check pod specifications - They merely have a *selector*, and they look for pods matching that selector - Yes, we can fool them by manually creating pods with the "right" labels - Bottom line: if we remove our `app=rng` label ... ... The pod "disappears" for its parent, which re-creates another pod to replace it .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- class: extra-details ## Isolation of replica sets and daemon sets - Since both the `rng` daemon set and the `rng` replica set use `app=rng` ... ... Why don't they "find" each other's pods? -- - *Replica sets* have a more specific selector, visible with `kubectl describe` (It looks like `app=rng,pod-template-hash=abcd1234`) - *Daemon sets* also have a more specific selector, but it's invisible (It looks like `app=rng,controller-revision-hash=abcd1234`) - As a result, each controller only "sees" the pods it manages .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Removing a pod from the load balancer - Currently, the `rng` service is defined by the `app=rng` selector - The only way to remove a pod is to remove or change the `app` label - ... But that will cause another pod to be created instead! - What's the solution? -- - We need to change the selector of the `rng` service! - Let's add another label to that selector (e.g. `active=yes`) .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Selectors with multiple labels - If a selector specifies multiple labels, they are understood as a logical *AND* (in other words: the pods must match all the labels) - We cannot have a logical *OR* (e.g. `app=api AND (release=prod OR release=preprod)`) - We can, however, apply as many extra labels as we want to our pods: - use selector `app=api AND prod-or-preprod=yes` - add `prod-or-preprod=yes` to both sets of pods - We will see later that in other places, we can use more advanced selectors .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## The plan 1. Add the label `active=yes` to all our `rng` pods 2. Update the selector for the `rng` service to also include `active=yes` 3. Toggle traffic to a pod by manually adding/removing the `active` label 4. Profit! *Note: if we swap steps 1 and 2, it will cause a short service disruption, because there will be a period of time during which the service selector won't match any pod. During that time, requests to the service will time out. By doing things in the order above, we guarantee that there won't be any interruption.* .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Adding labels to pods - We want to add the label `active=yes` to all pods that have `app=rng` - We could edit each pod one by one with `kubectl edit` ... - ... Or we could use `kubectl label` to label them all - `kubectl label` can use selectors itself .exercise[ - Add `active=yes` to all pods that have `app=rng`: ```bash kubectl label pods -l app=rng active=yes ``` ] .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Updating the service selector - We need to edit the service specification - Reminder: in the service definition, we will see `app: rng` in two places - the label of the service itself (we don't need to touch that one) - the selector of the service (that's the one we want to change) .exercise[ - Update the service to add `active: yes` to its selector: ```bash kubectl edit service rng ``` <!-- ```wait Please edit the object below``` ```keys /app: rng``` ```key ^J``` ```keys noactive: yes``` ```key ^[``` ] ```keys :wq``` ```key ^J``` --> ] -- ... And then we get *the weirdest error ever.* Why? .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## When the YAML parser is being too smart - YAML parsers try to help us: - `xyz` is the string `"xyz"` - `42` is the integer `42` - `yes` is the boolean value `true` - If we want the string `"42"` or the string `"yes"`, we have to quote them - So we have to use `active: "yes"` .footnote[For a good laugh: if we had used "ja", "oui", "si" ... as the value, it would have worked!] .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Updating the service selector, take 2 .exercise[ - Update the YAML manifest of the service - Add `active: "yes"` to its selector <!-- ```wait Please edit the object below``` ```keys /yes``` ```key ^J``` ```keys cw"yes"``` ```key ^[``` ] ```keys :wq``` ```key ^J``` --> ] This time it should work! If we did everything correctly, the web UI shouldn't show any change. .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Updating labels - We want to disable the pod that was created by the deployment - All we have to do, is remove the `active` label from that pod - To identify that pod, we can use its name - ... Or rely on the fact that it's the only one with a `pod-template-hash` label - Good to know: - `kubectl label ... foo=` doesn't remove a label (it sets it to an empty string) - to remove label `foo`, use `kubectl label ... foo-` - to change an existing label, we would need to add `--overwrite` .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Removing a pod from the load balancer .exercise[ - In one window, check the logs of that pod: ```bash POD=$(kubectl get pod -l app=rng,pod-template-hash -o name) kubectl logs --tail 1 --follow $POD ``` (We should see a steady stream of HTTP logs) <!-- ```wait HTTP/1.1``` ```tmux split-pane -v``` --> - In another window, remove the label from the pod: ```bash kubectl label pod -l app=rng,pod-template-hash active- ``` (The stream of HTTP logs should stop immediately) <!-- ```key ^D``` ```key ^C``` --> ] There might be a slight change in the web UI (since we removed a bit of capacity from the `rng` service). If we remove more pods, the effect should be more visible. .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- class: extra-details ## Updating the daemon set - If we scale up our cluster by adding new nodes, the daemon set will create more pods - These pods won't have the `active=yes` label - If we want these pods to have that label, we need to edit the daemon set spec - We can do that with e.g. `kubectl edit daemonset rng` .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- class: extra-details ## We've put resources in your resources - Reminder: a daemon set is a resource that creates more resources! - There is a difference between: - the label(s) of a resource (in the `metadata` block in the beginning) - the selector of a resource (in the `spec` block) - the label(s) of the resource(s) created by the first resource (in the `template` block) - We would need to update the selector and the template (metadata labels are not mandatory) - The template must match the selector (i.e. the resource will refuse to create resources that it will not select) .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Labels and debugging - When a pod is misbehaving, we can delete it: another one will be recreated - But we can also change its labels - It will be removed from the load balancer (it won't receive traffic anymore) - Another pod will be recreated immediately - But the problematic pod is still here, and we can inspect and debug it - We can even re-add it to the rotation if necessary (Very useful to troubleshoot intermittent and elusive bugs) .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- ## Labels and advanced rollout control - Conversely, we can add pods matching a service's selector - These pods will then receive requests and serve traffic - Examples: - one-shot pod with all debug flags enabled, to collect logs - pods created automatically, but added to rotation in a second step <br/> (by setting their label accordingly) - This gives us building blocks for canary and blue/green deployments .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- class: extra-details ## Advanced label selectors - As indicated earlier, service selectors are limited to a `AND` - But in many other places in the Kubernetes API, we can use complex selectors (e.g. Deployment, ReplicaSet, DaemonSet, NetworkPolicy ...) - These allow extra operations; specifically: - checking for presence (or absence) of a label - checking if a label is (or is not) in a given set - Relevant documentation: [Service spec](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.19/#servicespec-v1-core), [LabelSelector spec](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.19/#labelselector-v1-meta), [label selector doc](https://kubernetes.io/docs/concepts/overview/working-with-objects/labels/#label-selectors) .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- class: extra-details ## Example of advanced selector ```yaml theSelector: matchLabels: app: portal component: api matchExpressions: - key: release operator: In values: [ production, preproduction ] - key: signed-off-by operator: Exists ``` This selector matches pods that meet *all* the indicated conditions. `operator` can be `In`, `NotIn`, `Exists`, `DoesNotExist`. A `nil` selector matches *nothing*, a `{}` selector matches *everything*. <br/> (Because that means "match all pods that meet at least zero condition".) .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- class: extra-details ## Services and Endpoints - Each Service has a corresponding Endpoints resource (see `kubectl get endpoints` or `kubectl get ep`) - That Endpoints resource is used by various controllers (e.g. `kube-proxy` when setting up `iptables` rules for ClusterIP services) - These Endpoints are populated (and updated) with the Service selector - We can update the Endpoints manually, but our changes will get overwritten - ... Except if the Service selector is empty! .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- class: extra-details ## Empty Service selector - If a service selector is empty, Endpoints don't get updated automatically (but we can still set them manually) - This lets us create Services pointing to arbitrary destinations (potentially outside the cluster; or things that are not in pods) - Another use-case: the `kubernetes` service in the `default` namespace (its Endpoints are maintained automatically by the API server) ??? :EN:- Scaling with Daemon Sets :FR:- Utilisation de Daemon Sets .debug[[k8s/daemonset.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/daemonset.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/two-containers-on-a-truck.jpg)] --- name: toc-authoring-yaml class: title Authoring YAML .nav[ [Previous part](#toc-labels-and-selectors) | [Back to table of contents](#toc-part-4) | [Next part](#toc-exercise--writing-yaml) ] .debug[(automatically generated title slide)] --- # Authoring YAML - We have already generated YAML implicitly, with e.g.: - `kubectl run` - `kubectl create deployment` (and a few other `kubectl create` variants) - `kubectl expose` - When and why do we need to write our own YAML? - How do we write YAML from scratch? .debug[[k8s/authoring-yaml.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authoring-yaml.md)] --- ## The limits of generated YAML - Many advanced (and even not-so-advanced) features require to write YAML: - pods with multiple containers - resource limits - healthchecks - DaemonSets, StatefulSets - and more! - How do we access these features? .debug[[k8s/authoring-yaml.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authoring-yaml.md)] --- ## Various ways to write YAML - Completely from scratch with our favorite editor (yeah, right) - Dump an existing resource with `kubectl get -o yaml ...` (it is recommended to clean up the result) - Ask `kubectl` to generate the YAML (with a `kubectl create --dry-run -o yaml`) - Use The Docs, Luke (the documentation almost always has YAML examples) .debug[[k8s/authoring-yaml.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authoring-yaml.md)] --- ## Generating YAML from scratch - Start with a namespace: ```yaml kind: Namespace apiVersion: v1 metadata: name: hello ``` - We can use `kubectl explain` to see resource definitions: ```bash kubectl explain -r pod.spec ``` - Not the easiest option! .debug[[k8s/authoring-yaml.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authoring-yaml.md)] --- ## Dump the YAML for an existing resource - `kubectl get -o yaml` works! - A lot of fields in `metadata` are not necessary (`managedFields`, `resourceVersion`, `uid`, `creationTimestamp` ...) - Most objects will have a `status` field that is not necessary - Default or empty values can also be removed for clarity - This can be done manually or with the `kubectl-neat` plugin `kubectl get -o yaml ... | kubectl neat` .debug[[k8s/authoring-yaml.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authoring-yaml.md)] --- ## Generating YAML without creating resources - We can use the `--dry-run` option .exercise[ - Generate the YAML for a Deployment without creating it: ```bash kubectl create deployment web --image nginx --dry-run ``` - Optionally clean it up with `kubectl neat`, too ] Note: in recent versions of Kubernetes, we should use `--dry-run=client` (Or `--dry-run=server`; more on that later!) .debug[[k8s/authoring-yaml.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authoring-yaml.md)] --- class: extra-details ## Using `--dry-run` with `kubectl apply` - The `--dry-run` option can also be used with `kubectl apply` - However, it can be misleading (it doesn't do a "real" dry run) - Let's see what happens in the following scenario: - generate the YAML for a Deployment - tweak the YAML to transform it into a DaemonSet - apply that YAML to see what would actually be created .debug[[k8s/authoring-yaml.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authoring-yaml.md)] --- class: extra-details ## The limits of `kubectl apply --dry-run` .exercise[ - Generate the YAML for a deployment: ```bash kubectl create deployment web --image=nginx -o yaml > web.yaml ``` - Change the `kind` in the YAML to make it a `DaemonSet`: ```bash sed -i s/Deployment/DaemonSet/ web.yaml ``` - Ask `kubectl` what would be applied: ```bash kubectl apply -f web.yaml --dry-run --validate=false -o yaml ``` ] The resulting YAML doesn't represent a valid DaemonSet. .debug[[k8s/authoring-yaml.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authoring-yaml.md)] --- class: extra-details ## Server-side dry run - Since Kubernetes 1.13, we can use [server-side dry run and diffs](https://kubernetes.io/blog/2019/01/14/apiserver-dry-run-and-kubectl-diff/) - Server-side dry run will do all the work, but *not* persist to etcd (all validation and mutation hooks will be executed) .exercise[ - Try the same YAML file as earlier, with server-side dry run: ```bash kubectl apply -f web.yaml --dry-run=server --validate=false -o yaml ``` ] The resulting YAML doesn't have the `replicas` field anymore. Instead, it has the fields expected in a DaemonSet. .debug[[k8s/authoring-yaml.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authoring-yaml.md)] --- class: extra-details ## Advantages of server-side dry run - The YAML is verified much more extensively - The only step that is skipped is "write to etcd" - YAML that passes server-side dry run *should* apply successfully (unless the cluster state changes by the time the YAML is actually applied) - Validating or mutating hooks that have side effects can also be an issue .debug[[k8s/authoring-yaml.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authoring-yaml.md)] --- class: extra-details ## `kubectl diff` - Kubernetes 1.13 also introduced `kubectl diff` - `kubectl diff` does a server-side dry run, *and* shows differences .exercise[ - Try `kubectl diff` on the YAML that we tweaked earlier: ```bash kubectl diff -f web.yaml ``` <!-- ```wait status:``` --> ] Note: we don't need to specify `--validate=false` here. .debug[[k8s/authoring-yaml.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authoring-yaml.md)] --- ## Advantage of YAML - Using YAML (instead of `kubectl create <kind>`) allows to be *declarative* - The YAML describes the desired state of our cluster and applications - YAML can be stored, versioned, archived (e.g. in git repositories) - To change resources, change the YAML files (instead of using `kubectl edit`/`scale`/`label`/etc.) - Changes can be reviewed before being applied (with code reviews, pull requests ...) - This workflow is sometimes called "GitOps" (there are tools like Weave Flux or GitKube to facilitate it) .debug[[k8s/authoring-yaml.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authoring-yaml.md)] --- ## YAML in practice - Get started with `kubectl create deployment` and `kubectl expose` - Dump the YAML with `kubectl get -o yaml` - Tweak that YAML and `kubectl apply` it back - Store that YAML for reference (for further deployments) - Feel free to clean up the YAML: - remove fields you don't know - check that it still works! - That YAML will be useful later when using e.g. Kustomize or Helm ??? :EN:- Techniques to write YAML manifests :FR:- Comment écrire des *manifests* YAML .debug[[k8s/authoring-yaml.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authoring-yaml.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/wall-of-containers.jpeg)] --- name: toc-exercise--writing-yaml class: title Exercise — writing YAML .nav[ [Previous part](#toc-authoring-yaml) | [Back to table of contents](#toc-part-4) | [Next part](#toc-controlling-a-kubernetes-cluster-remotely) ] .debug[(automatically generated title slide)] --- # Exercise — writing YAML Let's write YAML manifests for the wordsmith app! It can be a single YAML file or multiple files in a directory. See next slides for testing instructions and hints. .debug[[k8s/exercise-yaml.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/exercise-yaml.md)] --- ## How to test our YAML If `XYZ` is that YAML file (or directory with YAML files), we should be able to: 1. Create a new namespace, e.g. `foo123` 2. Deploy wordsmith with a single command (e.g. `kubectl apply --namespace foo123 -f XYZ`) 3. Find out the connection information for `web` (e.g. `kubectl get service web --namespace`) 4. Connect to it and see the wordsmith app See next slide for hints. .debug[[k8s/exercise-yaml.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/exercise-yaml.md)] --- ## Strategies There are at least three methods to write our YAML. 1. Dump the YAML of existing wordsmith deployments and services. (we can dump YAML with `kubectl get -o yaml ...`) 2. Adapt existing YAML (from the docs or dockercoins). (for reference, kubercoins is at https://github.com/jpetazzo/kubercoins) 3. Write it entirely from scratch. See next slide for more hints. .debug[[k8s/exercise-yaml.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/exercise-yaml.md)] --- ## Adapting YAML *Scroll one slide at a time to see hints.* -- One option is to start with the YAML from kubercoins. (see https://github.com/jpetazzo/kubercoins) -- Adapt the YAML of a deployment (e.g. worker) to run "web". -- We need to change the name, labels, selectors, and image. -- Then adapt the YAML of a service (e.g. webui). -- We need to change the name, labels, selectors, possibly port number. -- Repeat for the other components. .debug[[k8s/exercise-yaml.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/exercise-yaml.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/Container-Ship-Freighter-Navigation-Elbe-Romance-1782991.jpg)] --- name: toc-controlling-a-kubernetes-cluster-remotely class: title Controlling a Kubernetes cluster remotely .nav[ [Previous part](#toc-exercise--writing-yaml) | [Back to table of contents](#toc-part-5) | [Next part](#toc-accessing-internal-services) ] .debug[(automatically generated title slide)] --- # Controlling a Kubernetes cluster remotely - `kubectl` can be used either on cluster instances or outside the cluster - Here, we are going to use `kubectl` from our local machine .debug[[k8s/localkubeconfig.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/localkubeconfig.md)] --- ## Requirements .warning[The exercises in this chapter should be done *on your local machine*.] - `kubectl` is officially available on Linux, macOS, Windows (and unofficially anywhere we can build and run Go binaries) - You may skip these exercises if you are following along from: - a tablet or phone - a web-based terminal - an environment where you can't install and run new binaries .debug[[k8s/localkubeconfig.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/localkubeconfig.md)] --- ## Installing `kubectl` - If you already have `kubectl` on your local machine, you can skip this .exercise[ <!-- ##VERSION## --> - Download the `kubectl` binary from one of these links: [Linux](https://storage.googleapis.com/kubernetes-release/release/v1.19.2/bin/linux/amd64/kubectl) | [macOS](https://storage.googleapis.com/kubernetes-release/release/v1.19.2/bin/darwin/amd64/kubectl) | [Windows](https://storage.googleapis.com/kubernetes-release/release/v1.19.2/bin/windows/amd64/kubectl.exe) - On Linux and macOS, make the binary executable with `chmod +x kubectl` (And remember to run it with `./kubectl` or move it to your `$PATH`) ] Note: if you are following along with a different platform (e.g. Linux on an architecture different from amd64, or with a phone or tablet), installing `kubectl` might be more complicated (or even impossible) so feel free to skip this section. .debug[[k8s/localkubeconfig.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/localkubeconfig.md)] --- ## Testing `kubectl` - Check that `kubectl` works correctly (before even trying to connect to a remote cluster!) .exercise[ - Ask `kubectl` to show its version number: ```bash kubectl version --client ``` ] The output should look like this: ``` Client Version: version.Info{Major:"1", Minor:"15", GitVersion:"v1.15.0", GitCommit:"e8462b5b5dc2584fdcd18e6bcfe9f1e4d970a529", GitTreeState:"clean", BuildDate:"2019-06-19T16:40:16Z", GoVersion:"go1.12.5", Compiler:"gc", Platform:"darwin/amd64"} ``` .debug[[k8s/localkubeconfig.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/localkubeconfig.md)] --- ## Preserving the existing `~/.kube/config` - If you already have a `~/.kube/config` file, rename it (we are going to overwrite it in the following slides!) - If you never used `kubectl` on your machine before: nothing to do! .exercise[ - Make a copy of `~/.kube/config`; if you are using macOS or Linux, you can do: ```bash cp ~/.kube/config ~/.kube/config.before.training ``` - If you are using Windows, you will need to adapt this command ] .debug[[k8s/localkubeconfig.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/localkubeconfig.md)] --- ## Copying the configuration file from `node1` - The `~/.kube/config` file that is on `node1` contains all the credentials we need - Let's copy it over! .exercise[ - Copy the file from `node1`; if you are using macOS or Linux, you can do: ``` scp `USER`@`X.X.X.X`:.kube/config ~/.kube/config # Make sure to replace X.X.X.X with the IP address of node1, # and USER with the user name used to log into node1! ``` - If you are using Windows, adapt these instructions to your SSH client ] .debug[[k8s/localkubeconfig.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/localkubeconfig.md)] --- ## Updating the server address - There is a good chance that we need to update the server address - To know if it is necessary, run `kubectl config view` - Look for the `server:` address: - if it matches the public IP address of `node1`, you're good! - if it is anything else (especially a private IP address), update it! - To update the server address, run: ```bash kubectl config set-cluster kubernetes --server=https://`X.X.X.X`:6443 # Make sure to replace X.X.X.X with the IP address of node1! ``` .debug[[k8s/localkubeconfig.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/localkubeconfig.md)] --- class: extra-details ## What if we get a certificate error? - Generally, the Kubernetes API uses a certificate that is valid for: - `kubernetes` - `kubernetes.default` - `kubernetes.default.svc` - `kubernetes.default.svc.cluster.local` - the ClusterIP address of the `kubernetes` service - the hostname of the node hosting the control plane (e.g. `node1`) - the IP address of the node hosting the control plane - On most clouds, the IP address of the node is an internal IP address - ... And we are going to connect over the external IP address - ... And that external IP address was not used when creating the certificate! .debug[[k8s/localkubeconfig.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/localkubeconfig.md)] --- class: extra-details ## Working around the certificate error - We need to tell `kubectl` to skip TLS verification (only do this with testing clusters, never in production!) - The following command will do the trick: ```bash kubectl config set-cluster kubernetes --insecure-skip-tls-verify ``` .debug[[k8s/localkubeconfig.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/localkubeconfig.md)] --- ## Checking that we can connect to the cluster - We can now run a couple of trivial commands to check that all is well .exercise[ - Check the versions of the local client and remote server: ```bash kubectl version ``` - View the nodes of the cluster: ```bash kubectl get nodes ``` ] We can now utilize the cluster exactly as if we're logged into a node, except that it's remote. ??? :EN:- Working with remote Kubernetes clusters :FR:- Travailler avec des *clusters* distants .debug[[k8s/localkubeconfig.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/localkubeconfig.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)] --- name: toc-accessing-internal-services class: title Accessing internal services .nav[ [Previous part](#toc-controlling-a-kubernetes-cluster-remotely) | [Back to table of contents](#toc-part-5) | [Next part](#toc-accessing-the-api-with-kubectl-proxy) ] .debug[(automatically generated title slide)] --- # Accessing internal services - When we are logged in on a cluster node, we can access internal services (by virtue of the Kubernetes network model: all nodes can reach all pods and services) - When we are accessing a remote cluster, things are different (generally, our local machine won't have access to the cluster's internal subnet) - How can we temporarily access a service without exposing it to everyone? -- - `kubectl proxy`: gives us access to the API, which includes a proxy for HTTP resources - `kubectl port-forward`: allows forwarding of TCP ports to arbitrary pods, services, ... .debug[[k8s/accessinternal.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/accessinternal.md)] --- ## Suspension of disbelief The exercises in this section assume that we have set up `kubectl` on our local machine in order to access a remote cluster. We will therefore show how to access services and pods of the remote cluster, from our local machine. You can also run these exercises directly on the cluster (if you haven't installed and set up `kubectl` locally). Running commands locally will be less useful (since you could access services and pods directly), but keep in mind that these commands will work anywhere as long as you have installed and set up `kubectl` to communicate with your cluster. .debug[[k8s/accessinternal.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/accessinternal.md)] --- ## `kubectl proxy` in theory - Running `kubectl proxy` gives us access to the entire Kubernetes API - The API includes routes to proxy HTTP traffic - These routes look like the following: `/api/v1/namespaces/<namespace>/services/<service>/proxy` - We just add the URI to the end of the request, for instance: `/api/v1/namespaces/<namespace>/services/<service>/proxy/index.html` - We can access `services` and `pods` this way .debug[[k8s/accessinternal.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/accessinternal.md)] --- ## `kubectl proxy` in practice - Let's access the `webui` service through `kubectl proxy` .exercise[ - Run an API proxy in the background: ```bash kubectl proxy & ``` - Access the `webui` service: ```bash curl localhost:8001/api/v1/namespaces/default/services/webui/proxy/index.html ``` - Terminate the proxy: ```bash kill %1 ``` ] .debug[[k8s/accessinternal.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/accessinternal.md)] --- ## `kubectl port-forward` in theory - What if we want to access a TCP service? - We can use `kubectl port-forward` instead - It will create a TCP relay to forward connections to a specific port (of a pod, service, deployment...) - The syntax is: `kubectl port-forward service/name_of_service local_port:remote_port` - If only one port number is specified, it is used for both local and remote ports .debug[[k8s/accessinternal.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/accessinternal.md)] --- ## `kubectl port-forward` in practice - Let's access our remote Redis server .exercise[ - Forward connections from local port 10000 to remote port 6379: ```bash kubectl port-forward svc/redis 10000:6379 & ``` - Connect to the Redis server: ```bash telnet localhost 10000 ``` - Issue a few commands, e.g. `INFO server` then `QUIT` <!-- ```wait Connected to localhost``` ```keys INFO server``` ```key ^J``` ```keys QUIT``` ```key ^J``` --> - Terminate the port forwarder: ```bash kill %1 ``` ] ??? :EN:- Securely accessing internal services :FR:- Accès sécurisé aux services internes :T: Accessing internal services from our local machine :Q: What's the advantage of "kubectl port-forward" compared to a NodePort? :A: It can forward arbitrary protocols :A: It doesn't require Kubernetes API credentials :A: It offers deterministic load balancing (instead of random) :A: ✔️It doesn't expose the service to the public :Q: What's the security concept behind "kubectl port-forward"? :A: ✔️We authenticate with the Kubernetes API, and it forwards connections on our behalf :A: It detects our source IP address, and only allows connections coming from it :A: It uses end-to-end mTLS (mutual TLS) to authenticate our connections :A: There is no security (as long as it's running, anyone can connect from anywhere) .debug[[k8s/accessinternal.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/accessinternal.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/aerial-view-of-containers.jpg)] --- name: toc-accessing-the-api-with-kubectl-proxy class: title Accessing the API with `kubectl proxy` .nav[ [Previous part](#toc-accessing-internal-services) | [Back to table of contents](#toc-part-5) | [Next part](#toc-rolling-updates) ] .debug[(automatically generated title slide)] --- # Accessing the API with `kubectl proxy` - The API requires us to authenticate.red[¹] - There are many authentication methods available, including: - TLS client certificates <br/> (that's what we've used so far) - HTTP basic password authentication <br/> (from a static file; not recommended) - various token mechanisms <br/> (detailed in the [documentation](https://kubernetes.io/docs/reference/access-authn-authz/authentication/#authentication-strategies)) .red[¹]OK, we lied. If you don't authenticate, you are considered to be user `system:anonymous`, which doesn't have any access rights by default. .debug[[k8s/kubectlproxy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlproxy.md)] --- ## Accessing the API directly - Let's see what happens if we try to access the API directly with `curl` .exercise[ - Retrieve the ClusterIP allocated to the `kubernetes` service: ```bash kubectl get svc kubernetes ``` - Replace the IP below and try to connect with `curl`: ```bash curl -k https://`10.96.0.1`/ ``` ] The API will tell us that user `system:anonymous` cannot access this path. .debug[[k8s/kubectlproxy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlproxy.md)] --- ## Authenticating to the API If we wanted to talk to the API, we would need to: - extract our TLS key and certificate information from `~/.kube/config` (the information is in PEM format, encoded in base64) - use that information to present our certificate when connecting (for instance, with `openssl s_client -key ... -cert ... -connect ...`) - figure out exactly which credentials to use (once we start juggling multiple clusters) - change that whole process if we're using another authentication method 🤔 There has to be a better way! .debug[[k8s/kubectlproxy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlproxy.md)] --- ## Using `kubectl proxy` for authentication - `kubectl proxy` runs a proxy in the foreground - This proxy lets us access the Kubernetes API without authentication (`kubectl proxy` adds our credentials on the fly to the requests) - This proxy lets us access the Kubernetes API over plain HTTP - This is a great tool to learn and experiment with the Kubernetes API - ... And for serious uses as well (suitable for one-shot scripts) - For unattended use, it's better to create a [service account](https://kubernetes.io/docs/tasks/configure-pod-container/configure-service-account/) .debug[[k8s/kubectlproxy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlproxy.md)] --- ## Trying `kubectl proxy` - Let's start `kubectl proxy` and then do a simple request with `curl`! .exercise[ - Start `kubectl proxy` in the background: ```bash kubectl proxy & ``` - Access the API's default route: ```bash curl localhost:8001 ``` <!-- ```wait /version``` ```key ^J``` --> - Terminate the proxy: ```bash kill %1 ``` ] The output is a list of available API routes. .debug[[k8s/kubectlproxy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlproxy.md)] --- ## OpenAPI (fka Swagger) - The Kubernetes API serves an OpenAPI Specification (OpenAPI was formerly known as Swagger) - OpenAPI has many advantages (generate client library code, generate test code ...) - For us, this means we can explore the API with [Swagger UI](https://swagger.io/tools/swagger-ui/) (for instance with the [Swagger UI add-on for Firefox](https://addons.mozilla.org/en-US/firefox/addon/swagger-ui-ff/)) .debug[[k8s/kubectlproxy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlproxy.md)] --- ## `kubectl proxy` is intended for local use - By default, the proxy listens on port 8001 (But this can be changed, or we can tell `kubectl proxy` to pick a port) - By default, the proxy binds to `127.0.0.1` (Making it unreachable from other machines, for security reasons) - By default, the proxy only accepts connections from: `^localhost$,^127\.0\.0\.1$,^\[::1\]$` - This is great when running `kubectl proxy` locally - Not-so-great when you want to connect to the proxy from a remote machine .debug[[k8s/kubectlproxy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlproxy.md)] --- class: extra-details ## Running `kubectl proxy` on a remote machine - If we wanted to connect to the proxy from another machine, we would need to: - bind to `INADDR_ANY` instead of `127.0.0.1` - accept connections from any address - This is achieved with: ``` kubectl proxy --port=8888 --address=0.0.0.0 --accept-hosts=.* ``` .warning[Do not do this on a real cluster: it opens full unauthenticated access!] .debug[[k8s/kubectlproxy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlproxy.md)] --- class: extra-details ## Security considerations - Running `kubectl proxy` openly is a huge security risk - It is slightly better to run the proxy where you need it (and copy credentials, e.g. `~/.kube/config`, to that place) - It is even better to use a limited account with reduced permissions .debug[[k8s/kubectlproxy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlproxy.md)] --- ## Good to know ... - `kubectl proxy` also gives access to all internal services - Specifically, services are exposed as such: ``` /api/v1/namespaces/<namespace>/services/<service>/proxy ``` - We can use `kubectl proxy` to access an internal service in a pinch (or, for non HTTP services, `kubectl port-forward`) - This is not very useful when running `kubectl` directly on the cluster (since we could connect to the services directly anyway) - But it is very powerful as soon as you run `kubectl` from a remote machine .debug[[k8s/kubectlproxy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubectlproxy.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/blue-containers.jpg)] --- name: toc-rolling-updates class: title Rolling updates .nav[ [Previous part](#toc-accessing-the-api-with-kubectl-proxy) | [Back to table of contents](#toc-part-5) | [Next part](#toc-healthchecks) ] .debug[(automatically generated title slide)] --- # Rolling updates - By default (without rolling updates), when a scaled resource is updated: - new pods are created - old pods are terminated - ... all at the same time - if something goes wrong, ¯\\\_(ツ)\_/¯ .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- ## Rolling updates - With rolling updates, when a Deployment is updated, it happens progressively - The Deployment controls multiple Replica Sets - Each Replica Set is a group of identical Pods (with the same image, arguments, parameters ...) - During the rolling update, we have at least two Replica Sets: - the "new" set (corresponding to the "target" version) - at least one "old" set - We can have multiple "old" sets (if we start another update before the first one is done) .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- ## Update strategy - Two parameters determine the pace of the rollout: `maxUnavailable` and `maxSurge` - They can be specified in absolute number of pods, or percentage of the `replicas` count - At any given time ... - there will always be at least `replicas`-`maxUnavailable` pods available - there will never be more than `replicas`+`maxSurge` pods in total - there will therefore be up to `maxUnavailable`+`maxSurge` pods being updated - We have the possibility of rolling back to the previous version <br/>(if the update fails or is unsatisfactory in any way) .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- ## Checking current rollout parameters - Recall how we build custom reports with `kubectl` and `jq`: .exercise[ - Show the rollout plan for our deployments: ```bash kubectl get deploy -o json | jq ".items[] | {name:.metadata.name} + .spec.strategy.rollingUpdate" ``` ] .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- ## Rolling updates in practice - As of Kubernetes 1.8, we can do rolling updates with: `deployments`, `daemonsets`, `statefulsets` - Editing one of these resources will automatically result in a rolling update - Rolling updates can be monitored with the `kubectl rollout` subcommand .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- ## Rolling out the new `worker` service .exercise[ - Let's monitor what's going on by opening a few terminals, and run: ```bash kubectl get pods -w kubectl get replicasets -w kubectl get deployments -w ``` <!-- ```wait NAME``` ```key ^C``` --> - Update `worker` either with `kubectl edit`, or by running: ```bash kubectl set image deploy worker worker=dockercoins/worker:v0.2 ``` ] -- That rollout should be pretty quick. What shows in the web UI? .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- ## Give it some time - At first, it looks like nothing is happening (the graph remains at the same level) - According to `kubectl get deploy -w`, the `deployment` was updated really quickly - But `kubectl get pods -w` tells a different story - The old `pods` are still here, and they stay in `Terminating` state for a while - Eventually, they are terminated; and then the graph decreases significantly - This delay is due to the fact that our worker doesn't handle signals - Kubernetes sends a "polite" shutdown request to the worker, which ignores it - After a grace period, Kubernetes gets impatient and kills the container (The grace period is 30 seconds, but [can be changed](https://kubernetes.io/docs/concepts/workloads/pods/pod/#termination-of-pods) if needed) .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- ## Rolling out something invalid - What happens if we make a mistake? .exercise[ - Update `worker` by specifying a non-existent image: ```bash kubectl set image deploy worker worker=dockercoins/worker:v0.3 ``` - Check what's going on: ```bash kubectl rollout status deploy worker ``` <!-- ```wait Waiting for deployment``` ```key ^C``` --> ] -- Our rollout is stuck. However, the app is not dead. (After a minute, it will stabilize to be 20-25% slower.) .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- ## What's going on with our rollout? - Why is our app a bit slower? - Because `MaxUnavailable=25%` ... So the rollout terminated 2 replicas out of 10 available - Okay, but why do we see 5 new replicas being rolled out? - Because `MaxSurge=25%` ... So in addition to replacing 2 replicas, the rollout is also starting 3 more - It rounded down the number of MaxUnavailable pods conservatively, <br/> but the total number of pods being rolled out is allowed to be 25+25=50% .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- class: extra-details ## The nitty-gritty details - We start with 10 pods running for the `worker` deployment - Current settings: MaxUnavailable=25% and MaxSurge=25% - When we start the rollout: - two replicas are taken down (as per MaxUnavailable=25%) - two others are created (with the new version) to replace them - three others are created (with the new version) per MaxSurge=25%) - Now we have 8 replicas up and running, and 5 being deployed - Our rollout is stuck at this point! .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- ## Checking the dashboard during the bad rollout If you didn't deploy the Kubernetes dashboard earlier, just skip this slide. .exercise[ - Connect to the dashboard that we deployed earlier - Check that we have failures in Deployments, Pods, and Replica Sets - Can we see the reason for the failure? ] .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- ## Recovering from a bad rollout - We could push some `v0.3` image (the pod retry logic will eventually catch it and the rollout will proceed) - Or we could invoke a manual rollback .exercise[ <!-- ```key ^C``` --> - Cancel the deployment and wait for the dust to settle: ```bash kubectl rollout undo deploy worker kubectl rollout status deploy worker ``` ] .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- ## Rolling back to an older version - We reverted to `v0.2` - But this version still has a performance problem - How can we get back to the previous version? .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- ## Multiple "undos" - What happens if we try `kubectl rollout undo` again? .exercise[ - Try it: ```bash kubectl rollout undo deployment worker ``` - Check the web UI, the list of pods ... ] 🤔 That didn't work. .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- ## Multiple "undos" don't work - If we see successive versions as a stack: - `kubectl rollout undo` doesn't "pop" the last element from the stack - it copies the N-1th element to the top - Multiple "undos" just swap back and forth between the last two versions! .exercise[ - Go back to v0.2 again: ```bash kubectl rollout undo deployment worker ``` ] .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- ## In this specific scenario - Our version numbers are easy to guess - What if we had used git hashes? - What if we had changed other parameters in the Pod spec? .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- ## Listing versions - We can list successive versions of a Deployment with `kubectl rollout history` .exercise[ - Look at our successive versions: ```bash kubectl rollout history deployment worker ``` ] We don't see *all* revisions. We might see something like 1, 4, 5. (Depending on how many "undos" we did before.) .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- ## Explaining deployment revisions - These revisions correspond to our Replica Sets - This information is stored in the Replica Set annotations .exercise[ - Check the annotations for our replica sets: ```bash kubectl describe replicasets -l app=worker | grep -A3 ^Annotations ``` ] .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- class: extra-details ## What about the missing revisions? - The missing revisions are stored in another annotation: `deployment.kubernetes.io/revision-history` - These are not shown in `kubectl rollout history` - We could easily reconstruct the full list with a script (if we wanted to!) .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- ## Rolling back to an older version - `kubectl rollout undo` can work with a revision number .exercise[ - Roll back to the "known good" deployment version: ```bash kubectl rollout undo deployment worker --to-revision=1 ``` - Check the web UI or the list of pods ] .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- class: extra-details ## Changing rollout parameters - We want to: - revert to `v0.1` - be conservative on availability (always have desired number of available workers) - go slow on rollout speed (update only one pod at a time) - give some time to our workers to "warm up" before starting more The corresponding changes can be expressed in the following YAML snippet: .small[ ```yaml spec: template: spec: containers: - name: worker image: dockercoins/worker:v0.1 strategy: rollingUpdate: maxUnavailable: 0 maxSurge: 1 minReadySeconds: 10 ``` ] .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- class: extra-details ## Applying changes through a YAML patch - We could use `kubectl edit deployment worker` - But we could also use `kubectl patch` with the exact YAML shown before .exercise[ .small[ - Apply all our changes and wait for them to take effect: ```bash kubectl patch deployment worker -p " spec: template: spec: containers: - name: worker image: dockercoins/worker:v0.1 strategy: rollingUpdate: maxUnavailable: 0 maxSurge: 1 minReadySeconds: 10 " kubectl rollout status deployment worker kubectl get deploy -o json worker | jq "{name:.metadata.name} + .spec.strategy.rollingUpdate" ``` ] ] ??? :EN:- Rolling updates :EN:- Rolling back a bad deployment :FR:- Mettre à jour un déploiement :FR:- Concept de *rolling update* et *rollback* :FR:- Paramétrer la vitesse de déploiement .debug[[k8s/rollout.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/rollout.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/chinook-helicopter-container.jpg)] --- name: toc-healthchecks class: title Healthchecks .nav[ [Previous part](#toc-rolling-updates) | [Back to table of contents](#toc-part-5) | [Next part](#toc-recording-deployment-actions) ] .debug[(automatically generated title slide)] --- # Healthchecks - Kubernetes provides two kinds of healthchecks: liveness and readiness - Healthchecks are *probes* that apply to *containers* (not to pods) - Each container can have two (optional) probes: - liveness = is this container dead or alive? - readiness = is this container ready to serve traffic? - Different probes are available (HTTP, TCP, program execution) - Let's see the difference and how to use them! .debug[[k8s/healthchecks.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks.md)] --- ## Liveness probe - Indicates if the container is dead or alive - A dead container cannot come back to life - If the liveness probe fails, the container is killed (to make really sure that it's really dead; no zombies or undeads!) - What happens next depends on the pod's `restartPolicy`: - `Never`: the container is not restarted - `OnFailure` or `Always`: the container is restarted .debug[[k8s/healthchecks.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks.md)] --- ## When to use a liveness probe - To indicate failures that can't be recovered - deadlocks (causing all requests to time out) - internal corruption (causing all requests to error) - Anything where our incident response would be "just restart/reboot it" .warning[**Do not** use liveness probes for problems that can't be fixed by a restart] - Otherwise we just restart our pods for no reason, creating useless load .debug[[k8s/healthchecks.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks.md)] --- ## Readiness probe - Indicates if the container is ready to serve traffic - If a container becomes "unready" it might be ready again soon - If the readiness probe fails: - the container is *not* killed - if the pod is a member of a service, it is temporarily removed - it is re-added as soon as the readiness probe passes again .debug[[k8s/healthchecks.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks.md)] --- ## When to use a readiness probe - To indicate failure due to an external cause - database is down or unreachable - mandatory auth or other backend service unavailable - To indicate temporary failure or unavailability - application can only service *N* parallel connections - runtime is busy doing garbage collection or initial data load - For processes that take a long time to start (more on that later) .debug[[k8s/healthchecks.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks.md)] --- ## Dependencies - If a web server depends on a database to function, and the database is down: - the web server's liveness probe should succeed - the web server's readiness probe should fail - Same thing for any hard dependency (without which the container can't work) .warning[**Do not** fail liveness probes for problems that are external to the container] .debug[[k8s/healthchecks.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks.md)] --- ## Timing and thresholds - Probes are executed at intervals of `periodSeconds` (default: 10) - The timeout for a probe is set with `timeoutSeconds` (default: 1) .warning[If a probe takes longer than that, it is considered as a FAIL] - A probe is considered successful after `successThreshold` successes (default: 1) - A probe is considered failing after `failureThreshold` failures (default: 3) - A probe can have an `initialDelaySeconds` parameter (default: 0) - Kubernetes will wait that amount of time before running the probe for the first time (this is important to avoid killing services that take a long time to start) .debug[[k8s/healthchecks.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks.md)] --- class: extra-details ## Startup probe - Kubernetes 1.16 introduces a third type of probe: `startupProbe` (it is in `alpha` in Kubernetes 1.16) - It can be used to indicate "container not ready *yet*" - process is still starting - loading external data, priming caches - Before Kubernetes 1.16, we had to use the `initialDelaySeconds` parameter (available for both liveness and readiness probes) - `initialDelaySeconds` is a rigid delay (always wait X before running probes) - `startupProbe` works better when a container start time can vary a lot .debug[[k8s/healthchecks.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks.md)] --- ## Different types of probes - HTTP request - specify URL of the request (and optional headers) - any status code between 200 and 399 indicates success - TCP connection - the probe succeeds if the TCP port is open - arbitrary exec - a command is executed in the container - exit status of zero indicates success .debug[[k8s/healthchecks.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks.md)] --- ## Benefits of using probes - Rolling updates proceed when containers are *actually ready* (as opposed to merely started) - Containers in a broken state get killed and restarted (instead of serving errors or timeouts) - Unavailable backends get removed from load balancer rotation (thus improving response times across the board) - If a probe is not defined, it's as if there was an "always successful" probe .debug[[k8s/healthchecks.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks.md)] --- ## Example: HTTP probe Here is a pod template for the `rng` web service of the DockerCoins app: ```yaml apiVersion: v1 kind: Pod metadata: name: rng-with-liveness spec: containers: - name: rng image: dockercoins/rng:v0.1 livenessProbe: httpGet: path: / port: 80 initialDelaySeconds: 10 periodSeconds: 1 ``` If the backend serves an error, or takes longer than 1s, 3 times in a row, it gets killed. .debug[[k8s/healthchecks.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks.md)] --- ## Example: exec probe Here is a pod template for a Redis server: ```yaml apiVersion: v1 kind: Pod metadata: name: redis-with-liveness spec: containers: - name: redis image: redis livenessProbe: exec: command: ["redis-cli", "ping"] ``` If the Redis process becomes unresponsive, it will be killed. .debug[[k8s/healthchecks.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks.md)] --- ## Questions to ask before adding healthchecks - Do we want liveness, readiness, both? (sometimes, we can use the same check, but with different failure thresholds) - Do we have existing HTTP endpoints that we can use? - Do we need to add new endpoints, or perhaps use something else? - Are our healthchecks likely to use resources and/or slow down the app? - Do they depend on additional services? (this can be particularly tricky, see next slide) .debug[[k8s/healthchecks.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks.md)] --- ## Healthchecks and dependencies - Liveness checks should not be influenced by the state of external services - All checks should reply quickly (by default, less than 1 second) - Otherwise, they are considered to fail - This might require to check the health of dependencies asynchronously (e.g. if a database or API might be healthy but still take more than 1 second to reply, we should check the status asynchronously and report a cached status) .debug[[k8s/healthchecks.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks.md)] --- ## Healthchecks for workers (In that context, worker = process that doesn't accept connections) - Readiness isn't useful (because workers aren't backends for a service) - Liveness may help us restart a broken worker, but how can we check it? - Embedding an HTTP server is a (potentially expensive) option - Using a "lease" file can be relatively easy: - touch a file during each iteration of the main loop - check the timestamp of that file from an exec probe - Writing logs (and checking them from the probe) also works ??? :EN:- Using healthchecks to improve availability :FR:- Utiliser des *healthchecks* pour améliorer la disponibilité .debug[[k8s/healthchecks.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks.md)] --- ## Adding healthchecks to an app - Let's add healthchecks to DockerCoins! - We will examine the questions of the previous slide - Then we will review each component individually to add healthchecks .debug[[k8s/healthchecks-more.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks-more.md)] --- ## Liveness, readiness, or both? - To answer that question, we need to see the app run for a while - Do we get temporary, recoverable glitches? → then use readiness - Or do we get hard lock-ups requiring a restart? → then use liveness - In the case of DockerCoins, we don't know yet! - Let's pick liveness .debug[[k8s/healthchecks-more.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks-more.md)] --- ## Do we have HTTP endpoints that we can use? - Each of the 3 web services (hasher, rng, webui) has a trivial route on `/` - These routes: - don't seem to perform anything complex or expensive - don't seem to call other services - Perfect! (See next slides for individual details) .debug[[k8s/healthchecks-more.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks-more.md)] --- - [hasher.rb](https://github.com/jpetazzo/container.training/blob/master/dockercoins/hasher/hasher.rb) ```ruby get '/' do "HASHER running on #{Socket.gethostname}\n" end ``` - [rng.py](https://github.com/jpetazzo/container.training/blob/master/dockercoins/rng/rng.py) ```python @app.route("/") def index(): return "RNG running on {}\n".format(hostname) ``` - [webui.js](https://github.com/jpetazzo/container.training/blob/master/dockercoins/webui/webui.js) ```javascript app.get('/', function (req, res) { res.redirect('/index.html'); }); ``` .debug[[k8s/healthchecks-more.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks-more.md)] --- ## Running DockerCoins - We will run DockerCoins in a new, separate namespace - We will use a set of YAML manifests and pre-built images - We will add our new liveness probe to the YAML of the `rng` DaemonSet - Then, we will deploy the application .debug[[k8s/healthchecks-more.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks-more.md)] --- ## Creating a new namespace - This will make sure that we don't collide / conflict with previous exercises .exercise[ - Create the yellow namespace: ```bash kubectl create namespace yellow ``` - Switch to that namespace: ```bash kns yellow ``` ] .debug[[k8s/healthchecks-more.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks-more.md)] --- ## Retrieving DockerCoins manifests - All the manifests that we need are on a convenient repository: https://github.com/jpetazzo/kubercoins .exercise[ - Clone that repository: ```bash cd ~ git clone https://github.com/jpetazzo/kubercoins ``` - Change directory to the repository: ```bash cd kubercoins ``` ] .debug[[k8s/healthchecks-more.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks-more.md)] --- ## A simple HTTP liveness probe This is what our liveness probe should look like: ```yaml containers: - name: ... image: ... livenessProbe: httpGet: path: / port: 80 initialDelaySeconds: 30 periodSeconds: 5 ``` This will give 30 seconds to the service to start. (Way more than necessary!) <br/> It will run the probe every 5 seconds. <br/> It will use the default timeout (1 second). <br/> It will use the default failure threshold (3 failed attempts = dead). <br/> It will use the default success threshold (1 successful attempt = alive). .debug[[k8s/healthchecks-more.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks-more.md)] --- ## Adding the liveness probe - Let's add the liveness probe, then deploy DockerCoins .exercise[ - Edit `rng-deployment.yaml` and add the liveness probe ```bash vim rng-deployment.yaml ``` - Load the YAML for all the resources of DockerCoins: ```bash kubectl apply -f . ``` ] .debug[[k8s/healthchecks-more.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks-more.md)] --- ## Testing the liveness probe - The rng service needs 100ms to process a request (because it is single-threaded and sleeps 0.1s in each request) - The probe timeout is set to 1 second - If we send more than 10 requests per second per backend, it will break - Let's generate traffic and see what happens! .exercise[ - Get the ClusterIP address of the rng service: ```bash kubectl get svc rng ``` ] .debug[[k8s/healthchecks-more.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks-more.md)] --- ## Monitoring the rng service - Each command below will show us what's happening on a different level .exercise[ - In one window, monitor cluster events: ```bash kubectl get events -w ``` - In another window, monitor the response time of rng: ```bash httping `<ClusterIP>` ``` - In another window, monitor pods status: ```bash kubectl get pods -w ``` ] .debug[[k8s/healthchecks-more.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks-more.md)] --- ## Generating traffic - Let's use `ab` to send concurrent requests to rng .exercise[ - In yet another window, generate traffic: ```bash ab -c 10 -n 1000 http://`<ClusterIP>`/1 ``` - Experiment with higher values of `-c` and see what happens ] - The `-c` parameter indicates the number of concurrent requests - The final `/1` is important to generate actual traffic (otherwise we would use the ping endpoint, which doesn't sleep 0.1s per request) .debug[[k8s/healthchecks-more.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks-more.md)] --- ## Discussion - Above a given threshold, the liveness probe starts failing (about 10 concurrent requests per backend should be plenty enough) - When the liveness probe fails 3 times in a row, the container is restarted - During the restart, there is *less* capacity available - ... Meaning that the other backends are likely to timeout as well - ... Eventually causing all backends to be restarted - ... And each fresh backend gets restarted, too - This goes on until the load goes down, or we add capacity *This wouldn't be a good healthcheck in a real application!* .debug[[k8s/healthchecks-more.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks-more.md)] --- ## Better healthchecks - We need to make sure that the healthcheck doesn't trip when performance degrades due to external pressure - Using a readiness check would have fewer effects (but it would still be an imperfect solution) - A possible combination: - readiness check with a short timeout / low failure threshold - liveness check with a longer timeout / higher failure threshold .debug[[k8s/healthchecks-more.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks-more.md)] --- ## Healthchecks for redis - A liveness probe is enough (it's not useful to remove a backend from rotation when it's the only one) - We could use an exec probe running `redis-cli ping` .debug[[k8s/healthchecks-more.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks-more.md)] --- class: extra-details ## Exec probes and zombies - When using exec probes, we should make sure that we have a *zombie reaper* 🤔🧐🧟 Wait, what? - When a process terminates, its parent must call `wait()`/`waitpid()` (this is how the parent process retrieves the child's exit status) - In the meantime, the process is in *zombie* state (the process state will show as `Z` in `ps`, `top` ...) - When a process is killed, its children are *orphaned* and attached to PID 1 - PID 1 has the responsibility of *reaping* these processes when they terminate - OK, but how does that affect us? .debug[[k8s/healthchecks-more.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks-more.md)] --- class: extra-details ## PID 1 in containers - On ordinary systems, PID 1 (`/sbin/init`) has logic to reap processes - In containers, PID 1 is typically our application process (e.g. Apache, the JVM, NGINX, Redis ...) - These *do not* take care of reaping orphans - If we use exec probes, we need to add a process reaper - We can add [tini](https://github.com/krallin/tini) to our images - Or [share the PID namespace between containers of a pod](https://kubernetes.io/docs/tasks/configure-pod-container/share-process-namespace/) (and have gcr.io/pause take care of the reaping) - Discussion of this in [Video - 10 Ways to Shoot Yourself in the Foot with Kubernetes, #9 Will Surprise You](https://www.youtube.com/watch?v=QKI-JRs2RIE) ??? :EN:- Adding healthchecks to an app :FR:- Ajouter des *healthchecks* à une application .debug[[k8s/healthchecks-more.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/healthchecks-more.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/container-cranes.jpg)] --- name: toc-recording-deployment-actions class: title Recording deployment actions .nav[ [Previous part](#toc-healthchecks) | [Back to table of contents](#toc-part-5) | [Next part](#toc-namespaces) ] .debug[(automatically generated title slide)] --- # Recording deployment actions - Some commands that modify a Deployment accept an optional `--record` flag (Example: `kubectl set image deployment worker worker=alpine --record`) - That flag will store the command line in the Deployment (Technically, using the annotation `kubernetes.io/change-cause`) - It gets copied to the corresponding ReplicaSet (Allowing to keep track of which command created or promoted this ReplicaSet) - We can view this information with `kubectl rollout history` .debug[[k8s/record.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/record.md)] --- ## Using `--record` - Let's make a couple of changes to a Deployment and record them .exercise[ - Roll back `worker` to image version 0.1: ```bash kubectl set image deployment worker worker=dockercoins/worker:v0.1 --record ``` - Promote it to version 0.2 again: ```bash kubectl set image deployment worker worker=dockercoins/worker:v0.2 --record ``` - View the change history: ```bash kubectl rollout history deployment worker ``` ] .debug[[k8s/record.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/record.md)] --- ## Pitfall #1: forgetting `--record` - What happens if we don't specify `--record`? .exercise[ - Promote `worker` to image version 0.3: ```bash kubectl set image deployment worker worker=dockercoins/worker:v0.3 ``` - View the change history: ```bash kubectl rollout history deployment worker ``` ] -- It recorded version 0.2 instead of 0.3! Why? .debug[[k8s/record.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/record.md)] --- ## How `--record` really works - `kubectl` adds the annotation `kubernetes.io/change-cause` to the Deployment - The Deployment controller copies that annotation to the ReplicaSet - `kubectl rollout history` shows the ReplicaSets' annotations - If we don't specify `--record`, the annotation is not updated - The previous value of that annotation is copied to the new ReplicaSet - In that case, the ReplicaSet annotation does not reflect reality! .debug[[k8s/record.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/record.md)] --- ## Pitfall #2: recording `scale` commands - What happens if we use `kubectl scale --record`? .exercise[ - Check the current history: ```bash kubectl rollout history deployment worker ``` - Scale the deployment: ```bash kubectl scale deployment worker --replicas=3 --record ``` - Check the change history again: ```bash kubectl rollout history deployment worker ``` ] -- The last entry in the history was overwritten by the `scale` command! Why? .debug[[k8s/record.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/record.md)] --- ## Actions that don't create a new ReplicaSet - The `scale` command updates the Deployment definition - But it doesn't create a new ReplicaSet - Using the `--record` flag sets the annotation like before - The annotation gets copied to the existing ReplicaSet - This overwrites the previous annotation that was there - In that case, we lose the previous change cause! .debug[[k8s/record.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/record.md)] --- ## Updating the annotation directly - Let's see what happens if we set the annotation manually .exercise[ - Annotate the Deployment: ```bash kubectl annotate deployment worker kubernetes.io/change-cause="Just for fun" ``` - Check that our annotation shows up in the change history: ```bash kubectl rollout history deployment worker ``` ] -- Our annotation shows up (and overwrote whatever was there before). .debug[[k8s/record.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/record.md)] --- ## Using change cause - It sounds like a good idea to use `--record`, but: *"Incorrect documentation is often worse than no documentation."* <br/> (Bertrand Meyer) - If we use `--record` once, we need to either: - use it every single time after that - or clear the Deployment annotation after using `--record` <br/> (subsequent changes will show up with a `<none>` change cause) - A safer way is to set it through our tooling .debug[[k8s/record.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/record.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/container-housing.jpg)] --- name: toc-namespaces class: title Namespaces .nav[ [Previous part](#toc-recording-deployment-actions) | [Back to table of contents](#toc-part-6) | [Next part](#toc-exposing-http-services-with-ingress-resources) ] .debug[(automatically generated title slide)] --- # Namespaces - We would like to deploy another copy of DockerCoins on our cluster - We could rename all our deployments and services: hasher → hasher2, redis → redis2, rng → rng2, etc. - That would require updating the code - There has to be a better way! -- - As hinted by the title of this section, we will use *namespaces* .debug[[k8s/namespaces.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/namespaces.md)] --- ## Identifying a resource - We cannot have two resources with the same name (or can we...?) -- - We cannot have two resources *of the same kind* with the same name (but it's OK to have an `rng` service, an `rng` deployment, and an `rng` daemon set) -- - We cannot have two resources of the same kind with the same name *in the same namespace* (but it's OK to have e.g. two `rng` services in different namespaces) -- - Except for resources that exist at the *cluster scope* (these do not belong to a namespace) .debug[[k8s/namespaces.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/namespaces.md)] --- ## Uniquely identifying a resource - For *namespaced* resources: the tuple *(kind, name, namespace)* needs to be unique - For resources at the *cluster scope*: the tuple *(kind, name)* needs to be unique .exercise[ - List resource types again, and check the NAMESPACED column: ```bash kubectl api-resources ``` ] .debug[[k8s/namespaces.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/namespaces.md)] --- ## Pre-existing namespaces - If we deploy a cluster with `kubeadm`, we have three or four namespaces: - `default` (for our applications) - `kube-system` (for the control plane) - `kube-public` (contains one ConfigMap for cluster discovery) - `kube-node-lease` (in Kubernetes 1.14 and later; contains Lease objects) - If we deploy differently, we may have different namespaces .debug[[k8s/namespaces.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/namespaces.md)] --- ## Creating namespaces - Let's see two identical methods to create a namespace .exercise[ - We can use `kubectl create namespace`: ```bash kubectl create namespace blue ``` - Or we can construct a very minimal YAML snippet: ```bash kubectl apply -f- <<EOF apiVersion: v1 kind: Namespace metadata: name: blue EOF ``` ] .debug[[k8s/namespaces.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/namespaces.md)] --- ## Using namespaces - We can pass a `-n` or `--namespace` flag to most `kubectl` commands: ```bash kubectl -n blue get svc ``` - We can also change our current *context* - A context is a *(user, cluster, namespace)* tuple - We can manipulate contexts with the `kubectl config` command .debug[[k8s/namespaces.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/namespaces.md)] --- ## Viewing existing contexts - On our training environments, at this point, there should be only one context .exercise[ - View existing contexts to see the cluster name and the current user: ```bash kubectl config get-contexts ``` ] - The current context (the only one!) is tagged with a `*` - What are NAME, CLUSTER, AUTHINFO, and NAMESPACE? .debug[[k8s/namespaces.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/namespaces.md)] --- ## What's in a context - NAME is an arbitrary string to identify the context - CLUSTER is a reference to a cluster (i.e. API endpoint URL, and optional certificate) - AUTHINFO is a reference to the authentication information to use (i.e. a TLS client certificate, token, or otherwise) - NAMESPACE is the namespace (empty string = `default`) .debug[[k8s/namespaces.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/namespaces.md)] --- ## Switching contexts - We want to use a different namespace - Solution 1: update the current context *This is appropriate if we need to change just one thing (e.g. namespace or authentication).* - Solution 2: create a new context and switch to it *This is appropriate if we need to change multiple things and switch back and forth.* - Let's go with solution 1! .debug[[k8s/namespaces.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/namespaces.md)] --- ## Updating a context - This is done through `kubectl config set-context` - We can update a context by passing its name, or the current context with `--current` .exercise[ - Update the current context to use the `blue` namespace: ```bash kubectl config set-context --current --namespace=blue ``` - Check the result: ```bash kubectl config get-contexts ``` ] .debug[[k8s/namespaces.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/namespaces.md)] --- ## Using our new namespace - Let's check that we are in our new namespace, then deploy a new copy of Dockercoins .exercise[ - Verify that the new context is empty: ```bash kubectl get all ``` ] .debug[[k8s/namespaces.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/namespaces.md)] --- ## Deploying DockerCoins with YAML files - The GitHub repository `jpetazzo/kubercoins` contains everything we need! .exercise[ - Clone the kubercoins repository: ```bash cd ~ git clone https://github.com/jpetazzo/kubercoins ``` - Create all the DockerCoins resources: ```bash kubectl create -f kubercoins ``` ] If the argument behind `-f` is a directory, all the files in that directory are processed. The subdirectories are *not* processed, unless we also add the `-R` flag. .debug[[k8s/namespaces.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/namespaces.md)] --- ## Viewing the deployed app - Let's see if this worked correctly! .exercise[ - Retrieve the port number allocated to the `webui` service: ```bash kubectl get svc webui ``` - Point our browser to http://X.X.X.X:3xxxx ] If the graph shows up but stays at zero, give it a minute or two! .debug[[k8s/namespaces.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/namespaces.md)] --- ## Namespaces and isolation - Namespaces *do not* provide isolation - A pod in the `green` namespace can communicate with a pod in the `blue` namespace - A pod in the `default` namespace can communicate with a pod in the `kube-system` namespace - CoreDNS uses a different subdomain for each namespace - Example: from any pod in the cluster, you can connect to the Kubernetes API with: `https://kubernetes.default.svc.cluster.local:443/` .debug[[k8s/namespaces.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/namespaces.md)] --- ## Isolating pods - Actual isolation is implemented with *network policies* - Network policies are resources (like deployments, services, namespaces...) - Network policies specify which flows are allowed: - between pods - from pods to the outside world - and vice-versa .debug[[k8s/namespaces.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/namespaces.md)] --- ## Switch back to the default namespace - Let's make sure that we don't run future exercises in the `blue` namespace .exercise[ - Switch back to the original context: ```bash kubectl config set-context --current --namespace= ``` ] Note: we could have used `--namespace=default` for the same result. .debug[[k8s/namespaces.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/namespaces.md)] --- ## Switching namespaces more easily - We can also use a little helper tool called `kubens`: ```bash # Switch to namespace foo kubens foo # Switch back to the previous namespace kubens - ``` - On our clusters, `kubens` is called `kns` instead (so that it's even fewer keystrokes to switch namespaces) .debug[[k8s/namespaces.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/namespaces.md)] --- ## `kubens` and `kubectx` - With `kubens`, we can switch quickly between namespaces - With `kubectx`, we can switch quickly between contexts - Both tools are simple shell scripts available from https://github.com/ahmetb/kubectx - On our clusters, they are installed as `kns` and `kctx` (for brevity and to avoid completion clashes between `kubectx` and `kubectl`) .debug[[k8s/namespaces.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/namespaces.md)] --- ## `kube-ps1` - It's easy to lose track of our current cluster / context / namespace - `kube-ps1` makes it easy to track these, by showing them in our shell prompt - It is installed on our training clusters, and when using [shpod](https://github.com/jpetazzo/shpod) - It gives us a prompt looking like this one: ``` [123.45.67.89] `(kubernetes-admin@kubernetes:default)` docker@node1 ~ ``` (The highlighted part is `context:namespace`, managed by `kube-ps1`) - Highly recommended if you work across multiple contexts or namespaces! .debug[[k8s/namespaces.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/namespaces.md)] --- ## Installing `kube-ps1` - It's a simple shell script available from https://github.com/jonmosco/kube-ps1 - It needs to be [installed in our profile/rc files](https://github.com/jonmosco/kube-ps1#installing) (instructions differ depending on platform, shell, etc.) - Once installed, it defines aliases called `kube_ps1`, `kubeon`, `kubeoff` (to selectively enable/disable it when needed) - Pro-tip: install it on your machine during the next break! ??? :EN:- Organizing resources with Namespaces :FR:- Organiser les ressources avec des *namespaces* .debug[[k8s/namespaces.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/namespaces.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/containers-by-the-water.jpg)] --- name: toc-exposing-http-services-with-ingress-resources class: title Exposing HTTP services with Ingress resources .nav[ [Previous part](#toc-namespaces) | [Back to table of contents](#toc-part-6) | [Next part](#toc-ingress-and-tls-certificates) ] .debug[(automatically generated title slide)] --- # Exposing HTTP services with Ingress resources - *Services* give us a way to access a pod or a set of pods - Services can be exposed to the outside world: - with type `NodePort` (on a port >30000) - with type `LoadBalancer` (allocating an external load balancer) - What about HTTP services? - how can we expose `webui`, `rng`, `hasher`? - the Kubernetes dashboard? - a new version of `webui`? .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Exposing HTTP services - If we use `NodePort` services, clients have to specify port numbers (i.e. http://xxxxx:31234 instead of just http://xxxxx) - `LoadBalancer` services are nice, but: - they are not available in all environments - they often carry an additional cost (e.g. they provision an ELB) - they require one extra step for DNS integration <br/> (waiting for the `LoadBalancer` to be provisioned; then adding it to DNS) - We could build our own reverse proxy .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Building a custom reverse proxy - There are many options available: Apache, HAProxy, Hipache, NGINX, Traefik, ... (look at [jpetazzo/aiguillage](https://github.com/jpetazzo/aiguillage) for a minimal reverse proxy configuration using NGINX) - Most of these options require us to update/edit configuration files after each change - Some of them can pick up virtual hosts and backends from a configuration store - Wouldn't it be nice if this configuration could be managed with the Kubernetes API? -- - Enter.red[¹] *Ingress* resources! .footnote[.red[¹] Pun maybe intended.] .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Ingress resources - Kubernetes API resource (`kubectl get ingress`/`ingresses`/`ing`) - Designed to expose HTTP services - Basic features: - load balancing - SSL termination - name-based virtual hosting - Can also route to different services depending on: - URI path (e.g. `/api`→`api-service`, `/static`→`assets-service`) - Client headers, including cookies (for A/B testing, canary deployment...) - and more! .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Principle of operation - Step 1: deploy an *ingress controller* - ingress controller = load balancer + control loop - the control loop watches over ingress resources, and configures the LB accordingly - Step 2: set up DNS - associate DNS entries with the load balancer address - Step 3: create *ingress resources* - the ingress controller picks up these resources and configures the LB - Step 4: profit! .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Ingress in action - We will deploy the Traefik ingress controller - this is an arbitrary choice - maybe motivated by the fact that Traefik releases are named after cheeses - For DNS, we will use [nip.io](http://nip.io/) - `*.1.2.3.4.nip.io` resolves to `1.2.3.4` - We will create ingress resources for various HTTP services .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Deploying pods listening on port 80 - We want our ingress load balancer to be available on port 80 - The best way to do that would be with a `LoadBalancer` service ... but it requires support from the underlying infrastructure - Instead, we are going to use the `hostNetwork` mode on the Traefik pods - Let's see what this `hostNetwork` mode is about ... .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Without `hostNetwork` - Normally, each pod gets its own *network namespace* (sometimes called sandbox or network sandbox) - An IP address is assigned to the pod - This IP address is routed/connected to the cluster network - All containers of that pod are sharing that network namespace (and therefore using the same IP address) .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## With `hostNetwork: true` - No network namespace gets created - The pod is using the network namespace of the host - It "sees" (and can use) the interfaces (and IP addresses) of the host - The pod can receive outside traffic directly, on any port - Downside: with most network plugins, network policies won't work for that pod - most network policies work at the IP address level - filtering that pod = filtering traffic from the node .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- class: extra-details ## Other techniques to expose port 80 - We could use pods specifying `hostPort: 80` ... but with most CNI plugins, this [doesn't work or requires additional setup](https://github.com/kubernetes/kubernetes/issues/23920) - We could use a `NodePort` service ... but that requires [changing the `--service-node-port-range` flag in the API server](https://kubernetes.io/docs/reference/command-line-tools-reference/kube-apiserver/) - We could create a service with an external IP ... this would work, but would require a few extra steps (figuring out the IP address and adding it to the service) .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Running Traefik - The [Traefik documentation](https://docs.traefik.io/user-guide/kubernetes/#deploy-trfik-using-a-deployment-or-daemonset) tells us to pick between Deployment and Daemon Set - We are going to use a Daemon Set so that each node can accept connections - We will do two minor changes to the [YAML provided by Traefik](https://github.com/containous/traefik/blob/v1.7/examples/k8s/traefik-ds.yaml): - enable `hostNetwork` - add a *toleration* so that Traefik also runs on `node1` .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Taints and tolerations - A *taint* is an attribute added to a node - It prevents pods from running on the node - ... Unless they have a matching *toleration* - When deploying with `kubeadm`: - a taint is placed on the node dedicated to the control plane - the pods running the control plane have a matching toleration .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- class: extra-details ## Checking taints on our nodes .exercise[ - Check our nodes specs: ```bash kubectl get node node1 -o json | jq .spec kubectl get node node2 -o json | jq .spec ``` ] We should see a result only for `node1` (the one with the control plane): ```json "taints": [ { "effect": "NoSchedule", "key": "node-role.kubernetes.io/master" } ] ``` .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- class: extra-details ## Understanding a taint - The `key` can be interpreted as: - a reservation for a special set of pods <br/> (here, this means "this node is reserved for the control plane") - an error condition on the node <br/> (for instance: "disk full," do not start new pods here!) - The `effect` can be: - `NoSchedule` (don't run new pods here) - `PreferNoSchedule` (try not to run new pods here) - `NoExecute` (don't run new pods and evict running pods) .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- class: extra-details ## Checking tolerations on the control plane .exercise[ - Check tolerations for CoreDNS: ```bash kubectl -n kube-system get deployments coredns -o json | jq .spec.template.spec.tolerations ``` ] The result should include: ```json { "effect": "NoSchedule", "key": "node-role.kubernetes.io/master" } ``` It means: "bypass the exact taint that we saw earlier on `node1`." .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- class: extra-details ## Special tolerations .exercise[ - Check tolerations on `kube-proxy`: ```bash kubectl -n kube-system get ds kube-proxy -o json | jq .spec.template.spec.tolerations ``` ] The result should include: ```json { "operator": "Exists" } ``` This one is a special case that means "ignore all taints and run anyway." .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Running Traefik on our cluster - We provide a YAML file (`k8s/traefik.yaml`) which is essentially the sum of: - [Traefik's Daemon Set resources](https://github.com/containous/traefik/blob/v1.7/examples/k8s/traefik-ds.yaml) (patched with `hostNetwork` and tolerations) - [Traefik's RBAC rules](https://github.com/containous/traefik/blob/v1.7/examples/k8s/traefik-rbac.yaml) allowing it to watch necessary API objects .exercise[ - Apply the YAML: ```bash kubectl apply -f ~/container.training/k8s/traefik.yaml ``` ] .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Checking that Traefik runs correctly - If Traefik started correctly, we now have a web server listening on each node .exercise[ - Check that Traefik is serving 80/tcp: ```bash curl localhost ``` ] We should get a `404 page not found` error. This is normal: we haven't provided any ingress rule yet. .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Setting up DNS - To make our lives easier, we will use [nip.io](http://nip.io) - Check out `http://cheddar.A.B.C.D.nip.io` (replacing A.B.C.D with the IP address of `node1`) - We should get the same `404 page not found` error (meaning that our DNS is "set up properly", so to speak!) .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Traefik web UI - Traefik provides a web dashboard - With the current install method, it's listening on port 8080 .exercise[ - Go to `http://node1:8080` (replacing `node1` with its IP address) <!-- ```open http://node1:8080``` --> ] .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Setting up host-based routing ingress rules - We are going to use `errm/cheese` images (there are [3 tags available](https://hub.docker.com/r/errm/cheese/tags/): wensleydale, cheddar, stilton) - These images contain a simple static HTTP server sending a picture of cheese - We will run 3 deployments (one for each cheese) - We will create 3 services (one for each deployment) - Then we will create 3 ingress rules (one for each service) - We will route `<name-of-cheese>.A.B.C.D.nip.io` to the corresponding deployment .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Running cheesy web servers .exercise[ - Run all three deployments: ```bash kubectl create deployment cheddar --image=errm/cheese:cheddar kubectl create deployment stilton --image=errm/cheese:stilton kubectl create deployment wensleydale --image=errm/cheese:wensleydale ``` - Create a service for each of them: ```bash kubectl expose deployment cheddar --port=80 kubectl expose deployment stilton --port=80 kubectl expose deployment wensleydale --port=80 ``` ] .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## What does an ingress resource look like? Here is a minimal host-based ingress resource: ```yaml apiVersion: networking.k8s.io/v1beta1 kind: Ingress metadata: name: cheddar spec: rules: - host: cheddar.`A.B.C.D`.nip.io http: paths: - path: / backend: serviceName: cheddar servicePort: 80 ``` (It is in `k8s/ingress.yaml`.) .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Creating our first ingress resources .exercise[ - Edit the file `~/container.training/k8s/ingress.yaml` - Replace A.B.C.D with the IP address of `node1` - Apply the file - Open http://cheddar.A.B.C.D.nip.io ] (An image of a piece of cheese should show up.) .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Creating the other ingress resources .exercise[ - Edit the file `~/container.training/k8s/ingress.yaml` - Replace `cheddar` with `stilton` (in `name`, `host`, `serviceName`) - Apply the file - Check that `stilton.A.B.C.D.nip.io` works correctly - Repeat for `wensleydale` ] .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- class: extra-details ## Using multiple ingress controllers - You can have multiple ingress controllers active simultaneously (e.g. Traefik and NGINX) - You can even have multiple instances of the same controller (e.g. one for internal, another for external traffic) - To indicate which ingress controller should be used by a given Ingress resouce: - before Kubernetes 1.18, use the `kubernetes.io/ingress.class` annotation - since Kubernetes 1.18, use the `ingressClassName` field <br/> (which should refer to an existing `IngressClass` resource) .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Ingress: the good - The traffic flows directly from the ingress load balancer to the backends - it doesn't need to go through the `ClusterIP` - in fact, we don't even need a `ClusterIP` (we can use a headless service) - The load balancer can be outside of Kubernetes (as long as it has access to the cluster subnet) - This allows the use of external (hardware, physical machines...) load balancers - Annotations can encode special features (rate-limiting, A/B testing, session stickiness, etc.) .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Ingress: the bad - Aforementioned "special features" are not standardized yet - Some controllers will support them; some won't - Even relatively common features (stripping a path prefix) can differ: - [traefik.ingress.kubernetes.io/rule-type: PathPrefixStrip](https://docs.traefik.io/user-guide/kubernetes/#path-based-routing) - [ingress.kubernetes.io/rewrite-target: /](https://github.com/kubernetes/contrib/tree/master/ingress/controllers/nginx/examples/rewrite) - The Ingress spec stabilized in Kubernetes 1.19 ... ... without specifying these features! 😭 .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## A special feature in action - We're going to see how to implement *canary releases* with Traefik - This feature is available on multiple ingress controllers - ... But it is configured very differently on each of them .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Canary releases - A *canary release* (or canary launch or canary deployment) is a release that will process only a small fraction of the workload - After deploying the canary, we compare its metrics to the normal release - If the metrics look good, the canary will progressively receive more traffic (until it gets 100% and becomes the new normal release) - If the metrics aren't good, the canary is automatically removed - When we deploy a bad release, only a tiny fraction of traffic is affected .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Various ways to implement canary - Example 1: canary for a microservice - 1% of all requests (sampled randomly) are sent to the canary - the remaining 99% are sent to the normal release - Example 2: canary for a web app - 1% of users are sent to the canary web site - the remaining 99% are sent to the normal release - Example 3: canary for shipping physical goods - 1% of orders are shipped with the canary process - the remaining 99% are shipped with the normal process - We're going to implement example 1 (per-request routing) .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Canary releases with Traefik - We need to deploy the canary and expose it with a separate service - Then, in the Ingress resource, we need: - multiple `paths` entries (one for each service, canary and normal) - an extra annotation indicating the weight of each service - If we want, we can send requests to more than 2 services - Let's send requests to our 3 cheesy services! .exercise[ - Create the resource shown on the next slide ] .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## The Ingress resource .small[ ```yaml apiVersion: networking.k8s.io/v1beta1 kind: Ingress metadata: name: cheeseplate annotations: traefik.ingress.kubernetes.io/service-weights: | cheddar: 50% wensleydale: 25% stilton: 25% spec: rules: - host: cheeseplate.`A.B.C.D`.nip.io http: paths: - path: / backend: serviceName: cheddar servicePort: 80 - path: / backend: serviceName: wensleydale servicePort: 80 - path: / backend: serviceName: stilton servicePort: 80 ``` ] .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- ## Testing the canary - Let's check the percentage of requests going to each service .exercise[ - Continuously send HTTP requests to the new ingress: ```bash while sleep 0.1; do curl -s http://cheeseplate.A.B.C.D.nip.io/ done ``` ] We should see a 50/25/25 request mix. .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- class: extra-details ## Load balancing fairness Note: if we use odd request ratios, the load balancing algorithm might appear to be broken on a small scale (when sending a small number of requests), but on a large scale (with many requests) it will be fair. For instance, with a 11%/89% ratio, we can see 79 requests going to the 89%-weighted service, and then requests alternating between the two services; then 79 requests again, etc. .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- class: extra-details ## Other ingress controllers *Just to illustrate how different things are ...* - With the NGINX ingress controller: - define two ingress ressources <br/> (specifying rules with the same host+path) - add `nginx.ingress.kubernetes.io/canary` annotations on each - With Linkerd2: - define two services - define an extra service for the weighted aggregate of the two - define a TrafficSplit (this is a CRD introduced by the SMI spec) .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- class: extra-details ## We need more than that What we saw is just one of the multiple building blocks that we need to achieve a canary release. We also need: - metrics (latency, performance ...) for our releases - automation to alter canary weights (increase canary weight if metrics look good; decrease otherwise) - a mechanism to manage the lifecycle of the canary releases (create them, promote them, delete them ...) For inspiration, check [flagger by Weave](https://github.com/weaveworks/flagger). ??? :EN:- The Ingress resource :FR:- La ressource *ingress* .debug[[k8s/ingress.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/distillery-containers.jpg)] --- name: toc-ingress-and-tls-certificates class: title Ingress and TLS certificates .nav[ [Previous part](#toc-exposing-http-services-with-ingress-resources) | [Back to table of contents](#toc-part-6) | [Next part](#toc-cert-manager) ] .debug[(automatically generated title slide)] --- # Ingress and TLS certificates - Most ingress controllers support TLS connections (in a way that is standard across controllers) - The TLS key and certificate are stored in a Secret - The Secret is then referenced in the Ingress resource: ```yaml spec: tls: - secretName: XXX hosts: - YYY rules: - ZZZ ``` .debug[[k8s/ingress-tls.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress-tls.md)] --- ## Obtaining a certificate - In the next section, we will need a TLS key and certificate - These usually come in [PEM](https://en.wikipedia.org/wiki/Privacy-Enhanced_Mail) format: ``` -----BEGIN CERTIFICATE----- MIIDATCCAemg... ... -----END CERTIFICATE----- ``` - We will see how to generate a self-signed certificate (easy, fast, but won't be recognized by web browsers) - We will also see how to obtain a certificate from [Let's Encrypt](https://letsencrypt.org/) (requires the cluster to be reachable through a domain name) .debug[[k8s/ingress-tls.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress-tls.md)] --- class: extra-details ## In production ... - A very popular option is to use the [cert-manager](https://cert-manager.io/docs/) operator - It's a flexible, modular approach to automated certificate management - For simplicity, in this section, we will use [certbot](https://certbot.eff.org/) - The method shown here works well for one-time certs, but lacks: - automation - renewal .debug[[k8s/ingress-tls.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress-tls.md)] --- ## Which domain to use - If you're doing this in a training: *the instructor will tell you what to use* - If you're doing this on your own Kubernetes cluster: *you should use a domain that points to your cluster* - More precisely: *you should use a domain that points to your ingress controller* - If you don't have a domain name, you can use [nip.io](https://nip.io/) (if your ingress controller is on 1.2.3.4, you can use `whatever.1.2.3.4.nip.io`) .debug[[k8s/ingress-tls.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress-tls.md)] --- ## Setting `$DOMAIN` - We will use `$DOMAIN` in the following section - Let's set it now .exercise[ - Set the `DOMAIN` environment variable: ```bash export DOMAIN=... ``` ] .debug[[k8s/ingress-tls.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress-tls.md)] --- ## Method 1, self-signed certificate - Thanks to `openssl`, generating a self-signed cert is just one command away! .exercise[ - Generate a key and certificate: ```bash openssl req \ -newkey rsa -nodes -keyout privkey.pem \ -x509 -days 30 -subj /CN=$DOMAIN/ -out cert.pem ``` ] This will create two files, `privkey.pem` and `cert.pem`. .debug[[k8s/ingress-tls.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress-tls.md)] --- ## Method 2, Let's Encrypt with certbot - `certbot` is an [ACME](https://tools.ietf.org/html/rfc8555) client (Automatic Certificate Management Environment) - We can use it to obtain certificates from Let's Encrypt - It needs to listen to port 80 (to complete the [HTTP-01 challenge](https://letsencrypt.org/docs/challenge-types/)) - If port 80 is already taken by our ingress controller, see method 3 .debug[[k8s/ingress-tls.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress-tls.md)] --- class: extra-details ## HTTP-01 challenge - `certbot` contacts Let's Encrypt, asking for a cert for `$DOMAIN` - Let's Encrypt gives a token to `certbot` - Let's Encrypt then tries to access the following URL: `http://$DOMAIN/.well-known/acme-challenge/<token>` - That URL needs to be routed to `certbot` - Once Let's Encrypt gets the response from `certbot`, it issues the certificate .debug[[k8s/ingress-tls.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress-tls.md)] --- ## Running certbot - There is a very convenient container image, `certbot/certbot` - Let's use a volume to get easy access to the generated key and certificate .exercise[ - Obtain a certificate from Let's Encrypt: ```bash EMAIL=your.address@example.com docker run --rm -p 80:80 -v $PWD/letsencrypt:/etc/letsencrypt \ certbot/certbot certonly \ -m $EMAIL \ --standalone --agree-tos -n \ --domain $DOMAIN \ --test-cert ``` ] This will get us a "staging" certificate. Remove `--test-cert` to obtain a *real* certificate. .debug[[k8s/ingress-tls.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress-tls.md)] --- ## Copying the key and certificate - If everything went fine: - the key and certificate files are in `letsencrypt/live/$DOMAIN` - they are owned by `root` .exercise[ - Grant ourselves permissions on these files: ```bash sudo chown -R $USER letsencrypt ``` - Copy the certificate and key to the current directory: ```bash cp letsencrypt/live/test/{cert,privkey}.pem . ``` ] .debug[[k8s/ingress-tls.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress-tls.md)] --- ## Method 3, certbot with Ingress - Sometimes, we can't simply listen to port 80: - we might already have an ingress controller there - our nodes might be on an internal network - But we can define an Ingress to route the HTTP-01 challenge to `certbot`! - Our Ingress needs to route all requests to `/.well-known/acme-challenge` to `certbot` - There are at least two ways to do that: - run `certbot` in a Pod (and extract the cert+key when it's done) - run `certbot` in a container on a node (and manually route traffic to it) - We're going to use the second option (mostly because it will give us an excuse to tinker with Endpoints resources!) .debug[[k8s/ingress-tls.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress-tls.md)] --- ## The plan - We need the following resources: - an Endpoints¹ listing a hard-coded IP address and port <br/>(where our `certbot` container will be listening) - a Service corresponding to that Endpoints - an Ingress sending requests to `/.well-known/acme-challenge/*` to that Service <br/>(we don't even need to include a domain name in it) - Then we need to start `certbot` so that it's listening on the right address+port .footnote[¹Endpoints is always plural, because even a single resource is a list of endpoints.] .debug[[k8s/ingress-tls.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress-tls.md)] --- ## Creating resources - We prepared a YAML file to create the three resources - However, the Endpoints needs to be adapted to put the current node's address .exercise[ - Edit `~/containers.training/k8s/certbot.yaml` (replace `A.B.C.D` with the current node's address) - Create the resources: ```bash kubectl apply -f ~/containers.training/k8s/certbot.yaml ``` ] .debug[[k8s/ingress-tls.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress-tls.md)] --- ## Obtaining the certificate - Now we can run `certbot`, listening on the port listed in the Endpoints (i.e. 8000) .exercise[ - Run `certbot`: ```bash EMAIL=your.address@example.com docker run --rm -p 8000:80 -v $PWD/letsencrypt:/etc/letsencrypt \ certbot/certbot certonly \ -m $EMAIL \ --standalone --agree-tos -n \ --domain $DOMAIN \ --test-cert ``` ] This is using the staging environment. Remove `--test-cert` to get a production certificate. .debug[[k8s/ingress-tls.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress-tls.md)] --- ## Copying the certificate - Just like in the previous method, the certificate is in `letsencrypt/live/$DOMAIN` (and owned by root) .exercise[ - Grand ourselves permissions on these files: ```bash sudo chown -R $USER letsencrypt ``` - Copy the certificate and key to the current directory: ```bash cp letsencrypt/live/$DOMAIN/{cert,privkey}.pem . ``` ] .debug[[k8s/ingress-tls.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress-tls.md)] --- ## Creating the Secret - We now have two files: - `privkey.pem` (the private key) - `cert.pem` (the certificate) - We can create a Secret to hold them .exercise[ - Create the Secret: ```bash kubectl create secret tls $DOMAIN --cert=cert.pem --key=privkey.pem ``` ] .debug[[k8s/ingress-tls.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress-tls.md)] --- ## Ingress with TLS - To enable TLS for an Ingress, we need to add a `tls` section to the Ingress: ```yaml spec: tls: - secretName: DOMAIN hosts: - DOMAIN rules: ... ``` - The list of hosts will be used by the ingress controller (to know which certificate to use with [SNI](https://en.wikipedia.org/wiki/Server_Name_Indication)) - Of course, the name of the secret can be different (here, for clarity and convenience, we set it to match the domain) .debug[[k8s/ingress-tls.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress-tls.md)] --- class: extra-details ## About the ingress controller - Many ingress controllers can use different "stores" for keys and certificates - Our ingress controller needs to be configured to use secrets (as opposed to, e.g., obtain certificates directly with Let's Encrypt) .debug[[k8s/ingress-tls.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress-tls.md)] --- ## Using the certificate .exercise[ - Edit the Ingress manifest, `~/container.training/k8s/ingress.yaml` - Uncomment the `tls` section - Update the `secretName` and `hosts` list - Create or update the Ingress: ```bash kubectl apply -f ~/container.training/k8s/ingress.yaml ``` - Check that the URL now works over `https` (it might take a minute to be picked up by the ingress controller) ] .debug[[k8s/ingress-tls.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress-tls.md)] --- ## Discussion *To repeat something mentioned earlier ...* - The methods presented here are for *educational purpose only* - In most production scenarios, the certificates will be obtained automatically - A very popular option is to use the [cert-manager](https://cert-manager.io/docs/) operator ??? :EN:- Ingress and TLS :FR:- Certificats TLS et *ingress* .debug[[k8s/ingress-tls.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/ingress-tls.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/lots-of-containers.jpg)] --- name: toc-cert-manager class: title cert-manager .nav[ [Previous part](#toc-ingress-and-tls-certificates) | [Back to table of contents](#toc-part-6) | [Next part](#toc-kustomize) ] .debug[(automatically generated title slide)] --- # cert-manager - cert-manager¹ facilitates certificate signing through the Kubernetes API: - we create a Certificate object (that's a CRD) - cert-manager creates a private key - it signs that key ... - ... or interacts with a certificate authority to obtain the signature - it stores the resulting key+cert in a Secret resource - These Secret resources can be used in many places (Ingress, mTLS, ...) .footnote[.red[¹]Always lower case, words separated with a dash; see the [style guide](https://cert-manager.io/docs/faq/style/_.)] .debug[[k8s/cert-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cert-manager.md)] --- ## Getting signatures - cert-manager can use multiple *Issuers* (another CRD), including: - self-signed - cert-manager acting as a CA - the [ACME protocol](https://en.wikipedia.org/wiki/Automated_Certificate_Management_Environment]) (notably used by Let's Encrypt) - [HashiCorp Vault](https://www.vaultproject.io/) - Multiple issuers can be configured simultaneously - Issuers can be available in a single namespace, or in the whole cluster (then we use the *ClusterIssuer* CRD) .debug[[k8s/cert-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cert-manager.md)] --- ## cert-manager in action - We will install cert-manager - We will create a ClusterIssuer to obtain certificates with Let's Encrypt (this will involve setting up an Ingress Controller) - We will create a Certificate request - cert-manager will honor that request and create a TLS Secret .debug[[k8s/cert-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cert-manager.md)] --- ## Installing cert-manager - It can be installed with a YAML manifest, or with Helm .exercise[ - Let's install the cert-manager Helm chart with this one-liner: ```bash helm install cert-manager cert-manager \ --repo https://charts.jetstack.io \ --create-namespace --namespace cert-manager \ --set installCRDs=true ``` ] - If you prefer to install with a single YAML file, that's fine too! (see [the documentation](https://cert-manager.io/docs/installation/kubernetes/#installing-with-regular-manifests) for instructions) .debug[[k8s/cert-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cert-manager.md)] --- ## ClusterIssuer manifest ```yaml apiVersion: cert-manager.io/v1 kind: ClusterIssuer metadata: name: letsencrypt-staging spec: acme: # Remember to update this if you use this manifest to obtain real certificates :) email: hello@example.com server: https://acme-staging-v02.api.letsencrypt.org/directory # To use the production environment, use the following line instead: #server: https://acme-v02.api.letsencrypt.org/directory privateKeySecretRef: name: issuer-letsencrypt-staging solvers: - http01: ingress: class: traefik ``` .debug[[k8s/cert-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cert-manager.md)] --- ## Creating the ClusterIssuer - The manifest shown on the previous slide is in [k8s/cm-clusterissuer.yaml](https://github.com/jpetazzo/container.training/tree/master/k8s/cm-clusterissuer.yaml) .exercise[ - Create the ClusterIssuer: ```bash kubectl apply -f ~/container.training/k8s/cm-clusterissuer.yaml ``` ] .debug[[k8s/cert-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cert-manager.md)] --- ## Certificate manifest ```yaml apiVersion: cert-manager.io/v1 kind: Certificate metadata: name: xyz.A.B.C.D.nip.io spec: secretName: xyz.A.B.C.D.nip.io dnsNames: - xyz.A.B.C.D.nip.io issuerRef: name: letsencrypt-staging kind: ClusterIssuer ``` - The `name`, `secretName`, and `dnsNames` don't have to match - There can be multiple `dnsNames` - The `issuerRef` must match the ClusterIssuer that we created earlier .debug[[k8s/cert-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cert-manager.md)] --- ## Creating the Certificate - The manifest shown on the previous slide is in [k8s/cm-certificate.yaml](https://github.com/jpetazzo/container.training/tree/master/k8s/cm-certificate.yaml) .exercise[ - Edit the Certificate to update the domain name (make sure to replace A.B.C.D with the IP address of one of your nodes!) - Create the Certificate: ```bash kubectl apply -f ~/container.training/k8s/cm-certificate.yaml ``` ] .debug[[k8s/cert-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cert-manager.md)] --- ## What's happening? - cert-manager will create: - the secret key - a Pod, a Service, and an Ingress to complete the HTTP challenge - then it waits for the challenge to complete .exercise[ - View the resources created by cert-manager: ```bash kubectl get pods,services,ingresses \ --selector=acme.cert-manager.io/http01-solver=true ``` ] .debug[[k8s/cert-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cert-manager.md)] --- ## HTTP challenge - The CA (in this case, Let's Encrypt) will fetch a particular URL: `http://<our-domain>/.well-known/acme-challenge/<token>` .exercise[ - Check the *path* of the Ingress in particular: ```bash kubectl describe ingress --selector=acme.cert-manager.io/http01-solver=true ``` ] .debug[[k8s/cert-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cert-manager.md)] --- ## What's missing ? -- An Ingress Controller! 😅 .exercise[ - Install an Ingress Controller: ```bash kubectl apply -f ~/container.training/k8s/traefik-v2.yaml ``` - Wait a little bit, and check that we now have a `kubernetes.io/tls` Secret: ```bash kubectl get secrets ``` ] .debug[[k8s/cert-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cert-manager.md)] --- class: extra-details ## Using the secret - For bonus points, try to use the secret in an Ingress! - This is what the manifest would look like: ```yaml apiVersion: networking.k8s.io/v1beta1 kind: Ingress metadata: name: xyz spec: tls: - secretName: xyz.A.B.C.D.nip.io hosts: - xyz.A.B.C.D.nip.io rules: ... ``` .debug[[k8s/cert-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cert-manager.md)] --- class: extra-details ## Automatic TLS Ingress with annotations - It is also possible to annotate Ingress resources for cert-manager - If we annotate an Ingress resource with `cert-manager.io/cluster-issuer=xxx`: - cert-manager will detect that annotation - it will obtain a certificate using the specified ClusterIssuer (`xxx`) - it will store the key and certificate in the specified Secret - Note: the Ingress still needs the `tls` section with `secretName` and `hosts` .debug[[k8s/cert-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cert-manager.md)] --- class: extra-details ## Let's Encrypt and nip.io - Let's Encrypt has [rate limits](https://letsencrypt.org/docs/rate-limits/) per domain (the limits only apply to the production environment, not staging) - There is a limit of 50 certificates per registered domain - If we try to use the production environment, we will probably hit the limit - It's fine to use the staging environment for these experiments (our certs won't validate in a browser, but we can always check the details of the cert to verify that it was issued by Let's Encrypt!) ??? :EN:- Obtaining certificates with cert-manager :FR:- Obtenir des certificats avec cert-manager :T: Obtaining TLS certificates with cert-manager .debug[[k8s/cert-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cert-manager.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/plastic-containers.JPG)] --- name: toc-kustomize class: title Kustomize .nav[ [Previous part](#toc-cert-manager) | [Back to table of contents](#toc-part-6) | [Next part](#toc-managing-stacks-with-helm) ] .debug[(automatically generated title slide)] --- # Kustomize - Kustomize lets us transform Kubernetes resources: *YAML + kustomize → new YAML* - Starting point = valid resource files (i.e. something that we could load with `kubectl apply -f`) - Recipe = a *kustomization* file (describing how to transform the resources) - Result = new resource files (that we can load with `kubectl apply -f`) .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## Pros and cons - Relatively easy to get started (just get some existing YAML files) - Easy to leverage existing "upstream" YAML files (or other *kustomizations*) - Somewhat integrated with `kubectl` (but only "somewhat" because of version discrepancies) - Less complex than e.g. Helm, but also less powerful - No central index like the Artifact Hub (but is there a need for it?) .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## Kustomize in a nutshell - Get some valid YAML (our "resources") - Write a *kustomization* (technically, a file named `kustomization.yaml`) - reference our resources - reference other kustomizations - add some *patches* - ... - Use that kustomization either with `kustomize build` or `kubectl apply -k` - Write new kustomizations referencing the first one to handle minor differences .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## A simple kustomization This features a Deployment, Service, and Ingress (in separate files), and a couple of patches (to change the number of replicas and the hostname used in the Ingress). ```yaml apiVersion: kustomize.config.k8s.io/v1beta1 kind: Kustomization patchesStrategicMerge: - scale-deployment.yaml - ingress-hostname.yaml resources: - deployment.yaml - service.yaml - ingress.yaml ``` On the next slide, let's see a more complex example ... .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## A more complex Kustomization .small[ ```yaml apiVersion: kustomize.config.k8s.io/v1beta1 kind: Kustomization commonAnnotations: mood: 😎 commonLabels: add-this-to-all-my-resources: please namePrefix: prod- patchesStrategicMerge: - prod-scaling.yaml - prod-healthchecks.yaml bases: - api/ - frontend/ - db/ - github.com/example/app?ref=tag-or-branch resources: - ingress.yaml - permissions.yaml configMapGenerator: - name: appconfig files: - global.conf - local.conf=prod.conf ``` ] .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## Glossary - A *base* is a kustomization that is referred to by other kustomizations - An *overlay* is a kustomization that refers to other kustomizations - A kustomization can be both a base and an overlay at the same time (a kustomization can refer to another, which can refer to a third) - A *patch* describes how to alter an existing resource (e.g. to change the image in a Deployment; or scaling parameters; etc.) - A *variant* is the final outcome of applying bases + overlays (See the [kustomize glossary](https://github.com/kubernetes-sigs/kustomize/blob/master/docs/glossary.md) for more definitions!) .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## What Kustomize *cannot* do - By design, there are a number of things that Kustomize won't do - For instance: - using command-line arguments or environment variables to generate a variant - overlays can only *add* resources, not *remove* them - See the full list of [eschewed features](https://github.com/kubernetes-sigs/kustomize/blob/master/docs/eschewedFeatures.md) for more details .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## Kustomize workflows - The Kustomize documentation proposes two different workflows - *Bespoke configuration* - base and overlays managed by the same team - *Off-the-shelf configuration* (OTS) - base and overlays managed by different teams - base is regularly updated by "upstream" (e.g. a vendor) - our overlays and patches should (hopefully!) apply cleanly - we may regularly update the base, or use a remote base .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## Remote bases - Kustomize can also use bases that are remote git repositories - Examples: github.com/jpetazzo/kubercoins (remote git repository) github.com/jpetazzo/kubercoins?ref=kustomize (specific tag or branch) - Note that this only works for kustomizations, not individual resources (the specified repository or directory must contain a `kustomization.yaml` file) .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- class: extra-details ## Hashicorp go-getter - Some versions of Kustomize support additional forms for remote resources - Examples: https://releases.hello.io/k/1.0.zip (remote archive) https://releases.hello.io/k/1.0.zip//some-subdir (subdirectory in archive) - This relies on [hashicorp/go-getter](https://github.com/hashicorp/go-getter#url-format) - ... But it prevents Kustomize inclusion in `kubectl` - Avoid them! - See [kustomize#3578](https://github.com/kubernetes-sigs/kustomize/issues/3578) for details .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## Managing `kustomization.yaml` - There are many ways to manage `kustomization.yaml` files, including: - web wizards like [Replicated Ship](https://www.replicated.com/ship/) - the `kustomize` CLI - opening the file with our favorite text editor - Let's see these in action! .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## An easy way to get started with Kustomize - We are going to use [Replicated Ship](https://www.replicated.com/ship/) to experiment with Kustomize - The [Replicated Ship CLI](https://github.com/replicatedhq/ship/releases) has been installed on our clusters - Replicated Ship has multiple workflows; here is what we will do: - initialize a Kustomize overlay from a remote GitHub repository - customize some values using the web UI provided by Ship - look at the resulting files and apply them to the cluster .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## Getting started with Ship - We need to run `ship init` in a new directory - `ship init` requires a URL to a remote repository containing Kubernetes YAML - It will clone that repository and start a web UI - Later, it can watch that repository and/or update from it - We will use the [jpetazzo/kubercoins](https://github.com/jpetazzo/kubercoins) repository (it contains all the DockerCoins resources as YAML files) .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## `ship init` .exercise[ - Change to a new directory: ```bash mkdir ~/kustomcoins cd ~/kustomcoins ``` - Run `ship init` with the kustomcoins repository: ```bash ship init https://github.com/jpetazzo/kubercoins ``` <!-- ```wait Open browser``` --> ] .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## Access the web UI - `ship init` tells us to connect on `localhost:8800` - We need to replace `localhost` with the address of our node (since we run on a remote machine) - Follow the steps in the web UI, and change one parameter (e.g. set the number of replicas in the worker Deployment) - Complete the web workflow, and go back to the CLI .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## Inspect the results - Look at the content of our directory - `base` contains the kubercoins repository + a `kustomization.yaml` file - `overlays/ship` contains the Kustomize overlay referencing the base + our patch(es) - `rendered.yaml` is a YAML bundle containing the patched application - `.ship` contains a state file used by Ship .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## Using the results - We can `kubectl apply -f rendered.yaml` (on any version of Kubernetes) - Starting with Kubernetes 1.14, we can apply the overlay directly with: ```bash kubectl apply -k overlays/ship ``` - But let's not do that for now! - We will create a new copy of DockerCoins in another namespace .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## Deploy DockerCoins with Kustomize .exercise[ - Create a new namespace: ```bash kubectl create namespace kustomcoins ``` - Deploy DockerCoins: ```bash kubectl apply -f rendered.yaml --namespace=kustomcoins ``` - Or, with Kubernetes 1.14, we can also do this: ```bash kubectl apply -k overlays/ship --namespace=kustomcoins ``` ] .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## Checking our new copy of DockerCoins - We can check the worker logs, or the web UI .exercise[ - Retrieve the NodePort number of the web UI: ```bash kubectl get service webui --namespace=kustomcoins ``` - Open it in a web browser - Look at the worker logs: ```bash kubectl logs deploy/worker --tail=10 --follow --namespace=kustomcoins ``` <!-- ```wait units of work done``` ```key ^C``` --> ] Note: it might take a minute or two for the worker to start. .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## Working with the `kustomize` CLI - This is another way to get started - General workflow: `kustomize create` to generate an empty `kustomization.yaml` file `kustomize edit add resource` to add Kubernetes YAML files to it `kustomize edit add patch` to add patches to said resources `kustomize build | kubectl apply -f-` or `kubectl apply -k .` .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## `kubectl` integration - Kustomize has been integrated in `kubectl` (since Kubernetes 1.14) - `kubectl kustomize` can apply a kustomization - commands that use `-f` can also use `-k` (`kubectl apply`/`delete`/...) - The `kustomize` tool is still needed if we want to use `create`, `edit`, ... - Kubernetes 1.14 to 1.20 uses Kustomize 2.0.3 - Kubernetes 1.21 jumps to Kustomize 4.1.2 - Future versions should track Kustomize updates more closely .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- class: extra-details ## Differences between 2.0.3 and later - Kustomize 2.1 / 3.0 deprecates `bases` (they should be listed in `resources`) (this means that "modern" `kustomize edit add resource` won't work with "old" `kubectl apply -k`) - Kustomize 2.1 introduces `replicas` and `envs` - Kustomize 3.1 introduces multipatches - Kustomize 3.2 introduce inline patches in `kustomization.yaml` - Kustomize 3.3 to 3.10 is mostly internal refactoring - Kustomize 4.0 drops go-getter again - Kustomize 4.1 allows patching kind and name .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## Scaling Instead of using a patch, scaling can be done like this: ```yaml apiVersion: kustomize.config.k8s.io/v1beta1 kind: Kustomization ... replicas: - name: worker count: 5 ``` It will automatically work with Deployments, ReplicaSets, StatefulSets. (For other resource types, fall back to a patch.) .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## Updating images Instead of using patches, images can be changed like this: ```yaml apiVersion: kustomize.config.k8s.io/v1beta1 kind: Kustomization ... images: - name: postgres newName: harbor.enix.io/my-postgres - name: dockercoins/worker newTag: v0.2 - name: dockercoins/hasher newName: registry.dockercoins.io/hasher newTag: v0.2 - name: alpine digest: sha256:24a0c4b4a4c0eb97a1aabb8e29f18e917d05abfe1b7a7c07857230879ce7d3d3 ``` .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## Updating images, pros and cons - Very convenient when the same image appears multiple times - Very convenient to define tags (or pin to hashes) outside of the main YAML - Doesn't support wildcard or generic substitutions: - cannot "replace `dockercoins/*` with `ghcr.io/dockercoins/*`" - cannot "tag all `dockercoins/*` with `v0.2`" - Only patches "well-known" image fields (won't work with CRDs referencing images) - Helm can deal with these scenarios, for instance: ```yaml image: {{ .Values.registry }}/worker:{{ .Values.version }} ``` .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## Advanced resource patching The example below shows how to: - patch multiple resources with a selector (new in Kustomize 3.1) - use an inline patch instead of a separate patch file (new in Kustomize 3.2) ```yaml apiVersion: kustomize.config.k8s.io/v1beta1 kind: Kustomization ... patches: - patch: |- - op: replace path: /spec/template/spec/containers/0/image value: alpine target: kind: Deployment labelSelector: "app" ``` (This replaces all images of Deployments matching the `app` selector with `alpine`.) .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## Advanced resource patching, pros and cons - Very convenient to patch an arbitrary number of resources - Very convenient to patch any kind of resource, including CRDs - Doesn't support "fine-grained" patching (e.g. image registry or tag) - Once again, Helm can do it: ```yaml image: {{ .Values.registry }}/worker:{{ .Values.version }} ``` .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- ## Differences with Helm - Helm charts generally require more upfront work (while kustomize "bases" are standard Kubernetes YAML) - ... But Helm charts are also more powerful; their templating language can: - conditionally include/exclude resources or blocks within resources - generate values by concatenating, hashing, transforming parameters - generate values or resources by iteration (`{{ range ... }}`) - access the Kubernetes API during template evaluation - [and much more](https://helm.sh/docs/chart_template_guide/) ??? :EN:- Packaging and running apps with Kustomize :FR:- *Packaging* d'applications avec Kustomize .debug[[k8s/kustomize.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kustomize.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/train-of-containers-1.jpg)] --- name: toc-managing-stacks-with-helm class: title Managing stacks with Helm .nav[ [Previous part](#toc-kustomize) | [Back to table of contents](#toc-part-6) | [Next part](#toc-helm-chart-format) ] .debug[(automatically generated title slide)] --- # Managing stacks with Helm - Helm is a (kind of!) package manager for Kubernetes - We can use it to: - find existing packages (called "charts") created by other folks - install these packages, configuring them for our particular setup - package our own things (for distribution or for internal use) - manage the lifecycle of these installs (rollback to previous version etc.) - It's a "CNCF graduate project", indicating a certain level of maturity (more on that later) .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- ## From `kubectl run` to YAML - We can create resources with one-line commands (`kubectl run`, `kubectl createa deployment`, `kubectl expose`...) - We can also create resources by loading YAML files (with `kubectl apply -f`, `kubectl create -f`...) - There can be multiple resources in a single YAML files (making them convenient to deploy entire stacks) - However, these YAML bundles often need to be customized (e.g.: number of replicas, image version to use, features to enable...) .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- ## Beyond YAML - Very often, after putting together our first `app.yaml`, we end up with: - `app-prod.yaml` - `app-staging.yaml` - `app-dev.yaml` - instructions indicating to users "please tweak this and that in the YAML" - That's where using something like [CUE](https://github.com/cuelang/cue/blob/v0.3.2/doc/tutorial/kubernetes/README.md), [Kustomize](https://kustomize.io/), or [Helm](https://helm.sh/) can help! - Now we can do something like this: ```bash helm install app ... --set this.parameter=that.value ``` .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- ## Other features of Helm - With Helm, we create "charts" - These charts can be used internally or distributed publicly - Public charts can be indexed through the [Artifact Hub](https://artifacthub.io/) - This gives us a way to find and install other folks' charts - Helm also gives us ways to manage the lifecycle of what we install: - keep track of what we have installed - upgrade versions, change parameters, roll back, uninstall - Furthermore, even if it's not "the" standard, it's definitely "a" standard! .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- ## CNCF graduation status - On April 30th 2020, Helm was the 10th project to *graduate* within the CNCF 🎉 (alongside Containerd, Prometheus, and Kubernetes itself) - This is an acknowledgement by the CNCF for projects that *demonstrate thriving adoption, an open governance process, <br/> and a strong commitment to community, sustainability, and inclusivity.* - See [CNCF announcement](https://www.cncf.io/announcement/2020/04/30/cloud-native-computing-foundation-announces-helm-graduation/) and [Helm announcement](https://helm.sh/blog/celebrating-helms-cncf-graduation/) .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- ## Helm concepts - `helm` is a CLI tool - It is used to find, install, upgrade *charts* - A chart is an archive containing templatized YAML bundles - Charts are versioned - Charts can be stored on private or public repositories .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- ## Differences between charts and packages - A package (deb, rpm...) contains binaries, libraries, etc. - A chart contains YAML manifests (the binaries, libraries, etc. are in the images referenced by the chart) - On most distributions, a package can only be installed once (installing another version replaces the installed one) - A chart can be installed multiple times - Each installation is called a *release* - This allows to install e.g. 10 instances of MongoDB (with potentially different versions and configurations) .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- class: extra-details ## Wait a minute ... *But, on my Debian system, I have Python 2 **and** Python 3. <br/> Also, I have multiple versions of the Postgres database engine!* Yes! But they have different package names: - `python2.7`, `python3.8` - `postgresql-10`, `postgresql-11` Good to know: the Postgres package in Debian includes provisions to deploy multiple Postgres servers on the same system, but it's an exception (and it's a lot of work done by the package maintainer, not by the `dpkg` or `apt` tools). .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- ## Helm 2 vs Helm 3 - Helm 3 was released [November 13, 2019](https://helm.sh/blog/helm-3-released/) - Charts remain compatible between Helm 2 and Helm 3 - The CLI is very similar (with minor changes to some commands) - The main difference is that Helm 2 uses `tiller`, a server-side component - Helm 3 doesn't use `tiller` at all, making it simpler (yay!) .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- class: extra-details ## With or without `tiller` - With Helm 3: - the `helm` CLI communicates directly with the Kubernetes API - it creates resources (deployments, services...) with our credentials - With Helm 2: - the `helm` CLI communicates with `tiller`, telling `tiller` what to do - `tiller` then communicates with the Kubernetes API, using its own credentials - This indirect model caused significant permissions headaches (`tiller` required very broad permissions to function) - `tiller` was removed in Helm 3 to simplify the security aspects .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- ## Installing Helm - If the `helm` CLI is not installed in your environment, install it .exercise[ - Check if `helm` is installed: ```bash helm ``` - If it's not installed, run the following command: ```bash curl https://raw.githubusercontent.com/kubernetes/helm/master/scripts/get-helm-3 \ | bash ``` ] (To install Helm 2, replace `get-helm-3` with `get`.) .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- class: extra-details ## Only if using Helm 2 ... - We need to install Tiller and give it some permissions - Tiller is composed of a *service* and a *deployment* in the `kube-system` namespace - They can be managed (installed, upgraded...) with the `helm` CLI .exercise[ - Deploy Tiller: ```bash helm init ``` ] At the end of the install process, you will see: ``` Happy Helming! ``` .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- class: extra-details ## Only if using Helm 2 ... - Tiller needs permissions to create Kubernetes resources - In a more realistic deployment, you might create per-user or per-team service accounts, roles, and role bindings .exercise[ - Grant `cluster-admin` role to `kube-system:default` service account: ```bash kubectl create clusterrolebinding add-on-cluster-admin \ --clusterrole=cluster-admin --serviceaccount=kube-system:default ``` ] (Defining the exact roles and permissions on your cluster requires a deeper knowledge of Kubernetes' RBAC model. The command above is fine for personal and development clusters.) .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- ## Charts and repositories - A *repository* (or repo in short) is a collection of charts - It's just a bunch of files (they can be hosted by a static HTTP server, or on a local directory) - We can add "repos" to Helm, giving them a nickname - The nickname is used when referring to charts on that repo (for instance, if we try to install `hello/world`, that means the chart `world` on the repo `hello`; and that repo `hello` might be something like https://blahblah.hello.io/charts/) .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- class: extra-details ## How to find charts, the old way - Helm 2 came with one pre-configured repo, the "stable" repo (located at https://charts.helm.sh/stable) - Helm 3 doesn't have any pre-configured repo - The "stable" repo mentioned above is now being deprecated - The new approach is to have fully decentralized repos - Repos can be indexed in the Artifact Hub (which supersedes the Helm Hub) .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- ## How to find charts, the new way - Go to the [Artifact Hub](https://artifacthub.io/packages/search?kind=0) (https://artifacthub.io) - Or use `helm search hub ...` from the CLI - Let's try to find a Helm chart for something called "OWASP Juice Shop"! (it is a famous demo app used in security challenges) .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- ## Finding charts from the CLI - We can use `helm search hub <keyword>` .exercise[ - Look for the OWASP Juice Shop app: ```bash helm search hub owasp juice ``` - Since the URLs are truncated, try with the YAML output: ```bash helm search hub owasp juice -o yaml ``` ] Then go to → https://artifacthub.io/packages/helm/seccurecodebox/juice-shop .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- ## Finding charts on the web - We can also use the Artifact Hub search feature .exercise[ - Go to https://artifacthub.io/ - In the search box on top, enter "owasp juice" - Click on the "juice-shop" result (not "multi-juicer" or "juicy-ctf") ] .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- ## Installing the chart - Click on the "Install" button, it will show instructions .exercise[ - First, add the repository for that chart: ```bash helm repo add juice https://charts.securecodebox.io ``` - Then, install the chart: ```bash helm install my-juice-shop juice/juice-shop ``` ] Note: it is also possible to install directly a chart, with `--repo https://...` .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- ## Charts and releases - "Installing a chart" means creating a *release* - In the previous exemple, the release was named "my-juice-shop" - We can also use `--generate-name` to ask Helm to generate a name for us .exercise[ - List the releases: ```bash helm list ``` - Check that we have a `my-juice-shop-...` Pod up and running: ```bash kubectl get pods ``` ] .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- class: extra-details ## Searching and installing with Helm 2 - Helm 2 doesn't have support for the Helm Hub - The `helm search` command only takes a search string argument (e.g. `helm search juice-shop`) - With Helm 2, the name is optional: `helm install juice/juice-shop` will automatically generate a name `helm install --name my-juice-shop juice/juice-shop` will specify a name .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- ## Viewing resources of a release - This specific chart labels all its resources with a `release` label - We can use a selector to see these resources .exercise[ - List all the resources created by this release: ```bash kubectl get all --selector=app.kubernetes.io/instance=my-juice-shop ``` ] Note: this label wasn't added automatically by Helm. <br/> It is defined in that chart. In other words, not all charts will provide this label. .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- ## Configuring a release - By default, `juice/juice-shop` creates a service of type `ClusterIP` - We would like to change that to a `NodePort` - We could use `kubectl edit service my-juice-shop`, but ... ... our changes would get overwritten next time we update that chart! - Instead, we are going to *set a value* - Values are parameters that the chart can use to change its behavior - Values have default values - Each chart is free to define its own values and their defaults .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- ## Checking possible values - We can inspect a chart with `helm show` or `helm inspect` .exercise[ - Look at the README for the app: ```bash helm show readme juice/juice-shop ``` - Look at the values and their defaults: ```bash helm show values juice/juice-shop ``` ] The `values` may or may not have useful comments. The `readme` may or may not have (accurate) explanations for the values. (If we're unlucky, there won't be any indication about how to use the values!) .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- ## Setting values - Values can be set when installing a chart, or when upgrading it - We are going to update `my-juice-shop` to change the type of the service .exercise[ - Update `my-juice-shop`: ```bash helm upgrade my-juice-shop juice/my-juice-shop \ --set service.type=NodePort ``` ] Note that we have to specify the chart that we use (`juice/my-juice-shop`), even if we just want to update some values. We can set multiple values. If we want to set many values, we can use `-f`/`--values` and pass a YAML file with all the values. All unspecified values will take the default values defined in the chart. .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- ## Connecting to the Juice Shop - Let's check the app that we just installed .exercise[ - Check the node port allocated to the service: ```bash kubectl get service my-juice-shop PORT=$(kubectl get service my-juice-shop -o jsonpath={..nodePort}) ``` - Connect to it: ```bash curl localhost:$PORT/ ``` ] ??? :EN:- Helm concepts :EN:- Installing software with Helm :EN:- Helm 2, Helm 3, and the Helm Hub :FR:- Fonctionnement général de Helm :FR:- Installer des composants via Helm :FR:- Helm 2, Helm 3, et le *Helm Hub* :T: Getting started with Helm and its concepts :Q: Which comparison is the most adequate? :A: Helm is a firewall, charts are access lists :A: ✔️Helm is a package manager, charts are packages :A: Helm is an artefact repository, charts are artefacts :A: Helm is a CI/CD platform, charts are CI/CD pipelines :Q: What's required to distribute a Helm chart? :A: A Helm commercial license :A: A Docker registry :A: An account on the Helm Hub :A: ✔️An HTTP server .debug[[k8s/helm-intro.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-intro.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/train-of-containers-2.jpg)] --- name: toc-helm-chart-format class: title Helm chart format .nav[ [Previous part](#toc-managing-stacks-with-helm) | [Back to table of contents](#toc-part-6) | [Next part](#toc-creating-a-basic-chart) ] .debug[(automatically generated title slide)] --- # Helm chart format - What exactly is a chart? - What's in it? - What would be involved in creating a chart? (we won't create a chart, but we'll see the required steps) .debug[[k8s/helm-chart-format.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-chart-format.md)] --- ## What is a chart - A chart is a set of files - Some of these files are mandatory for the chart to be viable (more on that later) - These files are typically packed in a tarball - These tarballs are stored in "repos" (which can be static HTTP servers) - We can install from a repo, from a local tarball, or an unpacked tarball (the latter option is preferred when developing a chart) .debug[[k8s/helm-chart-format.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-chart-format.md)] --- ## What's in a chart - A chart must have at least: - a `templates` directory, with YAML manifests for Kubernetes resources - a `values.yaml` file, containing (tunable) parameters for the chart - a `Chart.yaml` file, containing metadata (name, version, description ...) - Let's look at a simple chart for a basic demo app .debug[[k8s/helm-chart-format.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-chart-format.md)] --- ## Adding the repo - If you haven't done it before, you need to add the repo for that chart .exercise[ - Add the repo that holds the chart for the OWASP Juice Shop: ```bash helm repo add juice https://charts.securecodebox.io ``` ] .debug[[k8s/helm-chart-format.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-chart-format.md)] --- ## Downloading a chart - We can use `helm pull` to download a chart from a repo .exercise[ - Download the tarball for `juice/juice-shop`: ```bash helm pull juice/juice-shop ``` (This will create a file named `juice-shop-X.Y.Z.tgz`.) - Or, download + untar `juice/juice-shop`: ```bash helm pull juice/juice-shop --untar ``` (This will create a directory named `juice-shop`.) ] .debug[[k8s/helm-chart-format.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-chart-format.md)] --- ## Looking at the chart's content - Let's look at the files and directories in the `juice-shop` chart .exercise[ - Display the tree structure of the chart we just downloaded: ```bash tree juice-shop ``` ] We see the components mentioned above: `Chart.yaml`, `templates/`, `values.yaml`. .debug[[k8s/helm-chart-format.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-chart-format.md)] --- ## Templates - The `templates/` directory contains YAML manifests for Kubernetes resources (Deployments, Services, etc.) - These manifests can contain template tags (using the standard Go template library) .exercise[ - Look at the template file for the Service resource: ```bash cat juice-shop/templates/service.yaml ``` ] .debug[[k8s/helm-chart-format.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-chart-format.md)] --- ## Analyzing the template file - Tags are identified by `{{ ... }}` - `{{ template "x.y" }}` expands a [named template](https://helm.sh/docs/chart_template_guide/named_templates/#declaring-and-using-templates-with-define-and-template) (previously defined with `{{ define "x.y "}}...stuff...{{ end }}`) - The `.` in `{{ template "x.y" . }}` is the *context* for that named template (so that the named template block can access variables from the local context) - `{{ .Release.xyz }}` refers to [built-in variables](https://helm.sh/docs/chart_template_guide/builtin_objects/) initialized by Helm (indicating the chart name, version, whether we are installing or upgrading ...) - `{{ .Values.xyz }}` refers to tunable/settable [values](https://helm.sh/docs/chart_template_guide/values_files/) (more on that in a minute) .debug[[k8s/helm-chart-format.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-chart-format.md)] --- ## Values - Each chart comes with a [values file](https://helm.sh/docs/chart_template_guide/values_files/) - It's a YAML file containing a set of default parameters for the chart - The values can be accessed in templates with e.g. `{{ .Values.x.y }}` (corresponding to field `y` in map `x` in the values file) - The values can be set or overridden when installing or ugprading a chart: - with `--set x.y=z` (can be used multiple times to set multiple values) - with `--values some-yaml-file.yaml` (set a bunch of values from a file) - Charts following best practices will have values following specific patterns (e.g. having a `service` map allowing to set `service.type` etc.) .debug[[k8s/helm-chart-format.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-chart-format.md)] --- ## Other useful tags - `{{ if x }} y {{ end }}` allows to include `y` if `x` evaluates to `true` (can be used for e.g. healthchecks, annotations, or even an entire resource) - `{{ range x }} y {{ end }}` iterates over `x`, evaluating `y` each time (the elements of `x` are assigned to `.` in the range scope) - `{{- x }}`/`{{ x -}}` will remove whitespace on the left/right - The whole [Sprig](http://masterminds.github.io/sprig/) library, with additions: `lower` `upper` `quote` `trim` `default` `b64enc` `b64dec` `sha256sum` `indent` `toYaml` ... .debug[[k8s/helm-chart-format.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-chart-format.md)] --- ## Pipelines - `{{ quote blah }}` can also be expressed as `{{ blah | quote }}` - With multiple arguments, `{{ x y z }}` can be expressed as `{{ z | x y }}`) - Example: `{{ .Values.annotations | toYaml | indent 4 }}` - transforms the map under `annotations` into a YAML string - indents it with 4 spaces (to match the surrounding context) - Pipelines are not specific to Helm, but a feature of Go templates (check the [Go text/template documentation](https://golang.org/pkg/text/template/) for more details and examples) .debug[[k8s/helm-chart-format.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-chart-format.md)] --- ## README and NOTES.txt - At the top-level of the chart, it's a good idea to have a README - It will be viewable with e.g. `helm show readme juice/juice-shop` - In the `templates/` directory, we can also have a `NOTES.txt` file - When the template is installed (or upgraded), `NOTES.txt` is processed too (i.e. its `{{ ... }}` tags are evaluated) - It gets displayed after the install or upgrade - It's a great place to generate messages to tell the user: - how to connect to the release they just deployed - any passwords or other thing that we generated for them .debug[[k8s/helm-chart-format.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-chart-format.md)] --- ## Additional files - We can place arbitrary files in the chart (outside of the `templates/` directory) - They can be accessed in templates with `.Files` - They can be transformed into ConfigMaps or Secrets with `AsConfig` and `AsSecrets` (see [this example](https://helm.sh/docs/chart_template_guide/accessing_files/#configmap-and-secrets-utility-functions) in the Helm docs) .debug[[k8s/helm-chart-format.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-chart-format.md)] --- ## Hooks and tests - We can define *hooks* in our templates - Hooks are resources annotated with `"helm.sh/hook": NAME-OF-HOOK` - Hook names include `pre-install`, `post-install`, `test`, [and much more](https://helm.sh/docs/topics/charts_hooks/#the-available-hooks) - The resources defined in hooks are loaded at a specific time - Hook execution is *synchronous* (if the resource is a Job or Pod, Helm will wait for its completion) - This can be use for database migrations, backups, notifications, smoke tests ... - Hooks named `test` are executed only when running `helm test RELEASE-NAME` ??? :EN:- Helm charts format :FR:- Le format des *Helm charts* .debug[[k8s/helm-chart-format.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-chart-format.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/two-containers-on-a-truck.jpg)] --- name: toc-creating-a-basic-chart class: title Creating a basic chart .nav[ [Previous part](#toc-helm-chart-format) | [Back to table of contents](#toc-part-6) | [Next part](#toc-creating-better-helm-charts) ] .debug[(automatically generated title slide)] --- # Creating a basic chart - We are going to show a way to create a *very simplified* chart - In a real chart, *lots of things* would be templatized (Resource names, service types, number of replicas...) .exercise[ - Create a sample chart: ```bash helm create dockercoins ``` - Move away the sample templates and create an empty template directory: ```bash mv dockercoins/templates dockercoins/default-templates mkdir dockercoins/templates ``` ] .debug[[k8s/helm-create-basic-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-basic-chart.md)] --- ## Exporting the YAML for our application - The following section assumes that DockerCoins is currently running - If DockerCoins is not running, see next slide .exercise[ - Create one YAML file for each resource that we need: .small[ ```bash while read kind name; do kubectl get -o yaml $kind $name > dockercoins/templates/$name-$kind.yaml done <<EOF deployment worker deployment hasher daemonset rng deployment webui deployment redis service hasher service rng service webui service redis EOF ``` ] ] .debug[[k8s/helm-create-basic-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-basic-chart.md)] --- ## Obtaining DockerCoins YAML - If DockerCoins is not running, we can also obtain the YAML from a public repository .exercise[ - Clone the kubercoins repository: ```bash git clone https://github.com/jpetazzo/kubercoins ``` - Copy the YAML files to the `templates/` directory: ```bash cp kubercoins/*.yaml dockercoins/templates/ ``` ] .debug[[k8s/helm-create-basic-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-basic-chart.md)] --- ## Testing our helm chart .exercise[ - Let's install our helm chart! ``` helm install helmcoins dockercoins ``` (`helmcoins` is the name of the release; `dockercoins` is the local path of the chart) ] -- - Since the application is already deployed, this will fail: ``` Error: rendered manifests contain a resource that already exists. Unable to continue with install: existing resource conflict: kind: Service, namespace: default, name: hasher ``` - To avoid naming conflicts, we will deploy the application in another *namespace* .debug[[k8s/helm-create-basic-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-basic-chart.md)] --- ## Switching to another namespace - We need create a new namespace (Helm 2 creates namespaces automatically; Helm 3 doesn't anymore) - We need to tell Helm which namespace to use .exercise[ - Create a new namespace: ```bash kubectl create namespace helmcoins ``` - Deploy our chart in that namespace: ```bash helm install helmcoins dockercoins --namespace=helmcoins ``` ] .debug[[k8s/helm-create-basic-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-basic-chart.md)] --- ## Helm releases are namespaced - Let's try to see the release that we just deployed .exercise[ - List Helm releases: ```bash helm list ``` ] Our release doesn't show up! We have to specify its namespace (or switch to that namespace). .debug[[k8s/helm-create-basic-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-basic-chart.md)] --- ## Specifying the namespace - Try again, with the correct namespace .exercise[ - List Helm releases in `helmcoins`: ```bash helm list --namespace=helmcoins ``` ] .debug[[k8s/helm-create-basic-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-basic-chart.md)] --- ## Checking our new copy of DockerCoins - We can check the worker logs, or the web UI .exercise[ - Retrieve the NodePort number of the web UI: ```bash kubectl get service webui --namespace=helmcoins ``` - Open it in a web browser - Look at the worker logs: ```bash kubectl logs deploy/worker --tail=10 --follow --namespace=helmcoins ``` ] Note: it might take a minute or two for the worker to start. .debug[[k8s/helm-create-basic-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-basic-chart.md)] --- ## Discussion, shortcomings - Helm (and Kubernetes) best practices recommend to add a number of annotations (e.g. `app.kubernetes.io/name`, `helm.sh/chart`, `app.kubernetes.io/instance` ...) - Our basic chart doesn't have any of these - Our basic chart doesn't use any template tag - Does it make sense to use Helm in that case? - *Yes,* because Helm will: - track the resources created by the chart - save successive revisions, allowing us to rollback [Helm docs](https://helm.sh/docs/topics/chart_best_practices/labels/) and [Kubernetes docs](https://kubernetes.io/docs/concepts/overview/working-with-objects/common-labels/) have details about recommended annotations and labels. .debug[[k8s/helm-create-basic-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-basic-chart.md)] --- ## Cleaning up - Let's remove that chart before moving on .exercise[ - Delete the release (don't forget to specify the namespace): ```bash helm delete helmcoins --namespace=helmcoins ``` ] ??? :EN:- Writing a basic Helm chart for the whole app :FR:- Écriture d'un *chart* Helm simplifié .debug[[k8s/helm-create-basic-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-basic-chart.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/wall-of-containers.jpeg)] --- name: toc-creating-better-helm-charts class: title Creating better Helm charts .nav[ [Previous part](#toc-creating-a-basic-chart) | [Back to table of contents](#toc-part-6) | [Next part](#toc-helm-secrets) ] .debug[(automatically generated title slide)] --- # Creating better Helm charts - We are going to create a chart with the helper `helm create` - This will give us a chart implementing lots of Helm best practices (labels, annotations, structure of the `values.yaml` file ...) - We will use that chart as a generic Helm chart - We will use it to deploy DockerCoins - Each component of DockerCoins will have its own *release* - In other words, we will "install" that Helm chart multiple times (one time per component of DockerCoins) .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Creating a generic chart - Rather than starting from scratch, we will use `helm create` - This will give us a basic chart that we will customize .exercise[ - Create a basic chart: ```bash cd ~ helm create helmcoins ``` ] This creates a basic chart in the directory `helmcoins`. .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## What's in the basic chart? - The basic chart will create a Deployment and a Service - Optionally, it will also include an Ingress - If we don't pass any values, it will deploy the `nginx` image - We can override many things in that chart - Let's try to deploy DockerCoins components with that chart! .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Writing `values.yaml` for our components - We need to write one `values.yaml` file for each component (hasher, redis, rng, webui, worker) - We will start with the `values.yaml` of the chart, and remove what we don't need - We will create 5 files: hasher.yaml, redis.yaml, rng.yaml, webui.yaml, worker.yaml - In each file, we want to have: ```yaml image: repository: IMAGE-REPOSITORY-NAME tag: IMAGE-TAG ``` .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Getting started - For component X, we want to use the image dockercoins/X:v0.1 (for instance, for rng, we want to use the image dockercoins/rng:v0.1) - Exception: for redis, we want to use the official image redis:latest .exercise[ - Write YAML files for the 5 components, with the following model: ```yaml image: repository: `IMAGE-REPOSITORY-NAME` (e.g. dockercoins/worker) tag: `IMAGE-TAG` (e.g. v0.1) ``` ] .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Deploying DockerCoins components - For convenience, let's work in a separate namespace .exercise[ - Create a new namespace (if it doesn't already exist): ```bash kubectl create namespace helmcoins ``` - Switch to that namespace: ```bash kns helmcoins ``` ] .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Deploying the chart - To install a chart, we can use the following command: ```bash helm install COMPONENT-NAME CHART-DIRECTORY ``` - We can also use the following command, which is *idempotent*: ```bash helm upgrade COMPONENT-NAME CHART-DIRECTORY --install ``` .exercise[ - Install the 5 components of DockerCoins: ```bash for COMPONENT in hasher redis rng webui worker; do helm upgrade $COMPONENT helmcoins --install --values=$COMPONENT.yaml done ``` ] .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- class: extra-details ## "Idempotent" - Idempotent = that can be applied multiple times without changing the result (the word is commonly used in maths and computer science) - In this context, this means: - if the action (installing the chart) wasn't done, do it - if the action was already done, don't do anything - Ideally, when such an action fails, it can be retried safely (as opposed to, e.g., installing a new release each time we run it) - Other example: `kubectl -f some-file.yaml` .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Checking what we've done - Let's see if DockerCoins is working! .exercise[ - Check the logs of the worker: ```bash stern worker ``` - Look at the resources that were created: ```bash kubectl get all ``` ] There are *many* issues to fix! .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Can't pull image - It looks like our images can't be found .exercise[ - Use `kubectl describe` on any of the pods in error ] - We're trying to pull `rng:1.16.0` instead of `rng:v0.1`! - Where does that `1.16.0` tag come from? .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Inspecting our template - Let's look at the `templates/` directory (and try to find the one generating the Deployment resource) .exercise[ - Show the structure of the `helmcoins` chart that Helm generated: ```bash tree helmcoins ``` - Check the file `helmcoins/templates/deployment.yaml` - Look for the `image:` parameter ] *The image tag references `{{ .Chart.AppVersion }}`. Where does that come from?* .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## The `.Chart` variable - `.Chart` is a map corresponding to the values in `Chart.yaml` - Let's look for `AppVersion` there! .exercise[ - Check the file `helmcoins/Chart.yaml` - Look for the `appVersion:` parameter ] (Yes, the case is different between the template and the Chart file.) .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Using the correct tags - If we change `AppVersion` to `v0.1`, it will change for *all* deployments (including redis) - Instead, let's change the *template* to use `{{ .Values.image.tag }}` (to match what we've specified in our values YAML files) .exercise[ - Edit `helmcoins/templates/deployment.yaml` - Replace `{{ .Chart.AppVersion }}` with `{{ .Values.image.tag }}` ] .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Upgrading to use the new template - Technically, we just made a new version of the *chart* - To use the new template, we need to *upgrade* the release to use that chart .exercise[ - Upgrade all components: ```bash for COMPONENT in hasher redis rng webui worker; do helm upgrade $COMPONENT helmcoins done ``` - Check how our pods are doing: ```bash kubectl get pods ``` ] We should see all pods "Running". But ... not all of them are READY. .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Troubleshooting readiness - `hasher`, `rng`, `webui` should show up as `1/1 READY` - But `redis` and `worker` should show up as `0/1 READY` - Why? .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Troubleshooting pods - The easiest way to troubleshoot pods is to look at *events* - We can look at all the events on the cluster (with `kubectl get events`) - Or we can use `kubectl describe` on the objects that have problems (`kubectl describe` will retrieve the events related to the object) .exercise[ - Check the events for the redis pods: ```bash kubectl describe pod -l app.kubernetes.io/name=redis ``` ] It's failing both its liveness and readiness probes! .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Healthchecks - The default chart defines healthchecks doing HTTP requests on port 80 - That won't work for redis and worker (redis is not HTTP, and not on port 80; worker doesn't even listen) -- - We could remove or comment out the healthchecks - We could also make them conditional - This sounds more interesting, let's do that! .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Conditionals - We need to enclose the healthcheck block with: `{{ if false }}` at the beginning (we can change the condition later) `{{ end }}` at the end .exercise[ - Edit `helmcoins/templates/deployment.yaml` - Add `{{ if false }}` on the line before `livenessProbe` - Add `{{ end }}` after the `readinessProbe` section (see next slide for details) ] .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- This is what the new YAML should look like (added lines in yellow): ```yaml ports: - name: http containerPort: 80 protocol: TCP `{{ if false }}` livenessProbe: httpGet: path: / port: http readinessProbe: httpGet: path: / port: http `{{ end }}` resources: {{- toYaml .Values.resources | nindent 12 }} ``` .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Testing the new chart - We need to upgrade all the services again to use the new chart .exercise[ - Upgrade all components: ```bash for COMPONENT in hasher redis rng webui worker; do helm upgrade $COMPONENT helmcoins done ``` - Check how our pods are doing: ```bash kubectl get pods ``` ] Everything should now be running! .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## What's next? - Is this working now? .exercise[ - Let's check the logs of the worker: ```bash stern worker ``` ] This error might look familiar ... The worker can't resolve `redis`. Typically, that error means that the `redis` service doesn't exist. .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Checking services - What about the services created by our chart? .exercise[ - Check the list of services: ```bash kubectl get services ``` ] They are named `COMPONENT-helmcoins` instead of just `COMPONENT`. We need to change that! .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Where do the service names come from? - Look at the YAML template used for the services - It should be using `{{ include "helmcoins.fullname" }}` - `include` indicates a *template block* defined somewhere else .exercise[ - Find where that `fullname` thing is defined: ```bash grep define.*fullname helmcoins/templates/* ``` ] It should be in `_helpers.tpl`. We can look at the definition, but it's fairly complex ... .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Changing service names - Instead of that `{{ include }}` tag, let's use the name of the release - The name of the release is available as `{{ .Release.Name }}` .exercise[ - Edit `helmcoins/templates/service.yaml` - Replace the service name with `{{ .Release.Name }}` - Upgrade all the releases to use the new chart - Confirm that the services now have the right names ] .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Is it working now? - If we look at the worker logs, it appears that the worker is still stuck - What could be happening? -- - The redis service is not on port 80! - Let's see how the port number is set - We need to look at both the *deployment* template and the *service* template .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Service template - In the service template, we have the following section: ```yaml ports: - port: {{ .Values.service.port }} targetPort: http protocol: TCP name: http ``` - `port` is the port on which the service is "listening" (i.e. to which our code needs to connect) - `targetPort` is the port on which the pods are listening - The `name` is not important (it's OK if it's `http` even for non-HTTP traffic) .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Setting the redis port - Let's add a `service.port` value to the redis release .exercise[ - Edit `redis.yaml` to add: ```yaml service: port: 6379 ``` - Apply the new values file: ```bash helm upgrade redis helmcoins --values=redis.yaml ``` ] .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Deployment template - If we look at the deployment template, we see this section: ```yaml ports: - name: http containerPort: 80 protocol: TCP ``` - The container port is hard-coded to 80 - We'll change it to use the port number specified in the values .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Changing the deployment template .exercise[ - Edit `helmcoins/templates/deployment.yaml` - The line with `containerPort` should be: ```yaml containerPort: {{ .Values.service.port }} ``` ] .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Apply changes - Re-run the for loop to execute `helm upgrade` one more time - Check the worker logs - This time, it should be working! .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- ## Extra steps - We don't need to create a service for the worker - We can put the whole service block in a conditional (this will require additional changes in other files referencing the service) - We can set the webui to be a NodePort service - We can change the number of workers with `replicaCount` - And much more! ??? :EN:- Writing better Helm charts for app components :FR:- Écriture de *charts* composant par composant .debug[[k8s/helm-create-better-chart.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-create-better-chart.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/Container-Ship-Freighter-Navigation-Elbe-Romance-1782991.jpg)] --- name: toc-helm-secrets class: title Helm secrets .nav[ [Previous part](#toc-creating-better-helm-charts) | [Back to table of contents](#toc-part-6) | [Next part](#toc-exercise--helm-charts) ] .debug[(automatically generated title slide)] --- # Helm secrets - Helm can do *rollbacks*: - to previously installed charts - to previous sets of values - How and where does it store the data needed to do that? - Let's investigate! .debug[[k8s/helm-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-secrets.md)] --- ## Adding the repo - If you haven't done it before, you need to add the repo for that chart .exercise[ - Add the repo that holds the chart for the OWASP Juice Shop: ```bash helm repo add juice https://charts.securecodebox.io ``` ] .debug[[k8s/helm-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-secrets.md)] --- ## We need a release - We need to install something with Helm - Let's use the `juice/juice-shop` chart as an example .exercise[ - Install a release called `orange` with the chart `juice/juice-shop`: ```bash helm upgrade orange juice/juice-shop --install ``` - Let's upgrade that release, and change a value: ```bash helm upgrade orange juice/juice-shop --set ingress.enabled=true ``` ] .debug[[k8s/helm-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-secrets.md)] --- ## Release history - Helm stores successive revisions of each release .exercise[ - View the history for that release: ```bash helm history orange ``` ] Where does that come from? .debug[[k8s/helm-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-secrets.md)] --- ## Investigate - Possible options: - local filesystem (no, because history is visible from other machines) - persistent volumes (no, Helm works even without them) - ConfigMaps, Secrets? .exercise[ - Look for ConfigMaps and Secrets: ```bash kubectl get configmaps,secrets ``` ] -- We should see a number of secrets with TYPE `helm.sh/release.v1`. .debug[[k8s/helm-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-secrets.md)] --- ## Unpacking a secret - Let's find out what is in these Helm secrets .exercise[ - Examine the secret corresponding to the second release of `orange`: ```bash kubectl describe secret sh.helm.release.v1.orange.v2 ``` (`v1` is the secret format; `v2` means revision 2 of the `orange` release) ] There is a key named `release`. .debug[[k8s/helm-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-secrets.md)] --- ## Unpacking the release data - Let's see what's in this `release` thing! .exercise[ - Dump the secret: ```bash kubectl get secret sh.helm.release.v1.orange.v2 \ -o go-template='{{ .data.release }}' ``` ] Secrets are encoded in base64. We need to decode that! .debug[[k8s/helm-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-secrets.md)] --- ## Decoding base64 - We can pipe the output through `base64 -d` or use go-template's `base64decode` .exercise[ - Decode the secret: ```bash kubectl get secret sh.helm.release.v1.orange.v2 \ -o go-template='{{ .data.release | base64decode }}' ``` ] -- ... Wait, this *still* looks like base64. What's going on? -- Let's try one more round of decoding! .debug[[k8s/helm-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-secrets.md)] --- ## Decoding harder - Just add one more base64 decode filter .exercise[ - Decode it twice: ```bash kubectl get secret sh.helm.release.v1.orange.v2 \ -o go-template='{{ .data.release | base64decode | base64decode }}' ``` ] -- ... OK, that was *a lot* of binary data. What sould we do with it? .debug[[k8s/helm-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-secrets.md)] --- ## Guessing data type - We could use `file` to figure out the data type .exercise[ - Pipe the decoded release through `file -`: ```bash kubectl get secret sh.helm.release.v1.orange.v2 \ -o go-template='{{ .data.release | base64decode | base64decode }}' \ | file - ``` ] -- Gzipped data! It can be decoded with `gunzip -c`. .debug[[k8s/helm-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-secrets.md)] --- ## Uncompressing the data - Let's uncompress the data and save it to a file .exercise[ - Rerun the previous command, but with `| gunzip -c > release-info` : ```bash kubectl get secret sh.helm.release.v1.orange.v2 \ -o go-template='{{ .data.release | base64decode | base64decode }}' \ | gunzip -c > release-info ``` - Look at `release-info`: ```bash cat release-info ``` ] -- It's a bundle of ~~YAML~~ JSON. .debug[[k8s/helm-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-secrets.md)] --- ## Looking at the JSON If we inspect that JSON (e.g. with `jq keys release-info`), we see: - `chart` (contains the entire chart used for that release) - `config` (contains the values that we've set) - `info` (date of deployment, status messages) - `manifest` (YAML generated from the templates) - `name` (name of the release, so `orange`) - `namespace` (namespace where we deployed the release) - `version` (revision number within that release; starts at 1) The chart is in a structured format, but it's entirely captured in this JSON. .debug[[k8s/helm-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-secrets.md)] --- ## Conclusions - Helm stores each release information in a Secret in the namespace of the release - The secret is JSON object (gzipped and encoded in base64) - It contains the manifests generated for that release - ... And everything needed to rebuild these manifests (including the full source of the chart, and the values used) - This allows arbitrary rollbacks, as well as tweaking values even without having access to the source of the chart (or the chart repo) used for deployment ??? :EN:- Deep dive into Helm internals :FR:- Fonctionnement interne de Helm .debug[[k8s/helm-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/helm-secrets.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)] --- name: toc-exercise--helm-charts class: title Exercise — Helm charts .nav[ [Previous part](#toc-helm-secrets) | [Back to table of contents](#toc-part-6) | [Next part](#toc-cicd-with-gitlab) ] .debug[(automatically generated title slide)] --- # Exercise — Helm charts Let's write a Helm chart for wordsmith! We will need the YAML manifests that we wrote earlier. Level 1: create a chart to deploy wordsmith. Level 2: make it so that the number of replicas can be set with `--set replicas=X`. Level 3: change the colors of the lego bricks. (For level 3, you'll have to build/push your own images.) See next slide if you need hints! .debug[[k8s/exercise-helm.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/exercise-helm.md)] --- ## Hints *Scroll one slide at a time to see hints.* -- Use `helm create` to create a new chart. -- Delete the content of the `templates` directory and put your YAML instead. -- Install the resulting chart. Voilà! -- Use `{{ .Values.replicas }}` in the YAML manifest for `words`. -- Also add `replicas: 5` to `values.yaml` to provide a default value. .debug[[k8s/exercise-helm.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/exercise-helm.md)] --- ## Changing the color - Create an account on e.g. Docker Hub (e.g. `janedoe`) - Create an image repository (e.g. `janedoe/web`) - Change the images and/or CSS in `web/static` - Build and push - Trigger a rolling update using the image you just pushed .debug[[k8s/exercise-helm.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/exercise-helm.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/aerial-view-of-containers.jpg)] --- name: toc-cicd-with-gitlab class: title CI/CD with GitLab .nav[ [Previous part](#toc-exercise--helm-charts) | [Back to table of contents](#toc-part-6) | [Next part](#toc-network-policies) ] .debug[(automatically generated title slide)] --- # CI/CD with GitLab - In this section, we will see how to set up a CI/CD pipeline with GitLab (using a "self-hosted" GitLab; i.e. running on our Kubernetes cluster) - The big picture: - each time we push code to GitLab, it will be deployed in a staging environment - each time we push the `production` tag, it will be deployed in production .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## Disclaimers - We'll use GitLab here as an exemple, but there are many other options (e.g. some combination of Argo, Harbor, Tekton ...) - There are also hosted options (e.g. GitHub Actions and many others) - We'll use a specific pipeline and workflow, but it's purely arbitrary (treat it as a source of inspiration, not a model to be copied!) .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## Workflow overview - Push code to GitLab's git server - GitLab notices the `.gitlab-ci.yml` file, which defines our pipeline - Our pipeline can have multiple *stages* executed sequentially (e.g. lint, build, test, deploy ...) - Each stage can have multiple *jobs* executed in parallel (e.g. build images in parallel) - Each job will be executed in an independent *runner* pod .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## Pipeline overview - Our repository holds source code, Dockerfiles, and a Helm chart - *Lint* stage will check the Helm chart validity - *Build* stage will build container images (and push them to GitLab's integrated registry) - *Deploy* stage will deploy the Helm chart, using these images - Pushes to `production` will deploy to "the" production namespace - Pushes to other tags/branches will deploy to a namespace created on the fly - We will discuss shortcomings and alternatives and the end of this chapter! .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## Lots of requirements - We need *a lot* of components to pull this off: - a domain name - a storage class - a TLS-capable ingress controller - the cert-manager operator - GitLab itself - the GitLab pipeline - Wow, why?!? .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## I find your lack of TLS disturbing - We need a container registry (obviously!) - Docker (and other container engines) *require* TLS on the registry (with valid certificates) - A few options: - use a "real" TLS certificate (e.g. obtained with Let's Encrypt) - use a self-signed TLS certificate - communicate with the registry over localhost (TLS isn't required then) .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- class: extra-details ## Why not self-signed certs? - When using self-signed certs, we need to either: - add the cert (or CA) to trusted certs - disable cert validation - This needs to be done on *every client* connecting to the registry: - CI/CD pipeline (building and pushing images) - container engine (deploying the images) - other tools (e.g. container security scanner) - It's doable, but it's a lot of hacks (especially when adding more tools!) .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- class: extra-details ## Why not localhost? - TLS is usually not required when the registry is on localhost - We could expose the registry e.g. on a `NodePort` - ... And then tweak the CI/CD pipeline to use that instead - This is great when obtaining valid certs is difficult: - air-gapped or internal environments (that can't use Let's Encrypt) - no domain name available - Downside: the registry isn't easily or safely available from outside (the `NodePort` essentially defeats TLS) .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- class: extra-details ## Can we use `nip.io`? - We will use Let's Encrypt - Let's Encrypt has a quota of certificates per domain (in 2020, that was [50 certificates per week per domain](https://letsencrypt.org/docs/rate-limits/)) - So if we all use `nip.io`, we will probably run into that limit - But you can try and see if it works! .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## Ingress - We will assume that we have a domain name pointing to our cluster (i.e. with a wildcard record pointing to at least one node of the cluster) - We will get traffic in the cluster by leveraging `ExternalIPs` services (but it would be easy to use `LoadBalancer` services instead) - We will use Traefik as the ingress controller (but any other one should work too) - We will use cert-manager to obtain certificates with Let's Encrypt .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## Other details - We will deploy GitLab with its official Helm chart - It will still require a bunch of parameters and customization - We also need a Storage Class (unless our cluster already has one, of course) - We suggest the [Rancher local path provisioner](https://github.com/rancher/local-path-provisioner) .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## Setting everything up 1. `git clone https://github.com/jpetazzo/kubecoin` 2. `export EMAIL=xxx@example.com DOMAIN=awesome-kube-ci.io` (we need a real email address and a domain pointing to the cluster!) 3. `. setup-gitlab-on-k8s.rc` (this doesn't do anything, but defines a number of helper functions) 4. Execute each helper function, one after another (try `do_[TAB]` to see these functions) .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## Local Storage `do_1_localstorage` Applies the YAML directly from Rancher's repository. Annotate the Storage Class so that it becomes the default one. .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## Traefik `do_2_traefik_with_externalips` Install the official Traefik Helm chart. Instead of a `LoadBalancer` service, use a `ClusterIP` with `ExternalIPs`. Automatically infer the `ExternalIPs` from `kubectl get nodes`. Enable TLS. .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## cert-manager `do_3_certmanager` Install cert-manager using their official YAML. Easy-peasy. .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## Certificate issuers `do_4_issuers` Create a couple of `ClusterIssuer` resources for cert-manager. (One for the staging Let's Encrypt environment, one for production.) Note: this requires to specify a valid `$EMAIL` address! Note: if this fails, wait a bit and try again (cert-manager needs to be up). .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## GitLab `do_5_gitlab` Deploy GitLab using their official Helm chart. We pass a lot of parameters to this chart: - the domain name to use - disable GitLab's own ingress and cert-manager - annotate the ingress resources so that cert-manager kicks in - bind the shell service (git over SSH) to port 222 to avoid conflict - use ExternalIPs for that shell service Note: on modest cloud instances, it can take 10 minutes for GitLab to come up. We can check the status with `kubectl get pods --namespace=gitlab` .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## Log into GitLab and configure it `do_6_showlogin` This will get the GitLab root password (stored in a Secret). Then we need to: - log into GitLab - add our SSH key (top-right user menu → settings, then SSH keys on the left) - create a project (using the + menu next to the search bar on top) - go to project configuration (on the left, settings → CI/CD) - add a `KUBECONFIG` file variable with the content of our `.kube/config` file - go to settings → access tokens to create a read-only registry token - add variables `REGISTRY_USER` and `REGISTRY_PASSWORD` with that token - push our repo (`git remote add gitlab ...` then `git push gitlab ...`) .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## Monitoring progress and troubleshooting - Click on "CI/CD" in the left bar to view pipelines - If you see a permission issue mentioning `system:serviceaccount:gitlab:...`: *make sure you did set `KUBECONFIG` correctly!* - GitLab will create namespaces named `gl-<user>-<project>` - At the end of the deployment, the web UI will be available on some unique URL (`http://<user>-<project>-<githash>-gitlab.<domain>`) .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## Production - `git tag -f production && git push -f --tags` - Our CI/CD pipeline will deploy on the production URL (`http://<user>-<project>-gitlab.<domain>`) - It will do it *only* if that same git commit was pushed to staging first (look in the pipeline configuration file to see how it's done!) .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## Let's talk about build - There are many ways to build container images on Kubernetes - ~~And they all suck~~ Many of them have inconveniencing issues - Let's do a quick review! .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## Docker-based approaches - Bind-mount the Docker socket - very easy, but requires Docker Engine - build resource usage "evades" Kubernetes scheduler - insecure - Docker-in-Docker in a pod - requires privileged pod - insecure - approaches like rootless or sysbox might help in the future - External build host - more secure - requires resources outside of the Kubernetes cluster .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## Non-privileged builders - Kaniko - each build runs in its own containers or pod - no caching by default - registry-based caching is possible - BuildKit / `docker buildx` - can leverage Docker Engine or long-running Kubernetes worker pod - supports distributed, multi-arch build farms - basic caching out of the box - can also leverage registry-based caching .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## Other approaches - Ditch the Dockerfile! - bazel - jib - ko - etc. .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## Discussion - Our CI/CD workflow is just *one* of the many possibilities - It would be nice to add some actual unit or e2e tests - Map the production namespace to a "real" domain name - Automatically remove older staging environments (see e.g. [kube-janitor](https://codeberg.org/hjacobs/kube-janitor)) - Deploy production to a separate cluster - Better segregate permissions (don't give `cluster-admin` to the GitLab pipeline) .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## Pros - GitLab is an amazing, open source, all-in-one platform - Available as hosted, community, or enterprise editions - Rich ecosystem, very customizable - Can run on Kubernetes, or somewhere else .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- ## Cons - It can be difficult to use components separately (e.g. use a different registry, or a different job runner) - More than one way to configure it (it's not an opinionated platform) - Not "Kubernetes-native" (for instance, jobs are not Kubernetes jobs) - Job latency could be improved *Note: most of these drawbacks are the flip side of the "pros" on the previous slide!* ??? :EN:- CI/CD with GitLab :FR:- CI/CD avec GitLab .debug[[k8s/gitlab.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitlab.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/blue-containers.jpg)] --- name: toc-network-policies class: title Network policies .nav[ [Previous part](#toc-cicd-with-gitlab) | [Back to table of contents](#toc-part-7) | [Next part](#toc-authentication-and-authorization) ] .debug[(automatically generated title slide)] --- # Network policies - Namespaces help us to *organize* resources - Namespaces do not provide isolation - By default, every pod can contact every other pod - By default, every service accepts traffic from anyone - If we want this to be different, we need *network policies* .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## What's a network policy? A network policy is defined by the following things. - A *pod selector* indicating which pods it applies to e.g.: "all pods in namespace `blue` with the label `zone=internal`" - A list of *ingress rules* indicating which inbound traffic is allowed e.g.: "TCP connections to ports 8000 and 8080 coming from pods with label `zone=dmz`, and from the external subnet 4.42.6.0/24, except 4.42.6.5" - A list of *egress rules* indicating which outbound traffic is allowed A network policy can provide ingress rules, egress rules, or both. .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## How do network policies apply? - A pod can be "selected" by any number of network policies - If a pod isn't selected by any network policy, then its traffic is unrestricted (In other words: in the absence of network policies, all traffic is allowed) - If a pod is selected by at least one network policy, then all traffic is blocked ... ... unless it is explicitly allowed by one of these network policies .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- class: extra-details ## Traffic filtering is flow-oriented - Network policies deal with *connections*, not individual packets - Example: to allow HTTP (80/tcp) connections to pod A, you only need an ingress rule (You do not need a matching egress rule to allow response traffic to go through) - This also applies for UDP traffic (Allowing DNS traffic can be done with a single rule) - Network policy implementations use stateful connection tracking .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## Pod-to-pod traffic - Connections from pod A to pod B have to be allowed by both pods: - pod A has to be unrestricted, or allow the connection as an *egress* rule - pod B has to be unrestricted, or allow the connection as an *ingress* rule - As a consequence: if a network policy restricts traffic going from/to a pod, <br/> the restriction cannot be overridden by a network policy selecting another pod - This prevents an entity managing network policies in namespace A (but without permission to do so in namespace B) from adding network policies giving them access to namespace B .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## The rationale for network policies - In network security, it is generally considered better to "deny all, then allow selectively" (The other approach, "allow all, then block selectively" makes it too easy to leave holes) - As soon as one network policy selects a pod, the pod enters this "deny all" logic - Further network policies can open additional access - Good network policies should be scoped as precisely as possible - In particular: make sure that the selector is not too broad (Otherwise, you end up affecting pods that were otherwise well secured) .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## Our first network policy This is our game plan: - run a web server in a pod - create a network policy to block all access to the web server - create another network policy to allow access only from specific pods .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## Running our test web server .exercise[ - Let's use the `nginx` image: ```bash kubectl create deployment testweb --image=nginx ``` <!-- ```bash kubectl wait deployment testweb --for condition=available ``` --> - Find out the IP address of the pod with one of these two commands: ```bash kubectl get pods -o wide -l app=testweb IP=$(kubectl get pods -l app=testweb -o json | jq -r .items[0].status.podIP) ``` - Check that we can connect to the server: ```bash curl $IP ``` ] The `curl` command should show us the "Welcome to nginx!" page. .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## Adding a very restrictive network policy - The policy will select pods with the label `app=testweb` - It will specify an empty list of ingress rules (matching nothing) .exercise[ - Apply the policy in this YAML file: ```bash kubectl apply -f ~/container.training/k8s/netpol-deny-all-for-testweb.yaml ``` - Check if we can still access the server: ```bash curl $IP ``` <!-- ```wait curl``` ```key ^C``` --> ] The `curl` command should now time out. .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## Looking at the network policy This is the file that we applied: ```yaml kind: NetworkPolicy apiVersion: networking.k8s.io/v1 metadata: name: deny-all-for-testweb spec: podSelector: matchLabels: app: testweb ingress: [] ``` .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## Allowing connections only from specific pods - We want to allow traffic from pods with the label `run=testcurl` - Reminder: this label is automatically applied when we do `kubectl run testcurl ...` .exercise[ - Apply another policy: ```bash kubectl apply -f ~/container.training/k8s/netpol-allow-testcurl-for-testweb.yaml ``` ] .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## Looking at the network policy This is the second file that we applied: ```yaml kind: NetworkPolicy apiVersion: networking.k8s.io/v1 metadata: name: allow-testcurl-for-testweb spec: podSelector: matchLabels: app: testweb ingress: - from: - podSelector: matchLabels: run: testcurl ``` .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## Testing the network policy - Let's create pods with, and without, the required label .exercise[ - Try to connect to testweb from a pod with the `run=testcurl` label: ```bash kubectl run testcurl --rm -i --image=centos -- curl -m3 $IP ``` - Try to connect to testweb with a different label: ```bash kubectl run testkurl --rm -i --image=centos -- curl -m3 $IP ``` ] The first command will work (and show the "Welcome to nginx!" page). The second command will fail and time out after 3 seconds. (The timeout is obtained with the `-m3` option.) .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## An important warning - Some network plugins only have partial support for network policies - For instance, Weave added support for egress rules [in version 2.4](https://github.com/weaveworks/weave/pull/3313) (released in July 2018) - But only recently added support for ipBlock [in version 2.5](https://github.com/weaveworks/weave/pull/3367) (released in Nov 2018) - Unsupported features might be silently ignored (Making you believe that you are secure, when you're not) .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## Network policies, pods, and services - Network policies apply to *pods* - A *service* can select multiple pods (And load balance traffic across them) - It is possible that we can connect to some pods, but not some others (Because of how network policies have been defined for these pods) - In that case, connections to the service will randomly pass or fail (Depending on whether the connection was sent to a pod that we have access to or not) .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## Network policies and namespaces - A good strategy is to isolate a namespace, so that: - all the pods in the namespace can communicate together - other namespaces cannot access the pods - external access has to be enabled explicitly - Let's see what this would look like for the DockerCoins app! .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## Network policies for DockerCoins - We are going to apply two policies - The first policy will prevent traffic from other namespaces - The second policy will allow traffic to the `webui` pods - That's all we need for that app! .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## Blocking traffic from other namespaces This policy selects all pods in the current namespace. It allows traffic only from pods in the current namespace. (An empty `podSelector` means "all pods.") ```yaml kind: NetworkPolicy apiVersion: networking.k8s.io/v1 metadata: name: deny-from-other-namespaces spec: podSelector: {} ingress: - from: - podSelector: {} ``` .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## Allowing traffic to `webui` pods This policy selects all pods with label `app=webui`. It allows traffic from any source. (An empty `from` field means "all sources.") ```yaml kind: NetworkPolicy apiVersion: networking.k8s.io/v1 metadata: name: allow-webui spec: podSelector: matchLabels: app: webui ingress: - from: [] ``` .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## Applying both network policies - Both network policies are declared in the file `k8s/netpol-dockercoins.yaml` .exercise[ - Apply the network policies: ```bash kubectl apply -f ~/container.training/k8s/netpol-dockercoins.yaml ``` - Check that we can still access the web UI from outside <br/> (and that the app is still working correctly!) - Check that we can't connect anymore to `rng` or `hasher` through their ClusterIP ] Note: using `kubectl proxy` or `kubectl port-forward` allows us to connect regardless of existing network policies. This allows us to debug and troubleshoot easily, without having to poke holes in our firewall. .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## Cleaning up our network policies - The network policies that we have installed block all traffic to the default namespace - We should remove them, otherwise further exercises will fail! .exercise[ - Remove all network policies: ```bash kubectl delete networkpolicies --all ``` ] .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## Protecting the control plane - Should we add network policies to block unauthorized access to the control plane? (etcd, API server, etc.) -- - At first, it seems like a good idea ... -- - But it *shouldn't* be necessary: - not all network plugins support network policies - the control plane is secured by other methods (mutual TLS, mostly) - the code running in our pods can reasonably expect to contact the API <br/> (and it can do so safely thanks to the API permission model) - If we block access to the control plane, we might disrupt legitimate code - ...Without necessarily improving security .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## Tools and resources - [Cilium Network Policy Editor](https://editor.cilium.io/) - [Tufin Network Policy Viewer](https://orca.tufin.io/netpol/) - Two resources by [Ahmet Alp Balkan](https://ahmet.im/): - a [very good talk about network policies](https://www.youtube.com/watch?list=PLj6h78yzYM2P-3-xqvmWaZbbI1sW-ulZb&v=3gGpMmYeEO8) at KubeCon North America 2017 - a repository of [ready-to-use recipes](https://github.com/ahmetb/kubernetes-network-policy-recipes) for network policies .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- ## Documentation - As always, the [Kubernetes documentation](https://kubernetes.io/docs/concepts/services-networking/network-policies/) is a good starting point - The API documentation has a lot of detail about the format of various objects: <!-- ##VERSION## --> - [NetworkPolicy](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.20/#networkpolicy-v1-networking-k8s-io) - [NetworkPolicySpec](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.20/#networkpolicyspec-v1-networking-k8s-io) - [NetworkPolicyIngressRule](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.20/#networkpolicyingressrule-v1-networking-k8s-io) - etc. ??? :EN:- Isolating workloads with Network Policies :FR:- Isolation réseau avec les *network policies* .debug[[k8s/netpol.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/netpol.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/chinook-helicopter-container.jpg)] --- name: toc-authentication-and-authorization class: title Authentication and authorization .nav[ [Previous part](#toc-network-policies) | [Back to table of contents](#toc-part-7) | [Next part](#toc-the-csr-api) ] .debug[(automatically generated title slide)] --- # Authentication and authorization - In this section, we will: - define authentication and authorization - explain how they are implemented in Kubernetes - talk about tokens, certificates, service accounts, RBAC ... - But first: why do we need all this? .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## The need for fine-grained security - The Kubernetes API should only be available for identified users - we don't want "guest access" (except in very rare scenarios) - we don't want strangers to use our compute resources, delete our apps ... - our keys and passwords should not be exposed to the public - Users will often have different access rights - cluster admin (similar to UNIX "root") can do everything - developer might access specific resources, or a specific namespace - supervision might have read only access to *most* resources .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## Example: custom HTTP load balancer - Let's imagine that we have a custom HTTP load balancer for multiple apps - Each app has its own *Deployment* resource - By default, the apps are "sleeping" and scaled to zero - When a request comes in, the corresponding app gets woken up - After some inactivity, the app is scaled down again - This HTTP load balancer needs API access (to scale up/down) - What if *a wild vulnerability appears*? .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## Consequences of vulnerability - If the HTTP load balancer has the same API access as we do: *full cluster compromise (easy data leak, cryptojacking...)* - If the HTTP load balancer has `update` permissions on the Deployments: *defacement (easy), MITM / impersonation (medium to hard)* - If the HTTP load balancer only has permission to `scale` the Deployments: *denial-of-service* - All these outcomes are bad, but some are worse than others .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## Definitions - Authentication = verifying the identity of a person On a UNIX system, we can authenticate with login+password, SSH keys ... - Authorization = listing what they are allowed to do On a UNIX system, this can include file permissions, sudoer entries ... - Sometimes abbreviated as "authn" and "authz" - In good modular systems, these things are decoupled (so we can e.g. change a password or SSH key without having to reset access rights) .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## Authentication in Kubernetes - When the API server receives a request, it tries to authenticate it (it examines headers, certificates... anything available) - Many authentication methods are available and can be used simultaneously (we will see them on the next slide) - It's the job of the authentication method to produce: - the user name - the user ID - a list of groups - The API server doesn't interpret these; that'll be the job of *authorizers* .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## Authentication methods - TLS client certificates (that's what we've been doing with `kubectl` so far) - Bearer tokens (a secret token in the HTTP headers of the request) - [HTTP basic auth](https://en.wikipedia.org/wiki/Basic_access_authentication) (carrying user and password in an HTTP header; [deprecated since Kubernetes 1.19](https://github.com/kubernetes/kubernetes/pull/89069)) - Authentication proxy (sitting in front of the API and setting trusted headers) .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## Anonymous requests - If any authentication method *rejects* a request, it's denied (`401 Unauthorized` HTTP code) - If a request is neither rejected nor accepted by anyone, it's anonymous - the user name is `system:anonymous` - the list of groups is `[system:unauthenticated]` - By default, the anonymous user can't do anything (that's what you get if you just `curl` the Kubernetes API) .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## Authentication with TLS certificates - This is enabled in most Kubernetes deployments - The user name is derived from the `CN` in the client certificates - The groups are derived from the `O` fields in the client certificate - From the point of view of the Kubernetes API, users do not exist (i.e. they are not stored in etcd or anywhere else) - Users can be created (and added to groups) independently of the API - The Kubernetes API can be set up to use your custom CA to validate client certs .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- class: extra-details ## Viewing our admin certificate - Let's inspect the certificate we've been using all this time! .exercise[ - This command will show the `CN` and `O` fields for our certificate: ```bash kubectl config view \ --raw \ -o json \ | jq -r .users[0].user[\"client-certificate-data\"] \ | openssl base64 -d -A \ | openssl x509 -text \ | grep Subject: ``` ] Let's break down that command together! 😅 .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- class: extra-details ## Breaking down the command - `kubectl config view` shows the Kubernetes user configuration - `--raw` includes certificate information (which shows as REDACTED otherwise) - `-o json` outputs the information in JSON format - `| jq ...` extracts the field with the user certificate (in base64) - `| openssl base64 -d -A` decodes the base64 format (now we have a PEM file) - `| openssl x509 -text` parses the certificate and outputs it as plain text - `| grep Subject:` shows us the line that interests us → We are user `kubernetes-admin`, in group `system:masters`. (We will see later how and why this gives us the permissions that we have.) .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## User certificates in practice - The Kubernetes API server does not support certificate revocation (see issue [#18982](https://github.com/kubernetes/kubernetes/issues/18982)) - As a result, we don't have an easy way to terminate someone's access (if their key is compromised, or they leave the organization) - Option 1: re-create a new CA and re-issue everyone's certificates <br/> → Maybe OK if we only have a few users; no way otherwise - Option 2: don't use groups; grant permissions to individual users <br/> → Inconvenient if we have many users and teams; error-prone - Option 3: issue short-lived certificates (e.g. 24 hours) and renew them often <br/> → This can be facilitated by e.g. Vault or by the Kubernetes CSR API .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## Authentication with tokens - Tokens are passed as HTTP headers: `Authorization: Bearer and-then-here-comes-the-token` - Tokens can be validated through a number of different methods: - static tokens hard-coded in a file on the API server - [bootstrap tokens](https://kubernetes.io/docs/reference/access-authn-authz/bootstrap-tokens/) (special case to create a cluster or join nodes) - [OpenID Connect tokens](https://kubernetes.io/docs/reference/access-authn-authz/authentication/#openid-connect-tokens) (to delegate authentication to compatible OAuth2 providers) - service accounts (these deserve more details, coming right up!) .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## Service accounts - A service account is a user that exists in the Kubernetes API (it is visible with e.g. `kubectl get serviceaccounts`) - Service accounts can therefore be created / updated dynamically (they don't require hand-editing a file and restarting the API server) - A service account is associated with a set of secrets (the kind that you can view with `kubectl get secrets`) - Service accounts are generally used to grant permissions to applications, services... (as opposed to humans) .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- class: extra-details ## Token authentication in practice - We are going to list existing service accounts - Then we will extract the token for a given service account - And we will use that token to authenticate with the API .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- class: extra-details ## Listing service accounts .exercise[ - The resource name is `serviceaccount` or `sa` for short: ```bash kubectl get sa ``` ] There should be just one service account in the default namespace: `default`. .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- class: extra-details ## Finding the secret .exercise[ - List the secrets for the `default` service account: ```bash kubectl get sa default -o yaml SECRET=$(kubectl get sa default -o json | jq -r .secrets[0].name) ``` ] It should be named `default-token-XXXXX`. .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- class: extra-details ## Extracting the token - The token is stored in the secret, wrapped with base64 encoding .exercise[ - View the secret: ```bash kubectl get secret $SECRET -o yaml ``` - Extract the token and decode it: ```bash TOKEN=$(kubectl get secret $SECRET -o json \ | jq -r .data.token | openssl base64 -d -A) ``` ] .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- class: extra-details ## Using the token - Let's send a request to the API, without and with the token .exercise[ - Find the ClusterIP for the `kubernetes` service: ```bash kubectl get svc kubernetes API=$(kubectl get svc kubernetes -o json | jq -r .spec.clusterIP) ``` - Connect without the token: ```bash curl -k https://$API ``` - Connect with the token: ```bash curl -k -H "Authorization: Bearer $TOKEN" https://$API ``` ] .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- class: extra-details ## Results - In both cases, we will get a "Forbidden" error - Without authentication, the user is `system:anonymous` - With authentication, it is shown as `system:serviceaccount:default:default` - The API "sees" us as a different user - But neither user has any rights, so we can't do nothin' - Let's change that! .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## Authorization in Kubernetes - There are multiple ways to grant permissions in Kubernetes, called [authorizers](https://kubernetes.io/docs/reference/access-authn-authz/authorization/#authorization-modules): - [Node Authorization](https://kubernetes.io/docs/reference/access-authn-authz/node/) (used internally by kubelet; we can ignore it) - [Attribute-based access control](https://kubernetes.io/docs/reference/access-authn-authz/abac/) (powerful but complex and static; ignore it too) - [Webhook](https://kubernetes.io/docs/reference/access-authn-authz/webhook/) (each API request is submitted to an external service for approval) - [Role-based access control](https://kubernetes.io/docs/reference/access-authn-authz/rbac/) (associates permissions to users dynamically) - The one we want is the last one, generally abbreviated as RBAC .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## Role-based access control - RBAC allows to specify fine-grained permissions - Permissions are expressed as *rules* - A rule is a combination of: - [verbs](https://kubernetes.io/docs/reference/access-authn-authz/authorization/#determine-the-request-verb) like create, get, list, update, delete... - resources (as in "API resource," like pods, nodes, services...) - resource names (to specify e.g. one specific pod instead of all pods) - in some case, [subresources](https://kubernetes.io/docs/reference/access-authn-authz/rbac/#referring-to-resources) (e.g. logs are subresources of pods) .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## From rules to roles to rolebindings - A *role* is an API object containing a list of *rules* Example: role "external-load-balancer-configurator" can: - [list, get] resources [endpoints, services, pods] - [update] resources [services] - A *rolebinding* associates a role with a user Example: rolebinding "external-load-balancer-configurator": - associates user "external-load-balancer-configurator" - with role "external-load-balancer-configurator" - Yes, there can be users, roles, and rolebindings with the same name - It's a good idea for 1-1-1 bindings; not so much for 1-N ones .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## Cluster-scope permissions - API resources Role and RoleBinding are for objects within a namespace - We can also define API resources ClusterRole and ClusterRoleBinding - These are a superset, allowing us to: - specify actions on cluster-wide objects (like nodes) - operate across all namespaces - We can create Role and RoleBinding resources within a namespace - ClusterRole and ClusterRoleBinding resources are global .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## Pods and service accounts - A pod can be associated with a service account - by default, it is associated with the `default` service account - as we saw earlier, this service account has no permissions anyway - The associated token is exposed to the pod's filesystem (in `/var/run/secrets/kubernetes.io/serviceaccount/token`) - Standard Kubernetes tooling (like `kubectl`) will look for it there - So Kubernetes tools running in a pod will automatically use the service account .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## In practice - We are going to create a service account - We will use a default cluster role (`view`) - We will bind together this role and this service account - Then we will run a pod using that service account - In this pod, we will install `kubectl` and check our permissions .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## Creating a service account - We will call the new service account `viewer` (note that nothing prevents us from calling it `view`, like the role) .exercise[ - Create the new service account: ```bash kubectl create serviceaccount viewer ``` - List service accounts now: ```bash kubectl get serviceaccounts ``` ] .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## Binding a role to the service account - Binding a role = creating a *rolebinding* object - We will call that object `viewercanview` (but again, we could call it `view`) .exercise[ - Create the new role binding: ```bash kubectl create rolebinding viewercanview \ --clusterrole=view \ --serviceaccount=default:viewer ``` ] It's important to note a couple of details in these flags... .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## Roles vs Cluster Roles - We used `--clusterrole=view` - What would have happened if we had used `--role=view`? - we would have bound the role `view` from the local namespace <br/>(instead of the cluster role `view`) - the command would have worked fine (no error) - but later, our API requests would have been denied - This is a deliberate design decision (we can reference roles that don't exist, and create/update them later) .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## Users vs Service Accounts - We used `--serviceaccount=default:viewer` - What would have happened if we had used `--user=default:viewer`? - we would have bound the role to a user instead of a service account - again, the command would have worked fine (no error) - ...but our API requests would have been denied later - What's about the `default:` prefix? - that's the namespace of the service account - yes, it could be inferred from context, but... `kubectl` requires it .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## Testing - We will run an `alpine` pod and install `kubectl` there .exercise[ - Run a one-time pod: ```bash kubectl run eyepod --rm -ti --restart=Never \ --serviceaccount=viewer \ --image alpine ``` - Install `curl`, then use it to install `kubectl`: ```bash apk add --no-cache curl URLBASE=https://storage.googleapis.com/kubernetes-release/release KUBEVER=$(curl -s $URLBASE/stable.txt) curl -LO $URLBASE/$KUBEVER/bin/linux/amd64/kubectl chmod +x kubectl ``` ] .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## Running `kubectl` in the pod - We'll try to use our `view` permissions, then to create an object .exercise[ - Check that we can, indeed, view things: ```bash ./kubectl get all ``` - But that we can't create things: ``` ./kubectl create deployment testrbac --image=nginx ``` - Exit the container with `exit` or `^D` <!-- ```key ^D``` --> ] .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- ## Testing directly with `kubectl` - We can also check for permission with `kubectl auth can-i`: ```bash kubectl auth can-i list nodes kubectl auth can-i create pods kubectl auth can-i get pod/name-of-pod kubectl auth can-i get /url-fragment-of-api-request/ kubectl auth can-i '*' services ``` - And we can check permissions on behalf of other users: ```bash kubectl auth can-i list nodes \ --as some-user kubectl auth can-i list nodes \ --as system:serviceaccount:<namespace>:<name-of-service-account> ``` .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- class: extra-details ## Where does this `view` role come from? - Kubernetes defines a number of ClusterRoles intended to be bound to users - `cluster-admin` can do *everything* (think `root` on UNIX) - `admin` can do *almost everything* (except e.g. changing resource quotas and limits) - `edit` is similar to `admin`, but cannot view or edit permissions - `view` has read-only access to most resources, except permissions and secrets *In many situations, these roles will be all you need.* *You can also customize them!* .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- class: extra-details ## Customizing the default roles - If you need to *add* permissions to these default roles (or others), <br/> you can do it through the [ClusterRole Aggregation](https://kubernetes.io/docs/reference/access-authn-authz/rbac/#aggregated-clusterroles) mechanism - This happens by creating a ClusterRole with the following labels: ```yaml metadata: labels: rbac.authorization.k8s.io/aggregate-to-admin: "true" rbac.authorization.k8s.io/aggregate-to-edit: "true" rbac.authorization.k8s.io/aggregate-to-view: "true" ``` - This ClusterRole permissions will be added to `admin`/`edit`/`view` respectively - This is particulary useful when using CustomResourceDefinitions (since Kubernetes cannot guess which resources are sensitive and which ones aren't) .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- class: extra-details ## Where do our permissions come from? - When interacting with the Kubernetes API, we are using a client certificate - We saw previously that this client certificate contained: `CN=kubernetes-admin` and `O=system:masters` - Let's look for these in existing ClusterRoleBindings: ```bash kubectl get clusterrolebindings -o yaml | grep -e kubernetes-admin -e system:masters ``` (`system:masters` should show up, but not `kubernetes-admin`.) - Where does this match come from? .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- class: extra-details ## The `system:masters` group - If we eyeball the output of `kubectl get clusterrolebindings -o yaml`, we'll find out! - It is in the `cluster-admin` binding: ```bash kubectl describe clusterrolebinding cluster-admin ``` - This binding associates `system:masters` with the cluster role `cluster-admin` - And the `cluster-admin` is, basically, `root`: ```bash kubectl describe clusterrole cluster-admin ``` .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- class: extra-details ## Figuring out who can do what - For auditing purposes, sometimes we want to know who can perform which actions - There are a few tools to help us with that, available as `kubectl` plugins: - `kubectl who-can` / [kubectl-who-can](https://github.com/aquasecurity/kubectl-who-can) by Aqua Security - `kubectl access-matrix` / [Rakkess (Review Access)](https://github.com/corneliusweig/rakkess) by Cornelius Weig - `kubectl rbac-lookup` / [RBAC Lookup](https://github.com/FairwindsOps/rbac-lookup) by FairwindsOps - `kubectl` plugins can be installed and managed with `krew` - They can also be installed and executed as standalone programs ??? :EN:- Authentication and authorization in Kubernetes :EN:- Authentication with tokens and certificates :EN:- Authorization with RBAC (Role-Based Access Control) :EN:- Restricting permissions with Service Accounts :EN:- Working with Roles, Cluster Roles, Role Bindings, etc. :FR:- Identification et droits d'accès dans Kubernetes :FR:- Mécanismes d'identification par jetons et certificats :FR:- Le modèle RBAC *(Role-Based Access Control)* :FR:- Restreindre les permissions grâce aux *Service Accounts* :FR:- Comprendre les *Roles*, *Cluster Roles*, *Role Bindings*, etc. .debug[[k8s/authn-authz.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/authn-authz.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/container-cranes.jpg)] --- name: toc-the-csr-api class: title The CSR API .nav[ [Previous part](#toc-authentication-and-authorization) | [Back to table of contents](#toc-part-7) | [Next part](#toc-openid-connect) ] .debug[(automatically generated title slide)] --- # The CSR API - The Kubernetes API exposes CSR resources - We can use these resources to issue TLS certificates - First, we will go through a quick reminder about TLS certificates - Then, we will see how to obtain a certificate for a user - We will use that certificate to authenticate with the cluster - Finally, we will grant some privileges to that user .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## Reminder about TLS - TLS (Transport Layer Security) is a protocol providing: - encryption (to prevent eavesdropping) - authentication (using public key cryptography) - When we access an https:// URL, the server authenticates itself (it proves its identity to us; as if it were "showing its ID") - But we can also have mutual TLS authentication (mTLS) (client proves its identity to server; server proves its identity to client) .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## Authentication with certificates - To authenticate, someone (client or server) needs: - a *private key* (that remains known only to them) - a *public key* (that they can distribute) - a *certificate* (associating the public key with an identity) - A message encrypted with the private key can only be decrypted with the public key (and vice versa) - If I use someone's public key to encrypt/decrypt their messages, <br/> I can be certain that I am talking to them / they are talking to me - The certificate proves that I have the correct public key for them .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## Certificate generation workflow This is what I do if I want to obtain a certificate. 1. Create public and private keys. 2. Create a Certificate Signing Request (CSR). (The CSR contains the identity that I claim and a public key.) 3. Send that CSR to the Certificate Authority (CA). 4. The CA verifies that I can claim the identity in the CSR. 5. The CA generates my certificate and gives it to me. The CA (or anyone else) never needs to know my private key. .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## The CSR API - The Kubernetes API has a CertificateSigningRequest resource type (we can list them with e.g. `kubectl get csr`) - We can create a CSR object (= upload a CSR to the Kubernetes API) - Then, using the Kubernetes API, we can approve/deny the request - If we approve the request, the Kubernetes API generates a certificate - The certificate gets attached to the CSR object and can be retrieved .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## Using the CSR API - We will show how to use the CSR API to obtain user certificates - This will be a rather complex demo - ... And yet, we will take a few shortcuts to simplify it (but it will illustrate the general idea) - The demo also won't be automated (we would have to write extra code to make it fully functional) .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## Warning - The CSR API isn't really suited to issue user certificates - It is primarily intended to issue control plane certificates (for instance, deal with kubelet certificates renewal) - The API was expanded a bit in Kubernetes 1.19 to encompass broader usage - There are still lots of gaps in the spec (e.g. how to specify expiration in a standard way) - ... And no other implementation to this date (but [cert-manager](https://cert-manager.io/docs/faq/#kubernetes-has-a-builtin-certificatesigningrequest-api-why-not-use-that) might eventually get there!) .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## General idea - We will create a Namespace named "users" - Each user will get a ServiceAccount in that Namespace - That ServiceAccount will give read/write access to *one* CSR object - Users will use that ServiceAccount's token to submit a CSR - We will approve the CSR (or not) - Users can then retrieve their certificate from their CSR object - ...And use that certificate for subsequent interactions .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## Resource naming For a user named `jean.doe`, we will have: - ServiceAccount `jean.doe` in Namespace `users` - CertificateSigningRequest `user=jean.doe` - ClusterRole `user=jean.doe` giving read/write access to that CSR - ClusterRoleBinding `user=jean.doe` binding ClusterRole and ServiceAccount .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- class: extra-details ## About resource name constraints - Most Kubernetes identifiers and names are fairly restricted - They generally are DNS-1123 *labels* or *subdomains* (from [RFC 1123](https://tools.ietf.org/html/rfc1123)) - A label is lowercase letters, numbers, dashes; can't start or finish with a dash - A subdomain is one or multiple labels separated by dots - Some resources have more relaxed constraints, and can be "path segment names" (uppercase are allowed, as well as some characters like `#:?!,_`) - This includes RBAC objects (like Roles, RoleBindings...) and CSRs - See the [Identifiers and Names](https://github.com/kubernetes/community/blob/master/contributors/design-proposals/architecture/identifiers.md) design document and the [Object Names and IDs](https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#path-segment-names) documentation page for more details .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## Creating the user's resources .warning[If you want to use another name than `jean.doe`, update the YAML file!] .exercise[ - Create the global namespace for all users: ```bash kubectl create namespace users ``` - Create the ServiceAccount, ClusterRole, ClusterRoleBinding for `jean.doe`: ```bash kubectl apply -f ~/container.training/k8s/user=jean.doe.yaml ``` ] .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## Extracting the user's token - Let's obtain the user's token and give it to them (the token will be their password) .exercise[ - List the user's secrets: ```bash kubectl --namespace=users describe serviceaccount jean.doe ``` - Show the user's token: ```bash kubectl --namespace=users describe secret `jean.doe-token-xxxxx` ``` ] .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## Configure `kubectl` to use the token - Let's create a new context that will use that token to access the API .exercise[ - Add a new identity to our kubeconfig file: ```bash kubectl config set-credentials token:jean.doe --token=... ``` - Add a new context using that identity: ```bash kubectl config set-context jean.doe --user=token:jean.doe --cluster=`kubernetes` ``` (Make sure to adapt the cluster name if yours is different!) - Use that context: ```bash kubectl config use-context jean.doe ``` ] .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## Access the API with the token - Let's check that our access rights are set properly .exercise[ - Try to access any resource: ```bash kubectl get pods ``` (This should tell us "Forbidden") - Try to access "our" CertificateSigningRequest: ```bash kubectl get csr user=jean.doe ``` (This should tell us "NotFound") ] .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## Create a key and a CSR - There are many tools to generate TLS keys and CSRs - Let's use OpenSSL; it's not the best one, but it's installed everywhere (many people prefer cfssl, easyrsa, or other tools; that's fine too!) .exercise[ - Generate the key and certificate signing request: ```bash openssl req -newkey rsa:2048 -nodes -keyout key.pem \ -new -subj /CN=jean.doe/O=devs/ -out csr.pem ``` ] The command above generates: - a 2048-bit RSA key, without encryption, stored in key.pem - a CSR for the name `jean.doe` in group `devs` .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## Inside the Kubernetes CSR object - The Kubernetes CSR object is a thin wrapper around the CSR PEM file - The PEM file needs to be encoded to base64 on a single line (we will use `base64 -w0` for that purpose) - The Kubernetes CSR object also needs to list the right "usages" (these are flags indicating how the certificate can be used) .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## Sending the CSR to Kubernetes .exercise[ - Generate and create the CSR resource: ```bash kubectl apply -f - <<EOF apiVersion: certificates.k8s.io/v1beta1 kind: CertificateSigningRequest metadata: name: user=jean.doe spec: request: $(base64 -w0 < csr.pem) usages: - digital signature - key encipherment - client auth EOF ``` ] .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## Adjusting certificate expiration - By default, the CSR API generates certificates valid 1 year - We want to generate short-lived certificates, so we will lower that to 1 hour - Fow now, this is configured [through an experimental controller manager flag](https://github.com/kubernetes/kubernetes/issues/67324) .exercise[ - Edit the static pod definition for the controller manager: ```bash sudo vim /etc/kubernetes/manifests/kube-controller-manager.yaml ``` - In the list of flags, add the following line: ```bash - --experimental-cluster-signing-duration=1h ``` ] .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## Verifying and approving the CSR - Let's inspect the CSR, and if it is valid, approve it .exercise[ - Switch back to `cluster-admin`: ```bash kctx - ``` - Inspect the CSR: ```bash kubectl describe csr user=jean.doe ``` - Approve it: ```bash kubectl certificate approve user=jean.doe ``` ] .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## Obtaining the certificate .exercise[ - Switch back to the user's identity: ```bash kctx - ``` - Retrieve the updated CSR object and extract the certificate: ```bash kubectl get csr user=jean.doe \ -o jsonpath={.status.certificate} \ | base64 -d > cert.pem ``` - Inspect the certificate: ```bash openssl x509 -in cert.pem -text -noout ``` ] .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## Using the certificate .exercise[ - Add the key and certificate to kubeconfig: ```bash kubectl config set-credentials cert:jean.doe --embed-certs \ --client-certificate=cert.pem --client-key=key.pem ``` - Update the user's context to use the key and cert to authenticate: ```bash kubectl config set-context jean.doe --user cert:jean.doe ``` - Confirm that we are seen as `jean.doe` (but don't have permissions): ```bash kubectl get pods ``` ] .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## What's missing? We have just shown, step by step, a method to issue short-lived certificates for users. To be usable in real environments, we would need to add: - a kubectl helper to automatically generate the CSR and obtain the cert (and transparently renew the cert when needed) - a Kubernetes controller to automatically validate and approve CSRs (checking that the subject and groups are valid) - a way for the users to know the groups to add to their CSR (e.g.: annotations on their ServiceAccount + read access to the ServiceAccount) .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- ## Is this realistic? - Larger organizations typically integrate with their own directory - The general principle, however, is the same: - users have long-term credentials (password, token, ...) - they use these credentials to obtain other, short-lived credentials - This provides enhanced security: - the long-term credentials can use long passphrases, 2FA, HSM... - the short-term credentials are more convenient to use - we get strong security *and* convenience - Systems like Vault also have certificate issuance mechanisms ??? :EN:- Generating user certificates with the CSR API :FR:- Génération de certificats utilisateur avec la CSR API .debug[[k8s/csr-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/csr-api.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/container-housing.jpg)] --- name: toc-openid-connect class: title OpenID Connect .nav[ [Previous part](#toc-the-csr-api) | [Back to table of contents](#toc-part-7) | [Next part](#toc-pod-security-policies) ] .debug[(automatically generated title slide)] --- # OpenID Connect - The Kubernetes API server can perform authentication with OpenID connect - This requires an *OpenID provider* (external authorization server using the OAuth 2.0 protocol) - We can use a third-party provider (e.g. Google) or run our own (e.g. Dex) - We are going to give an overview of the protocol - We will show it in action (in a simplified scenario) .debug[[k8s/openid-connect.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openid-connect.md)] --- ## Workflow overview - We want to access our resources (a Kubernetes cluster) - We authenticate with the OpenID provider - we can do this directly (e.g. by going to https://accounts.google.com) - or maybe a kubectl plugin can open a browser page on our behalf - After authenticating us, the OpenID provider gives us: - an *id token* (a short-lived signed JSON Web Token, see next slide) - a *refresh token* (to renew the *id token* when needed) - We can now issue requests to the Kubernetes API with the *id token* - The API server will verify that token's content to authenticate us .debug[[k8s/openid-connect.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openid-connect.md)] --- ## JSON Web Tokens - A JSON Web Token (JWT) has three parts: - a header specifying algorithms and token type - a payload (indicating who issued the token, for whom, which purposes...) - a signature generated by the issuer (the issuer = the OpenID provider) - Anyone can verify a JWT without contacting the issuer (except to obtain the issuer's public key) - Pro tip: we can inspect a JWT with https://jwt.io/ .debug[[k8s/openid-connect.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openid-connect.md)] --- ## How the Kubernetes API uses JWT - Server side - enable OIDC authentication - indicate which issuer (provider) should be allowed - indicate which audience (or "client id") should be allowed - optionally, map or prefix user and group names - Client side - obtain JWT as described earlier - pass JWT as authentication token - renew JWT when needed (using the refresh token) .debug[[k8s/openid-connect.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openid-connect.md)] --- ## Demo time! - We will use [Google Accounts](https://accounts.google.com) as our OpenID provider - We will use the [Google OAuth Playground](https://developers.google.com/oauthplayground) as the "audience" or "client id" - We will obtain a JWT through Google Accounts and the OAuth Playground - We will enable OIDC in the Kubernetes API server - We will use the JWT to authenticate .footnote[If you can't or won't use a Google account, you can try to adapt this to another provider.] .debug[[k8s/openid-connect.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openid-connect.md)] --- ## Checking the API server logs - The API server logs will be particularly useful in this section (they will indicate e.g. why a specific token is rejected) - Let's keep an eye on the API server output! .exercise[ - Tail the logs of the API server: ```bash kubectl logs kube-apiserver-node1 --follow --namespace=kube-system ``` ] .debug[[k8s/openid-connect.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openid-connect.md)] --- ## Authenticate with the OpenID provider - We will use the Google OAuth Playground for convenience - In a real scenario, we would need our own OAuth client instead of the playground (even if we were still using Google as the OpenID provider) .exercise[ - Open the Google OAuth Playground: ``` https://developers.google.com/oauthplayground/ ``` - Enter our own custom scope in the text field: ``` https://www.googleapis.com/auth/userinfo.email ``` - Click on "Authorize APIs" and allow the playground to access our email address ] .debug[[k8s/openid-connect.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openid-connect.md)] --- ## Obtain our JSON Web Token - The previous step gave us an "authorization code" - We will use it to obtain tokens .exercise[ - Click on "Exchange authorization code for tokens" ] - The JWT is the very long `id_token` that shows up on the right hand side (it is a base64-encoded JSON object, and should therefore start with `eyJ`) .debug[[k8s/openid-connect.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openid-connect.md)] --- ## Using our JSON Web Token - We need to create a context (in kubeconfig) for our token (if we just add the token or use `kubectl --token`, our certificate will still be used) .exercise[ - Create a new authentication section in kubeconfig: ```bash kubectl config set-credentials myjwt --token=eyJ... ``` - Try to use it: ```bash kubectl --user=myjwt get nodes ``` ] We should get an `Unauthorized` response, since we haven't enabled OpenID Connect in the API server yet. We should also see `invalid bearer token` in the API server log output. .debug[[k8s/openid-connect.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openid-connect.md)] --- ## Enabling OpenID Connect - We need to add a few flags to the API server configuration - These two are mandatory: `--oidc-issuer-url` → URL of the OpenID provider `--oidc-client-id` → app requesting the authentication <br/>(in our case, that's the ID for the Google OAuth Playground) - This one is optional: `--oidc-username-claim` → which field should be used as user name <br/>(we will use the user's email address instead of an opaque ID) - See the [API server documentation](https://kubernetes.io/docs/reference/access-authn-authz/authentication/#configuring-the-api-server ) for more details about all available flags .debug[[k8s/openid-connect.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openid-connect.md)] --- ## Updating the API server configuration - The instructions below will work for clusters deployed with kubeadm (or where the control plane is deployed in static pods) - If your cluster is deployed differently, you will need to adapt them .exercise[ - Edit `/etc/kubernetes/manifests/kube-apiserver.yaml` - Add the following lines to the list of command-line flags: ```yaml - --oidc-issuer-url=https://accounts.google.com - --oidc-client-id=407408718192.apps.googleusercontent.com - --oidc-username-claim=email ``` ] .debug[[k8s/openid-connect.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openid-connect.md)] --- ## Restarting the API server - The kubelet monitors the files in `/etc/kubernetes/manifests` - When we save the pod manifest, kubelet will restart the corresponding pod (using the updated command line flags) .exercise[ - After making the changes described on the previous slide, save the file - Issue a simple command (like `kubectl version`) until the API server is back up (it might take between a few seconds and one minute for the API server to restart) - Restart the `kubectl logs` command to view the logs of the API server ] .debug[[k8s/openid-connect.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openid-connect.md)] --- ## Using our JSON Web Token - Now that the API server is set up to recognize our token, try again! .exercise[ - Try an API command with our token: ```bash kubectl --user=myjwt get nodes kubectl --user=myjwt get pods ``` ] We should see a message like: ``` Error from server (Forbidden): nodes is forbidden: User "jean.doe@gmail.com" cannot list resource "nodes" in API group "" at the cluster scope ``` → We were successfully *authenticated*, but not *authorized*. .debug[[k8s/openid-connect.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openid-connect.md)] --- ## Authorizing our user - As an extra step, let's grant read access to our user - We will use the pre-defined ClusterRole `view` .exercise[ - Create a ClusterRoleBinding allowing us to view resources: ```bash kubectl create clusterrolebinding i-can-view \ --user=`jean.doe@gmail.com` --clusterrole=view ``` (make sure to put *your* Google email address there) - Confirm that we can now list pods with our token: ```bash kubectl --user=myjwt get pods ``` ] .debug[[k8s/openid-connect.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openid-connect.md)] --- ## From demo to production .warning[This was a very simplified demo! In a real deployment...] - We wouldn't use the Google OAuth Playground - We *probably* wouldn't even use Google at all (it doesn't seem to provide a way to include groups!) - Some popular alternatives: - [Dex](https://github.com/dexidp/dex), [Keycloak](https://www.keycloak.org/) (self-hosted) - [Okta](https://developer.okta.com/docs/how-to/creating-token-with-groups-claim/#step-five-decode-the-jwt-to-verify) (SaaS) - We would use a helper (like the [kubelogin](https://github.com/int128/kubelogin) plugin) to automatically obtain tokens .debug[[k8s/openid-connect.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openid-connect.md)] --- class: extra-details ## Service Account tokens - The tokens used by Service Accounts are JWT tokens as well - They are signed and verified using a special service account key pair .exercise[ - Extract the token of a service account in the current namespace: ```bash kubectl get secrets -o jsonpath={..token} | base64 -d ``` - Copy-paste the token to a verification service like https://jwt.io - Notice that it says "Invalid Signature" ] .debug[[k8s/openid-connect.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openid-connect.md)] --- class: extra-details ## Verifying Service Account tokens - JSON Web Tokens embed the URL of the "issuer" (=OpenID provider) - The issuer provides its public key through a well-known discovery endpoint (similar to https://accounts.google.com/.well-known/openid-configuration) - There is no such endpoint for the Service Account key pair - But we can provide the public key ourselves for verification .debug[[k8s/openid-connect.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openid-connect.md)] --- class: extra-details ## Verifying a Service Account token - On clusters provisioned with kubeadm, the Service Account key pair is: `/etc/kubernetes/pki/sa.key` (used by the controller manager to generate tokens) `/etc/kubernetes/pki/sa.pub` (used by the API server to validate the same tokens) .exercise[ - Display the public key used to sign Service Account tokens: ```bash sudo cat /etc/kubernetes/pki/sa.pub ``` - Copy-paste the key in the "verify signature" area on https://jwt.io - It should now say "Signature Verified" ] ??? :EN:- Authenticating with OIDC :FR:- S'identifier avec OIDC .debug[[k8s/openid-connect.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openid-connect.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/containers-by-the-water.jpg)] --- name: toc-pod-security-policies class: title Pod Security Policies .nav[ [Previous part](#toc-openid-connect) | [Back to table of contents](#toc-part-7) | [Next part](#toc-securing-the-control-plane) ] .debug[(automatically generated title slide)] --- # Pod Security Policies - By default, our pods and containers can do *everything* (including taking over the entire cluster) - We are going to show an example of a malicious pod - Then we will explain how to avoid this with PodSecurityPolicies - We will enable PodSecurityPolicies on our cluster - We will create a couple of policies (restricted and permissive) - Finally we will see how to use them to improve security on our cluster .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## Setting up a namespace - For simplicity, let's work in a separate namespace - Let's create a new namespace called "green" .exercise[ - Create the "green" namespace: ```bash kubectl create namespace green ``` - Change to that namespace: ```bash kns green ``` ] .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## Creating a basic Deployment - Just to check that everything works correctly, deploy NGINX .exercise[ - Create a Deployment using the official NGINX image: ```bash kubectl create deployment web --image=nginx ``` - Confirm that the Deployment, ReplicaSet, and Pod exist, and that the Pod is running: ```bash kubectl get all ``` ] .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## One example of malicious pods - We will now show an escalation technique in action - We will deploy a DaemonSet that adds our SSH key to the root account (on *each* node of the cluster) - The Pods of the DaemonSet will do so by mounting `/root` from the host .exercise[ - Check the file `k8s/hacktheplanet.yaml` with a text editor: ```bash vim ~/container.training/k8s/hacktheplanet.yaml ``` - If you would like, change the SSH key (by changing the GitHub user name) ] .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## Deploying the malicious pods - Let's deploy our "exploit"! .exercise[ - Create the DaemonSet: ```bash kubectl create -f ~/container.training/k8s/hacktheplanet.yaml ``` - Check that the pods are running: ```bash kubectl get pods ``` - Confirm that the SSH key was added to the node's root account: ```bash sudo cat /root/.ssh/authorized_keys ``` ] .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## Cleaning up - Before setting up our PodSecurityPolicies, clean up that namespace .exercise[ - Remove the DaemonSet: ```bash kubectl delete daemonset hacktheplanet ``` - Remove the Deployment: ```bash kubectl delete deployment web ``` ] .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## Pod Security Policies in theory - To use PSPs, we need to activate their specific *admission controller* - That admission controller will intercept each pod creation attempt - It will look at: - *who/what* is creating the pod - which PodSecurityPolicies they can use - which PodSecurityPolicies can be used by the Pod's ServiceAccount - Then it will compare the Pod with each PodSecurityPolicy one by one - If a PodSecurityPolicy accepts all the parameters of the Pod, it is created - Otherwise, the Pod creation is denied and it won't even show up in `kubectl get pods` .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## Pod Security Policies fine print - With RBAC, using a PSP corresponds to the verb `use` on the PSP (that makes sense, right?) - If no PSP is defined, no Pod can be created (even by cluster admins) - Pods that are already running are *not* affected - If we create a Pod directly, it can use a PSP to which *we* have access - If the Pod is created by e.g. a ReplicaSet or DaemonSet, it's different: - the ReplicaSet / DaemonSet controllers don't have access to *our* policies - therefore, we need to give access to the PSP to the Pod's ServiceAccount .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## Pod Security Policies in practice - We are going to enable the PodSecurityPolicy admission controller - At that point, we won't be able to create any more pods (!) - Then we will create a couple of PodSecurityPolicies - ...And associated ClusterRoles (giving `use` access to the policies) - Then we will create RoleBindings to grant these roles to ServiceAccounts - We will verify that we can't run our "exploit" anymore .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## Enabling Pod Security Policies - To enable Pod Security Policies, we need to enable their *admission plugin* - This is done by adding a flag to the API server - On clusters deployed with `kubeadm`, the control plane runs in static pods - These pods are defined in YAML files located in `/etc/kubernetes/manifests` - Kubelet watches this directory - Each time a file is added/removed there, kubelet creates/deletes the corresponding pod - Updating a file causes the pod to be deleted and recreated .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## Updating the API server flags - Let's edit the manifest for the API server pod .exercise[ - Have a look at the static pods: ```bash ls -l /etc/kubernetes/manifests ``` - Edit the one corresponding to the API server: ```bash sudo vim /etc/kubernetes/manifests/kube-apiserver.yaml ``` <!-- ```wait apiVersion``` --> ] .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## Adding the PSP admission plugin - There should already be a line with `--enable-admission-plugins=...` - Let's add `PodSecurityPolicy` on that line .exercise[ - Locate the line with `--enable-admission-plugins=` - Add `PodSecurityPolicy` It should read: `--enable-admission-plugins=NodeRestriction,PodSecurityPolicy` - Save, quit <!-- ```keys /--enable-admission-plugins=``` ```key ^J``` ```key $``` ```keys a,PodSecurityPolicy``` ```key Escape``` ```keys :wq``` ```key ^J``` --> ] .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## Waiting for the API server to restart - The kubelet detects that the file was modified - It kills the API server pod, and starts a new one - During that time, the API server is unavailable .exercise[ - Wait until the API server is available again ] .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## Check that the admission plugin is active - Normally, we can't create any Pod at this point .exercise[ - Try to create a Pod directly: ```bash kubectl run testpsp1 --image=nginx --restart=Never ``` <!-- ```wait forbidden: no providers available``` --> - Try to create a Deployment: ```bash kubectl create deployment testpsp2 --image=nginx ``` - Look at existing resources: ```bash kubectl get all ``` ] We can get hints at what's happening by looking at the ReplicaSet and Events. .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## Introducing our Pod Security Policies - We will create two policies: - privileged (allows everything) - restricted (blocks some unsafe mechanisms) - For each policy, we also need an associated ClusterRole granting *use* .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## Creating our Pod Security Policies - We have a couple of files, each defining a PSP and associated ClusterRole: - k8s/psp-privileged.yaml: policy `privileged`, role `psp:privileged` - k8s/psp-restricted.yaml: policy `restricted`, role `psp:restricted` .exercise[ - Create both policies and their associated ClusterRoles: ```bash kubectl create -f ~/container.training/k8s/psp-restricted.yaml kubectl create -f ~/container.training/k8s/psp-privileged.yaml ``` ] - The privileged policy comes from [the Kubernetes documentation](https://kubernetes.io/docs/concepts/policy/pod-security-policy/#example-policies) - The restricted policy is inspired by that same documentation page .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## Check that we can create Pods again - We haven't bound the policy to any user yet - But `cluster-admin` can implicitly `use` all policies .exercise[ - Check that we can now create a Pod directly: ```bash kubectl run testpsp3 --image=nginx --restart=Never ``` - Create a Deployment as well: ```bash kubectl create deployment testpsp4 --image=nginx ``` - Confirm that the Deployment is *not* creating any Pods: ```bash kubectl get all ``` ] .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## What's going on? - We can create Pods directly (thanks to our root-like permissions) - The Pods corresponding to a Deployment are created by the ReplicaSet controller - The ReplicaSet controller does *not* have root-like permissions - We need to either: - grant permissions to the ReplicaSet controller *or* - grant permissions to our Pods' ServiceAccount - The first option would allow *anyone* to create pods - The second option will allow us to scope the permissions better .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## Binding the restricted policy - Let's bind the role `psp:restricted` to ServiceAccount `green:default` (aka the default ServiceAccount in the green Namespace) - This will allow Pod creation in the green Namespace (because these Pods will be using that ServiceAccount automatically) .exercise[ - Create the following RoleBinding: ```bash kubectl create rolebinding psp:restricted \ --clusterrole=psp:restricted \ --serviceaccount=green:default ``` ] .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## Trying it out - The Deployments that we created earlier will *eventually* recover (the ReplicaSet controller will retry to create Pods once in a while) - If we create a new Deployment now, it should work immediately .exercise[ - Create a simple Deployment: ```bash kubectl create deployment testpsp5 --image=nginx ``` - Look at the Pods that have been created: ```bash kubectl get all ``` ] .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## Trying to hack the cluster - Let's create the same DaemonSet we used earlier .exercise[ - Create a hostile DaemonSet: ```bash kubectl create -f ~/container.training/k8s/hacktheplanet.yaml ``` - Look at the state of the namespace: ```bash kubectl get all ``` ] .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- class: extra-details ## What's in our restricted policy? - The restricted PSP is similar to the one provided in the docs, but: - it allows containers to run as root - it doesn't drop capabilities - Many containers run as root by default, and would require additional tweaks - Many containers use e.g. `chown`, which requires a specific capability (that's the case for the NGINX official image, for instance) - We still block: hostPath, privileged containers, and much more! .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- class: extra-details ## The case of static pods - If we list the pods in the `kube-system` namespace, `kube-apiserver` is missing - However, the API server is obviously running (otherwise, `kubectl get pods --namespace=kube-system` wouldn't work) - The API server Pod is created directly by kubelet (without going through the PSP admission plugin) - Then, kubelet creates a "mirror pod" representing that Pod in etcd - That "mirror pod" creation goes through the PSP admission plugin - And it gets blocked! - This can be fixed by binding `psp:privileged` to group `system:nodes` .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## .warning[Before moving on...] - Our cluster is currently broken (we can't create pods in namespaces kube-system, default, ...) - We need to either: - disable the PSP admission plugin - allow use of PSP to relevant users and groups - For instance, we could: - bind `psp:restricted` to the group `system:authenticated` - bind `psp:privileged` to the ServiceAccount `kube-system:default` .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- ## Fixing the cluster - Let's disable the PSP admission plugin .exercise[ - Edit the Kubernetes API server static pod manifest - Remove the PSP admission plugin - This can be done with this one-liner: ```bash sudo sed -i s/,PodSecurityPolicy// /etc/kubernetes/manifests/kube-apiserver.yaml ``` ] ??? :EN:- Preventing privilege escalation with Pod Security Policies :FR:- Limiter les droits des conteneurs avec les *Pod Security Policies* .debug[[k8s/podsecuritypolicy.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/podsecuritypolicy.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/distillery-containers.jpg)] --- name: toc-securing-the-control-plane class: title Securing the control plane .nav[ [Previous part](#toc-pod-security-policies) | [Back to table of contents](#toc-part-7) | [Next part](#toc-volumes) ] .debug[(automatically generated title slide)] --- # Securing the control plane - Many components accept connections (and requests) from others: - API server - etcd - kubelet - We must secure these connections: - to deny unauthorized requests - to prevent eavesdropping secrets, tokens, and other sensitive information - Disabling authentication and/or authorization is **strongly discouraged** (but it's possible to do it, e.g. for learning / troubleshooting purposes) .debug[[k8s/control-plane-auth.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/control-plane-auth.md)] --- ## Authentication and authorization - Authentication (checking "who you are") is done with mutual TLS (both the client and the server need to hold a valid certificate) - Authorization (checking "what you can do") is done in different ways - the API server implements a sophisticated permission logic (with RBAC) - some services will defer authorization to the API server (through webhooks) - some services require a certificate signed by a particular CA / sub-CA .debug[[k8s/control-plane-auth.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/control-plane-auth.md)] --- ## In practice - We will review the various communication channels in the control plane - We will describe how they are secured - When TLS certificates are used, we will indicate: - which CA signs them - what their subject (CN) should be, when applicable - We will indicate how to configure security (client- and server-side) .debug[[k8s/control-plane-auth.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/control-plane-auth.md)] --- ## etcd peers - Replication and coordination of etcd happens on a dedicated port (typically port 2380; the default port for normal client connections is 2379) - Authentication uses TLS certificates with a separate sub-CA (otherwise, anyone with a Kubernetes client certificate could access etcd!) - The etcd command line flags involved are: `--peer-client-cert-auth=true` to activate it `--peer-cert-file`, `--peer-key-file`, `--peer-trusted-ca-file` .debug[[k8s/control-plane-auth.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/control-plane-auth.md)] --- ## etcd clients - The only¹ thing that connects to etcd is the API server - Authentication uses TLS certificates with a separate sub-CA (for the same reasons as for etcd inter-peer authentication) - The etcd command line flags involved are: `--client-cert-auth=true` to activate it `--trusted-ca-file`, `--cert-file`, `--key-file` - The API server command line flags involved are: `--etcd-cafile`, `--etcd-certfile`, `--etcd-keyfile` .footnote[¹Technically, there is also the etcd healthcheck. Let's ignore it for now.] .debug[[k8s/control-plane-auth.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/control-plane-auth.md)] --- ## etcd authorization - etcd supports RBAC, but Kubernetes doesn't use it by default (note: etcd RBAC is completely different from Kubernetes RBAC!) - By default, etcd access is "all or nothing" (if you have a valid certificate, you get in) - Be very careful if you use the same root CA for etcd and other things (if etcd trusts the root CA, then anyone with a valid cert gets full etcd access) - For more details, check the following resources: - [etcd documentation on authentication](https://etcd.io/docs/current/op-guide/authentication/) - [PKI The Wrong Way](https://www.youtube.com/watch?v=gcOLDEzsVHI) at KubeCon NA 2020 .debug[[k8s/control-plane-auth.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/control-plane-auth.md)] --- ## API server clients - The API server has a sophisticated authentication and authorization system - For connections coming from other components of the control plane: - authentication uses certificates (trusting the certificates' subject or CN) - authorization uses whatever mechanism is enabled (most oftentimes, RBAC) - The relevant API server flags are: `--client-ca-file`, `--tls-cert-file`, `--tls-private-key-file` - Each component connecting to the API server takes a `--kubeconfig` flag (to specify a kubeconfig file containing the CA cert, client key, and client cert) - Yes, that kubeconfig file follows the same format as our `~/.kube/config` file! .debug[[k8s/control-plane-auth.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/control-plane-auth.md)] --- ## Kubelet and API server - Communication between kubelet and API server can be established both ways - Kubelet → API server: - kubelet registers itself ("hi, I'm node42, do you have work for me?") - connection is kept open and re-established if it breaks - that's how the kubelet knows which pods to start/stop - API server → kubelet: - used to retrieve logs, exec, attach to containers .debug[[k8s/control-plane-auth.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/control-plane-auth.md)] --- ## Kubelet → API server - Kubelet is started with `--kubeconfig` with API server information - The client certificate of the kubelet will typically have: `CN=system:node:<nodename>` and groups `O=system:nodes` - Nothing special on the API server side (it will authenticate like any other client) .debug[[k8s/control-plane-auth.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/control-plane-auth.md)] --- ## API server → kubelet - Kubelet is started with the flag `--client-ca-file` (typically using the same CA as the API server) - API server will use a dedicated key pair when contacting kubelet (specified with `--kubelet-client-certificate` and `--kubelet-client-key`) - Authorization uses webhooks (enabled with `--authorization-mode=Webhook` on kubelet) - The webhook server is the API server itself (the kubelet sends back a request to the API server to ask, "can this person do that?") .debug[[k8s/control-plane-auth.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/control-plane-auth.md)] --- ## Scheduler - The scheduler connects to the API server like an ordinary client - The certificate of the scheduler will have `CN=system:kube-scheduler` .debug[[k8s/control-plane-auth.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/control-plane-auth.md)] --- ## Controller manager - The controller manager is also a normal client to the API server - Its certificate will have `CN=system:kube-controller-manager` - If we use the CSR API, the controller manager needs the CA cert and key (passed with flags `--cluster-signing-cert-file` and `--cluster-signing-key-file`) - We usually want the controller manager to generate tokens for service accounts - These tokens deserve some details (on the next slide!) .debug[[k8s/control-plane-auth.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/control-plane-auth.md)] --- class: extra-details ## How are these permissions set up? - A bunch of roles and bindings are defined as constants in the API server code: [auth/authorizer/rbac/bootstrappolicy/policy.go](https://github.com/kubernetes/kubernetes/blob/release-1.19/plugin/pkg/auth/authorizer/rbac/bootstrappolicy/policy.go#L188) - They are created automatically when the API server starts: [registry/rbac/rest/storage_rbac.go](https://github.com/kubernetes/kubernetes/blob/release-1.19/pkg/registry/rbac/rest/storage_rbac.go#L140) - We must use the correct Common Names (`CN`) for the control plane certificates (since the bindings defined above refer to these common names) .debug[[k8s/control-plane-auth.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/control-plane-auth.md)] --- ## Service account tokens - Each time we create a service account, the controller manager generates a token - These tokens are JWT tokens, signed with a particular key - These tokens are used for authentication with the API server (and therefore, the API server needs to be able to verify their integrity) - This uses another keypair: - the private key (used for signature) is passed to the controller manager <br/>(using flags `--service-account-private-key-file` and `--root-ca-file`) - the public key (used for verification) is passed to the API server <br/>(using flag `--service-account-key-file`) .debug[[k8s/control-plane-auth.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/control-plane-auth.md)] --- ## kube-proxy - kube-proxy is "yet another API server client" - In many clusters, it runs as a Daemon Set - In that case, it will have its own Service Account and associated permissions - It will authenticate using the token of that Service Account .debug[[k8s/control-plane-auth.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/control-plane-auth.md)] --- ## Webhooks - We mentioned webhooks earlier; how does that really work? - The Kubernetes API has special resource types to check permissions - One of them is SubjectAccessReview - To check if a particular user can do a particular action on a particular resource: - we prepare a SubjectAccessReview object - we send that object to the API server - the API server responds with allow/deny (and optional explanations) - Using webhooks for authorization = sending SAR to authorize each request .debug[[k8s/control-plane-auth.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/control-plane-auth.md)] --- ## Subject Access Review Here is an example showing how to check if `jean.doe` can `get` some `pods` in `kube-system`: ```bash kubectl -v9 create -f- <<EOF apiVersion: authorization.k8s.io/v1beta1 kind: SubjectAccessReview spec: user: jean.doe group: - foo - bar resourceAttributes: #group: blah.k8s.io namespace: kube-system resource: pods verb: get #name: web-xyz1234567-pqr89 EOF ``` ??? :EN:- Control plane authentication :FR:- Sécurisation du plan de contrôle .debug[[k8s/control-plane-auth.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/control-plane-auth.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/lots-of-containers.jpg)] --- name: toc-volumes class: title Volumes .nav[ [Previous part](#toc-securing-the-control-plane) | [Back to table of contents](#toc-part-8) | [Next part](#toc-exercise--configmaps) ] .debug[(automatically generated title slide)] --- # Volumes - Volumes are special directories that are mounted in containers - Volumes can have many different purposes: - share files and directories between containers running on the same machine - share files and directories between containers and their host - centralize configuration information in Kubernetes and expose it to containers - manage credentials and secrets and expose them securely to containers - store persistent data for stateful services - access storage systems (like Ceph, EBS, NFS, Portworx, and many others) .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- class: extra-details ## Kubernetes volumes vs. Docker volumes - Kubernetes and Docker volumes are very similar (the [Kubernetes documentation](https://kubernetes.io/docs/concepts/storage/volumes/) says otherwise ... <br/> but it refers to Docker 1.7, which was released in 2015!) - Docker volumes allow us to share data between containers running on the same host - Kubernetes volumes allow us to share data between containers in the same pod - Both Docker and Kubernetes volumes enable access to storage systems - Kubernetes volumes are also used to expose configuration and secrets - Docker has specific concepts for configuration and secrets <br/> (but under the hood, the technical implementation is similar) - If you're not familiar with Docker volumes, you can safely ignore this slide! .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- ## Volumes ≠ Persistent Volumes - Volumes and Persistent Volumes are related, but very different! - *Volumes*: - appear in Pod specifications (we'll see that in a few slides) - do not exist as API resources (**cannot** do `kubectl get volumes`) - *Persistent Volumes*: - are API resources (**can** do `kubectl get persistentvolumes`) - correspond to concrete volumes (e.g. on a SAN, EBS, etc.) - cannot be associated with a Pod directly; but through a Persistent Volume Claim - won't be discussed further in this section .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- ## Adding a volume to a Pod - We will start with the simplest Pod manifest we can find - We will add a volume to that Pod manifest - We will mount that volume in a container in the Pod - By default, this volume will be an `emptyDir` (an empty directory) - It will "shadow" the directory where it's mounted .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- ## Our basic Pod ```yaml apiVersion: v1 kind: Pod metadata: name: nginx-without-volume spec: containers: - name: nginx image: nginx ``` This is a MVP! (Minimum Viable Pod😉) It runs a single NGINX container. .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- ## Trying the basic pod .exercise[ - Create the Pod: ```bash kubectl create -f ~/container.training/k8s/nginx-1-without-volume.yaml ``` <!-- ```bash kubectl wait pod/nginx-without-volume --for condition=ready ``` --> - Get its IP address: ```bash IPADDR=$(kubectl get pod nginx-without-volume -o jsonpath={.status.podIP}) ``` - Send a request with curl: ```bash curl $IPADDR ``` ] (We should see the "Welcome to NGINX" page.) .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- ## Adding a volume - We need to add the volume in two places: - at the Pod level (to declare the volume) - at the container level (to mount the volume) - We will declare a volume named `www` - No type is specified, so it will default to `emptyDir` (as the name implies, it will be initialized as an empty directory at pod creation) - In that pod, there is also a container named `nginx` - That container mounts the volume `www` to path `/usr/share/nginx/html/` .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- ## The Pod with a volume ```yaml apiVersion: v1 kind: Pod metadata: name: nginx-with-volume spec: volumes: - name: www containers: - name: nginx image: nginx volumeMounts: - name: www mountPath: /usr/share/nginx/html/ ``` .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- ## Trying the Pod with a volume .exercise[ - Create the Pod: ```bash kubectl create -f ~/container.training/k8s/nginx-2-with-volume.yaml ``` <!-- ```bash kubectl wait pod/nginx-with-volume --for condition=ready ``` --> - Get its IP address: ```bash IPADDR=$(kubectl get pod nginx-with-volume -o jsonpath={.status.podIP}) ``` - Send a request with curl: ```bash curl $IPADDR ``` ] (We should now see a "403 Forbidden" error page.) .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- ## Populating the volume with another container - Let's add another container to the Pod - Let's mount the volume in *both* containers - That container will populate the volume with static files - NGINX will then serve these static files - To populate the volume, we will clone the Spoon-Knife repository - this repository is https://github.com/octocat/Spoon-Knife - it's very popular (more than 100K stars!) .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- ## Sharing a volume between two containers .small[ ```yaml apiVersion: v1 kind: Pod metadata: name: nginx-with-git spec: volumes: - name: www containers: - name: nginx image: nginx volumeMounts: - name: www mountPath: /usr/share/nginx/html/ - name: git image: alpine command: [ "sh", "-c", "apk add git && git clone https://github.com/octocat/Spoon-Knife /www" ] volumeMounts: - name: www mountPath: /www/ restartPolicy: OnFailure ``` ] .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- ## Sharing a volume, explained - We added another container to the pod - That container mounts the `www` volume on a different path (`/www`) - It uses the `alpine` image - When started, it installs `git` and clones the `octocat/Spoon-Knife` repository (that repository contains a tiny HTML website) - As a result, NGINX now serves this website .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- ## Trying the shared volume - This one will be time-sensitive! - We need to catch the Pod IP address *as soon as it's created* - Then send a request to it *as fast as possible* .exercise[ - Watch the pods (so that we can catch the Pod IP address) ```bash kubectl get pods -o wide --watch ``` <!-- ```wait NAME``` ```tmux split-pane -v``` --> ] .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- ## Shared volume in action .exercise[ - Create the pod: ```bash kubectl create -f ~/container.training/k8s/nginx-3-with-git.yaml ``` <!-- ```bash kubectl wait pod/nginx-with-git --for condition=initialized``` ```bash IP=$(kubectl get pod nginx-with-git -o jsonpath={.status.podIP})``` --> - As soon as we see its IP address, access it: ```bash curl `$IP` ``` <!-- ```bash /bin/sleep 5``` --> - A few seconds later, the state of the pod will change; access it again: ```bash curl `$IP` ``` ] The first time, we should see "403 Forbidden". The second time, we should see the HTML file from the Spoon-Knife repository. .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- ## Explanations - Both containers are started at the same time - NGINX starts very quickly (it can serve requests immediately) - But at this point, the volume is empty (NGINX serves "403 Forbidden") - The other containers installs git and clones the repository (this takes a bit longer) - When the other container is done, the volume holds the repository (NGINX serves the HTML file) .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- ## The devil is in the details - The default `restartPolicy` is `Always` - This would cause our `git` container to run again ... and again ... and again (with an exponential back-off delay, as explained [in the documentation](https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle/#restart-policy)) - That's why we specified `restartPolicy: OnFailure` .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- ## Inconsistencies - There is a short period of time during which the website is not available (because the `git` container hasn't done its job yet) - With a bigger website, we could get inconsistent results (where only a part of the content is ready) - In real applications, this could cause incorrect results - How can we avoid that? .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- ## Init Containers - We can define containers that should execute *before* the main ones - They will be executed in order (instead of in parallel) - They must all succeed before the main containers are started - This is *exactly* what we need here! - Let's see one in action .footnote[See [Init Containers](https://kubernetes.io/docs/concepts/workloads/pods/init-containers/) documentation for all the details.] .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- ## Defining Init Containers .small[ ```yaml apiVersion: v1 kind: Pod metadata: name: nginx-with-init spec: volumes: - name: www containers: - name: nginx image: nginx volumeMounts: - name: www mountPath: /usr/share/nginx/html/ initContainers: - name: git image: alpine command: [ "sh", "-c", "apk add git && git clone https://github.com/octocat/Spoon-Knife /www" ] volumeMounts: - name: www mountPath: /www/ ``` ] .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- ## Trying the init container .exercise[ - Create the pod: ```bash kubectl create -f ~/container.training/k8s/nginx-4-with-init.yaml ``` - Try to send HTTP requests as soon as the pod comes up <!-- ```key ^D``` ```key ^C``` --> ] - This time, instead of "403 Forbidden" we get a "connection refused" - NGINX doesn't start until the git container has done its job - We never get inconsistent results (a "half-ready" container) .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- ## Other uses of init containers - Load content - Generate configuration (or certificates) - Database migrations - Waiting for other services to be up (to avoid flurry of connection errors in main container) - etc. .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- ## Volume lifecycle - The lifecycle of a volume is linked to the pod's lifecycle - This means that a volume is created when the pod is created - This is mostly relevant for `emptyDir` volumes (other volumes, like remote storage, are not "created" but rather "attached" ) - A volume survives across container restarts - A volume is destroyed (or, for remote storage, detached) when the pod is destroyed ??? :EN:- Sharing data between containers with volumes :EN:- When and how to use Init Containers :FR:- Partager des données grâce aux volumes :FR:- Quand et comment utiliser un *Init Container* .debug[[k8s/volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/volumes.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/plastic-containers.JPG)] --- name: toc-exercise--configmaps class: title Exercise — ConfigMaps .nav[ [Previous part](#toc-volumes) | [Back to table of contents](#toc-part-8) | [Next part](#toc-building-images-with-the-docker-engine) ] .debug[(automatically generated title slide)] --- # Exercise — ConfigMaps - In this exercise, we will use a ConfigMap to store static assets - While there are some circumstances where this can be useful ... - ... It is generally **not** a good idea! - Once you've read that warning, check the next slide for instructions :) .debug[[k8s/exercise-configmap.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/exercise-configmap.md)] --- ## Exercise — ConfigMaps This will use the wordsmith app. We want to store the static files (served by `web`) in a ConfigMap. 1. Transform the `static` directory into a ConfigMap. (https://github.com/jpetazzo/wordsmith/tree/master/web/static) 2. Find out where that `static` directory is located in `web`. (for instance, by using `kubectl exec` to investigate) 3. Update the definition of the `web` Deployment to use the ConfigMap. (note: fonts and images will be broken; that's OK) 4. Make a minor change in the ConfigMap (e.g. change the text color) .debug[[k8s/exercise-configmap.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/exercise-configmap.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/train-of-containers-1.jpg)] --- name: toc-building-images-with-the-docker-engine class: title Building images with the Docker Engine .nav[ [Previous part](#toc-exercise--configmaps) | [Back to table of contents](#toc-part-8) | [Next part](#toc-building-images-with-kaniko) ] .debug[(automatically generated title slide)] --- # Building images with the Docker Engine - Until now, we have built our images manually, directly on a node - We are going to show how to build images from within the cluster (by executing code in a container controlled by Kubernetes) - We are going to use the Docker Engine for that purpose - To access the Docker Engine, we will mount the Docker socket in our container - After building the image, we will push it to our self-hosted registry .debug[[k8s/build-with-docker.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/build-with-docker.md)] --- ## Resource specification for our builder pod .small[ ```yaml apiVersion: v1 kind: Pod metadata: name: build-image spec: restartPolicy: OnFailure containers: - name: docker-build image: docker env: - name: REGISTRY_PORT value: "`3XXXX`" command: ["sh", "-c"] args: - | apk add --no-cache git && mkdir /workspace && git clone https://github.com/jpetazzo/container.training /workspace && docker build -t localhost:$REGISTRY_PORT/worker /workspace/dockercoins/worker && docker push localhost:$REGISTRY_PORT/worker volumeMounts: - name: docker-socket mountPath: /var/run/docker.sock volumes: - name: docker-socket hostPath: path: /var/run/docker.sock ``` ] .debug[[k8s/build-with-docker.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/build-with-docker.md)] --- ## Breaking down the pod specification (1/2) - `restartPolicy: OnFailure` prevents the build from running in an infinite lopo - We use the `docker` image (so that the `docker` CLI is available) - We rely on the fact that the `docker` image is based on `alpine` (which is why we use `apk` to install `git`) - The port for the registry is passed through an environment variable (this avoids repeating it in the specification, which would be error-prone) .warning[The environment variable has to be a string, so the `"`s are mandatory!] .debug[[k8s/build-with-docker.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/build-with-docker.md)] --- ## Breaking down the pod specification (2/2) - The volume `docker-socket` is declared with a `hostPath`, indicating a bind-mount - It is then mounted in the container onto the default Docker socket path - We show a interesting way to specify the commands to run in the container: - the command executed will be `sh -c <args>` - `args` is a list of strings - `|` is used to pass a multi-line string in the YAML file .debug[[k8s/build-with-docker.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/build-with-docker.md)] --- ## Running our pod - Let's try this out! .exercise[ - Check the port used by our self-hosted registry: ```bash kubectl get svc registry ``` - Edit `~/container.training/k8s/docker-build.yaml` to put the port number - Schedule the pod by applying the resource file: ```bash kubectl apply -f ~/container.training/k8s/docker-build.yaml ``` - Watch the logs: ```bash stern build-image ``` <!-- ```longwait latest: digest: sha256:``` ```key ^C``` --> ] .debug[[k8s/build-with-docker.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/build-with-docker.md)] --- ## What's missing? What do we need to change to make this production-ready? - Build from a long-running container (e.g. a `Deployment`) triggered by web hooks (the payload of the web hook could indicate the repository to build) - Build a specific branch or tag; tag image accordingly - Handle repositories where the Dockerfile is not at the root (or containing multiple Dockerfiles) - Expose build logs so that troubleshooting is straightforward -- 🤔 That seems like a lot of work! -- That's why services like Docker Hub (with [automated builds](https://docs.docker.com/docker-hub/builds/)) are helpful. <br/> They handle the whole "code repository → Docker image" workflow. .debug[[k8s/build-with-docker.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/build-with-docker.md)] --- ## Things to be aware of - This is talking directly to a node's Docker Engine to build images - It bypasses resource allocation mechanisms used by Kubernetes (but you can use *taints* and *tolerations* to dedicate builder nodes) - Be careful not to introduce conflicts when naming images (e.g. do not allow the user to specify the image names!) - Your builds are going to be *fast* (because they will leverage Docker's caching system) .debug[[k8s/build-with-docker.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/build-with-docker.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/train-of-containers-2.jpg)] --- name: toc-building-images-with-kaniko class: title Building images with Kaniko .nav[ [Previous part](#toc-building-images-with-the-docker-engine) | [Back to table of contents](#toc-part-8) | [Next part](#toc-managing-configuration) ] .debug[(automatically generated title slide)] --- # Building images with Kaniko - [Kaniko](https://github.com/GoogleContainerTools/kaniko) is an open source tool to build container images within Kubernetes - It can build an image using any standard Dockerfile - The resulting image can be pushed to a registry or exported as a tarball - It doesn't require any particular privilege (and can therefore run in a regular container in a regular pod) - This combination of features is pretty unique (most other tools use different formats, or require elevated privileges) .debug[[k8s/build-with-kaniko.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/build-with-kaniko.md)] --- ## Kaniko in practice - Kaniko provides an "executor image", `gcr.io/kaniko-project/executor` - When running that image, we need to specify at least: - the path to the build context (=the directory with our Dockerfile) - the target image name (including the registry address) - Simplified example: ``` docker run \ -v ...:/workspace gcr.io/kaniko-project/executor \ --context=/workspace \ --destination=registry:5000/image_name:image_tag ``` .debug[[k8s/build-with-kaniko.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/build-with-kaniko.md)] --- ## Running Kaniko in a Docker container - Let's build the image for the DockerCoins `worker` service with Kaniko .exercise[ - Find the port number for our self-hosted registry: ```bash kubectl get svc registry PORT=$(kubectl get svc registry -o json | jq .spec.ports[0].nodePort) ``` - Run Kaniko: ```bash docker run --net host \ -v ~/container.training/dockercoins/worker:/workspace \ gcr.io/kaniko-project/executor \ --context=/workspace \ --destination=127.0.0.1:$PORT/worker-kaniko:latest ``` ] We use `--net host` so that we can connect to the registry over `127.0.0.1`. .debug[[k8s/build-with-kaniko.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/build-with-kaniko.md)] --- ## Running Kaniko in a Kubernetes pod - We need to mount or copy the build context to the pod - We are going to build straight from the git repository (to avoid depending on files sitting on a node, outside of containers) - We need to `git clone` the repository before running Kaniko - We are going to use two containers sharing a volume: - a first container to `git clone` the repository to the volume - a second container to run Kaniko, using the content of the volume - However, we need the first container to be done before running the second one 🤔 How could we do that? .debug[[k8s/build-with-kaniko.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/build-with-kaniko.md)] --- ## [Init Containers](https://kubernetes.io/docs/concepts/workloads/pods/init-containers/) to the rescue - A pod can have a list of `initContainers` - `initContainers` are executed in the specified order - Each Init Container needs to complete (exit) successfully - If any Init Container fails (non-zero exit status) the pod fails (what happens next depends on the pod's `restartPolicy`) - After all Init Containers have run successfully, normal `containers` are started - We are going to execute the `git clone` operation in an Init Container .debug[[k8s/build-with-kaniko.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/build-with-kaniko.md)] --- ## Our Kaniko builder pod .small[ ```yaml apiVersion: v1 kind: Pod metadata: name: kaniko-build spec: initContainers: - name: git-clone image: alpine command: ["sh", "-c"] args: - | apk add --no-cache git && git clone git://github.com/jpetazzo/container.training /workspace volumeMounts: - name: workspace mountPath: /workspace containers: - name: build-image image: gcr.io/kaniko-project/executor:latest args: - "--context=/workspace/dockercoins/rng" - "--insecure" - "--destination=registry:5000/rng-kaniko:latest" volumeMounts: - name: workspace mountPath: /workspace volumes: - name: workspace ``` ] .debug[[k8s/build-with-kaniko.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/build-with-kaniko.md)] --- ## Explanations - We define a volume named `workspace` (using the default `emptyDir` provider) - That volume is mounted to `/workspace` in both our containers - The `git-clone` Init Container installs `git` and runs `git clone` - The `build-image` container executes Kaniko - We use our self-hosted registry DNS name (`registry`) - We add `--insecure` to use plain HTTP to talk to the registry .debug[[k8s/build-with-kaniko.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/build-with-kaniko.md)] --- ## Running our Kaniko builder pod - The YAML for the pod is in `k8s/kaniko-build.yaml` .exercise[ - Create the pod: ```bash kubectl apply -f ~/container.training/k8s/kaniko-build.yaml ``` - Watch the logs: ```bash stern kaniko ``` <!-- ```longwait registry:5000/rng-kaniko:latest:``` ```key ^C``` --> ] .debug[[k8s/build-with-kaniko.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/build-with-kaniko.md)] --- ## Discussion *What should we use? The Docker build technique shown earlier? Kaniko? Something else?* - The Docker build technique is simple, and has the potential to be very fast - However, it doesn't play nice with Kubernetes resource limits - Kaniko plays nice with resource limits - However, it's slower (there is no caching at all) - The ultimate building tool will probably be [Jessica Frazelle](https://twitter.com/jessfraz)'s [img](https://github.com/genuinetools/img) builder (it depends on upstream changes that are not in Kubernetes 1.11.2 yet) But ... is it all about [speed](https://github.com/AkihiroSuda/buildbench/issues/1)? (No!) .debug[[k8s/build-with-kaniko.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/build-with-kaniko.md)] --- ## The big picture - For starters: the [Docker Hub automated builds](https://docs.docker.com/docker-hub/builds/) are very easy to set up - link a GitHub repository with the Docker Hub - each time you push to GitHub, an image gets build on the Docker Hub - If this doesn't work for you: why? - too slow (I'm far from `us-east-1`!) → consider using your cloud provider's registry - I'm not using a cloud provider → ok, perhaps you need to self-host then - I need fancy features (e.g. CI) → consider something like GitLab .debug[[k8s/build-with-kaniko.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/build-with-kaniko.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/two-containers-on-a-truck.jpg)] --- name: toc-managing-configuration class: title Managing configuration .nav[ [Previous part](#toc-building-images-with-kaniko) | [Back to table of contents](#toc-part-9) | [Next part](#toc-managing-secrets) ] .debug[(automatically generated title slide)] --- # Managing configuration - Some applications need to be configured (obviously!) - There are many ways for our code to pick up configuration: - command-line arguments - environment variables - configuration files - configuration servers (getting configuration from a database, an API...) - ... and more (because programmers can be very creative!) - How can we do these things with containers and Kubernetes? .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Passing configuration to containers - There are many ways to pass configuration to code running in a container: - baking it into a custom image - command-line arguments - environment variables - injecting configuration files - exposing it over the Kubernetes API - configuration servers - Let's review these different strategies! .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Baking custom images - Put the configuration in the image (it can be in a configuration file, but also `ENV` or `CMD` actions) - It's easy! It's simple! - Unfortunately, it also has downsides: - multiplication of images - different images for dev, staging, prod ... - minor reconfigurations require a whole build/push/pull cycle - Avoid doing it unless you don't have the time to figure out other options .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Command-line arguments - Indicate what should run in the container - Pass `command` and/or `args` in the container options in a Pod's template - Both `command` and `args` are arrays - Example ([source](https://github.com/jpetazzo/container.training/blob/main/k8s/consul-1.yaml#L70)): ```yaml args: - "agent" - "-bootstrap-expect=3" - "-retry-join=provider=k8s label_selector=\"app=consul\" namespace=\"$(NS)\"" - "-client=0.0.0.0" - "-data-dir=/consul/data" - "-server" - "-ui" ``` .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## `args` or `command`? - Use `command` to override the `ENTRYPOINT` defined in the image - Use `args` to keep the `ENTRYPOINT` defined in the image (the parameters specified in `args` are added to the `ENTRYPOINT`) - In doubt, use `command` - It is also possible to use *both* `command` and `args` (they will be strung together, just like `ENTRYPOINT` and `CMD`) - See the [docs](https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#notes) to see how they interact together .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Command-line arguments, pros & cons - Works great when options are passed directly to the running program (otherwise, a wrapper script can work around the issue) - Works great when there aren't too many parameters (to avoid a 20-lines `args` array) - Requires documentation and/or understanding of the underlying program ("which parameters and flags do I need, again?") - Well-suited for mandatory parameters (without default values) - Not ideal when we need to pass a real configuration file anyway .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Environment variables - Pass options through the `env` map in the container specification - Example: ```yaml env: - name: ADMIN_PORT value: "8080" - name: ADMIN_AUTH value: Basic - name: ADMIN_CRED value: "admin:0pensesame!" ``` .warning[`value` must be a string! Make sure that numbers and fancy strings are quoted.] 🤔 Why this weird `{name: xxx, value: yyy}` scheme? It will be revealed soon! .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## The downward API - In the previous example, environment variables have fixed values - We can also use a mechanism called the *downward API* - The downward API allows exposing pod or container information - either through special files (we won't show that for now) - or through environment variables - The value of these environment variables is computed when the container is started - Remember: environment variables won't (can't) change after container start - Let's see a few concrete examples! .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Exposing the pod's namespace ```yaml - name: MY_POD_NAMESPACE valueFrom: fieldRef: fieldPath: metadata.namespace ``` - Useful to generate FQDN of services (in some contexts, a short name is not enough) - For instance, the two commands should be equivalent: ``` curl api-backend curl api-backend.$MY_POD_NAMESPACE.svc.cluster.local ``` .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Exposing the pod's IP address ```yaml - name: MY_POD_IP valueFrom: fieldRef: fieldPath: status.podIP ``` - Useful if we need to know our IP address (we could also read it from `eth0`, but this is more solid) .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Exposing the container's resource limits ```yaml - name: MY_MEM_LIMIT valueFrom: resourceFieldRef: containerName: test-container resource: limits.memory ``` - Useful for runtimes where memory is garbage collected - Example: the JVM (the memory available to the JVM should be set with the `-Xmx ` flag) - Best practice: set a memory limit, and pass it to the runtime - Note: recent versions of the JVM can do this automatically (see [JDK-8146115](https://bugs.java.com/bugdatabase/view_bug.do?bug_id=JDK-8146115)) and [this blog post](https://very-serio.us/2017/12/05/running-jvms-in-kubernetes/) for detailed examples) .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## More about the downward API - [This documentation page](https://kubernetes.io/docs/tasks/inject-data-application/environment-variable-expose-pod-information/) tells more about these environment variables - And [this one](https://kubernetes.io/docs/tasks/inject-data-application/downward-api-volume-expose-pod-information/) explains the other way to use the downward API (through files that get created in the container filesystem) - That second link also includes a list of all the fields that can be used with the downward API .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Environment variables, pros and cons - Works great when the running program expects these variables - Works great for optional parameters with reasonable defaults (since the container image can provide these defaults) - Sort of auto-documented (we can see which environment variables are defined in the image, and their values) - Can be (ab)used with longer values ... - ... You *can* put an entire Tomcat configuration file in an environment ... - ... But *should* you? (Do it if you really need to, we're not judging! But we'll see better ways.) .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Injecting configuration files - Sometimes, there is no way around it: we need to inject a full config file - Kubernetes provides a mechanism for that purpose: `configmaps` - A configmap is a Kubernetes resource that exists in a namespace - Conceptually, it's a key/value map (values are arbitrary strings) - We can think about them in (at least) two different ways: - as holding entire configuration file(s) - as holding individual configuration parameters *Note: to hold sensitive information, we can use "Secrets", which are another type of resource behaving very much like configmaps. We'll cover them just after!* .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Configmaps storing entire files - In this case, each key/value pair corresponds to a configuration file - Key = name of the file - Value = content of the file - There can be one key/value pair, or as many as necessary (for complex apps with multiple configuration files) - Examples: ``` # Create a configmap with a single key, "app.conf" kubectl create configmap my-app-config --from-file=app.conf # Create a configmap with a single key, "app.conf" but another file kubectl create configmap my-app-config --from-file=app.conf=app-prod.conf # Create a configmap with multiple keys (one per file in the config.d directory) kubectl create configmap my-app-config --from-file=config.d/ ``` .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Configmaps storing individual parameters - In this case, each key/value pair corresponds to a parameter - Key = name of the parameter - Value = value of the parameter - Examples: ``` # Create a configmap with two keys kubectl create cm my-app-config \ --from-literal=foreground=red \ --from-literal=background=blue # Create a configmap from a file containing key=val pairs kubectl create cm my-app-config \ --from-env-file=app.conf ``` .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Exposing configmaps to containers - Configmaps can be exposed as plain files in the filesystem of a container - this is achieved by declaring a volume and mounting it in the container - this is particularly effective for configmaps containing whole files - Configmaps can be exposed as environment variables in the container - this is achieved with the downward API - this is particularly effective for configmaps containing individual parameters - Let's see how to do both! .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Passing a configuration file with a configmap - We will start a load balancer powered by HAProxy - We will use the [official `haproxy` image](https://hub.docker.com/_/haproxy/) - It expects to find its configuration in `/usr/local/etc/haproxy/haproxy.cfg` - We will provide a simple HAproxy configuration, `k8s/haproxy.cfg` - It listens on port 80, and load balances connections between IBM and Google .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Creating the configmap .exercise[ - Go to the `k8s` directory in the repository: ```bash cd ~/container.training/k8s ``` - Create a configmap named `haproxy` and holding the configuration file: ```bash kubectl create configmap haproxy --from-file=haproxy.cfg ``` - Check what our configmap looks like: ```bash kubectl get configmap haproxy -o yaml ``` ] .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Using the configmap We are going to use the following pod definition: ```yaml apiVersion: v1 kind: Pod metadata: name: haproxy spec: volumes: - name: config configMap: name: haproxy containers: - name: haproxy image: haproxy volumeMounts: - name: config mountPath: /usr/local/etc/haproxy/ ``` .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Using the configmap - The resource definition from the previous slide is in `k8s/haproxy.yaml` .exercise[ - Create the HAProxy pod: ```bash kubectl apply -f ~/container.training/k8s/haproxy.yaml ``` <!-- ```hide kubectl wait pod haproxy --for condition=ready``` --> - Check the IP address allocated to the pod: ```bash kubectl get pod haproxy -o wide IP=$(kubectl get pod haproxy -o json | jq -r .status.podIP) ``` ] .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Testing our load balancer - The load balancer will send: - half of the connections to Google - the other half to IBM .exercise[ - Access the load balancer a few times: ```bash curl $IP curl $IP curl $IP ``` ] We should see connections served by Google, and others served by IBM. <br/> (Each server sends us a redirect page. Look at the URL that they send us to!) .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Exposing configmaps with the downward API - We are going to run a Docker registry on a custom port - By default, the registry listens on port 5000 - This can be changed by setting environment variable `REGISTRY_HTTP_ADDR` - We are going to store the port number in a configmap - Then we will expose that configmap as a container environment variable .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Creating the configmap .exercise[ - Our configmap will have a single key, `http.addr`: ```bash kubectl create configmap registry --from-literal=http.addr=0.0.0.0:80 ``` - Check our configmap: ```bash kubectl get configmap registry -o yaml ``` ] .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Using the configmap We are going to use the following pod definition: ```yaml apiVersion: v1 kind: Pod metadata: name: registry spec: containers: - name: registry image: registry env: - name: REGISTRY_HTTP_ADDR valueFrom: configMapKeyRef: name: registry key: http.addr ``` .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- ## Using the configmap - The resource definition from the previous slide is in `k8s/registry.yaml` .exercise[ - Create the registry pod: ```bash kubectl apply -f ~/container.training/k8s/registry.yaml ``` <!-- ```hide kubectl wait pod registry --for condition=ready``` --> - Check the IP address allocated to the pod: ```bash kubectl get pod registry -o wide IP=$(kubectl get pod registry -o json | jq -r .status.podIP) ``` - Confirm that the registry is available on port 80: ```bash curl $IP/v2/_catalog ``` ] ??? :EN:- Managing application configuration :EN:- Exposing configuration with the downward API :EN:- Exposing configuration with Config Maps :FR:- Gérer la configuration des applications :FR:- Configuration au travers de la *downward API* :FR:- Configurer les applications avec des *Config Maps* .debug[[k8s/configuration.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/configuration.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/wall-of-containers.jpeg)] --- name: toc-managing-secrets class: title Managing secrets .nav[ [Previous part](#toc-managing-configuration) | [Back to table of contents](#toc-part-9) | [Next part](#toc-stateful-sets) ] .debug[(automatically generated title slide)] --- # Managing secrets - Sometimes our code needs sensitive information: - passwords - API tokens - TLS keys - ... - *Secrets* can be used for that purpose - Secrets and ConfigMaps are very similar .debug[[k8s/secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/secrets.md)] --- ## Similarities between ConfigMap and Secrets - ConfigMap and Secrets are key-value maps (a Secret can contain zero, one, or many key-value pairs) - They can both be exposed with the downward API or volumes - They can both be created with YAML or with a CLI command (`kubectl create configmap` / `kubectl create secret`) .debug[[k8s/secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/secrets.md)] --- ## ConfigMap and Secrets are different resources - They can have different RBAC permissions (e.g. the default `view` role can read ConfigMaps but not Secrets) - They indicate a different *intent*: *"You should use secrets for things which are actually secret like API keys, credentials, etc., and use config map for not-secret configuration data."* *"In the future there will likely be some differentiators for secrets like rotation or support for backing the secret API w/ HSMs, etc."* (Source: [the author of both features](https://stackoverflow.com/a/36925553/580281 )) .debug[[k8s/secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/secrets.md)] --- ## Secrets have an optional *type* - The type indicates which keys must exist in the secrets, for instance: `kubernetes.io/tls` requires `tls.crt` and `tls.key` `kubernetes.io/basic-auth` requires `username` and `password` `kubernetes.io/ssh-auth` requires `ssh-privatekey` `kubernetes.io/dockerconfigjson` requires `.dockerconfigjson` `kubernetes.io/service-account-token` requires `token`, `namespace`, `ca.crt` (the whole list is in [the documentation](https://kubernetes.io/docs/concepts/configuration/secret/#secret-types)) - This is merely for our (human) convenience: “Ah yes, this secret is a ...” .debug[[k8s/secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/secrets.md)] --- ## Accessing private repositories - Let's see how to access an image on private registry! - These images are protected by a username + password (on some registries, it's token + password, but it's the same thing) - To access a private image, we need to: - create a secret - reference that secret in a Pod template - or reference that secret in a ServiceAccount used by a Pod .debug[[k8s/secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/secrets.md)] --- ## In practice - Let's try to access an image on a private registry! - image = docker-registry.enix.io/jpetazzo/private:latest - user = reader - password = VmQvqdtXFwXfyy4Jb5DR .exercise[ - Create a Deployment using that image: ```bash kubectl create deployment priv \ --image=docker-registry.enix.io/jpetazzo/private ``` - Check that the Pod won't start: ```bash kubectl get pods --selector=app=priv ``` ] .debug[[k8s/secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/secrets.md)] --- ## Creating a secret - Let's create a secret with the information provided earlier .exercise[ - Create the registry secret: ```bash kubectl create secret docker-registry enix \ --docker-server=docker-registry.enix.io \ --docker-username=reader \ --docker-password=VmQvqdtXFwXfyy4Jb5DR ``` ] Why do we have to specify the registry address? If we use multiple sets of credentials for different registries, it prevents leaking the credentials of one registry to *another* registry. .debug[[k8s/secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/secrets.md)] --- ## Using the secret - The first way to use a secret is to add it to `imagePullSecrets` (in the `spec` section of a Pod template) .exercise[ - Patch the `priv` Deployment that we created earlier: ```bash kubectl patch deploy priv --patch=' spec: template: spec: imagePullSecrets: - name: enix ' ``` ] .debug[[k8s/secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/secrets.md)] --- ## Checking the results .exercise[ - Confirm that our Pod can now start correctly: ```bash kubectl get pods --selector=app=priv ``` ] .debug[[k8s/secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/secrets.md)] --- ## Another way to use the secret - We can add the secret to the ServiceAccount - This is convenient to automatically use credentials for *all* pods (as long as they're using a specific ServiceAccount, of course) .exercise[ - Add the secret to the ServiceAccount: ```bash kubectl patch serviceaccount default --patch=' imagePullSecrets: - name: enix ' ``` ] .debug[[k8s/secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/secrets.md)] --- ## Secrets are displayed with base64 encoding - When shown with e.g. `kubectl get secrets -o yaml`, secrets are base64-encoded - Likewise, when defining it with YAML, `data` values are base64-encoded - Example: ```yaml kind: Secret apiVersion: v1 metadata: name: pin-codes data: onetwothreefour: MTIzNA== zerozerozerozero: MDAwMA== ``` - Keep in mind that this is just *encoding*, not *encryption* - It is very easy to [automatically extract and decode secrets](https://medium.com/@mveritym/decoding-kubernetes-secrets-60deed7a96a3) .debug[[k8s/secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/secrets.md)] --- class: extra-details ## Using `stringData` - When creating a Secret, it is possible to bypass base64 - Just use `stringData` instead of `data`: ```yaml kind: Secret apiVersion: v1 metadata: name: pin-codes stringData: onetwothreefour: 1234 zerozerozerozero: 0000 ``` - It will show up as base64 if you `kubectl get -o yaml` - No `type` was specified, so it defaults to `Opaque` .debug[[k8s/secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/secrets.md)] --- class: extra-details ## Encryption at rest - It is possible to [encrypted secrets at rest](https://kubernetes.io/docs/tasks/administer-cluster/encrypt-data/) - This means that secrets will be safe if someone ... - steals our etcd servers - steals our backups - snoops the e.g. iSCSI link between our etcd servers and SAN - However, starting the API server will now require human intervention (to provide the decryption keys) - This is only for extremely regulated environments (military, nation states...) .debug[[k8s/secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/secrets.md)] --- class: extra-details ## Immutable ConfigMaps and Secrets - Since Kubernetes 1.19, it is possible to mark a ConfigMap or Secret as *immutable* ```bash kubectl patch configmap xyz --patch='{"immutable": true}' ``` - This brings performance improvements when using lots of ConfigMaps and Secrets (lots = tens of thousands) - Once a ConfigMap or Secret has been marked as immutable: - its content cannot be changed anymore - the `immutable` field can't be changed back either - the only way to change it is to delete and re-create it - Pods using it will have to be re-created as well ??? :EN:- Handling passwords and tokens safely :FR:- Manipulation de mots de passe, clés API etc. .debug[[k8s/secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/secrets.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/Container-Ship-Freighter-Navigation-Elbe-Romance-1782991.jpg)] --- name: toc-stateful-sets class: title Stateful sets .nav[ [Previous part](#toc-managing-secrets) | [Back to table of contents](#toc-part-9) | [Next part](#toc-running-a-consul-cluster) ] .debug[(automatically generated title slide)] --- # Stateful sets - Stateful sets are a type of resource in the Kubernetes API (like pods, deployments, services...) - They offer mechanisms to deploy scaled stateful applications - At a first glance, they look like *deployments*: - a stateful set defines a pod spec and a number of replicas *R* - it will make sure that *R* copies of the pod are running - that number can be changed while the stateful set is running - updating the pod spec will cause a rolling update to happen - But they also have some significant differences .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Stateful sets unique features - Pods in a stateful set are numbered (from 0 to *R-1*) and ordered - They are started and updated in order (from 0 to *R-1*) - A pod is started (or updated) only when the previous one is ready - They are stopped in reverse order (from *R-1* to 0) - Each pod knows its identity (i.e. which number it is in the set) - Each pod can discover the IP address of the others easily - The pods can persist data on attached volumes 🤔 Wait a minute ... Can't we already attach volumes to pods and deployments? .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Revisiting volumes - [Volumes](https://kubernetes.io/docs/concepts/storage/volumes/) are used for many purposes: - sharing data between containers in a pod - exposing configuration information and secrets to containers - accessing storage systems - Let's see examples of the latter usage .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Volumes types - There are many [types of volumes](https://kubernetes.io/docs/concepts/storage/volumes/#types-of-volumes) available: - public cloud storage (GCEPersistentDisk, AWSElasticBlockStore, AzureDisk...) - private cloud storage (Cinder, VsphereVolume...) - traditional storage systems (NFS, iSCSI, FC...) - distributed storage (Ceph, Glusterfs, Portworx...) - Using a persistent volume requires: - creating the volume out-of-band (outside of the Kubernetes API) - referencing the volume in the pod description, with all its parameters .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Using a cloud volume Here is a pod definition using an AWS EBS volume (that has to be created first): ```yaml apiVersion: v1 kind: Pod metadata: name: pod-using-my-ebs-volume spec: containers: - image: ... name: container-using-my-ebs-volume volumeMounts: - mountPath: /my-ebs name: my-ebs-volume volumes: - name: my-ebs-volume awsElasticBlockStore: volumeID: vol-049df61146c4d7901 fsType: ext4 ``` .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Using an NFS volume Here is another example using a volume on an NFS server: ```yaml apiVersion: v1 kind: Pod metadata: name: pod-using-my-nfs-volume spec: containers: - image: ... name: container-using-my-nfs-volume volumeMounts: - mountPath: /my-nfs name: my-nfs-volume volumes: - name: my-nfs-volume nfs: server: 192.168.0.55 path: "/exports/assets" ``` .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Shortcomings of volumes - Their lifecycle (creation, deletion...) is managed outside of the Kubernetes API (we can't just use `kubectl apply/create/delete/...` to manage them) - If a Deployment uses a volume, all replicas end up using the same volume - That volume must then support concurrent access - some volumes do (e.g. NFS servers support multiple read/write access) - some volumes support concurrent reads - some volumes support concurrent access for colocated pods - What we really need is a way for each replica to have its own volume .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Individual volumes - The Pods of a Stateful set can have individual volumes (i.e. in a Stateful set with 3 replicas, there will be 3 volumes) - These volumes can be either: - allocated from a pool of pre-existing volumes (disks, partitions ...) - created dynamically using a storage system - This introduces a bunch of new Kubernetes resource types: Persistent Volumes, Persistent Volume Claims, Storage Classes (and also `volumeClaimTemplates`, that appear within Stateful Set manifests!) .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Stateful set recap - A Stateful sets manages a number of identical pods (like a Deployment) - These pods are numbered, and started/upgraded/stopped in a specific order - These pods are aware of their number (e.g., #0 can decide to be the primary, and #1 can be secondary) - These pods can find the IP addresses of the other pods in the set (through a *headless service*) - These pods can each have their own persistent storage (Deployments cannot do that) .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)] --- name: toc-running-a-consul-cluster class: title Running a Consul cluster .nav[ [Previous part](#toc-stateful-sets) | [Back to table of contents](#toc-part-9) | [Next part](#toc-persistent-volumes-claims) ] .debug[(automatically generated title slide)] --- # Running a Consul cluster - Here is a good use-case for Stateful sets! - We are going to deploy a Consul cluster with 3 nodes - Consul is a highly-available key/value store (like etcd or Zookeeper) - One easy way to bootstrap a cluster is to tell each node: - the addresses of other nodes - how many nodes are expected (to know when quorum is reached) .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Bootstrapping a Consul cluster *After reading the Consul documentation carefully (and/or asking around), we figure out the minimal command-line to run our Consul cluster.* ``` consul agent -data-dir=/consul/data -client=0.0.0.0 -server -ui \ -bootstrap-expect=3 \ -retry-join=`X.X.X.X` \ -retry-join=`Y.Y.Y.Y` ``` - Replace X.X.X.X and Y.Y.Y.Y with the addresses of other nodes - A node can add its own address (it will work fine) - ... Which means that we can use the same command-line on all nodes (convenient!) .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Cloud Auto-join - Since version 1.4.0, Consul can use the Kubernetes API to find its peers - This is called [Cloud Auto-join] - Instead of passing an IP address, we need to pass a parameter like this: ``` consul agent -retry-join "provider=k8s label_selector=\"app=consul\"" ``` - Consul needs to be able to talk to the Kubernetes API - We can provide a `kubeconfig` file - If Consul runs in a pod, it will use the *service account* of the pod [Cloud Auto-join]: https://www.consul.io/docs/agent/cloud-auto-join.html#kubernetes-k8s- .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Setting up Cloud auto-join - We need to create a service account for Consul - We need to create a role that can `list` and `get` pods - We need to bind that role to the service account - And of course, we need to make sure that Consul pods use that service account .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Putting it all together - The file `k8s/consul-1.yaml` defines the required resources (service account, role, role binding, service, stateful set) - Inspired by this [excellent tutorial](https://github.com/kelseyhightower/consul-on-kubernetes) by Kelsey Hightower (many features from the original tutorial were removed for simplicity) .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Running our Consul cluster - We'll use the provided YAML file .exercise[ - Create the stateful set and associated service: ```bash kubectl apply -f ~/container.training/k8s/consul-1.yaml ``` - Check the logs as the pods come up one after another: ```bash stern consul ``` <!-- ```wait Synced node info``` ```key ^C``` --> - Check the health of the cluster: ```bash kubectl exec consul-0 -- consul members ``` ] .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Caveats - The scheduler may place two Consul pods on the same node - if that node fails, we lose two Consul pods at the same time - this will cause the cluster to fail - Scaling down the cluster will cause it to fail - when a Consul member leaves the cluster, it needs to inform the others - otherwise, the last remaining node doesn't have quorum and stops functioning - This Consul cluster doesn't use real persistence yet - data is stored in the containers' ephemeral filesystem - if a pod fails, its replacement starts from a blank slate .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Improving pod placement - We need to tell the scheduler: *do not put two of these pods on the same node!* - This is done with an `affinity` section like the following one: ```yaml affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchLabels: app: consul topologyKey: kubernetes.io/hostname ``` .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Using a lifecycle hook - When a Consul member leaves the cluster, it needs to execute: ```bash consul leave ``` - This is done with a `lifecycle` section like the following one: ```yaml lifecycle: preStop: exec: command: [ "sh", "-c", "consul leave" ] ``` .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Running a better Consul cluster - Let's try to add the scheduling constraint and lifecycle hook - We can do that in the same namespace or another one (as we like) - If we do that in the same namespace, we will see a rolling update (pods will be replaced one by one) .exercise[ - Deploy a better Consul cluster: ```bash kubectl apply -f ~/container.training/k8s/consul-2.yaml ``` ] .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Still no persistence, though - We aren't using actual persistence yet (no `volumeClaimTemplate`, Persistent Volume, etc.) - What happens if we lose a pod? - a new pod gets rescheduled (with an empty state) - the new pod tries to connect to the two others - it will be accepted (after 1-2 minutes of instability) - and it will retrieve the data from the other pods .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Failure modes - What happens if we lose two pods? - manual repair will be required - we will need to instruct the remaining one to act solo - then rejoin new pods - What happens if we lose three pods? (aka all of them) - we lose all the data (ouch) - If we run Consul without persistent storage, backups are a good idea! .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/aerial-view-of-containers.jpg)] --- name: toc-persistent-volumes-claims class: title Persistent Volumes Claims .nav[ [Previous part](#toc-running-a-consul-cluster) | [Back to table of contents](#toc-part-9) | [Next part](#toc-local-persistent-volumes) ] .debug[(automatically generated title slide)] --- # Persistent Volumes Claims - Our Pods can use a special volume type: a *Persistent Volume Claim* - A Persistent Volume Claim (PVC) is also a Kubernetes resource (visible with `kubectl get persistentvolumeclaims` or `kubectl get pvc`) - A PVC is not a volume; it is a *request for a volume* - It should indicate at least: - the size of the volume (e.g. "5 GiB") - the access mode (e.g. "read-write by a single pod") .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## What's in a PVC? - A PVC contains at least: - a list of *access modes* (ReadWriteOnce, ReadOnlyMany, ReadWriteMany) - a size (interpreted as the minimal storage space needed) - It can also contain optional elements: - a selector (to restrict which actual volumes it can use) - a *storage class* (used by dynamic provisioning, more on that later) .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## What does a PVC look like? Here is a manifest for a basic PVC: ```yaml kind: PersistentVolumeClaim apiVersion: v1 metadata: name: my-claim spec: accessModes: - ReadWriteOnce resources: requests: storage: 1Gi ``` .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Using a Persistent Volume Claim Here is a Pod definition like the ones shown earlier, but using a PVC: ```yaml apiVersion: v1 kind: Pod metadata: name: pod-using-a-claim spec: containers: - image: ... name: container-using-a-claim volumeMounts: - mountPath: /my-vol name: my-volume volumes: - name: my-volume persistentVolumeClaim: claimName: my-claim ``` .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Creating and using Persistent Volume Claims - PVCs can be created manually and used explicitly (as shown on the previous slides) - They can also be created and used through Stateful Sets (this will be shown later) .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Lifecycle of Persistent Volume Claims - When a PVC is created, it starts existing in "Unbound" state (without an associated volume) - A Pod referencing an unbound PVC will not start (the scheduler will wait until the PVC is bound to place it) - A special controller continuously monitors PVCs to associate them with PVs - If no PV is available, one must be created: - manually (by operator intervention) - using a *dynamic provisioner* (more on that later) .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- class: extra-details ## Which PV gets associated to a PVC? - The PV must satisfy the PVC constraints (access mode, size, optional selector, optional storage class) - The PVs with the closest access mode are picked - Then the PVs with the closest size - It is possible to specify a `claimRef` when creating a PV (this will associate it to the specified PVC, but only if the PV satisfies all the requirements of the PVC; otherwise another PV might end up being picked) - For all the details about the PersistentVolumeClaimBinder, check [this doc](https://github.com/kubernetes/community/blob/master/contributors/design-proposals/storage/persistent-storage.md#matching-and-binding) .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Persistent Volume Claims and Stateful sets - A Stateful set can define one (or more) `volumeClaimTemplate` - Each `volumeClaimTemplate` will create one Persistent Volume Claim per pod - Each pod will therefore have its own individual volume - These volumes are numbered (like the pods) - Example: - a Stateful set is named `db` - it is scaled to replicas - it has a `volumeClaimTemplate` named `data` - then it will create pods `db-0`, `db-1`, `db-2` - these pods will have volumes named `data-db-0`, `data-db-1`, `data-db-2` .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Persistent Volume Claims are sticky - When updating the stateful set (e.g. image upgrade), each pod keeps its volume - When pods get rescheduled (e.g. node failure), they keep their volume (this requires a storage system that is not node-local) - These volumes are not automatically deleted (when the stateful set is scaled down or deleted) - If a stateful set is scaled back up later, the pods get their data back .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Dynamic provisioners - A *dynamic provisioner* monitors unbound PVCs - It can create volumes (and the corresponding PV) on the fly - This requires the PVCs to have a *storage class* (annotation `volume.beta.kubernetes.io/storage-provisioner`) - A dynamic provisioner only acts on PVCs with the right storage class (it ignores the other ones) - Just like `LoadBalancer` services, dynamic provisioners are optional (i.e. our cluster may or may not have one pre-installed) .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## What's a Storage Class? - A Storage Class is yet another Kubernetes API resource (visible with e.g. `kubectl get storageclass` or `kubectl get sc`) - It indicates which *provisioner* to use (which controller will create the actual volume) - And arbitrary parameters for that provisioner (replication levels, type of disk ... anything relevant!) - Storage Classes are required if we want to use [dynamic provisioning](https://kubernetes.io/docs/concepts/storage/dynamic-provisioning/) (but we can also create volumes manually, and ignore Storage Classes) .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## The default storage class - At most one storage class can be marked as the default class (by annotating it with `storageclass.kubernetes.io/is-default-class=true`) - When a PVC is created, it will be annotated with the default storage class (unless it specifies an explicit storage class) - This only happens at PVC creation (existing PVCs are not updated when we mark a class as the default one) .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Dynamic provisioning setup This is how we can achieve fully automated provisioning of persistent storage. 1. Configure a storage system. (It needs to have an API, or be capable of automated provisioning of volumes.) 2. Install a dynamic provisioner for this storage system. (This is some specific controller code.) 3. Create a Storage Class for this system. (It has to match what the dynamic provisioner is expecting.) 4. Annotate the Storage Class to be the default one. .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- ## Dynamic provisioning usage After setting up the system (previous slide), all we need to do is: *Create a Stateful Set that makes use of a `volumeClaimTemplate`.* This will trigger the following actions. 1. The Stateful Set creates PVCs according to the `volumeClaimTemplate`. 2. The Stateful Set creates Pods using these PVCs. 3. The PVCs are automatically annotated with our Storage Class. 4. The dynamic provisioner provisions volumes and creates the corresponding PVs. 5. The PersistentVolumeClaimBinder associates the PVs and the PVCs together. 6. PVCs are now bound, the Pods can start. ??? :EN:- Deploying apps with Stateful Sets :EN:- Example: deploying a Consul cluster :EN:- Understanding Persistent Volume Claims and Storage Classes :FR:- Déployer une application avec un *Stateful Set* :FR:- Example : lancer un cluster Consul :FR:- Comprendre les *Persistent Volume Claims* et *Storage Classes* .debug[[k8s/statefulsets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/statefulsets.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/blue-containers.jpg)] --- name: toc-local-persistent-volumes class: title Local Persistent Volumes .nav[ [Previous part](#toc-persistent-volumes-claims) | [Back to table of contents](#toc-part-9) | [Next part](#toc-openebs-) ] .debug[(automatically generated title slide)] --- # Local Persistent Volumes - We want to run that Consul cluster *and* actually persist data - But we don't have a distributed storage system - We are going to use local volumes instead (similar conceptually to `hostPath` volumes) - We can use local volumes without installing extra plugins - However, they are tied to a node - If that node goes down, the volume becomes unavailable .debug[[k8s/local-persistent-volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/local-persistent-volumes.md)] --- ## With or without dynamic provisioning - We will deploy a Consul cluster *with* persistence - That cluster's StatefulSet will create PVCs - These PVCs will remain unbound¹, until we will create local volumes manually (we will basically do the job of the dynamic provisioner) - Then, we will see how to automate that with a dynamic provisioner .footnote[¹Unbound = without an associated Persistent Volume.] .debug[[k8s/local-persistent-volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/local-persistent-volumes.md)] --- ## If we have a dynamic provisioner ... - The labs in this section assume that we *do not* have a dynamic provisioner - If we do have one, we need to disable it .exercise[ - Check if we have a dynamic provisioner: ```bash kubectl get storageclass ``` - If the output contains a line with `(default)`, run this command: ```bash kubectl annotate sc storageclass.kubernetes.io/is-default-class- --all ``` - Check again that it is no longer marked as `(default)` ] .debug[[k8s/local-persistent-volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/local-persistent-volumes.md)] --- ## Deploying Consul - Let's use a new manifest for our Consul cluster - The only differences between that file and the previous one are: - `volumeClaimTemplate` defined in the Stateful Set spec - the corresponding `volumeMounts` in the Pod spec .exercise[ - Apply the persistent Consul YAML file: ```bash kubectl apply -f ~/container.training/k8s/consul-3.yaml ``` ] .debug[[k8s/local-persistent-volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/local-persistent-volumes.md)] --- ## Observing the situation - Let's look at Persistent Volume Claims and Pods .exercise[ - Check that we now have an unbound Persistent Volume Claim: ```bash kubectl get pvc ``` - We don't have any Persistent Volume: ```bash kubectl get pv ``` - The Pod `consul-0` is not scheduled yet: ```bash kubectl get pods -o wide ``` ] *Hint: leave these commands running with `-w` in different windows.* .debug[[k8s/local-persistent-volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/local-persistent-volumes.md)] --- ## Explanations - In a Stateful Set, the Pods are started one by one - `consul-1` won't be created until `consul-0` is running - `consul-0` has a dependency on an unbound Persistent Volume Claim - The scheduler won't schedule the Pod until the PVC is bound (because the PVC might be bound to a volume that is only available on a subset of nodes; for instance EBS are tied to an availability zone) .debug[[k8s/local-persistent-volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/local-persistent-volumes.md)] --- ## Creating Persistent Volumes - Let's create 3 local directories (`/mnt/consul`) on node2, node3, node4 - Then create 3 Persistent Volumes corresponding to these directories .exercise[ - Create the local directories: ```bash for NODE in node2 node3 node4; do ssh $NODE sudo mkdir -p /mnt/consul done ``` - Create the PV objects: ```bash kubectl apply -f ~/container.training/k8s/volumes-for-consul.yaml ``` ] .debug[[k8s/local-persistent-volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/local-persistent-volumes.md)] --- ## Check our Consul cluster - The PVs that we created will be automatically matched with the PVCs - Once a PVC is bound, its pod can start normally - Once the pod `consul-0` has started, `consul-1` can be created, etc. - Eventually, our Consul cluster is up, and backend by "persistent" volumes .exercise[ - Check that our Consul clusters has 3 members indeed: ```bash kubectl exec consul-0 -- consul members ``` ] .debug[[k8s/local-persistent-volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/local-persistent-volumes.md)] --- ## Devil is in the details (1/2) - The size of the Persistent Volumes is bogus (it is used when matching PVs and PVCs together, but there is no actual quota or limit) .debug[[k8s/local-persistent-volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/local-persistent-volumes.md)] --- ## Devil is in the details (2/2) - This specific example worked because we had exactly 1 free PV per node: - if we had created multiple PVs per node ... - we could have ended with two PVCs bound to PVs on the same node ... - which would have required two pods to be on the same node ... - which is forbidden by the anti-affinity constraints in the StatefulSet - To avoid that, we need to associated the PVs with a Storage Class that has: ```yaml volumeBindingMode: WaitForFirstConsumer ``` (this means that a PVC will be bound to a PV only after being used by a Pod) - See [this blog post](https://kubernetes.io/blog/2018/04/13/local-persistent-volumes-beta/) for more details .debug[[k8s/local-persistent-volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/local-persistent-volumes.md)] --- ## Bulk provisioning - It's not practical to manually create directories and PVs for each app - We *could* pre-provision a number of PVs across our fleet - We could even automate that with a Daemon Set: - creating a number of directories on each node - creating the corresponding PV objects - We also need to recycle volumes - ... This can quickly get out of hand .debug[[k8s/local-persistent-volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/local-persistent-volumes.md)] --- ## Dynamic provisioning - We could also write our own provisioner, which would: - watch the PVCs across all namespaces - when a PVC is created, create a corresponding PV on a node - Or we could use one of the dynamic provisioners for local persistent volumes (for instance the [Rancher local path provisioner](https://github.com/rancher/local-path-provisioner)) .debug[[k8s/local-persistent-volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/local-persistent-volumes.md)] --- ## Strategies for local persistent volumes - Remember, when a node goes down, the volumes on that node become unavailable - High availability will require another layer of replication (like what we've just seen with Consul; or primary/secondary; etc) - Pre-provisioning PVs makes sense for machines with local storage (e.g. cloud instance storage; or storage directly attached to a physical machine) - Dynamic provisioning makes sense for large number of applications (when we can't or won't dedicate a whole disk to a volume) - It's possible to mix both (using distinct Storage Classes) ??? :EN:- Static vs dynamic volume provisioning :EN:- Example: local persistent volume provisioner :FR:- Création statique ou dynamique de volumes :FR:- Exemple : création de volumes locaux .debug[[k8s/local-persistent-volumes.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/local-persistent-volumes.md)] --- ``` Invalid content: k8s/portworx. ``` --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/chinook-helicopter-container.jpg)] --- name: toc-openebs- class: title OpenEBS .nav[ [Previous part](#toc-local-persistent-volumes) | [Back to table of contents](#toc-part-9) | [Next part](#toc-centralized-logging) ] .debug[(automatically generated title slide)] --- # OpenEBS - [OpenEBS] is a popular open-source storage solution for Kubernetes - Uses the concept of "Container Attached Storage" (1 volume = 1 dedicated controller pod + a set of replica pods) - Supports a wide range of storage engines: - LocalPV: local volumes (hostpath or device), no replication - Jiva: for lighter workloads with basic cloning/snapshotting - cStor: more powerful engine that also supports resizing, RAID, disk pools ... - [Mayastor]: newer, even more powerful engine with NVMe and vhost-user support [OpenEBS]: https://openebs.io/ [Mayastor]: https://github.com/openebs/MayaStor#mayastor .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- class: extra-details ## What are all these storage engines? - LocalPV is great if we want good performance, no replication, easy setup (it is similar to the Rancher local path provisioner) - Jiva is great if we want replication and easy setup (data is stored in containers' filesystems) - cStor is more powerful and flexible, but requires more extensive setup - Mayastor is designed to achieve extreme performance levels (with the right hardware and disks) - The OpenEBS documentation has a [good comparison of engines] to help us pick [good comparison of engines]: https://docs.openebs.io/docs/next/casengines.html#cstor-vs-jiva-vs-localpv-features-comparison .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## Installing OpenEBS with Helm - The OpenEBS control plane can be installed with Helm - It will run as a set of containers on Kubernetes worker nodes .exercise[ - Install OpenEBS: ```bash helm upgrade --install openebs openebs \ --repo https://openebs.github.io/charts \ --namespace openebs --create-namespace ``` ] .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## Checking what was installed - Wait a little bit ... .exercise[ - Look at the pods in the `openebs` namespace: ```bash kubectl get pods --namespace openebs ``` - And the StorageClasses that were created: ```bash kubectl get sc ``` ] .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## The default StorageClasses - OpenEBS typically creates three default StorageClasses - `openebs-jiva-default` provisions 3 replicated Jiva pods per volume - data is stored in `/openebs` in the replica pods - `/openebs` is a localpath volume mapped to `/var/openebs/pvc-...` on the node - `openebs-hostpath` uses LocalPV with local directories - volumes are hostpath volumes created in `/var/openebs/local` on each node - `openebs-device` uses LocalPV with local block devices - requires available disks and/or a bit of extra configuration - the default configuration filters out loop, LVM, MD devices .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## When do we need custom StorageClasses? - To store LocalPV hostpath volumes on a different path on the host - To change the number of replicated Jiva pods - To use a different Jiva pool (i.e. a different path on the host to store the Jiva volumes) - To create a cStor pool - ... .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- class: extra-details ## Defining a custom StorageClass Example for a LocalPV hostpath class using an extra mount on `/mnt/vol001`: ```yaml apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: localpv-hostpath-mntvol001 annotations: openebs.io/cas-type: local cas.openebs.io/config: | - name: BasePath value: "/mnt/vol001" - name: StorageType value: "hostpath" provisioner: openebs.io/local ``` - `provisioner` needs to be set accordingly - Storage engine is chosen by specifying the annotation `openebs.io/cas-type` - Storage engine configuration is set with the annotation `cas.openebs.io/config` .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## Checking the default hostpath StorageClass - Let's inspect the StorageClass that OpenEBS created for us .exercise[ - Let's look at the OpenEBS LocalPV hostpath StorageClass: ```bash kubectl get storageclass openebs-hostpath -o yaml ``` ] .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## Create a host path PVC - Let's create a Persistent Volume Claim using an explicit StorageClass .exercise[ ```bash kubectl apply -f - <<EOF kind: PersistentVolumeClaim apiVersion: v1 metadata: name: local-hostpath-pvc spec: storageClassName: openebs-hostpath accessModes: - ReadWriteOnce resources: requests: storage: 1G EOF ``` ] .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## Making sure that a PV was created for our PVC - Normally, the `openebs-hostpath` StorageClass created a PV for our PVC .exercise[ - Look at the PV and PVC: ```bash kubectl get pv,pvc ``` ] .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## Create a Pod to consume the PV .exercise[ - Create a Pod using that PVC: ```bash kubectl apply -f ~/container.training/k8s/openebs-pod.yaml ``` - Here are the sections that declare and use the volume: ```yaml volumes: - name: my-storage persistentVolumeClaim: claimName: local-hostpath-pvc containers: ... volumeMounts: - mountPath: /mnt/storage name: my-storage ``` ] .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## Verify that data is written on the node - Let's find the file written by the Pod on the node where the Pod is running .exercise[ - Get the worker node where the pod is located ```bash kubectl get pod openebs-local-hostpath-pod -ojsonpath={.spec.nodeName} ``` - SSH into the node - Check the volume content ```bash sudo tail /var/openebs/local/pvc-*/greet.txt ``` ] .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## Heads up! - The following labs and exercises will use the Jiva storage class - This storage class creates 3 replicas by default - It uses *anti-affinity* placement constraits to put these replicas on different nodes - **This requires a cluster with multiple nodes!** - It also requires the iSCSI client (aka *initiator*) to be installed on the nodes - On many platforms, the iSCSI client is preinstalled and will start automatically - If it doesn't, you might want to check [this documentation page] for details [this documentation page]: https://docs.openebs.io/docs/next/prerequisites.html .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## The default StorageClass - The PVC that we defined earlier specified an explicit StorageClass - We can also set a default StorageClass - It will then be used for all PVC that *don't* specify and explicit StorageClass - This is done with the annotation `storageclass.kubernetes.io/is-default-class` .exercise[ - Check if we have a default StorageClass: ```bash kubectl get storageclasses ``` ] - The default StorageClass (if there is one) is shown with `(default)` .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## Setting a default StorageClass - Let's set the default StorageClass to use `openebs-jiva-default` .exercise[ - Remove the annotation (just in case we already have a default class): ```bash kubectl annotate storageclass storageclass.kubernetes.io/is-default-class- --all ``` - Annotate the Jiva StorageClass: ```bash kubectl annotate storageclasses \ openebs-jiva-default storageclass.kubernetes.io/is-default-class=true ``` - Check the result: ```bash kuectl get storageclasses ``` ] .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## Creating a Pod using the Jiva class - We will create a Pod running PostgreSQL, using the default class .exercise[ - Create the Pod: ```bash kubectl apply -f ~/container.training/k8s/postgres.yaml ``` - Wait for the PV, PVC, and Pod to be up: ```bash watch kubectl get pv,pvc,pod ``` - We can also check what's going on in the `openebs` namespace: ```bash watch kubectl get pods --namespace openebs ``` ] .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## Node failover ⚠️ This will partially break your cluster! - We are going to disconnect the node running PostgreSQL from the cluster - We will see what happens, and how to recover - We will not reconnect the node to the cluster - This whole lab will take at least 10-15 minutes (due to various timeouts) ⚠️ Only do this lab at the very end, when you don't want to run anything else after! .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## Disconnecting the node from the cluster .exercise[ - Find out where the Pod is running, and SSH into that node: ```bash kubectl get pod postgres-0 -o jsonpath={.spec.nodeName} ssh nodeX ``` - Check the name of the network interface: ```bash sudo ip route ls default ``` - The output should look like this: ``` default via 10.10.0.1 `dev ensX` proto dhcp src 10.10.0.13 metric 100 ``` - Shutdown the network interface: ```bash sudo ip link set ensX down ``` ] .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## Watch what's going on - Let's look at the status of Nodes, Pods, and Events .exercise[ - In a first pane/tab/window, check Nodes and Pods: ```bash watch kubectl get nodes,pods -o wide ``` - In another pane/tab/window, check Events: ```bash kubectl get events --watch ``` ] .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## Node Ready → NotReady - After \~30 seconds, the control plane stops receiving heartbeats from the Node - The Node is marked NotReady - It is not *schedulable* anymore (the scheduler won't place new pods there, except some special cases) - All Pods on that Node are also *not ready* (they get removed from service Endpoints) - ... But nothing else happens for now (the control plane is waiting: maybe the Node will come back shortly?) .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## Pod eviction - After \~5 minutes, the control plane will evict most Pods from the Node - These Pods are now `Terminating` - The Pods controlled by e.g. ReplicaSets are automatically moved (or rather: new Pods are created to replace them) - But nothing happens to the Pods controlled by StatefulSets at this point (they remain `Terminating` forever) - Why? 🤔 -- - This is to avoid *split brain scenarios* .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- class: extra-details ## Split brain 🧠⚡️🧠 - Imagine that we create a replacement pod `postgres-0` on another Node - And 15 minutes later, the Node is reconnected and the original `postgres-0` comes back - Which one is the "right" one? - What if they have conflicting data? 😱 - We *cannot* let that happen! - Kubernetes won't do it - ... Unless we tell it to .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## The Node is gone - One thing we can do, is tell Kubernetes "the Node won't come back" (there are other methods; but this one is the simplest one here) - This is done with a simple `kubectl delete node` .exercise[ - `kubectl delete` the Node that we disconnected ] .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## Pod rescheduling - Kubernetes removes the Node - After a brief period of time (\~1 minute) the "Terminating" Pods are removed - A replacement Pod is created on another Node - ... But it doens't start yet! - Why? 🤔 .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- ## Multiple attachment - By default, a disk can only be attached to one Node at a time (sometimes it's a hardware or API limitation; sometimes enforced in software) - In our Events, we should see `FailedAttachVolume` and `FailedMount` messages - After \~5 more minutes, the disk will be force-detached from the old Node - ... Which will allow attaching it to the new Node! 🎉 - The Pod will then be able to start - Failover is complete! ??? :EN:- Understanding Container Attached Storage (CAS) :EN:- Deploying stateful apps with OpenEBS :FR:- Comprendre le "Container Attached Storage" (CAS) :FR:- Déployer une application "stateful" avec OpenEBS .debug[[k8s/openebs.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/openebs.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/container-cranes.jpg)] --- name: toc-centralized-logging class: title Centralized logging .nav[ [Previous part](#toc-openebs-) | [Back to table of contents](#toc-part-10) | [Next part](#toc-collecting-metrics-with-prometheus) ] .debug[(automatically generated title slide)] --- # Centralized logging - Using `kubectl` or `stern` is simple; but it has drawbacks: - when a node goes down, its logs are not available anymore - we can only dump or stream logs; we want to search/index/count... - We want to send all our logs to a single place - We want to parse them (e.g. for HTTP logs) and index them - We want a nice web dashboard -- - We are going to deploy an EFK stack .debug[[k8s/logs-centralized.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/logs-centralized.md)] --- ## What is EFK? - EFK is three components: - ElasticSearch (to store and index log entries) - Fluentd (to get container logs, process them, and put them in ElasticSearch) - Kibana (to view/search log entries with a nice UI) - The only component that we need to access from outside the cluster will be Kibana .debug[[k8s/logs-centralized.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/logs-centralized.md)] --- ## Deploying EFK on our cluster - We are going to use a YAML file describing all the required resources .exercise[ - Load the YAML file into our cluster: ```bash kubectl apply -f ~/container.training/k8s/efk.yaml ``` ] If we [look at the YAML file](https://github.com/jpetazzo/container.training/blob/master/k8s/efk.yaml), we see that it creates a daemon set, two deployments, two services, and a few roles and role bindings (to give fluentd the required permissions). .debug[[k8s/logs-centralized.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/logs-centralized.md)] --- ## The itinerary of a log line (before Fluentd) - A container writes a line on stdout or stderr - Both are typically piped to the container engine (Docker or otherwise) - The container engine reads the line, and sends it to a logging driver - The timestamp and stream (stdout or stderr) is added to the log line - With the default configuration for Kubernetes, the line is written to a JSON file (`/var/log/containers/pod-name_namespace_container-id.log`) - That file is read when we invoke `kubectl logs`; we can access it directly too .debug[[k8s/logs-centralized.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/logs-centralized.md)] --- ## The itinerary of a log line (with Fluentd) - Fluentd runs on each node (thanks to a daemon set) - It bind-mounts `/var/log/containers` from the host (to access these files) - It continuously scans this directory for new files; reads them; parses them - Each log line becomes a JSON object, fully annotated with extra information: <br/>container id, pod name, Kubernetes labels... - These JSON objects are stored in ElasticSearch - ElasticSearch indexes the JSON objects - We can access the logs through Kibana (and perform searches, counts, etc.) .debug[[k8s/logs-centralized.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/logs-centralized.md)] --- ## Accessing Kibana - Kibana offers a web interface that is relatively straightforward - Let's check it out! .exercise[ - Check which `NodePort` was allocated to Kibana: ```bash kubectl get svc kibana ``` - With our web browser, connect to Kibana ] .debug[[k8s/logs-centralized.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/logs-centralized.md)] --- ## Using Kibana *Note: this is not a Kibana workshop! So this section is deliberately very terse.* - The first time you connect to Kibana, you must "configure an index pattern" - Just use the one that is suggested, `@timestamp`.red[*] - Then click "Discover" (in the top-left corner) - You should see container logs - Advice: in the left column, select a few fields to display, e.g.: `kubernetes.host`, `kubernetes.pod_name`, `stream`, `log` .red[*]If you don't see `@timestamp`, it's probably because no logs exist yet. <br/>Wait a bit, and double-check the logging pipeline! .debug[[k8s/logs-centralized.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/logs-centralized.md)] --- ## Caveat emptor We are using EFK because it is relatively straightforward to deploy on Kubernetes, without having to redeploy or reconfigure our cluster. But it doesn't mean that it will always be the best option for your use-case. If you are running Kubernetes in the cloud, you might consider using the cloud provider's logging infrastructure (if it can be integrated with Kubernetes). The deployment method that we will use here has been simplified: there is only one ElasticSearch node. In a real deployment, you might use a cluster, both for performance and reliability reasons. But this is outside of the scope of this chapter. The YAML file that we used creates all the resources in the `default` namespace, for simplicity. In a real scenario, you will create the resources in the `kube-system` namespace or in a dedicated namespace. ??? :EN:- Centralizing logs :FR:- Centraliser les logs .debug[[k8s/logs-centralized.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/logs-centralized.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/container-housing.jpg)] --- name: toc-collecting-metrics-with-prometheus class: title Collecting metrics with Prometheus .nav[ [Previous part](#toc-centralized-logging) | [Back to table of contents](#toc-part-10) | [Next part](#toc-prometheus-and-grafana) ] .debug[(automatically generated title slide)] --- # Collecting metrics with Prometheus - Prometheus is an open-source monitoring system including: - multiple *service discovery* backends to figure out which metrics to collect - a *scraper* to collect these metrics - an efficient *time series database* to store these metrics - a specific query language (PromQL) to query these time series - an *alert manager* to notify us according to metrics values or trends - We are going to use it to collect and query some metrics on our Kubernetes cluster .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Why Prometheus? - We don't endorse Prometheus more or less than any other system - It's relatively well integrated within the cloud-native ecosystem - It can be self-hosted (this is useful for tutorials like this) - It can be used for deployments of varying complexity: - one binary and 10 lines of configuration to get started - all the way to thousands of nodes and millions of metrics .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Exposing metrics to Prometheus - Prometheus obtains metrics and their values by querying *exporters* - An exporter serves metrics over HTTP, in plain text - This is what the *node exporter* looks like: http://demo.robustperception.io:9100/metrics - Prometheus itself exposes its own internal metrics, too: http://demo.robustperception.io:9090/metrics - If you want to expose custom metrics to Prometheus: - serve a text page like these, and you're good to go - libraries are available in various languages to help with quantiles etc. .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## How Prometheus gets these metrics - The *Prometheus server* will *scrape* URLs like these at regular intervals (by default: every minute; can be more/less frequent) - The list of URLs to scrape (the *scrape targets*) is defined in configuration .footnote[Worried about the overhead of parsing a text format? <br/> Check this [comparison](https://github.com/RichiH/OpenMetrics/blob/master/markdown/protobuf_vs_text.md) of the text format with the (now deprecated) protobuf format!] .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Defining scrape targets This is maybe the simplest configuration file for Prometheus: ```yaml scrape_configs: - job_name: 'prometheus' static_configs: - targets: ['localhost:9090'] ``` - In this configuration, Prometheus collects its own internal metrics - A typical configuration file will have multiple `scrape_configs` - In this configuration, the list of targets is fixed - A typical configuration file will use dynamic service discovery .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Service discovery This configuration file will leverage existing DNS `A` records: ```yaml scrape_configs: - ... - job_name: 'node' dns_sd_configs: - names: ['api-backends.dc-paris-2.enix.io'] type: 'A' port: 9100 ``` - In this configuration, Prometheus resolves the provided name(s) (here, `api-backends.dc-paris-2.enix.io`) - Each resulting IP address is added as a target on port 9100 .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Dynamic service discovery - In the DNS example, the names are re-resolved at regular intervals - As DNS records are created/updated/removed, scrape targets change as well - Existing data (previously collected metrics) is not deleted - Other service discovery backends work in a similar fashion .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Other service discovery mechanisms - Prometheus can connect to e.g. a cloud API to list instances - Or to the Kubernetes API to list nodes, pods, services ... - Or a service like Consul, Zookeeper, etcd, to list applications - The resulting configurations files are *way more complex* (but don't worry, we won't need to write them ourselves) .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Time series database - We could wonder, "why do we need a specialized database?" - One metrics data point = metrics ID + timestamp + value - With a classic SQL or noSQL data store, that's at least 160 bits of data + indexes - Prometheus is way more efficient, without sacrificing performance (it will even be gentler on the I/O subsystem since it needs to write less) - Would you like to know more? Check this video: [Storage in Prometheus 2.0](https://www.youtube.com/watch?v=C4YV-9CrawA) by [Goutham V](https://twitter.com/putadent) at DC17EU .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Checking if Prometheus is installed - Before trying to install Prometheus, let's check if it's already there .exercise[ - Look for services with a label `app=prometheus` across all namespaces: ```bash kubectl get services --selector=app=prometheus --all-namespaces ``` ] If we see a `NodePort` service called `prometheus-server`, we're good! (We can then skip to "Connecting to the Prometheus web UI".) .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Running Prometheus on our cluster We need to: - Run the Prometheus server in a pod (using e.g. a Deployment to ensure that it keeps running) - Expose the Prometheus server web UI (e.g. with a NodePort) - Run the *node exporter* on each node (with a Daemon Set) - Set up a Service Account so that Prometheus can query the Kubernetes API - Configure the Prometheus server (storing the configuration in a Config Map for easy updates) .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Helm charts to the rescue - To make our lives easier, we are going to use a Helm chart - The Helm chart will take care of all the steps explained above (including some extra features that we don't need, but won't hurt) .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Step 1: install Helm - If we already installed Helm earlier, this command won't break anything .exercise[ - Install the Helm CLI: ```bash curl https://raw.githubusercontent.com/kubernetes/helm/master/scripts/get-helm-3 \ | bash ``` ] .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Step 2: add the `prometheus-community` repo - This will add the repository containing the chart for Prometheus - This command is idempotent (it won't break anything if the repository was already added) .exercise[ - Add the repository: ```bash helm repo add prometheus-community \ https://prometheus-community.github.io/helm-charts ``` ] .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Step 3: install Prometheus - The following command, just like the previous ones, is idempotent (it won't error out if Prometheus is already installed) .exercise[ - Install Prometheus on our cluster: ```bash helm upgrade prometheus prometheus-community/prometheus \ --install \ --namespace kube-system \ --set server.service.type=NodePort \ --set server.service.nodePort=30090 \ --set server.persistentVolume.enabled=false \ --set alertmanager.enabled=false ``` ] Curious about all these flags? They're explained in the next slide. .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- class: extra-details ## Explaining all the Helm flags - `helm upgrade prometheus` → upgrade the release named `prometheus` ... <br/> (a "release" is an instance of an app deployed with Helm) - `prometheus-community/...` → of a chart located in the `prometheus-community` repo ... - `.../prometheus` → in that repo, get the chart named `prometheus` ... - `--install` → if the app doesn't exist, create it ... - `--namespace kube-system` → put it in that specific namespace ... - ... and set the following *values* when rendering the chart's templates: - `server.service.type=NodePort` → expose the Prometheus server with a NodePort - `server.service.nodePort=30090` → set the specific NodePort number to use - `server.persistentVolume.enabled=false` → do not use a PersistentVolumeClaim - `alertmanager.enabled=false` → disable the alert manager entirely .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Connecting to the Prometheus web UI - Let's connect to the web UI and see what we can do .exercise[ - Figure out the NodePort that was allocated to the Prometheus server: ```bash kubectl get svc --all-namespaces | grep prometheus-server ``` - With your browser, connect to that port ] .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Querying some metrics - This is easy... if you are familiar with PromQL .exercise[ - Click on "Graph", and in "expression", paste the following: ``` sum by (instance) ( irate( container_cpu_usage_seconds_total{ pod_name=~"worker.*" }[5m] ) ) ``` ] - Click on the blue "Execute" button and on the "Graph" tab just below - We see the cumulated CPU usage of worker pods for each node <br/> (if we just deployed Prometheus, there won't be much data to see, though) .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Getting started with PromQL - We can't learn PromQL in just 5 minutes - But we can cover the basics to get an idea of what is possible (and have some keywords and pointers) - We are going to break down the query above (building it one step at a time) .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Graphing one metric across all tags This query will show us CPU usage across all containers: ``` container_cpu_usage_seconds_total ``` - The suffix of the metrics name tells us: - the unit (seconds of CPU) - that it's the total used since the container creation - Since it's a "total," it is an increasing quantity (we need to compute the derivative if we want e.g. CPU % over time) - We see that the metrics retrieved have *tags* attached to them .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Selecting metrics with tags This query will show us only metrics for worker containers: ``` container_cpu_usage_seconds_total{pod_name=~"worker.*"} ``` - The `=~` operator allows regex matching - We select all the pods with a name starting with `worker` (it would be better to use labels to select pods; more on that later) - The result is a smaller set of containers .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Transforming counters in rates This query will show us CPU usage % instead of total seconds used: ``` 100*irate(container_cpu_usage_seconds_total{pod_name=~"worker.*"}[5m]) ``` - The [`irate`](https://prometheus.io/docs/prometheus/latest/querying/functions/#irate) operator computes the "per-second instant rate of increase" - `rate` is similar but allows decreasing counters and negative values - with `irate`, if a counter goes back to zero, we don't get a negative spike - The `[5m]` tells how far to look back if there is a gap in the data - And we multiply with `100*` to get CPU % usage .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Aggregation operators This query sums the CPU usage per node: ``` sum by (instance) ( irate(container_cpu_usage_seconds_total{pod_name=~"worker.*"}[5m]) ) ``` - `instance` corresponds to the node on which the container is running - `sum by (instance) (...)` computes the sum for each instance - Note: all the other tags are collapsed (in other words, the resulting graph only shows the `instance` tag) - PromQL supports many more [aggregation operators](https://prometheus.io/docs/prometheus/latest/querying/operators/#aggregation-operators) .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## What kind of metrics can we collect? - Node metrics (related to physical or virtual machines) - Container metrics (resource usage per container) - Databases, message queues, load balancers, ... (check out this [list of exporters](https://prometheus.io/docs/instrumenting/exporters/)!) - Instrumentation (=deluxe `printf` for our code) - Business metrics (customers served, revenue, ...) .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- class: extra-details ## Node metrics - CPU, RAM, disk usage on the whole node - Total number of processes running, and their states - Number of open files, sockets, and their states - I/O activity (disk, network), per operation or volume - Physical/hardware (when applicable): temperature, fan speed... - ...and much more! .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- class: extra-details ## Container metrics - Similar to node metrics, but not totally identical - RAM breakdown will be different - active vs inactive memory - some memory is *shared* between containers, and specially accounted for - I/O activity is also harder to track - async writes can cause deferred "charges" - some page-ins are also shared between containers For details about container metrics, see: <br/> http://jpetazzo.github.io/2013/10/08/docker-containers-metrics/ .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- class: extra-details ## Application metrics - Arbitrary metrics related to your application and business - System performance: request latency, error rate... - Volume information: number of rows in database, message queue size... - Business data: inventory, items sold, revenue... .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- class: extra-details ## Detecting scrape targets - Prometheus can leverage Kubernetes service discovery (with proper configuration) - Services or pods can be annotated with: - `prometheus.io/scrape: true` to enable scraping - `prometheus.io/port: 9090` to indicate the port number - `prometheus.io/path: /metrics` to indicate the URI (`/metrics` by default) - Prometheus will detect and scrape these (without needing a restart or reload) .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Querying labels - What if we want to get metrics for containers belonging to a pod tagged `worker`? - The cAdvisor exporter does not give us Kubernetes labels - Kubernetes labels are exposed through another exporter - We can see Kubernetes labels through metrics `kube_pod_labels` (each container appears as a time series with constant value of `1`) - Prometheus *kind of* supports "joins" between time series - But only if the names of the tags match exactly .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Unfortunately ... - The cAdvisor exporter uses tag `pod_name` for the name of a pod - The Kubernetes service endpoints exporter uses tag `pod` instead - See [this blog post](https://www.robustperception.io/exposing-the-software-version-to-prometheus) or [this other one](https://www.weave.works/blog/aggregating-pod-resource-cpu-memory-usage-arbitrary-labels-prometheus/) to see how to perform "joins" - Alas, Prometheus cannot "join" time series with different labels (see [Prometheus issue #2204](https://github.com/prometheus/prometheus/issues/2204) for the rationale) - There is a workaround involving relabeling, but it's "not cheap" - see [this comment](https://github.com/prometheus/prometheus/issues/2204#issuecomment-261515520) for an overview - or [this blog post](https://5pi.de/2017/11/09/use-prometheus-vector-matching-to-get-kubernetes-utilization-across-any-pod-label/) for a complete description of the process .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- ## In practice - Grafana is a beautiful (and useful) frontend to display all kinds of graphs - Not everyone needs to know Prometheus, PromQL, Grafana, etc. - But in a team, it is valuable to have at least one person who know them - That person can set up queries and dashboards for the rest of the team - It's a little bit like knowing how to optimize SQL queries, Dockerfiles... Don't panic if you don't know these tools! ...But make sure at least one person in your team is on it 💯 ??? :EN:- Collecting metrics with Prometheus :FR:- Collecter des métriques avec Prometheus .debug[[k8s/prometheus.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/containers-by-the-water.jpg)] --- name: toc-prometheus-and-grafana class: title Prometheus and Grafana .nav[ [Previous part](#toc-collecting-metrics-with-prometheus) | [Back to table of contents](#toc-part-10) | [Next part](#toc-resource-limits) ] .debug[(automatically generated title slide)] --- # Prometheus and Grafana - What if we want metrics retention, view graphs, trends? - A very popular combo is Prometheus+Grafana: - Prometheus as the "metrics engine" - Grafana to display comprehensive dashboards - Prometheus also has an alert-manager component to trigger alerts (we won't talk about that one) .debug[[k8s/prometheus-stack.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus-stack.md)] --- ## Installing Prometheus and Grafana - A complete metrics stack needs at least: - the Prometheus server (collects metrics and stores them efficiently) - a collection of *exporters* (exposing metrics to Prometheus) - Grafana - a collection of Grafana dashboards (building them from scratch is tedious) - The Helm chart `kube-prometheus-stack` combines all these elements - ... So we're going to use it to deploy our metrics stack! .debug[[k8s/prometheus-stack.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus-stack.md)] --- ## Installing `kube-prometheus-stack` - Let's install that stack *directly* from its repo (without doing `helm repo add` first) - Otherwise, keep the same naming strategy: ```bash helm upgrade --install kube-prometheus-stack kube-prometheus-stack \ --namespace kube-prometheus-stack --create-namespace \ --repo https://prometheus-community.github.io/helm-charts ``` - This will take a minute... - Then check what was installed: ```bash kubectl get all --namespace kube-prometheus-stack ``` .debug[[k8s/prometheus-stack.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus-stack.md)] --- ## Exposing Grafana - Let's create an Ingress for Grafana ```bash kubectl create ingress --namespace kube-prometheus-stack grafana \ --rule=grafana.`cloudnative.party`/*=kube-prometheus-stack-grafana:80 ``` (as usual, make sure to use *your* domain name above) - Connect to Grafana (remember that the DNS record might take a few minutes to come up) .debug[[k8s/prometheus-stack.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus-stack.md)] --- ## Grafana credentials - What could the login and password be? - Let's look at the Secrets available in the namespace: ```bash kubectl get secrets --namespace kube-prometheus-stack ``` - There is a `kube-prometheus-stack-grafana` that looks promising! - Decode the Secret: ```bash kubectl get secret --namespace kube-prometheus-stack \ kube-prometheus-stack-grafana -o json | jq '.data | map_values(@base64d)' ``` - If you don't have the `jq` tool mentioned above, don't worry... -- - The login/password is hardcoded to `admin`/`prom-operator` 😬 .debug[[k8s/prometheus-stack.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus-stack.md)] --- ## Grafana dashboards - Once logged in, click on the "Dashboards" icon on the left (it's the one that looks like four squares) - Then click on the "Manage" entry - Then click on "Kubernetes / Compute Resources / Cluster" - This gives us a breakdown of resource usage by Namespace - Feel free to explore the other dashboards! ??? :EN:- Installing Prometheus and Grafana :FR:- Installer Prometheus et Grafana :T: Observing our cluster with Prometheus and Grafana :Q: What's the relationship between Prometheus and Grafana? :A: Prometheus collects and graphs metrics; Grafana sends alerts :A: ✔️Prometheus collects metrics; Grafana displays them on dashboards :A: Prometheus collects and graphs metrics; Grafana is its configuration interface :A: Grafana collects and graphs metrics; Prometheus sends alerts .debug[[k8s/prometheus-stack.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/prometheus-stack.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/distillery-containers.jpg)] --- name: toc-resource-limits class: title Resource Limits .nav[ [Previous part](#toc-prometheus-and-grafana) | [Back to table of contents](#toc-part-10) | [Next part](#toc-defining-min-max-and-default-resources) ] .debug[(automatically generated title slide)] --- # Resource Limits - We can attach resource indications to our pods (or rather: to the *containers* in our pods) - We can specify *limits* and/or *requests* - We can specify quantities of CPU and/or memory .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## CPU vs memory - CPU is a *compressible resource* (it can be preempted immediately without adverse effect) - Memory is an *incompressible resource* (it needs to be swapped out to be reclaimed; and this is costly) - As a result, exceeding limits will have different consequences for CPU and memory .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Exceeding CPU limits - CPU can be reclaimed instantaneously (in fact, it is preempted hundreds of times per second, at each context switch) - If a container uses too much CPU, it can be throttled (it will be scheduled less often) - The processes in that container will run slower (or rather: they will not run faster) .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- class: extra-details ## CPU limits implementation details - A container with a CPU limit will be "rationed" by the kernel - Every `cfs_period_us`, it will receive a CPU quota, like an "allowance" (that interval defaults to 100ms) - Once it has used its quota, it will be stalled until the next period - This can easily result in throttling for bursty workloads (see details on next slide) .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- class: extra-details ## A bursty example - Web service receives one request per minute - Each request takes 1 second of CPU - Average load: 1.66% - Let's say we set a CPU limit of 10% - This means CPU quotas of 10ms every 100ms - Obtaining the quota for 1 second of CPU will take 10 seconds - Observed latency will be 10 seconds (... actually 9.9s) instead of 1 second (real-life scenarios will of course be less extreme, but they do happen!) .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- class: extra-details ## Multi-core scheduling details - Each core gets a small share of the container's CPU quota (this avoids locking and contention on the "global" quota for the container) - By default, the kernel distributes that quota to CPUs in 5ms increments (tunable with `kernel.sched_cfs_bandwidth_slice_us`) - If a containerized process (or thread) uses up its local CPU quota: *it gets more from the "global" container quota (if there's some left)* - If it "yields" (e.g. sleeps for I/O) before using its local CPU quota: *the quota is **soon** returned to the "global" container quota, **minus** 1ms* .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- class: extra-details ## Low quotas on machines with many cores - The local CPU quota is not immediately returned to the global quota - this reduces locking and contention on the global quota - but this can cause starvation when many threads/processes become runnable - That 1ms that "stays" on the local CPU quota is often useful - if the thread/process becomes runnable, it can be scheduled immediately - again, this reduces locking and contention on the global quota - but if the thread/process doesn't become runnable, it is wasted! - this can become a huge problem on machines with many cores .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- class: extra-details ## CPU limits in a nutshell - Beware if you run small bursty workloads on machines with many cores! ("highly-threaded, user-interactive, non-cpu bound applications") - Check the `nr_throttled` and `throttled_time` metrics in `cpu.stat` - Possible solutions/workarounds: - be generous with the limits - make sure your kernel has the [appropriate patch](https://lkml.org/lkml/2019/5/17/581) - use [static CPU manager policy](https://kubernetes.io/docs/tasks/administer-cluster/cpu-management-policies/#static-policy) For more details, check [this blog post](https://erickhun.com/posts/kubernetes-faster-services-no-cpu-limits/) or these ones ([part 1](https://engineering.indeedblog.com/blog/2019/12/unthrottled-fixing-cpu-limits-in-the-cloud/), [part 2](https://engineering.indeedblog.com/blog/2019/12/cpu-throttling-regression-fix/)). .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Exceeding memory limits - Memory needs to be swapped out before being reclaimed - "Swapping" means writing memory pages to disk, which is very slow - On a classic system, a process that swaps can get 1000x slower (because disk I/O is 1000x slower than memory I/O) - Exceeding the memory limit (even by a small amount) can reduce performance *a lot* - Kubernetes *does not support swap* (more on that later!) - Exceeding the memory limit will cause the container to be killed .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Limits vs requests - Limits are "hard limits" (they can't be exceeded) - a container exceeding its memory limit is killed - a container exceeding its CPU limit is throttled - Requests are used for scheduling purposes - a container using *less* than what it requested will never be killed or throttled - the scheduler uses the requested sizes to determine placement - the resources requested by all pods on a node will never exceed the node size .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Pod quality of service Each pod is assigned a QoS class (visible in `status.qosClass`). - If limits = requests: - as long as the container uses less than the limit, it won't be affected - if all containers in a pod have *(limits=requests)*, QoS is considered "Guaranteed" - If requests < limits: - as long as the container uses less than the request, it won't be affected - otherwise, it might be killed/evicted if the node gets overloaded - if at least one container has *(requests<limits)*, QoS is considered "Burstable" - If a pod doesn't have any request nor limit, QoS is considered "BestEffort" .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Quality of service impact - When a node is overloaded, BestEffort pods are killed first - Then, Burstable pods that exceed their requests - Burstable and Guaranteed pods below their requests are never killed (except if their node fails) - If we only use Guaranteed pods, no pod should ever be killed (as long as they stay within their limits) (Pod QoS is also explained in [this page](https://kubernetes.io/docs/tasks/configure-pod-container/quality-service-pod/) of the Kubernetes documentation and in [this blog post](https://medium.com/google-cloud/quality-of-service-class-qos-in-kubernetes-bb76a89eb2c6).) .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Where is my swap? - The semantics of memory and swap limits on Linux cgroups are complex - With cgroups v1, it's not possible to disable swap for a cgroup (the closest option is to [reduce "swappiness"](https://unix.stackexchange.com/questions/77939/turning-off-swapping-for-only-one-process-with-cgroups)) - It is possible with cgroups v2 (see the [kernel docs](https://www.kernel.org/doc/html/latest/admin-guide/cgroup-v2.html) and the [fbatx docs](https://facebookmicrosites.github.io/cgroup2/docs/memory-controller.html#using-swap)) - Cgroups v2 aren't widely deployed yet - The architects of Kubernetes wanted to ensure that Guaranteed pods never swap - The simplest solution was to disable swap entirely .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Alternative point of view - Swap enables paging¹ of anonymous² memory - Even when swap is disabled, Linux will still page memory for: - executables, libraries - mapped files - Disabling swap *will reduce performance and available resources* - For a good time, read [kubernetes/kubernetes#53533](https://github.com/kubernetes/kubernetes/issues/53533) - Also read this [excellent blog post about swap](https://jvns.ca/blog/2017/02/17/mystery-swap/) ¹Paging: reading/writing memory pages from/to disk to reclaim physical memory ²Anonymous memory: memory that is not backed by files or blocks .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Enabling swap anyway - If you don't care that pods are swapping, you can enable swap - You will need to add the flag `--fail-swap-on=false` to kubelet (otherwise, it won't start!) .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Specifying resources - Resource requests are expressed at the *container* level - CPU is expressed in "virtual CPUs" (corresponding to the virtual CPUs offered by some cloud providers) - CPU can be expressed with a decimal value, or even a "milli" suffix (so 100m = 0.1) - Memory is expressed in bytes - Memory can be expressed with k, M, G, T, ki, Mi, Gi, Ti suffixes (corresponding to 10^3, 10^6, 10^9, 10^12, 2^10, 2^20, 2^30, 2^40) .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Specifying resources in practice This is what the spec of a Pod with resources will look like: ```yaml containers: - name: httpenv image: jpetazzo/httpenv resources: limits: memory: "100Mi" cpu: "100m" requests: memory: "100Mi" cpu: "10m" ``` This set of resources makes sure that this service won't be killed (as long as it stays below 100 MB of RAM), but allows its CPU usage to be throttled if necessary. .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Default values - If we specify a limit without a request: the request is set to the limit - If we specify a request without a limit: there will be no limit (which means that the limit will be the size of the node) - If we don't specify anything: the request is zero and the limit is the size of the node *Unless there are default values defined for our namespace!* .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## We need default resource values - If we do not set resource values at all: - the limit is "the size of the node" - the request is zero - This is generally *not* what we want - a container without a limit can use up all the resources of a node - if the request is zero, the scheduler can't make a smart placement decision - To address this, we can set default values for resources - This is done with a LimitRange object .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/lots-of-containers.jpg)] --- name: toc-defining-min-max-and-default-resources class: title Defining min, max, and default resources .nav[ [Previous part](#toc-resource-limits) | [Back to table of contents](#toc-part-10) | [Next part](#toc-namespace-quotas) ] .debug[(automatically generated title slide)] --- # Defining min, max, and default resources - We can create LimitRange objects to indicate any combination of: - min and/or max resources allowed per pod - default resource *limits* - default resource *requests* - maximal burst ratio (*limit/request*) - LimitRange objects are namespaced - They apply to their namespace only .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## LimitRange example ```yaml apiVersion: v1 kind: LimitRange metadata: name: my-very-detailed-limitrange spec: limits: - type: Container min: cpu: "100m" max: cpu: "2000m" memory: "1Gi" default: cpu: "500m" memory: "250Mi" defaultRequest: cpu: "500m" ``` .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Example explanation The YAML on the previous slide shows an example LimitRange object specifying very detailed limits on CPU usage, and providing defaults on RAM usage. Note the `type: Container` line: in the future, it might also be possible to specify limits per Pod, but it's not [officially documented yet](https://github.com/kubernetes/website/issues/9585). .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## LimitRange details - LimitRange restrictions are enforced only when a Pod is created (they don't apply retroactively) - They don't prevent creation of e.g. an invalid Deployment or DaemonSet (but the pods will not be created as long as the LimitRange is in effect) - If there are multiple LimitRange restrictions, they all apply together (which means that it's possible to specify conflicting LimitRanges, <br/>preventing any Pod from being created) - If a LimitRange specifies a `max` for a resource but no `default`, <br/>that `max` value becomes the `default` limit too .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/plastic-containers.JPG)] --- name: toc-namespace-quotas class: title Namespace quotas .nav[ [Previous part](#toc-defining-min-max-and-default-resources) | [Back to table of contents](#toc-part-10) | [Next part](#toc-limiting-resources-in-practice) ] .debug[(automatically generated title slide)] --- # Namespace quotas - We can also set quotas per namespace - Quotas apply to the total usage in a namespace (e.g. total CPU limits of all pods in a given namespace) - Quotas can apply to resource limits and/or requests (like the CPU and memory limits that we saw earlier) - Quotas can also apply to other resources: - "extended" resources (like GPUs) - storage size - number of objects (number of pods, services...) .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Creating a quota for a namespace - Quotas are enforced by creating a ResourceQuota object - ResourceQuota objects are namespaced, and apply to their namespace only - We can have multiple ResourceQuota objects in the same namespace - The most restrictive values are used .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Limiting total CPU/memory usage - The following YAML specifies an upper bound for *limits* and *requests*: ```yaml apiVersion: v1 kind: ResourceQuota metadata: name: a-little-bit-of-compute spec: hard: requests.cpu: "10" requests.memory: 10Gi limits.cpu: "20" limits.memory: 20Gi ``` These quotas will apply to the namespace where the ResourceQuota is created. .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Limiting number of objects - The following YAML specifies how many objects of specific types can be created: ```yaml apiVersion: v1 kind: ResourceQuota metadata: name: quota-for-objects spec: hard: pods: 100 services: 10 secrets: 10 configmaps: 10 persistentvolumeclaims: 20 services.nodeports: 0 services.loadbalancers: 0 count/roles.rbac.authorization.k8s.io: 10 ``` (The `count/` syntax allows limiting arbitrary objects, including CRDs.) .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## YAML vs CLI - Quotas can be created with a YAML definition - ...Or with the `kubectl create quota` command - Example: ```bash kubectl create quota my-resource-quota --hard=pods=300,limits.memory=300Gi ``` - With both YAML and CLI form, the values are always under the `hard` section (there is no `soft` quota) .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Viewing current usage When a ResourceQuota is created, we can see how much of it is used: ``` kubectl describe resourcequota my-resource-quota Name: my-resource-quota Namespace: default Resource Used Hard -------- ---- ---- pods 12 100 services 1 5 services.loadbalancers 0 0 services.nodeports 0 0 ``` .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Advanced quotas and PriorityClass - Since Kubernetes 1.12, it is possible to create PriorityClass objects - Pods can be assigned a PriorityClass - Quotas can be linked to a PriorityClass - This allows us to reserve resources for pods within a namespace - For more details, check [this documentation page](https://kubernetes.io/docs/concepts/policy/resource-quotas/#resource-quota-per-priorityclass) .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/train-of-containers-1.jpg)] --- name: toc-limiting-resources-in-practice class: title Limiting resources in practice .nav[ [Previous part](#toc-namespace-quotas) | [Back to table of contents](#toc-part-10) | [Next part](#toc-checking-node-and-pod-resource-usage) ] .debug[(automatically generated title slide)] --- # Limiting resources in practice - We have at least three mechanisms: - requests and limits per Pod - LimitRange per namespace - ResourceQuota per namespace - Let's see a simple recommendation to get started with resource limits .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Set a LimitRange - In each namespace, create a LimitRange object - Set a small default CPU request and CPU limit (e.g. "100m") - Set a default memory request and limit depending on your most common workload - for Java, Ruby: start with "1G" - for Go, Python, PHP, Node: start with "250M" - Set upper bounds slightly below your expected node size (80-90% of your node size, with at least a 500M memory buffer) .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Set a ResourceQuota - In each namespace, create a ResourceQuota object - Set generous CPU and memory limits (e.g. half the cluster size if the cluster hosts multiple apps) - Set generous objects limits - these limits should not be here to constrain your users - they should catch a runaway process creating many resources - example: a custom controller creating many pods .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Observe, refine, iterate - Observe the resource usage of your pods (we will see how in the next chapter) - Adjust individual pod limits - If you see trends: adjust the LimitRange (rather than adjusting every individual set of pod limits) - Observe the resource usage of your namespaces (with `kubectl describe resourcequota ...`) - Rinse and repeat regularly .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Viewing a namespace limits and quotas - `kubectl describe namespace` will display resource limits and quotas .exercise[ - Try it out: ```bash kubectl describe namespace default ``` - View limits and quotas for *all* namespaces: ```bash kubectl describe namespace ``` ] .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Additional resources - [A Practical Guide to Setting Kubernetes Requests and Limits](http://blog.kubecost.com/blog/requests-and-limits/) - explains what requests and limits are - provides guidelines to set requests and limits - gives PromQL expressions to compute good values <br/>(our app needs to be running for a while) - [Kube Resource Report](https://github.com/hjacobs/kube-resource-report/) - generates web reports on resource usage - [static demo](https://hjacobs.github.io/kube-resource-report/sample-report/output/index.html) | [live demo](https://kube-resource-report.demo.j-serv.de/applications.html) ??? :EN:- Setting compute resource limits :EN:- Defining default policies for resource usage :EN:- Managing cluster allocation and quotas :EN:- Resource management in practice :FR:- Allouer et limiter les ressources des conteneurs :FR:- Définir des ressources par défaut :FR:- Gérer les quotas de ressources au niveau du cluster :FR:- Conseils pratiques .debug[[k8s/resource-limits.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/resource-limits.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/train-of-containers-2.jpg)] --- name: toc-checking-node-and-pod-resource-usage class: title Checking Node and Pod resource usage .nav[ [Previous part](#toc-limiting-resources-in-practice) | [Back to table of contents](#toc-part-10) | [Next part](#toc-cluster-sizing) ] .debug[(automatically generated title slide)] --- # Checking Node and Pod resource usage - We've installed a few things on our cluster so far - How much resources (CPU, RAM) are we using? - We need metrics! .exercise[ - Let's try the following command: ```bash kubectl top nodes ``` ] .debug[[k8s/metrics-server.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/metrics-server.md)] --- ## Is metrics-server installed? - If we see a list of nodes, with CPU and RAM usage: *great, metrics-server is installed!* - If we see `error: Metrics API not available`: *metrics-server isn't installed, so we'll install it!* .debug[[k8s/metrics-server.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/metrics-server.md)] --- ## The resource metrics pipeline - The `kubectl top` command relies on the Metrics API - The Metrics API is part of the "[resource metrics pipeline]" - The Metrics API isn't served (built into) the Kubernetes API server - It is made available through the [aggregation layer] - It is usually served by a component called metrics-server - It is optional (Kubernetes can function without it) - It is necessary for some features (like the Horizontal Pod Autoscaler) [resource metrics pipeline]: https://kubernetes.io/docs/tasks/debug-application-cluster/resource-metrics-pipeline/ [aggregation layer]: https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/ .debug[[k8s/metrics-server.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/metrics-server.md)] --- ## Other ways to get metrics - We could use a SAAS like Datadog, New Relic... - We could use a self-hosted solution like Prometheus - Or we could use metrics-server - What's special about metrics-server? .debug[[k8s/metrics-server.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/metrics-server.md)] --- ## Pros/cons Cons: - no data retention (no history data, just instant numbers) - only CPU and RAM of nodes and pods (no disk or network usage or I/O...) Pros: - very lightweight - doesn't require storage - used by Kubernetes autoscaling .debug[[k8s/metrics-server.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/metrics-server.md)] --- ## Why metrics-server - We may install something fancier later (think: Prometheus with Grafana) - But metrics-server will work in *minutes* - It will barely use resources on our cluster - It's required for autoscaling anyway .debug[[k8s/metrics-server.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/metrics-server.md)] --- ## How metric-server works - It runs a single Pod - That Pod will fetch metrics from all our Nodes - It will expose them through the Kubernetes API agregation layer (we won't say much more about that agregation layer; that's fairly advanced stuff!) .debug[[k8s/metrics-server.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/metrics-server.md)] --- ## Installing metrics-server - In a lot of places, this is done with a little bit of custom YAML (derived from the [official installation instructions](https://github.com/kubernetes-sigs/metrics-server#installation)) - We're going to use Helm one more time: ```bash helm upgrade --install metrics-server bitnami/metrics-server \ --create-namespace --namespace metrics-server \ --set apiService.create=true \ --set extraArgs.kubelet-insecure-tls=true \ --set extraArgs.kubelet-preferred-address-types=InternalIP ``` - What are these options for? .debug[[k8s/metrics-server.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/metrics-server.md)] --- ## Installation options - `apiService.create=true` register `metrics-server` with the Kubernetes agregation layer (create an entry that will show up in `kubectl get apiservices`) - `extraArgs.kubelet-insecure-tls=true` when connecting to nodes to collect their metrics, don't check kubelet TLS certs (because most kubelet certs include the node name, but not its IP address) - `extraArgs.kubelet-preferred-address-types=InternalIP` when connecting to nodes, use their internal IP address instead of node name (because the latter requires an internal DNS, which is rarely configured) .debug[[k8s/metrics-server.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/metrics-server.md)] --- ## Testing metrics-server - After a minute or two, metrics-server should be up - We should now be able to check Nodes resource usage: ```bash kubectl top nodes ``` - And Pods resource usage, too: ```bash kubectl top pods --all-namespaces ``` .debug[[k8s/metrics-server.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/metrics-server.md)] --- ## Keep some padding - The RAM usage that we see should correspond more or less to the Resident Set Size - Our pods also need some extra space for buffers, caches... - Do not aim for 100% memory usage! - Some more realistic targets: 50% (for workloads with disk I/O and leveraging caching) 90% (on very big nodes with mostly CPU-bound workloads) 75% (anywhere in between!) .debug[[k8s/metrics-server.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/metrics-server.md)] --- ## Other tools - kube-capacity is a great CLI tool to view resources (https://github.com/robscott/kube-capacity) - It can show resource and limits, and compare them with usage - It can show utilization per node, or per pod - kube-resource-report can generate HTML reports (https://github.com/hjacobs/kube-resource-report) ??? :EN:- The resource metrics pipeline :EN:- Installing metrics-server :EN:- Le *resource metrics pipeline* :FR:- Installtion de metrics-server .debug[[k8s/metrics-server.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/metrics-server.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/two-containers-on-a-truck.jpg)] --- name: toc-cluster-sizing class: title Cluster sizing .nav[ [Previous part](#toc-checking-node-and-pod-resource-usage) | [Back to table of contents](#toc-part-10) | [Next part](#toc-the-horizontal-pod-autoscaler) ] .debug[(automatically generated title slide)] --- # Cluster sizing - What happens when the cluster gets full? - How can we scale up the cluster? - Can we do it automatically? - What are other methods to address capacity planning? .debug[[k8s/cluster-sizing.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-sizing.md)] --- ## When are we out of resources? - kubelet monitors node resources: - memory - node disk usage (typically the root filesystem of the node) - image disk usage (where container images and RW layers are stored) - For each resource, we can provide two thresholds: - a hard threshold (if it's met, it provokes immediate action) - a soft threshold (provokes action only after a grace period) - Resource thresholds and grace periods are configurable (by passing kubelet command-line flags) .debug[[k8s/cluster-sizing.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-sizing.md)] --- ## What happens then? - If disk usage is too high: - kubelet will try to remove terminated pods - then, it will try to *evict* pods - If memory usage is too high: - it will try to evict pods - The node is marked as "under pressure" - This temporarily prevents new pods from being scheduled on the node .debug[[k8s/cluster-sizing.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-sizing.md)] --- ## Which pods get evicted? - kubelet looks at the pods' QoS and PriorityClass - First, pods with BestEffort QoS are considered - Then, pods with Burstable QoS exceeding their *requests* (but only if the exceeding resource is the one that is low on the node) - Finally, pods with Guaranteed QoS, and Burstable pods within their requests - Within each group, pods are sorted by PriorityClass - If there are pods with the same PriorityClass, they are sorted by usage excess (i.e. the pods whose usage exceeds their requests the most are evicted first) .debug[[k8s/cluster-sizing.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-sizing.md)] --- class: extra-details ## Eviction of Guaranteed pods - *Normally*, pods with Guaranteed QoS should not be evicted - A chunk of resources is reserved for node processes (like kubelet) - It is expected that these processes won't use more than this reservation - If they do use more resources anyway, all bets are off! - If this happens, kubelet must evict Guaranteed pods to preserve node stability (or Burstable pods that are still within their requested usage) .debug[[k8s/cluster-sizing.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-sizing.md)] --- ## What happens to evicted pods? - The pod is terminated - It is marked as `Failed` at the API level - If the pod was created by a controller, the controller will recreate it - The pod will be recreated on another node, *if there are resources available!* - For more details about the eviction process, see: - [this documentation page](https://kubernetes.io/docs/tasks/administer-cluster/out-of-resource/) about resource pressure and pod eviction, - [this other documentation page](https://kubernetes.io/docs/concepts/configuration/pod-priority-preemption/) about pod priority and preemption. .debug[[k8s/cluster-sizing.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-sizing.md)] --- ## What if there are no resources available? - Sometimes, a pod cannot be scheduled anywhere: - all the nodes are under pressure, - or the pod requests more resources than are available - The pod then remains in `Pending` state until the situation improves .debug[[k8s/cluster-sizing.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-sizing.md)] --- ## Cluster scaling - One way to improve the situation is to add new nodes - This can be done automatically with the [Cluster Autoscaler](https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler) - The autoscaler will automatically scale up: - if there are pods that failed to be scheduled - The autoscaler will automatically scale down: - if nodes have a low utilization for an extended period of time .debug[[k8s/cluster-sizing.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-sizing.md)] --- ## Restrictions, gotchas ... - The Cluster Autoscaler only supports a few cloud infrastructures (see [here](https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler/cloudprovider) for a list) - The Cluster Autoscaler cannot scale down nodes that have pods using: - local storage - affinity/anti-affinity rules preventing them from being rescheduled - a restrictive PodDisruptionBudget .debug[[k8s/cluster-sizing.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-sizing.md)] --- ## Other way to do capacity planning - "Running Kubernetes without nodes" - Systems like [Virtual Kubelet](https://virtual-kubelet.io/) or [Kiyot](https://static.elotl.co/docs/latest/kiyot/kiyot.html) can run pods using on-demand resources - Virtual Kubelet can leverage e.g. ACI or Fargate to run pods - Kiyot runs pods in ad-hoc EC2 instances (1 instance per pod) - Economic advantage (no wasted capacity) - Security advantage (stronger isolation between pods) Check [this blog post](http://jpetazzo.github.io/2019/02/13/running-kubernetes-without-nodes-with-kiyot/) for more details. ??? :EN:- What happens when the cluster is at, or over, capacity :EN:- Cluster sizing and scaling :FR:- Ce qui se passe quand il n'y a plus assez de ressources :FR:- Dimensionner et redimensionner ses clusters .debug[[k8s/cluster-sizing.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-sizing.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/wall-of-containers.jpeg)] --- name: toc-the-horizontal-pod-autoscaler class: title The Horizontal Pod Autoscaler .nav[ [Previous part](#toc-cluster-sizing) | [Back to table of contents](#toc-part-10) | [Next part](#toc-extending-the-kubernetes-api) ] .debug[(automatically generated title slide)] --- # The Horizontal Pod Autoscaler - What is the Horizontal Pod Autoscaler, or HPA? - It is a controller that can perform *horizontal* scaling automatically - Horizontal scaling = changing the number of replicas (adding/removing pods) - Vertical scaling = changing the size of individual replicas (increasing/reducing CPU and RAM per pod) - Cluster scaling = changing the size of the cluster (adding/removing nodes) .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## Principle of operation - Each HPA resource (or "policy") specifies: - which object to monitor and scale (e.g. a Deployment, ReplicaSet...) - min/max scaling ranges (the max is a safety limit!) - a target resource usage (e.g. the default is CPU=80%) - The HPA continuously monitors the CPU usage for the related object - It computes how many pods should be running: `TargetNumOfPods = ceil(sum(CurrentPodsCPUUtilization) / Target)` - It scales the related object up/down to this target number of pods .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## Pre-requirements - The metrics server needs to be running (i.e. we need to be able to see pod metrics with `kubectl top pods`) - The pods that we want to autoscale need to have resource requests (because the target CPU% is not absolute, but relative to the request) - The latter actually makes a lot of sense: - if a Pod doesn't have a CPU request, it might be using 10% of CPU... - ...but only because there is no CPU time available! - this makes sure that we won't add pods to nodes that are already resource-starved .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## Testing the HPA - We will start a CPU-intensive web service - We will send some traffic to that service - We will create an HPA policy - The HPA will automatically scale up the service for us .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## A CPU-intensive web service - Let's use `jpetazzo/busyhttp` (it is a web server that will use 1s of CPU for each HTTP request) .exercise[ - Deploy the web server: ```bash kubectl create deployment busyhttp --image=jpetazzo/busyhttp ``` - Expose it with a ClusterIP service: ```bash kubectl expose deployment busyhttp --port=80 ``` - Get the ClusterIP allocated to the service: ```bash kubectl get svc busyhttp ``` ] .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## Monitor what's going on - Let's start a bunch of commands to watch what is happening .exercise[ - Monitor pod CPU usage: ```bash watch kubectl top pods -l app=busyhttp ``` <!-- ```wait NAME``` ```tmux split-pane -v``` ```bash CLUSTERIP=$(kubectl get svc busyhttp -o jsonpath={.spec.clusterIP})``` --> - Monitor service latency: ```bash httping http://`$CLUSTERIP`/ ``` <!-- ```wait connected to``` ```tmux split-pane -v``` --> - Monitor cluster events: ```bash kubectl get events -w ``` <!-- ```wait Normal``` ```tmux split-pane -v``` ```bash CLUSTERIP=$(kubectl get svc busyhttp -o jsonpath={.spec.clusterIP})``` --> ] .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## Send traffic to the service - We will use `ab` (Apache Bench) to send traffic .exercise[ - Send a lot of requests to the service, with a concurrency level of 3: ```bash ab -c 3 -n 100000 http://`$CLUSTERIP`/ ``` <!-- ```wait be patient``` ```tmux split-pane -v``` ```tmux selectl even-vertical``` --> ] The latency (reported by `httping`) should increase above 3s. The CPU utilization should increase to 100%. (The server is single-threaded and won't go above 100%.) .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## Create an HPA policy - There is a helper command to do that for us: `kubectl autoscale` .exercise[ - Create the HPA policy for the `busyhttp` deployment: ```bash kubectl autoscale deployment busyhttp --max=10 ``` ] By default, it will assume a target of 80% CPU usage. This can also be set with `--cpu-percent=`. -- *The autoscaler doesn't seem to work. Why?* .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## What did we miss? - The events stream gives us a hint, but to be honest, it's not very clear: `missing request for cpu` - We forgot to specify a resource request for our Deployment! - The HPA target is not an absolute CPU% - It is relative to the CPU requested by the pod .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## Adding a CPU request - Let's edit the deployment and add a CPU request - Since our server can use up to 1 core, let's request 1 core .exercise[ - Edit the Deployment definition: ```bash kubectl edit deployment busyhttp ``` <!-- ```wait Please edit``` ```keys /resources``` ```key ^J``` ```keys $xxxo requests:``` ```key ^J``` ```key Space``` ```key Space``` ```keys cpu: "1"``` ```key Escape``` ```keys :wq``` ```key ^J``` --> - In the `containers` list, add the following block: ```yaml resources: requests: cpu: "1" ``` ] .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## Results - After saving and quitting, a rolling update happens (if `ab` or `httping` exits, make sure to restart it) - It will take a minute or two for the HPA to kick in: - the HPA runs every 30 seconds by default - it needs to gather metrics from the metrics server first - If we scale further up (or down), the HPA will react after a few minutes: - it won't scale up if it already scaled in the last 3 minutes - it won't scale down if it already scaled in the last 5 minutes .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## What about other metrics? - The HPA in API group `autoscaling/v1` only supports CPU scaling - The HPA in API group `autoscaling/v2beta2` supports metrics from various API groups: - metrics.k8s.io, aka metrics server (per-Pod CPU and RAM) - custom.metrics.k8s.io, custom metrics per Pod - external.metrics.k8s.io, external metrics (not associated to Pods) - Kubernetes doesn't implement any of these API groups - Using these metrics requires [registering additional APIs](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/#support-for-metrics-apis) - The metrics provided by metrics server are standard; everything else is custom - For more details, see [this great blog post](https://medium.com/uptime-99/kubernetes-hpa-autoscaling-with-custom-and-external-metrics-da7f41ff7846) or [this talk](https://www.youtube.com/watch?v=gSiGFH4ZnS8) .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## Cleanup - Since `busyhttp` uses CPU cycles, let's stop it before moving on .exercise[ - Delete the `busyhttp` Deployment: ```bash kubectl delete deployment busyhttp ``` <!-- ```key ^D``` ```key ^C``` ```key ^D``` ```key ^C``` ```key ^D``` ```key ^C``` ```key ^D``` ```key ^C``` --> ] ??? :EN:- Auto-scaling resources :FR:- *Auto-scaling* (dimensionnement automatique) des ressources .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/Container-Ship-Freighter-Navigation-Elbe-Romance-1782991.jpg)] --- name: toc-extending-the-kubernetes-api class: title Extending the Kubernetes API .nav[ [Previous part](#toc-the-horizontal-pod-autoscaler) | [Back to table of contents](#toc-part-11) | [Next part](#toc-api-server-internals) ] .debug[(automatically generated title slide)] --- # Extending the Kubernetes API There are multiple ways to extend the Kubernetes API. We are going to cover: - Controllers - Dynamic Admission Webhooks - Custom Resource Definitions (CRDs) - The Aggregation Layer But first, let's re(re)visit the API server ... .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Revisiting the API server - The Kubernetes API server is a central point of the control plane - Everything connects to the API server: - users (that's us, but also automation like CI/CD) - kubelets - network components (e.g. `kube-proxy`, pod network, NPC) - controllers; lots of controllers .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Some controllers - `kube-controller-manager` runs built-on controllers (watching Deployments, Nodes, ReplicaSets, and much more) - `kube-scheduler` runs the scheduler (it's conceptually not different from another controller) - `cloud-controller-manager` takes care of "cloud stuff" (e.g. provisioning load balancers, persistent volumes...) - Some components mentioned above are also controllers (e.g. Network Policy Controller) .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- ## More controllers - Cloud resources can also be managed by additional controllers (e.g. the [AWS Load Balancer Controller](https://github.com/kubernetes-sigs/aws-load-balancer-controller)) - Leveraging Ingress resources requires an Ingress Controller (many options available here; we can even install multiple ones!) - Many add-ons (including CRDs and operators) have controllers as well 🤔 *What's even a controller ?!?* .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- ## What's a controller? According to the [documentation](https://kubernetes.io/docs/concepts/architecture/controller/): *Controllers are **control loops** that<br/> **watch** the state of your cluster,<br/> then make or request changes where needed.* *Each controller tries to move the current cluster state closer to the desired state.* .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- ## What controllers do - Watch resources - Make changes: - purely at the API level (e.g. Deployment, ReplicaSet controllers) - and/or configure resources (e.g. `kube-proxy`) - and/or provision resources (e.g. load balancer controller) .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Extending Kubernetes with controllers - Random example: - watch resources like Deployments, Services ... - read annotations to configure monitoring - Technically, this is not extending the API (but it can still be very useful!) .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Other ways to extend Kubernetes - Prevent or alter API requests before resources are committed to storage: *Admission Control* - Create new resource types leveraging Kubernetes storage facilities: *Custom Resource Definitions* - Create new resource types with different storage or different semantics: *Aggregation Layer* - Spoiler alert: often, we will combine multiple techniques (and involve controllers as well!) .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Admission controllers - Admission controllers can vet or transform API requests - The diagram on the next slide shows the path of an API request (courtesy of Banzai Cloud) .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- class: pic ![API request lifecycle](images/api-request-lifecycle.png) .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Types of admission controllers - *Validating* admission controllers can accept/reject the API call - *Mutating* admission controllers can modify the API request payload - Both types can also trigger additional actions (e.g. automatically create a Namespace if it doesn't exist) - There are a number of built-in admission controllers (see [documentation](https://kubernetes.io/docs/reference/access-authn-authz/admission-controllers/#what-does-each-admission-controller-do) for a list) - We can also dynamically define and register our own .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- class: extra-details ## Some built-in admission controllers - ServiceAccount: automatically adds a ServiceAccount to Pods that don't explicitly specify one - LimitRanger: applies resource constraints specified by LimitRange objects when Pods are created - NamespaceAutoProvision: automatically creates namespaces when an object is created in a non-existent namespace *Note: #1 and #2 are enabled by default; #3 is not.* .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Dynamic Admission Control - We can set up *admission webhooks* to extend the behavior of the API server - The API server will submit incoming API requests to these webhooks - These webhooks can be *validating* or *mutating* - Webhooks can be set up dynamically (without restarting the API server) - To setup a dynamic admission webhook, we create a special resource: a `ValidatingWebhookConfiguration` or a `MutatingWebhookConfiguration` - These resources are created and managed like other resources (i.e. `kubectl create`, `kubectl get`...) .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Webhook Configuration - A ValidatingWebhookConfiguration or MutatingWebhookConfiguration contains: - the address of the webhook - the authentication information to use with the webhook - a list of rules - The rules indicate for which objects and actions the webhook is triggered (to avoid e.g. triggering webhooks when setting up webhooks) - The webhook server can be hosted in or out of the cluster .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Dynamic Admission Examples - Policy control ([Kyverno](https://kyverno.io/), [Open Policy Agent](https://www.openpolicyagent.org/docs/latest/)) - Sidecar injection (Used by some service meshes) - Type validation (More on this later, in the CRD section) .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Kubernetes API types - Almost everything in Kubernetes is materialized by a resource - Resources have a type (or "kind") (similar to strongly typed languages) - We can see existing types with `kubectl api-resources` - We can list resources of a given type with `kubectl get <type>` .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Creating new types - We can create new types with Custom Resource Definitions (CRDs) - CRDs are created dynamically (without recompiling or restarting the API server) - CRDs themselves are resources: - we can create a new type with `kubectl create` and some YAML - we can see all our custom types with `kubectl get crds` - After we create a CRD, the new type works just like built-in types .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Examples - Representing composite resources (e.g. clusters like databases, messages queues ...) - Representing external resources (e.g. virtual machines, object store buckets, domain names ...) - Representing configuration for controllers and operators (e.g. custom Ingress resources, certificate issuers, backups ...) - Alternate representations of other objects; services and service instances (e.g. encrypted secret, git endpoints ...) .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- ## The aggregation layer - We can delegate entire parts of the Kubernetes API to external servers - This is done by creating APIService resources (check them with `kubectl get apiservices`!) - The APIService resource maps a type (kind) and version to an external service - All requests concerning that type are sent (proxied) to the external service - This allows to have resources like CRDs, but that aren't stored in etcd - Example: `metrics-server` .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Why? - Using a CRD for live metrics would be extremely inefficient (etcd **is not** a metrics store; write performance is way too slow) - Instead, `metrics-server`: - collects metrics from kubelets - stores them in memory - exposes them as PodMetrics and NodeMetrics (in API group metrics.k8s.io) - is registered as an APIService .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Drawbacks - Requires a server - ... that implements a non-trivial API (aka the Kubernetes API semantics) - If we need REST semantics, CRDs are probably way simpler - *Sometimes* synchronizing external state with CRDs might do the trick (unless we want the external state to be our single source of truth) .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Service catalog - *Service catalog* is another extension mechanism - It's not extending the Kubernetes API strictly speaking (but it still provides new features!) - It doesn't create new types; it uses: - ClusterServiceBroker - ClusterServiceClass - ClusterServicePlan - ServiceInstance - ServiceBinding - It uses the Open service broker API .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Documentation - [Custom Resource Definitions: when to use them](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) - [Custom Resources Definitions: how to use them](https://kubernetes.io/docs/tasks/access-kubernetes-api/custom-resources/custom-resource-definitions/) - [Service Catalog](https://kubernetes.io/docs/concepts/extend-kubernetes/service-catalog/) - [Built-in Admission Controllers](https://kubernetes.io/docs/reference/access-authn-authz/admission-controllers/) - [Dynamic Admission Controllers](https://kubernetes.io/docs/reference/access-authn-authz/extensible-admission-controllers/) - [Aggregation Layer](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/) ??? :EN:- Overview of Kubernetes API extensions :FR:- Comment étendre l'API Kubernetes .debug[[k8s/extending-api.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/extending-api.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)] --- name: toc-api-server-internals class: title API server internals .nav[ [Previous part](#toc-extending-the-kubernetes-api) | [Back to table of contents](#toc-part-11) | [Next part](#toc-custom-resource-definitions) ] .debug[(automatically generated title slide)] --- # API server internals - Understanding the internals of the API server is useful.red[¹]: - when extending the Kubernetes API server (CRDs, webhooks...) - when running Kubernetes at scale - Let's dive into a bit of code! .footnote[.red[¹]And by *useful*, we mean *strongly recommended or else...*] .debug[[k8s/apiserver-deepdive.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/apiserver-deepdive.md)] --- ## The main handler - The API server parses its configuration, and builds a `GenericAPIServer` - ... which contains an `APIServerHandler` ([src](https://github.com/kubernetes/apiserver/blob/release-1.19/pkg/server/handler.go#L37 )) - ... which contains a couple of `http.Handler` fields - Requests go through: - `FullhandlerChain` (a series of HTTP filters, see next slide) - `Director` (switches the request to `GoRestfulContainer` or `NonGoRestfulMux`) - `GoRestfulContainer` is for "normal" APIs; integrates nicely with OpenAPI - `NonGoRestfulMux` is for everything else (e.g. proxy, delegation) .debug[[k8s/apiserver-deepdive.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/apiserver-deepdive.md)] --- ## The chain of handlers - API requests go through a complex chain of filters ([src](https://github.com/kubernetes/apiserver/blob/release-1.19/pkg/server/config.go#L671)) (note when reading that code: requests start at the bottom and go up) - This is where authentication, authorization, and admission happen (as well as a few other things!) - Let's review an arbitrary selection of some of these handlers! *In the following slides, the handlers are in chronological order.* *Note: handlers are nested; so they can act at the beginning and end of a request.* .debug[[k8s/apiserver-deepdive.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/apiserver-deepdive.md)] --- ## `WithPanicRecovery` - Reminder about Go: there is no exception handling in Go; instead: - functions typically return a composite `(SomeType, error)` type - when things go really bad, the code can call `panic()` - `panic()` can be caught with `recover()` <br/> (but this is almost never used like an exception handler!) - The API server code is not supposed to `panic()` - But just in case, we have that handler to prevent (some) crashes .debug[[k8s/apiserver-deepdive.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/apiserver-deepdive.md)] --- ## `WithRequestInfo` ([src](https://github.com/kubernetes/apiserver/blob/release-1.19/pkg/endpoints/request/requestinfo.go#L163)) - Parse out essential information: API group, version, namespace, resource, subresource, verb ... - WithRequestInfo: parse out API group+version, Namespace, resource, subresource ... - Maps HTTP verbs (GET, PUT, ...) to Kubernetes verbs (list, get, watch, ...) .debug[[k8s/apiserver-deepdive.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/apiserver-deepdive.md)] --- class: extra-details ## HTTP verb mapping - POST → create - PUT → update - PATCH → patch - DELETE <br/> → delete (if a resource name is specified) <br/> → deletecollection (otherwise) - GET, HEAD <br/> → get (if a resource name is specified) <br/> → list (otherwise) <br/> → watch (if the `?watch=true` option is specified) .debug[[k8s/apiserver-deepdive.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/apiserver-deepdive.md)] --- ## `WithWaitGroup`, - When we shutdown, tells clients (with in-flight requests) to retry - only for "short" requests - for long running requests, the client needs to do more - Long running requests include `watch` verb, `proxy` sub-resource (See also `WithTimeoutForNonLongRunningRequests`) .debug[[k8s/apiserver-deepdive.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/apiserver-deepdive.md)] --- ## AuthN and AuthZ - `WithAuthentication`: the request goes through a *chain* of authenticators ([src](https://github.com/kubernetes/apiserver/blob/release-1.19/pkg/endpoints/filters/authentication.go#L38)) - WithAudit - WithImpersonation: used for e.g. `kubectl ... --as another.user` - WithPriorityAndFairness or WithMaxInFlightLimit (`system:masters` can bypass these) - WithAuthorization .debug[[k8s/apiserver-deepdive.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/apiserver-deepdive.md)] --- ## After all these handlers ... - We get to the "director" mentioned above - Api Groups get installed into the "gorestfulhandler" ([src](https://github.com/kubernetes/apiserver/blob/release-1.19/pkg/server/genericapiserver.go#L423)) - REST-ish resources are managed by various handlers (in [this directory](https://github.com/kubernetes/apiserver/blob/release-1.19/pkg/endpoints/handlers/)) - These files show us the code path for each type of request .debug[[k8s/apiserver-deepdive.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/apiserver-deepdive.md)] --- class: extra-details ## Request code path - [create.go](https://github.com/kubernetes/apiserver/blob/release-1.19/pkg/endpoints/handlers/create.go): decode to HubGroupVersion; admission; mutating admission; store - [delete.go](https://github.com/kubernetes/apiserver/blob/release-1.19/pkg/endpoints/handlers/delete.go): validating admission only; deletion - [get.go](https://github.com/kubernetes/apiserver/blob/release-1.19/pkg/endpoints/handlers/get.go) (get, list): directly fetch from rest storage abstraction - [patch.go](https://github.com/kubernetes/apiserver/blob/release-1.19/pkg/endpoints/handlers/patch.go): admission; mutating admission; patch - [update.go](https://github.com/kubernetes/apiserver/blob/release-1.19/pkg/endpoints/handlers/update.go): decode to HubGroupVersion; admission; mutating admission; store - [watch.go](https://github.com/kubernetes/apiserver/blob/release-1.19/pkg/endpoints/handlers/watch.go): similar to get.go, but with watch logic (HubGroupVersion = in-memory, "canonical" version.) ??? :EN:- Kubernetes API server internals :FR:- Fonctionnement interne du serveur API .debug[[k8s/apiserver-deepdive.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/apiserver-deepdive.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/aerial-view-of-containers.jpg)] --- name: toc-custom-resource-definitions class: title Custom Resource Definitions .nav[ [Previous part](#toc-api-server-internals) | [Back to table of contents](#toc-part-11) | [Next part](#toc-the-aggregation-layer) ] .debug[(automatically generated title slide)] --- # Custom Resource Definitions - CRDs are one of the (many) ways to extend the API - CRDs can be defined dynamically (no need to recompile or reload the API server) - A CRD is defined with a CustomResourceDefinition resource (CustomResourceDefinition is conceptually similar to a *metaclass*) .debug[[k8s/crd.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/crd.md)] --- ## A very simple CRD The file [k8s/coffee-1.yaml](https://github.com/jpetazzo/container.training/tree/master/k8s/coffee-1.yaml) describes a very simple CRD representing different kinds of coffee: ```yaml apiVersion: apiextensions.k8s.io/v1beta1 kind: CustomResourceDefinition metadata: name: coffees.container.training spec: group: container.training version: v1alpha1 scope: Namespaced names: plural: coffees singular: coffee kind: Coffee shortNames: - cof ``` .debug[[k8s/crd.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/crd.md)] --- ## Creating a CRD - Let's create the Custom Resource Definition for our Coffee resource .exercise[ - Load the CRD: ```bash kubectl apply -f ~/container.training/k8s/coffee-1.yaml ``` - Confirm that it shows up: ```bash kubectl get crds ``` ] .debug[[k8s/crd.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/crd.md)] --- ## Creating custom resources The YAML below defines a resource using the CRD that we just created: ```yaml kind: Coffee apiVersion: container.training/v1alpha1 metadata: name: arabica spec: taste: strong ``` .exercise[ - Create a few types of coffee beans: ```bash kubectl apply -f ~/container.training/k8s/coffees.yaml ``` ] .debug[[k8s/crd.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/crd.md)] --- ## Viewing custom resources - By default, `kubectl get` only shows name and age of custom resources .exercise[ - View the coffee beans that we just created: ```bash kubectl get coffees ``` ] - We'll see in a bit how to improve that .debug[[k8s/crd.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/crd.md)] --- ## What can we do with CRDs? There are many possibilities! - *Operators* encapsulate complex sets of resources (e.g.: a PostgreSQL replicated cluster; an etcd cluster... <br/> see [awesome operators](https://github.com/operator-framework/awesome-operators) and [OperatorHub](https://operatorhub.io/) to find more) - Custom use-cases like [gitkube](https://gitkube.sh/) - creates a new custom type, `Remote`, exposing a git+ssh server - deploy by pushing YAML or Helm charts to that remote - Replacing built-in types with CRDs (see [this lightning talk by Tim Hockin](https://www.youtube.com/watch?v=ji0FWzFwNhA)) .debug[[k8s/crd.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/crd.md)] --- ## What's next? - Creating a basic CRD is quick and easy - But there is a lot more that we can (and probably should) do: - improve input with *data validation* - improve output with *custom columns* - And of course, we probably need a *controller* to go with our CRD! (otherwise, we're just using the Kubernetes API as a fancy data store) .debug[[k8s/crd.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/crd.md)] --- ## Additional printer columns - We can specify `additionalPrinterColumns` in the CRD - This is similar to `-o custom-columns` (map a column name to a path in the object, e.g. `.spec.taste`) ```yaml additionalPrinterColumns: - jsonPath: .spec.taste description: Subjective taste of that kind of coffee bean name: Taste type: string - jsonPath: .metadata.creationTimestamp name: Age type: date ``` .debug[[k8s/crd.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/crd.md)] --- ## Using additional printer columns - Let's update our CRD using [k8s/coffee-3.yaml](https://github.com/jpetazzo/container.training/tree/master/k8s/coffee-3.yaml) .exercise[ - Update the CRD: ```bash kubectl apply -f ~/container.training/k8s/coffee-3.yaml ``` - Look at our Coffee resources: ```bash kubectl get coffees ``` ] Note: we can update a CRD without having to re-create the corresponding resources. (Good news, right?) .debug[[k8s/crd.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/crd.md)] --- ## Data validation - By default, CRDs are not *validated* (we can put anything we want in the `spec`) - When creating a CRD, we can pass an OpenAPI v3 schema (which will then be used to validate resources) - More advanced validation can also be done with admission webhooks, e.g.: - consistency between parameters - advanced integer filters (e.g. odd number of replicas) - things that can change in one direction but not the other .debug[[k8s/crd.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/crd.md)] --- ## OpenAPI v3 schema example This is what we have in [k8s/coffee-3.yaml](https://github.com/jpetazzo/container.training/tree/master/k8s/coffee-3.yaml): ```yaml schema: openAPIV3Schema: type: object required: [ spec ] properties: spec: type: object properties: taste: description: Subjective taste of that kind of coffee bean type: string required: [ taste ] ``` .debug[[k8s/crd.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/crd.md)] --- ## Validation *a posteriori* - Some of the "coffees" that we defined earlier *do not* pass validation - How is that possible? -- - Validation happens at *admission* (when resources get written into the database) - Therefore, we can have "invalid" resources in etcd (they are invalid from the CRD perspective, but the CRD can be changed) 🤔 How should we handle that ? .debug[[k8s/crd.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/crd.md)] --- ## Versions - If the data format changes, we can roll out a new version of the CRD (e.g. go from `v1alpha1` to `v1alpha2`) - In a CRD we can specify the versions that exist, that are *served*, and *stored* - multiple versions can be *served* - only one can be *stored* - Kubernetes doesn't automatically migrate the content of the database - However, it can convert between versions when resources are read/written .debug[[k8s/crd.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/crd.md)] --- ## Conversion - When *creating* a new resource, the *stored* version is used (if we create it with another version, it gets converted) - When *getting* or *watching* resources, the *requested* version is used (if it is stored with another version, it gets converted) - By default, "conversion" only changes the `apiVersion` field - ... But we can register *conversion webhooks* (see [that doc page](https://kubernetes.io/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definition-versioning/#webhook-conversion) for details) .debug[[k8s/crd.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/crd.md)] --- ## Migrating database content - We need to *serve* a version as long as we *store* objects in that version (=as long as the database has at least one object with that version) - If we want to "retire" a version, we need to migrate these objects first - All we have to do is to read and re-write them (the [kube-storage-version-migrator](https://github.com/kubernetes-sigs/kube-storage-version-migrator) tool can help) .debug[[k8s/crd.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/crd.md)] --- ## What's next? - Generally, when creating a CRD, we also want to run a *controller* (otherwise nothing will happen when we create resources of that type) - The controller will typically *watch* our custom resources (and take action when they are created/updated) .debug[[k8s/crd.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/crd.md)] --- ## CRDs in the wild - [gitkube](https://storage.googleapis.com/gitkube/gitkube-setup-stable.yaml) - [A redis operator](https://github.com/amaizfinance/redis-operator/blob/master/deploy/crds/k8s_v1alpha1_redis_crd.yaml) - [cert-manager](https://github.com/jetstack/cert-manager/releases/download/v1.0.4/cert-manager.yaml) *How big are these YAML files?* *What's the size (e.g. in lines) of each resource?* .debug[[k8s/crd.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/crd.md)] --- ## CRDs in practice - Production-grade CRDs can be extremely verbose (because of the openAPI schema validation) - This can (and usually will) be managed by a framework .debug[[k8s/crd.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/crd.md)] --- ## (Ab)using the API server - If we need to store something "safely" (as in: in etcd), we can use CRDs - This gives us primitives to read/write/list objects (and optionally validate them) - The Kubernetes API server can run on its own (without the scheduler, controller manager, and kubelets) - By loading CRDs, we can have it manage totally different objects (unrelated to containers, clusters, etc.) ??? :EN:- Custom Resource Definitions (CRDs) :FR:- Les CRDs *(Custom Resource Definitions)* .debug[[k8s/crd.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/crd.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/blue-containers.jpg)] --- name: toc-the-aggregation-layer class: title The Aggregation Layer .nav[ [Previous part](#toc-custom-resource-definitions) | [Back to table of contents](#toc-part-11) | [Next part](#toc-dynamic-admission-control) ] .debug[(automatically generated title slide)] --- # The Aggregation Layer - The aggregation layer is a way to extend the Kubernetes API - It is similar to CRDs - it lets us define new resource types - these resources can then be used with `kubectl` and other clients - The implementation is very different - CRDs are handled within the API server - the aggregation layer offloads requests to another process - They are designed for very different use-cases .debug[[k8s/aggregation-layer.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/aggregation-layer.md)] --- ## CRDs vs aggregation layer - The Kubernetes API is a REST-ish API with a hierarchical structure - It can be extended with Custom Resource Definifions (CRDs) - Custom resources are managed by the Kubernetes API server - we don't need to write code - the API server does all the heavy lifting - these resources are persisted in Kubernetes' "standard" database <br/> (for most installations, that's `etcd`) - We can also define resources that are *not* managed by the API server (the API server merely proxies the requests to another server) .debug[[k8s/aggregation-layer.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/aggregation-layer.md)] --- ## Which one is best? - For things that "map" well to objects stored in a traditional database: *probably CRDs* - For things that "exist" only in Kubernetes and don't represent external resources: *probably CRDs* - For things that are read-only, at least from Kubernetes' perspective: *probably aggregation layer* - For things that can't be stored in etcd because of size or access patterns: *probably aggregation layer* .debug[[k8s/aggregation-layer.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/aggregation-layer.md)] --- ## How are resources organized? - Let's have a look at the Kubernetes API hierarchical structure - We'll ask `kubectl` to show us the exacts requests that it's making .exercise[ - Check the URI for a cluster-scope, "core" resource, e.g. a Node: ```bash kubectl -v6 get node node1 ``` - Check the URI for a cluster-scope, "non-core" resource, e.g. a ClusterRole: ```bash kubectl -v6 get clusterrole view ``` ] .debug[[k8s/aggregation-layer.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/aggregation-layer.md)] --- ## Core vs non-core - This is the structure of the URIs that we just checked: ``` /api/v1/nodes/node1 ↑ ↑ ↑ `version` `kind` `name` /apis/rbac.authorization.k8s.io/v1/clusterroles/view ↑ ↑ ↑ ↑ `group` `version` `kind` `name` ``` - There is no group for "core" resources - Or, we could say that the group, `core`, is implied .debug[[k8s/aggregation-layer.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/aggregation-layer.md)] --- ## Group-Version-Kind - In the API server, the Group-Version-Kind triple maps to a Go type (look for all the "GVK" occurrences in the source code!) - In the API server URI router, the GVK is parsed "relatively early" (so that the server can know which resource we're talking about) - "Well, actually ..." Things are a bit more complicated, see next slides! .debug[[k8s/aggregation-layer.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/aggregation-layer.md)] --- class: extra-details ## Namespaced resources - What about namespaced resources? .exercise[ - Check the URI for a namespaced, "core" resource, e.g. a Service: ```bash kubectl -v6 get service kubernetes --namespace default ``` ] - Here are what namespaced resources URIs look like: ``` /api/v1/namespaces/default/services/kubernetes ↑ ↑ ↑ ↑ `version` `namespace` `kind` `name` /apis/apps/v1/namespaces/kube-system/daemonsets/kube-proxy ↑ ↑ ↑ ↑ ↑ `group` `version` `namespace` `kind` `name` ``` .debug[[k8s/aggregation-layer.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/aggregation-layer.md)] --- class: extra-details ## Subresources - Many resources have *subresources*, for instance: - `/status` (decouples status updates from other updates) - `/scale` (exposes a consistent interface for autoscalers) - `/proxy` (allows access to HTTP resources) - `/portforward` (used by `kubectl port-forward`) - `/logs` (access pod logs) - These are added at the end of the URI .debug[[k8s/aggregation-layer.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/aggregation-layer.md)] --- class: extra-details ## Accessing a subresource .exercise[ - List `kube-proxy` pods: ```bash kubectl get pods --namespace=kube-system --selector=k8s-app=kube-proxy PODNAME=$( kubectl get pods --namespace=kube-system --selector=k8s-app=kube-proxy \ -o json | jq -r .items[0].metadata.name) ``` - Execute a command in a pod, showing the API requests: ```bash kubectl -v6 exec --namespace=kube-system $PODNAME -- echo hello world ``` ] -- The full request looks like: ``` POST https://.../api/v1/namespaces/kube-system/pods/kube-proxy-c7rlw/exec? command=echo&command=hello&command=world&container=kube-proxy&stderr=true&stdout=true ``` .debug[[k8s/aggregation-layer.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/aggregation-layer.md)] --- ## Listing what's supported on the server - There are at least three useful commands to introspect the API server .exercise[ - List resources types, their group, kind, short names, and scope: ```bash kubectl api-resources ``` - List API groups + versions: ```bash kubectl api-versions ``` - List APIServices: ```bash kubectl get apiservices ``` ] -- 🤔 What's the difference between the last two? .debug[[k8s/aggregation-layer.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/aggregation-layer.md)] --- ## API registration - `kubectl api-versions` shows all API groups, including `apiregistration.k8s.io` - `kubectl get apiservices` shows the "routing table" for API requests - The latter doesn't show `apiregistration.k8s.io` (APIServices belong to `apiregistration.k8s.io`) - Most API groups are `Local` (handled internally by the API server) - If we're running the `metrics-server`, it should handle `metrics.k8s.io` - This is an API group handled *outside* of the API server - This is the *aggregation layer!* .debug[[k8s/aggregation-layer.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/aggregation-layer.md)] --- ## Finding resources The following assumes that `metrics-server` is deployed on your cluster. .exercise[ - Check that the metrics.k8s.io is registered with `metrics-server`: ```bash kubectl get apiservices | grep metrics.k8s.io ``` - Check the resource kinds registered in the metrics.k8s.io group: ```bash kubectl api-resources --api-group=metrics.k8s.io ``` ] (If the output of either command is empty, install `metrics-server` first.) .debug[[k8s/aggregation-layer.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/aggregation-layer.md)] --- ## `nodes` vs `nodes` - We can have multiple resources with the same name .exercise[ - Look for resources named `node`: ```bash kubectl api-resources | grep -w nodes ``` - Compare the output of both commands: ```bash kubectl get nodes kubectl get nodes.metrics.k8s.io ``` ] -- 🤔 What are the second kind of nodes? How can we see what's really in them? .debug[[k8s/aggregation-layer.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/aggregation-layer.md)] --- ## Node vs NodeMetrics - `nodes.metrics.k8s.io` (aka NodeMetrics) don't have fancy *printer columns* - But we can look at the raw data (with `-o json` or `-o yaml`) .exercise[ - Look at NodeMetrics objects with one of these commands: ```bash kubectl get -o yaml nodes.metrics.k8s.io kubectl get -o yaml NodeMetrics ``` ] -- 💡 Alright, these are the live metrics (CPU, RAM) for our nodes. .debug[[k8s/aggregation-layer.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/aggregation-layer.md)] --- ## An easier way to consume metrics - We might have seen these metrics before ... With an easier command! -- .exercise[ - Display node metrics: ```bash kubectl top nodes ``` - Check which API requests happen behind the scenes: ```bash kubectl top nodes -v6 ``` ] .debug[[k8s/aggregation-layer.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/aggregation-layer.md)] --- ## Aggregation layer in practice - We can write an API server to handle a subset of the Kubernetes API - Then we can register that server by creating an APIService resource .exercise[ - Check the definition used for the `metrics-server`: ```bash kubectl describe apiservices v1beta1.metrics.k8s.io ``` ] - Group priority is used when multiple API groups provide similar kinds (e.g. `nodes` and `nodes.metrics.k8s.io` as seen earlier) .debug[[k8s/aggregation-layer.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/aggregation-layer.md)] --- ## Authentication flow - We have two Kubernetes API servers: - "aggregator" (the main one; clients connect to it) - "aggregated" (the one providing the extra API; aggregator connects to it) - Aggregator deals with client authentication - Aggregator authenticates with aggregated using mutual TLS - Aggregator passes (/forwards/proxies/...) requests to aggregated - Aggregated performs authorization by calling back aggregator ("can subject X perform action Y on resource Z?") [This doc page](https://kubernetes.io/docs/tasks/extend-kubernetes/configure-aggregation-layer/#authentication-flow) has very nice swim lanes showing that flow. .debug[[k8s/aggregation-layer.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/aggregation-layer.md)] --- ## Discussion - Aggregation layer is great for metrics (fast-changing, ephemeral data, that would be outrageously bad for etcd) - It *could* be a good fit to expose other REST APIs as a pass-thru (but it's more common to see CRDs instead) ??? :EN:- The aggregation layer :FR:- Étendre l'API avec le *aggregation layer* .debug[[k8s/aggregation-layer.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/aggregation-layer.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/chinook-helicopter-container.jpg)] --- name: toc-dynamic-admission-control class: title Dynamic Admission Control .nav[ [Previous part](#toc-the-aggregation-layer) | [Back to table of contents](#toc-part-11) | [Next part](#toc-operators) ] .debug[(automatically generated title slide)] --- # Dynamic Admission Control - This is one of the many ways to extend the Kubernetes API - High level summary: dynamic admission control relies on webhooks that are ... - dynamic (can be added/removed on the fly) - running inside our outside the cluster - *validating* (yay/nay) or *mutating* (can change objects that are created/updated) - selective (can be configured to apply only to some kinds, some selectors...) - mandatory or optional (should it block operations when webhook is down?) - Used for themselves (e.g. policy enforcement) or as part of operators .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## Use cases Some examples ... - Stand-alone admission controllers *validating:* policy enforcement (e.g. quotas, naming conventions ...) *mutating:* inject or provide default values (e.g. pod presets) - Admission controllers part of a greater system *validating:* advanced typing for operators *mutating:* inject sidecars for service meshes .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## You said *dynamic?* - Some admission controllers are built in the API server - They are enabled/disabled through Kubernetes API server configuration (e.g. `--enable-admission-plugins`/`--disable-admission-plugins` flags) - Here, we're talking about *dynamic* admission controllers - They can be added/remove while the API server is running (without touching the configuration files or even having access to them) - This is done through two kinds of cluster-scope resources: ValidatingWebhookConfiguration and MutatingWebhookConfiguration .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## You said *webhooks?* - A ValidatingWebhookConfiguration or MutatingWebhookConfiguration contains: - a resource filter <br/> (e.g. "all pods", "deployments in namespace xyz", "everything"...) - an operations filter <br/> (e.g. CREATE, UPDATE, DELETE) - the address of the webhook server - Each time an operation matches the filters, it is sent to the webhook server .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## What gets sent exactly? - The API server will `POST` a JSON object to the webhook - That object will be a Kubernetes API message with `kind` `AdmissionReview` - It will contain a `request` field, with, notably: - `request.uid` (to be used when replying) - `request.object` (the object created/deleted/changed) - `request.oldObject` (when an object is modified) - `request.userInfo` (who was making the request to the API in the first place) (See [the documentation](https://kubernetes.io/docs/reference/access-authn-authz/extensible-admission-controllers/#request) for a detailed example showing more fields.) .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## How should the webhook respond? - By replying with another `AdmissionReview` in JSON - It should have a `response` field, with, notably: - `response.uid` (matching the `request.uid`) - `response.allowed` (`true`/`false`) - `response.status.message` (optional string; useful when denying requests) - `response.patchType` (when a mutating webhook changes the object; e.g. `json`) - `response.patch` (the patch, encoded in base64) .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## What if the webhook *does not* respond? - If "something bad" happens, the API server follows the `failurePolicy` option - this is a per-webhook option (specified in the webhook configuration) - it can be `Fail` (the default) or `Ignore` ("allow all, unmodified") - What's "something bad"? - webhook responds with something invalid - webhook takes more than 10 seconds to respond <br/> (this can be changed with `timeoutSeconds` field in the webhook config) - webhook is down or has invalid certificates <br/> (TLS! It's not just a good idea; for admission control, it's the law!) .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## What did you say about TLS? - The webhook configuration can indicate: - either `url` of the webhook server (has to begin with `https://`) - or `service.name` and `service.namespace` of a Service on the cluster - In the latter case, the Service has to accept TLS connections on port 443 - It has to use a certificate with CN `<name>.<namespace>.svc` (**and** a `subjectAltName` extension with `DNS:<name>.<namespace>.svc`) - The certificate needs to be valid (signed by a CA trusted by the API server) ... alternatively, we can pass a `caBundle` in the webhook configuration .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## Webhook server inside or outside - "Outside" webhook server is defined with `url` option - convenient for external webooks (e.g. tamper-resistent audit trail) - also great for initial development (e.g. with ngrok) - requires outbound connectivity (duh) and can become a SPOF - "Inside" webhook server is defined with `service` option - convenient when the webhook needs to be deployed and managed on the cluster - also great for air gapped clusters - development can be harder (but tools like [Tilt](https://tilt.dev) can help) .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## Developing a simple admission webhook - We're going to register a custom webhook! - First, we'll just dump the `AdmissionRequest` object (using a little Node app) - Then, we'll implement a strict policy on a specific label (using a little Flask app) - Development will happen in local containers, plumbed with ngrok - The we will deploy to the cluster 🔥 .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## Running the webhook locally - We prepared a Docker Compose file to start the whole stack (the Node "echo" app, the Flask app, and one ngrok tunnel for each of them) .exercise[ - Go to the webhook directory: ```bash cd ~/container.training/webhooks/admission ``` - Start the webhook in Docker containers: ```bash docker-compose up ``` ] *Note the URL in `ngrok-echo_1` looking like `url=https://xxxx.ngrok.io`.* .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- class: extra-details ## What's ngrok? - Ngrok provides secure tunnels to access local services - Example: run `ngrok http 1234` - `ngrok` will display a publicly-available URL (e.g. https://xxxxyyyyzzzz.ngrok.io) - Connections to https://xxxxyyyyzzzz.ngrok.io will terminate at `localhost:1234` - Basic product is free; extra features (vanity domains, end-to-end TLS...) for $$$ - Perfect to develop our webhook! - Probably not for production, though (webhook requests and responses now pass through the ngrok platform) .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## Update the webhook configuration - We have a webhook configuration in `k8s/webhook-configuration.yaml` - We need to update the configuration with the correct `url` .exercise[ - Edit the webhook configuration manifest: ```bash vim k8s/webhook-configuration.yaml ``` - **Uncomment** the `url:` line - **Update** the `.ngrok.io` URL with the URL shown by Compose - Save and quit ] .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## Register the webhook configuration - Just after we register the webhook, it will be called for each matching request (CREATE and UPDATE on Pods in all namespaces) - The `failurePolicy` is `Ignore` (so if the webhook server is down, we can still create pods) .exercise[ - Register the webhook: ```bash kubectl apply -f k8s/webhook-configuration.yaml ``` ] It is strongly recommended to tail the logs of the API server while doing that. .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## Create a pod - Let's create a pod and try to set a `color` label .exercise[ - Create a pod named `chroma`: ```bash kubectl run --restart=Never chroma --image=nginx ``` - Add a label `color` set to `pink`: ```bash kubectl label pod chroma color=pink ``` ] We should see the `AdmissionReview` objects in the Compose logs. Note: the webhook doesn't do anything (other than printing the request payload). .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## Use the "real" admission webhook - We have a small Flask app implementing a particular policy on pod labels: - if a pod sets a label `color`, it must be `blue`, `green`, `red` - once that `color` label is set, it cannot be removed or changed - That Flask app was started when we did `docker-compose up` earlier - It is exposed through its own ngrok tunnel - We are going to use that webhook instead of the other one (by changing only the `url` field in the ValidatingWebhookConfiguration) .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## Update the webhook configuration .exercise[ - First, check the ngrok URL of the tunnel for the Flask app: ```bash docker-compose logs ngrok-flask ``` - Then, edit the webhook configuration: ```bash kubectl edit validatingwebhookconfiguration admission.container.training ``` - Find the `url:` field with the `.ngrok.io` URL and update it - Save and quit; the new configuration is applied immediately ] .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## Verify the behavior of the webhook - Try to create a few pods and/or change labels on existing pods - What happens if we try to make changes to the earlier pod? (the one that has `label=pink`) .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## Deploying the webhook on the cluster - Let's see what's needed to self-host the webhook server! - The webhook needs to be reachable through a Service on our cluster - The Service needs to accept TLS connections on port 443 - We need a proper TLS certificate: - with the right `CN` and `subjectAltName` (`<servicename>.<namespace>.svc`) - signed by a trusted CA - We can either use a "real" CA, or use the `caBundle` option to specify the CA cert (the latter makes it easy to use self-signed certs) .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## In practice - We're going to generate a key pair and a self-signed certificate - We will store them in a Secret - We will run the webhook in a Deployment, exposed with a Service - We will update the webhook configuration to use that Service - The Service will be named `admission`, in Namespace `webhooks` (keep in mind that the ValidatingWebhookConfiguration itself is at cluster scope) .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## Let's get to work! .exercise[ - Make sure we're in the right directory: ```bash cd ~/container.training/webhooks/admission ``` - Create the namespace: ```bash kubectl create namespace webhooks ``` - Switch to the namespace: ```bash kubectl config set-context --current --namespace=webhooks ``` ] .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## Deploying the webhook - *Normally,* we would author an image for this - Since our webhook is just *one* Python source file ... ... we'll store it in a ConfigMap, and install dependencies on the fly .exercise[ - Load the webhook source in a ConfigMap: ```bash kubectl create configmap admission --from-file=flask/webhook.py ``` - Create the Deployment and Service: ```bash kubectl apply -f k8s/webhook-server.yaml ``` ] .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## Generating the key pair and certificate - Let's call OpenSSL to the rescue! (of course, there are plenty others options; e.g. `cfssl`) .exercise[ - Generate a self-signed certificate: ```bash NAMESPACE=webhooks SERVICE=admission CN=$SERVICE.$NAMESPACE.svc openssl req -x509 -newkey rsa:4096 -nodes -keyout key.pem -out cert.pem \ -days 30 -subj /CN=$CN -addext subjectAltName=DNS:$CN ``` - Load up the key and cert in a Secret: ```bash kubectl create secret tls admission --cert=cert.pem --key=key.pem ``` ] .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## Update the webhook configuration - Let's reconfigure the webhook to use our Service instead of ngrok .exercise[ - Edit the webhook configuration manifest: ```bash vim k8s/webhook-configuration.yaml ``` - Comment out the `url:` line - Uncomment the `service:` section - Save, quit - Update the webhook configuration: ```bash kubectl apply -f k8s/webhook-configuration.yaml ``` ] .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## Add our self-signed cert to the `caBundle` - The API server won't accept our self-signed certificate - We need to add it to the `caBundle` field in the webhook configuration - The `caBundle` will be our `cert.pem` file, encoded in base64 .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- Shell to the rescue! .exercise[ - Load up our cert and encode it in base64: ```bash CA=$(base64 -w0 < cert.pem) ``` - Define a patch operation to update the `caBundle`: ```bash PATCH='[{ "op": "replace", "path": "/webhooks/0/clientConfig/caBundle", "value":"'$CA'" }]' ``` - Patch the webhook configuration: ```bash kubectl patch validatingwebhookconfiguration \ admission.webhook.container.training \ --type='json' -p="$PATCH" ``` ] .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- ## Try it out! - Keep an eye on the API server logs - Tail the logs of the pod running the webhook server - Create a few pods; we should see requests in the webhook server logs - Check that the label `color` is enforced correctly (it should only allow values of `red`, `green`, `blue`) ??? :EN:- Dynamic admission control with webhooks :FR:- Contrôle d'admission dynamique (webhooks) .debug[[k8s/admission.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/admission.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/container-cranes.jpg)] --- name: toc-operators class: title Operators .nav[ [Previous part](#toc-dynamic-admission-control) | [Back to table of contents](#toc-part-11) | [Next part](#toc-designing-an-operator) ] .debug[(automatically generated title slide)] --- # Operators *An operator represents **human operational knowledge in software,** <br/> to reliably manage an application. — [CoreOS](https://coreos.com/blog/introducing-operators.html)* Examples: - Deploying and configuring replication with MySQL, PostgreSQL ... - Setting up Elasticsearch, Kafka, RabbitMQ, Zookeeper ... - Reacting to failures when intervention is needed - Scaling up and down these systems .debug[[k8s/operators.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators.md)] --- ## What are they made from? - Operators combine two things: - Custom Resource Definitions - controller code watching the corresponding resources and acting upon them - A given operator can define one or multiple CRDs - The controller code (control loop) typically runs within the cluster (running as a Deployment with 1 replica is a common scenario) - But it could also run elsewhere (nothing mandates that the code run on the cluster, as long as it has API access) .debug[[k8s/operators.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators.md)] --- ## Why use operators? - Kubernetes gives us Deployments, StatefulSets, Services ... - These mechanisms give us building blocks to deploy applications - They work great for services that are made of *N* identical containers (like stateless ones) - They also work great for some stateful applications like Consul, etcd ... (with the help of highly persistent volumes) - They're not enough for complex services: - where different containers have different roles - where extra steps have to be taken when scaling or replacing containers .debug[[k8s/operators.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators.md)] --- ## Use-cases for operators - Systems with primary/secondary replication Examples: MariaDB, MySQL, PostgreSQL, Redis ... - Systems where different groups of nodes have different roles Examples: ElasticSearch, MongoDB ... - Systems with complex dependencies (that are themselves managed with operators) Examples: Flink or Kafka, which both depend on Zookeeper .debug[[k8s/operators.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators.md)] --- ## More use-cases - Representing and managing external resources (Example: [AWS S3 Operator](https://operatorhub.io/operator/awss3-operator-registry)) - Managing complex cluster add-ons (Example: [Istio operator](https://operatorhub.io/operator/istio)) - Deploying and managing our applications' lifecycles (more on that later) .debug[[k8s/operators.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators.md)] --- ## How operators work - An operator creates one or more CRDs (i.e., it creates new "Kinds" of resources on our cluster) - The operator also runs a *controller* that will watch its resources - Each time we create/update/delete a resource, the controller is notified (we could write our own cheap controller with `kubectl get --watch`) .debug[[k8s/operators.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators.md)] --- ## Deploying our apps with operators - It is very simple to deploy with `kubectl create deployment` / `kubectl expose` - We can unlock more features by writing YAML and using `kubectl apply` - Kustomize or Helm let us deploy in multiple environments (and adjust/tweak parameters in each environment) - We can also use an operator to deploy our application .debug[[k8s/operators.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators.md)] --- ## Pros and cons of deploying with operators - The app definition and configuration is persisted in the Kubernetes API - Multiple instances of the app can be manipulated with `kubectl get` - We can add labels, annotations to the app instances - Our controller can execute custom code for any lifecycle event - However, we need to write this controller - We need to be careful about changes (what happens when the resource `spec` is updated?) .debug[[k8s/operators.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators.md)] --- ## Operators are not magic - Look at this ElasticSearch resource definition: [k8s/eck-elasticsearch.yaml](https://github.com/jpetazzo/container.training/tree/master/k8s/eck-elasticsearch.yaml) - What should happen if we flip the TLS flag? Twice? - What should happen if we add another group of nodes? - What if we want different images or parameters for the different nodes? *Operators can be very powerful. <br/> But we need to know exactly the scenarios that they can handle.* ??? :EN:- Kubernetes operators :FR:- Les opérateurs .debug[[k8s/operators.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/container-housing.jpg)] --- name: toc-designing-an-operator class: title Designing an operator .nav[ [Previous part](#toc-operators) | [Back to table of contents](#toc-part-11) | [Next part](#toc-kubebuilder) ] .debug[(automatically generated title slide)] --- # Designing an operator - Once we understand CRDs and operators, it's tempting to use them everywhere - Yes, we can do (almost) everything with operators ... - ... But *should we?* - Very often, the answer is **“no!”** - Operators are powerful, but significantly more complex than other solutions .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- ## When should we (not) use operators? - Operators are great if our app needs to react to cluster events (nodes or pods going down, and requiring extensive reconfiguration) - Operators *might* be helpful to encapsulate complexity (manipulate one single custom resource for an entire stack) - Operators are probably overkill if a Helm chart would suffice - That being said, if we really want to write an operator ... Read on! .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- ## What does it take to write an operator? - Writing a quick-and-dirty operator, or a POC/MVP, is easy - Writing a robust operator is hard - We will describe the general idea - We will identify some of the associated challenges - We will list a few tools that can help us .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Top-down vs. bottom-up - Both approaches are possible - Let's see what they entail, and their respective pros and cons .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Top-down approach - Start with high-level design (see next slide) - Pros: - can yield cleaner design that will be more robust - Cons: - must be able to anticipate all the events that might happen - design will be better only to the extent of what we anticipated - hard to anticipate if we don't have production experience .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- ## High-level design - What are we solving? (e.g.: geographic databases backed by PostGIS with Redis caches) - What are our use-cases, stories? (e.g.: adding/resizing caches and read replicas; load balancing queries) - What kind of outage do we want to address? (e.g.: loss of individual node, pod, volume) - What are our *non-features*, the things we don't want to address? (e.g.: loss of datacenter/zone; differentiating between read and write queries; <br/> cache invalidation; upgrading to newer major versions of Redis, PostGIS, PostgreSQL) .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Low-level design - What Custom Resource Definitions do we need? (one, many?) - How will we store configuration information? (part of the CRD spec fields, annotations, other?) - Do we need to store state? If so, where? - state that is small and doesn't change much can be stored via the Kubernetes API <br/> (e.g.: leader information, configuration, credentials) - things that are big and/or change a lot should go elsewhere <br/> (e.g.: metrics, bigger configuration file like GeoIP) .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- class: extra-details ## What can we store via the Kubernetes API? - The API server stores most Kubernetes resources in etcd - Etcd is designed for reliability, not for performance - If our storage needs exceed what etcd can offer, we need to use something else: - either directly - or by extending the API server <br/>(for instance by using the agregation layer, like [metrics server](https://github.com/kubernetes-incubator/metrics-server) does) .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Bottom-up approach - Start with existing Kubernetes resources (Deployment, Stateful Set...) - Run the system in production - Add scripts, automation, to facilitate day-to-day operations - Turn the scripts into an operator - Pros: simpler to get started; reflects actual use-cases - Cons: can result in convoluted designs requiring extensive refactor .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- ## General idea - Our operator will watch its CRDs *and associated resources* - Drawing state diagrams and finite state automata helps a lot - It's OK if some transitions lead to a big catch-all "human intervention" - Over time, we will learn about new failure modes and add to these diagrams - It's OK to start with CRD creation / deletion and prevent any modification (that's the easy POC/MVP we were talking about) - *Presentation* and *validation* will help our users (more on that later) .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Challenges - Reacting to infrastructure disruption can seem hard at first - Kubernetes gives us a lot of primitives to help: - Pods and Persistent Volumes will *eventually* recover - Stateful Sets give us easy ways to "add N copies" of a thing - The real challenges come with configuration changes (i.e., what to do when our users update our CRDs) - Keep in mind that [some] of the [largest] cloud [outages] haven't been caused by [natural catastrophes], or even code bugs, but by configuration changes [some]: https://www.datacenterdynamics.com/news/gcp-outage-mainone-leaked-google-cloudflare-ip-addresses-china-telecom/ [largest]: https://aws.amazon.com/message/41926/ [outages]: https://aws.amazon.com/message/65648/ [natural catastrophes]: https://www.datacenterknowledge.com/amazon/aws-says-it-s-never-seen-whole-data-center-go-down .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Configuration changes - It is helpful to analyze and understand how Kubernetes controllers work: - watch resource for modifications - compare desired state (CRD) and current state - issue actions to converge state - Configuration changes will probably require *another* state diagram or FSA - Again, it's OK to have transitions labeled as "unsupported" (i.e. reject some modifications because we can't execute them) .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Tools - CoreOS / RedHat Operator Framework [GitHub](https://github.com/operator-framework) | [Blog](https://developers.redhat.com/blog/2018/12/18/introduction-to-the-kubernetes-operator-framework/) | [Intro talk](https://www.youtube.com/watch?v=8k_ayO1VRXE) | [Deep dive talk](https://www.youtube.com/watch?v=fu7ecA2rXmc) | [Simple example](https://medium.com/faun/writing-your-first-kubernetes-operator-8f3df4453234) - Kubernetes Operator Pythonic Framework (KOPF) [GitHub](https://github.com/nolar/kopf) | [Docs](https://kopf.readthedocs.io/) | [Step-by-step tutorial](https://kopf.readthedocs.io/en/stable/walkthrough/problem/) - Mesosphere Kubernetes Universal Declarative Operator (KUDO) [GitHub](https://github.com/kudobuilder/kudo) | [Blog](https://mesosphere.com/blog/announcing-maestro-a-declarative-no-code-approach-to-kubernetes-day-2-operators/) | [Docs](https://kudo.dev/) | [Zookeeper example](https://github.com/kudobuilder/frameworks/tree/master/repo/stable/zookeeper) - Kubebuilder (Go, very close to the Kubernetes API codebase) [GitHub](https://github.com/kubernetes-sigs/kubebuilder) | [Book](https://book.kubebuilder.io/) .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Validation - By default, a CRD is "free form" (we can put pretty much anything we want in it) - When creating a CRD, we can provide an OpenAPI v3 schema ([Example](https://github.com/amaizfinance/redis-operator/blob/master/deploy/crds/k8s_v1alpha1_redis_crd.yaml#L34)) - The API server will then validate resources created/edited with this schema - If we need a stronger validation, we can use a Validating Admission Webhook: - run an [admission webhook server](https://kubernetes.io/docs/reference/access-authn-authz/extensible-admission-controllers/#write-an-admission-webhook-server) to receive validation requests - register the webhook by creating a [ValidatingWebhookConfiguration](https://kubernetes.io/docs/reference/access-authn-authz/extensible-admission-controllers/#configure-admission-webhooks-on-the-fly) - each time the API server receives a request matching the configuration, <br/>the request is sent to our server for validation .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Presentation - By default, `kubectl get mycustomresource` won't display much information (just the name and age of each resource) - When creating a CRD, we can specify additional columns to print ([Example](https://github.com/amaizfinance/redis-operator/blob/master/deploy/crds/k8s_v1alpha1_redis_crd.yaml#L6), [Docs](https://kubernetes.io/docs/tasks/access-kubernetes-api/custom-resources/custom-resource-definitions/#additional-printer-columns)) - By default, `kubectl describe mycustomresource` will also be generic - `kubectl describe` can show events related to our custom resources (for that, we need to create Event resources, and fill the `involvedObject` field) - For scalable resources, we can define a `scale` sub-resource - This will enable the use of `kubectl scale` and other scaling-related operations .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- ## About scaling - It is possible to use the HPA (Horizontal Pod Autoscaler) with CRDs - But it is not always desirable - The HPA works very well for homogenous, stateless workloads - For other workloads, your mileage may vary - Some systems can scale across multiple dimensions (for instance: increase number of replicas, or number of shards?) - If autoscaling is desired, the operator will have to take complex decisions (example: Zalando's Elasticsearch Operator ([Video](https://www.youtube.com/watch?v=lprE0J0kAq0))) .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Versioning - As our operator evolves over time, we may have to change the CRD (add, remove, change fields) - Like every other resource in Kubernetes, [custom resources are versioned](https://kubernetes.io/docs/tasks/access-kubernetes-api/custom-resources/custom-resource-definition-versioning/ ) - When creating a CRD, we need to specify a *list* of versions - Versions can be marked as `stored` and/or `served` .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Stored version - Exactly one version has to be marked as the `stored` version - As the name implies, it is the one that will be stored in etcd - Resources in storage are never converted automatically (we need to read and re-write them ourselves) - Yes, this means that we can have different versions in etcd at any time - Our code needs to handle all the versions that still exist in storage .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Served versions - By default, the Kubernetes API will serve resources "as-is" (using their stored version) - It will assume that all versions are compatible storage-wise (i.e. that the spec and fields are compatible between versions) - We can provide [conversion webhooks](https://kubernetes.io/docs/tasks/access-kubernetes-api/custom-resources/custom-resource-definition-versioning/#webhook-conversion) to "translate" requests (the alternative is to upgrade all stored resources and stop serving old versions) .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Operator reliability - Remember that the operator itself must be resilient (e.g.: the node running it can fail) - Our operator must be able to restart and recover gracefully - Do not store state locally (unless we can reconstruct that state when we restart) - As indicated earlier, we can use the Kubernetes API to store data: - in the custom resources themselves - in other resources' annotations .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Beyond CRDs - CRDs cannot use custom storage (e.g. for time series data) - CRDs cannot support arbitrary subresources (like logs or exec for Pods) - CRDs cannot support protobuf (for faster, more efficient communication) - If we need these things, we can use the [aggregation layer](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/) instead - The aggregation layer proxies all requests below a specific path to another server (this is used e.g. by the metrics server) - [This documentation page](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/#choosing-a-method-for-adding-custom-resources) compares the features of CRDs and API aggregation ??? :EN:- Guidelines to design our own operators :FR:- Comment concevoir nos propres opérateurs .debug[[k8s/operators-design.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/operators-design.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/containers-by-the-water.jpg)] --- name: toc-kubebuilder class: title Kubebuilder .nav[ [Previous part](#toc-designing-an-operator) | [Back to table of contents](#toc-part-11) | [Next part](#toc-sealed-secrets) ] .debug[(automatically generated title slide)] --- # Kubebuilder - Writing a quick and dirty operator is (relatively) easy - Doing it right, however ... -- - We need: - proper CRD with schema validation - controller performing a reconcilation loop - manage errors, retries, dependencies between resources - maybe webhooks for admission and/or conversion 😱 .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Frameworks - There are a few frameworks available out there: - [kubebuilder](https://github.com/kubernetes-sigs/kubebuilder) ([book](https://book.kubebuilder.io/)): go-centric, very close to Kubernetes' core types - [operator-framework](https://operatorframework.io/): higher level; also supports Ansible and Helm - [KUDO](https://kudo.dev/): declarative operators written in YAML - [KOPF](https://kopf.readthedocs.io/en/latest/): operators in Python - ... .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Kubebuilder workflow - Kubebuilder will create scaffolding for us (Go stubs for types and controllers) - Then we edit these types and controllers files - Kubebuilder generates CRD manifests from our type definitions (and regenerates the manifests whenver we update the types) - It also gives us tools to quickly run the controller against a cluster (not necessarily *on* the cluster) .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Our objective - We're going to implement a *useless machine* [basic example](https://www.youtube.com/watch?v=aqAUmgE3WyM) | [playful example](https://www.youtube.com/watch?v=kproPsch7i0) | [advanced example](https://www.youtube.com/watch?v=Nqk_nWAjBus) | [another advanced example](https://www.youtube.com/watch?v=eLtUB8ncEnA) - A machine manifest will look like this: ```yaml kind: Machine apiVersion: useless.container.training/v1alpha1 metadata: name: machine-1 spec: # Our useless operator will change that to "down" switchPosition: up ``` - Each time we change the `switchPosition`, the operator will move it back to `down` (This is inspired by the [uselessoperator](https://github.com/tilt-dev/uselessoperator) written by [L Körbes](https://twitter.com/ellenkorbes). Highly recommend!💯) .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- class: extra-details ## Local vs remote - Building Go code can be a little bit slow on our modest lab VMs - It will typically be *much* faster on a local machine - All the demos and labs in this section will run fine either way! .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Preparation - Install Go (on our VMs: `sudo snap install go --classic`) - Install kubebuilder ([get a release](https://github.com/kubernetes-sigs/kubebuilder/releases/), untar, move the `kubebuilder` binary to the `$PATH`) - Initialize our workspace: ```bash mkdir useless cd useless go mod init container.training/useless kubebuilder init --domain container.training ``` .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Create scaffolding - Create a type and corresponding controller: ```bash kubebuilder create api --group useless --version v1alpha1 --kind Machine ``` - Answer `y` to both questions - Then we need to edit the type that just got created! .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Edit type Edit `api/v1alpha1/machine_types.go`. Add the `switchPosition` field in the `spec` structure: ```go // MachineSpec defines the desired state of Machine type MachineSpec struct { // Position of the switch on the machine, for instance up or down. SwitchPosition string ``json:"switchPosition,omitempty"`` } ``` ⚠️ The backticks above should be simple backticks, not double-backticks. Sorry. .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Go markers We can use Go *marker comments* to give `controller-gen` extra details about how to handle our type, for instance: ``` // +kubebuilder:object:root=true // +kubebuilder:subresource:status // +kubebuilder:printcolumn:JSONPath=".spec.switchPosition",name=Position,type=string ``` (See [marker syntax](https://book.kubebuilder.io/reference/markers.html), [CRD generation](https://book.kubebuilder.io/reference/markers/crd.html), [CRD validation](https://book.kubebuilder.io/reference/markers/crd-validation.html) ) .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- class: extra-details ## Using CRD v1 - By default, kubebuilder generates v1alpha1 CRDs - If we want to generate v1 CRDs: - edit `Makefile` - update `crd:crdVersions=v1` .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Installing the CRD After making these changes, we can run `make install`. This will build the Go code, but also: - generate the CRD manifest - and apply the manifest to the cluster .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Creating a machine Edit `config/samples/useless_v1alpha1_machine.yaml`: ```yaml kind: Machine apiVersion: useless.container.training/v1alpha1 metadata: name: machine-1 spec: # Our useless operator will change that to "down" switchPosition: up ``` ... and apply it to the cluster. .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Designing the controller - Our controller needs to: - notice when a `switchPosition` is not `down` - move it to `down` when that happens - Later, we can add fancy improvements (wait a bit before moving it, etc.) .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Reconciler logic - Kubebuilder will call our *reconciler* when necessary - When necessary = when changes happen ... - on our resource - or resources that it *watches* (related resources) - After "doing stuff", the reconciler can return ... - `ctrl.Result{},nil` = all is good - `ctrl.Result{Requeue...},nil` = all is good, but call us back in a bit - `ctrl.Result{},err` = something's wrong, try again later .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Loading an object Open `controllers/machine_controller.go` and add that code in the `Reconcile` method: ```go var machine uselessv1alpha1.Machine if err := r.Get(ctx, req.NamespacedName, &machine); err != nil { log.Info("error getting object") return ctrl.Result{}, err } r.Log.Info( "reconciling", "machine", req.NamespaceName, "switchPosition", machine.Spec.SwitchPosition, ) ``` .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Running the controller Our controller is not done yet, but let's try what we have right now! This will compile the controller and run it: ``` make run ``` Then: - create a machine - change the `switchPosition` - delete the machine -- 🤔 .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## `IgnoreNotFound` When we are called for object deletion, the object has *already* been deleted. (Unless we're using finalizers, but that's another story.) When we return `err`, the controller will try to access the object ... ... We need to tell it to *not* do that. Don't just return `err`, but instead, wrap it around `client.IgnoreNotFound`: ```go return ctrl.Result{}, client.IgnoreNotFound(err) ``` Update the code, `make run` again, create/change/delete again. -- 🎉 .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Updating the machine Let's try to update the machine like this: ```go if machine.Spec.SwitchPosition != "down" { machine.Spec.SwitchPosition = "down" if err := r.Update(ctx, &machine); err != nil { log.Info("error updating switch position") return ctrl.Result{}, client.IgnoreNotFound(err) } } ``` Again - update, `make run`, test. .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Spec vs Status - Spec = desired state - Status = observed state - If Status is lost, the controller should be able to reconstruct it (maybe with degraded behavior in the meantime) - Status will almost always be a sub-resource (so that it can be updated separately "cheaply") .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- class: extra-details ## Spec vs Status (in depth) - The `/status` subresource is handled differently by the API server - Updates to `/status` don't alter the rest of the object - Conversely, updates to the object ignore changes in the status (See [the docs](https://kubernetes.io/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/#status-subresource) for the fine print.) .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## "Improving" our controller - We want to wait a few seconds before flipping the switch - Let's add the following line of code to the controller: ```go time.Sleep(5 * time.Second) ``` - `make run`, create a few machines, observe what happens -- 💡 Concurrency! .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Controller logic - Our controller shouldn't block (think "event loop") - There is a queue of objects that need to be reconciled - We can ask to be put back on the queue for later processing - When we need to block (wait for something to happen), two options: - ask for a *requeue* ("call me back later") - yield because we know we will be notified by another resource .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## To requeue ... `return ctrl.Result{RequeueAfter: 1 * time.Second}` - That means: "try again in 1 second, and I will check if progress was made" - This *does not* guarantee that we will be called exactly 1 second later: - we might be called before (if other changes happen) - we might be called after (if the controller is busy with other objects) - If we are waiting for another resource to change, there is an even better way! .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## ... or not to requeue `return ctrl.Result{}, nil` - That means: "no need to set an alarm; we'll be notified some other way" - Use this if we are waiting for another resource to update (e.g. a LoadBalancer to be provisioned, a Pod to be ready...) - For this to work, we need to set a *watch* (more on that later) .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## "Improving" our controller, take 2 - Let's store in the machine status the moment when we saw it ```go // +kubebuilder:printcolumn:JSONPath=".status.seenAt",name=Seen,type=date type MachineStatus struct { // Time at which the machine was noticed by our controller. SeenAt *metav1.Time ``json:"seenAt,omitempty"`` } ``` ⚠️ The backticks above should be simple backticks, not double-backticks. Sorry. Note: `date` fields don't display timestamps in the future. (That's why for this example it's simpler to use `seenAt` rather than `changeAt`.) .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Set `seenAt` Let's add the following block in our reconciler: ```go if machine.Status.SeenAt == nil { now := metav1.Now() machine.Status.SeenAt = &now if err := r.Status().Update(ctx, &machine); err != nil { log.Info("error updating status.seenAt") return ctrl.Result{}, client.IgnoreNotFound(err) } return ctrl.Result{RequeueAfter: 5 * time.Second}, nil } ``` (If needed, add `metav1 "k8s.io/apimachinery/pkg/apis/meta/v1"` to our imports.) .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Use `seenAt` Our switch-position-changing code can now become: ```go if machine.Spec.SwitchPosition != "down" { now := metav1.Now() changeAt := machine.Status.SeenAt.Time.Add(5 * time.Second) if now.Time.After(changeAt) { machine.Spec.SwitchPosition = "down" if err := r.Update(ctx, &machine); err != nil { log.Info("error updating switch position") return ctrl.Result{}, client.IgnoreNotFound(err) } } } ``` `make run`, create a few machines, tweak their switches. .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Owner and dependents - Next, let's see how to have relationships between objects! - We will now have two kinds of objects: machines, and switches - Machines should have *at least* one switch, possibly *multiple ones* - The position will now be stored in the switch, not the machine - The machine will also expose the combined state of the switches - The switches will be tied to their machine through a label (See next slide for an example) .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Switches and machines ``` [jp@hex ~]$ kubectl get machines NAME SWITCHES POSITIONS machine-cz2vl 3 ddd machine-vf4xk 1 d [jp@hex ~]$ kubectl get switches --show-labels NAME POSITION SEEN LABELS switch-6wmjw down machine=machine-cz2vl switch-b8csg down machine=machine-cz2vl switch-fl8dq down machine=machine-cz2vl switch-rc59l down machine=machine-vf4xk ``` (The field `status.positions` shows the first letter of the `position` of each switch.) .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Tasks Create the new resource type (but don't create a controller): ```bash kubebuilder create api --group useless --version v1alpha1 --kind Switch ``` Update `machine_types.go` and `switch_types.go`. Implement the logic so that the controller flips all switches down immediately. Then change it so that a given machine doesn't flip more than one switch every 5 seconds. See next slides for hints! .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Listing objects We can use the `List` method with filters: ```go var switches uselessv1alpha1.SwitchList if err := r.List(ctx, &switches, client.InNamespace(req.Namespace), client.MatchingLabels{"machine": req.Name}, ); err != nil { log.Error(err, "unable to list switches of the machine") return ctrl.Result{}, client.IgnoreNotFound(err) } log.Info("Found switches", "switches", switches) ``` .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Creating objects We can use the `Create` method to create a new object: ```go sw := uselessv1alpha1.Switch{ TypeMeta: metav1.TypeMeta{ APIVersion: uselessv1alpha1.GroupVersion.String(), Kind: "Switch", }, ObjectMeta: metav1.ObjectMeta{ GenerateName: "switch-", Namespace: machine.Namespace, Labels: map[string]string{"machine": machine.Name}, }, Spec: uselessv1alpha1.SwitchSpec{ Position: "down", }, } if err := r.Create(ctx, &sw); err != nil { ... ``` .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Watches - Our controller will correctly flip switches when it starts - It will also react to machine updates - But it won't react if we directly touch the switches! - By default, it only monitors machines, not switches - We need to tell it to watch switches - We also need to tell it how to map a switch to its machine .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Mapping a switch to its machine Define the following helper function: ```go func (r *MachineReconciler) machineOfSwitch(obj handler.MapObject) []ctrl.Request { r.Log.Debug("mos", "obj", obj) return []ctrl.Request{ ctrl.Request{ NamespacedName: types.NamespacedName{ Name: obj.Meta.GetLabels()["machine"], Namespace: obj.Meta.GetNamespace(), }, }, } } ``` .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Telling the controller to watch switches Update the `SetupWithManager` method in the controller: ```go func (r *MachineReconciler) SetupWithManager(mgr ctrl.Manager) error { return ctrl.NewControllerManagedBy(mgr). For(&uselessv1alpha1.Machine{}). Owns(&uselessv1alpha1.Switch{}). Watches( &source.Kind{Type: &uselessv1alpha1.Switch{}}, &handler.EnqueueRequestsFromMapFunc{ ToRequests: handler.ToRequestsFunc(r.machineOfSwitch), }). Complete(r) } ``` After this, our controller should now react to switch changes. .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Bonus points - Handle "scale down" of a machine (by deleting extraneous switches) - Automatically delete switches when a machine is deleted (ideally, using ownership information) - Test corner cases (e.g. changing a switch label) .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- ## Acknowledgements - Useless Operator, by [L Körbes](https://twitter.com/ellenkorbes) [code](https://github.com/tilt-dev/uselessoperator) | [video (EN)](https://www.youtube.com/watch?v=85dKpsFFju4) | [video (PT)](https://www.youtube.com/watch?v=Vt7Eg4wWNDw) - Zero To Operator, by [Solly Ross](https://twitter.com/directxman12) [code](https://pres.metamagical.dev/kubecon-us-2019/code) | [video](https://www.youtube.com/watch?v=KBTXBUVNF2I) | [slides](https://pres.metamagical.dev/kubecon-us-2019/) - The [kubebuilder book](https://book.kubebuilder.io/) ??? :EN:- Implementing an operator with kubebuilder :FR:- Implémenter un opérateur avec kubebuilder .debug[[k8s/kubebuilder.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kubebuilder.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/distillery-containers.jpg)] --- name: toc-sealed-secrets class: title Sealed Secrets .nav[ [Previous part](#toc-kubebuilder) | [Back to table of contents](#toc-part-11) | [Next part](#toc-exercise--sealed-secrets) ] .debug[(automatically generated title slide)] --- # Sealed Secrets - Kubernetes provides the "Secret" resource to store credentials, keys, passwords ... - Secrets can be protected with RBAC (e.g. "you can write secrets, but only the app's service account can read them") - [Sealed Secrets](https://github.com/bitnami-labs/sealed-secrets) is an operator that lets us store secrets in code repositories - It uses asymetric cryptography: - anyone can *encrypt* a secret - only the cluster can *decrypt* a secret .debug[[k8s/sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/sealed-secrets.md)] --- ## Principle - The Sealed Secrets operator uses a *public* and a *private* key - The public key is available publicly (duh!) - We use the public key to encrypt secrets into a SealedSecret resource - the SealedSecret resource can be stored in a code repo (even a public one) - The SealedSecret resource is `kubectl apply`'d to the cluster - The Sealed Secrets controller decrypts the SealedSecret with the private key (this creates a classic Secret resource) - Nobody else can decrypt secrets, since only the controller has the private key .debug[[k8s/sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/sealed-secrets.md)] --- ## In action - We will install the Sealed Secrets operator - We will generate a Secret - We will "seal" that Secret (generate a SealedSecret) - We will load that SealedSecret on the cluster - We will check that we now have a Secret .debug[[k8s/sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/sealed-secrets.md)] --- ## Installing the operator - The official installation is done through a single YAML file - There is also a Helm chart if you prefer that .exercise[ - Install the operator: .small[ ```bash kubectl apply -f \ https://github.com/bitnami-labs/sealed-secrets/releases/download/v0.13.1/controller.yaml ``` ] ] Note: it installs into `kube-system` by default. If you change that, you will also need to inform `kubeseal` later on. .debug[[k8s/sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/sealed-secrets.md)] --- ## Creating a Secret - Let's create a normal (unencrypted) secret .exercise[ - Create a Secret with a couple of API tokens: ```bash kubectl create secret generic awskey \ --from-literal=AWS_ACCESS_KEY_ID=AKI... \ --from-literal=AWS_SECRET_ACCESS_KEY=abc123xyz... \ --dry-run=client -o yaml > secret-aws.yaml ``` ] - Note the `--dry-run` and `-o yaml` (we're just generating YAML, not sending the secrets to our Kubernetes cluster) - We could also write the YAML from scratch or generate it with other tools .debug[[k8s/sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/sealed-secrets.md)] --- ## Creating a Sealed Secret - This is done with the `kubeseal` tool - It will obtain the public key from the cluster .exercise[ - Create the Sealed Secret: ```bash kubeseal < secret-aws.yaml > sealed-secret-aws.json ``` ] - The file `sealed-secret-aws.json` can be committed to your public repo (if you prefer YAML output, you can add `-o yaml`) .debug[[k8s/sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/sealed-secrets.md)] --- ## Using a Sealed Secret - Now let's `kubectl apply` that Sealed Secret to the cluster - The Sealed Secret controller will "unseal" it for us .exercise[ - Check that our Secret doesn't exist (yet): ```bash kubectl get secrets ``` - Load the Sealed Secret into the cluster: ```bash kubectl create -f sealed-secret-aws.json ``` - Check that the secret is now available: ```bash kubectl get secrets ``` ] .debug[[k8s/sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/sealed-secrets.md)] --- ## Tweaking secrets - Let's see what happens if we try to rename the Secret (or use it in a different namespace) .exercise[ - Delete both the Secret and the SealedSecret - Edit `sealed-secret-aws.json` - Change the name of the secret, or its namespace (both in the SealedSecret metadata and in the Secret template) - `kubectl apply -f` the new JSON file and observe the results 🤔 ] .debug[[k8s/sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/sealed-secrets.md)] --- ## Sealed Secrets are *scoped* - A SealedSecret cannot be renamed or moved to another namespace (at least, not by default!) - Otherwise, it would allow to evade RBAC rules: - if I can view Secrets in namespace `myapp` but not in namespace `yourapp` - I could take a SealedSecret belonging to namespace `yourapp` - ... and deploy it in `myapp` - ... and view the resulting decrypted Secret! - This can be changed with `--scope namespace-wide` or `--scope cluster-wide` .debug[[k8s/sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/sealed-secrets.md)] --- ## Working offline - We can obtain the public key from the server (technically, as a PEM certificate) - Then we can use that public key offline (without contacting the server) - Relevant commands: `kubeseal --fetch-cert > seal.pem` `kubeseal --cert seal.pem < secret.yaml > sealedsecret.json` .debug[[k8s/sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/sealed-secrets.md)] --- ## Key rotation - The controller generate new keys every month by default - The keys are kept as TLS Secrets in the `kube-system` namespace (named `sealed-secrets-keyXXXXX`) - When keys are "rotated", old decryption keys are kept (otherwise we can't decrypt previously-generated SealedSecrets) .debug[[k8s/sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/sealed-secrets.md)] --- ## Key compromise - If the *sealing* key (obtained with `--fetch-cert` is compromised): *we don't need to do anything (it's a public key!)* - However, if the *unsealing* key (the TLS secret in `kube-system`) is compromised ... *we need to:* - rotate the key - rotate the SealedSecrets that were encrypted with that key <br/> (as they are compromised) .debug[[k8s/sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/sealed-secrets.md)] --- ## Rotating the key - By default, new keys are generated every 30 days - To force the generation of a new key "right now": - obtain an RFC1123 timestamp with `date -R` - edit Deployment `sealed-secrets-controller` (in `kube-system`) - add `--key-cutoff-time=TIMESTAMP` to the command-line - *Then*, rotate the SealedSecrets that were encrypted with it (generate new Secrets, then encrypt them with the new key) .debug[[k8s/sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/sealed-secrets.md)] --- ## Discussion (the good) - The footprint of the operator is rather small: - only one CRD - one Deployment, one Service - a few RBAC-related objects .debug[[k8s/sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/sealed-secrets.md)] --- ## Discussion (the less good) - Events could be improved - `no key to decrypt secret` when there is a name/namespace mismatch - no event indicating that a SealedSecret was successfully unsealed - Key rotation could be improved (how to find secrets corresponding to a key?) - If the sealing keys are lost, it's impossible to unseal the SealedSecrets (e.g. cluster reinstall) - ... Which means that we need to back up the sealing keys - ... Which means that we need to be super careful with these backups! .debug[[k8s/sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/sealed-secrets.md)] --- ## Other approaches - [Kamus](https://kamus.soluto.io/) ([git](https://github.com/Soluto/kamus)) offers "zero-trust" secrets (the cluster cannot decrypt secrets; only the application can decrypt them) - [Vault](https://learn.hashicorp.com/tutorials/vault/kubernetes-sidecar?in=vault/kubernetes) can do ... a lot - dynamic secrets (generated on the fly for a consumer) - certificate management - integration outside of Kubernetes - and much more! ??? :EN:- The Sealed Secrets Operator :FR:- L'opérateur *Sealed Secrets* .debug[[k8s/sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/sealed-secrets.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/lots-of-containers.jpg)] --- name: toc-exercise--sealed-secrets class: title Exercise — sealed secrets .nav[ [Previous part](#toc-sealed-secrets) | [Back to table of contents](#toc-part-11) | [Next part](#toc-policy-management-with-kyverno) ] .debug[(automatically generated title slide)] --- # Exercise — sealed secrets This is a "combo exercise" to practice the following concepts: - Secrets (mounting them in containers) - RBAC (granting specific permissions to specific users) - Operators (specifically, sealed secrets) - Migrations (copying/transferring resources from a cluster to another) For this exercise, you will need two clusters. (It can be two local clusters.) We will call them "source cluster" and "target cluster". .debug[[k8s/exercise-sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/exercise-sealed-secrets.md)] --- ## Step 1 (easy) - Install the sealed secrets operator on both clusters - On source cluster, create a Namespace called `dev` - Create two sealed secrets, `verysecure` and `veryverysecure` (the content doesn't matter; put a random string of your choice) - Create a Deployment called `app` using both secrets (use a mount or environment variables; whatever you prefer!) - Verify that the secrets are available to the Deployment .debug[[k8s/exercise-sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/exercise-sealed-secrets.md)] --- ## Step 2 (medium) - Create another Namespace called `prod` (on the source cluster) - Create the same Deployment `app` using both secrets - Verify that the secrets are available to the Deployment .debug[[k8s/exercise-sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/exercise-sealed-secrets.md)] --- ## Step 3 (hard) - On the target cluster, create a Namespace called `prod` - Create the `app` Deployment and both sealed secrets (do not copy the Secrets; only the sealed secrets) - Check the next slide if you need a hint! -- - You will have to copy the Sealed Secret private key .debug[[k8s/exercise-sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/exercise-sealed-secrets.md)] --- ## Step 4 (medium) On the target cluster, create the Namespace `dev`. Let's say that user `alice` has access to the target cluster. (You can use `kubectl --as=alice` to impersonate her.) We want Alice to be able to: - deploy the whole application - access the `verysecure` secret - but *not* the `veryverysecure` secret .debug[[k8s/exercise-sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/exercise-sealed-secrets.md)] --- ## Step 5 (hard) - Make sure that Alice can view the logs of the Deployment - Can you think of a way for Alice to access the `veryverysecure` Secret? (check next slide for a hint) -- - `kubectl exec`, maybe? -- - Can you think of a way to prevent that? .debug[[k8s/exercise-sealed-secrets.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/exercise-sealed-secrets.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/plastic-containers.JPG)] --- name: toc-policy-management-with-kyverno class: title Policy Management with Kyverno .nav[ [Previous part](#toc-exercise--sealed-secrets) | [Back to table of contents](#toc-part-11) | [Next part](#toc-an-elasticsearch-operator) ] .debug[(automatically generated title slide)] --- # Policy Management with Kyverno - The Kubernetes permission management system is very flexible ... - ... But it can't express *everything!* - Examples: - forbid using `:latest` image tag - enforce that each Deployment, Service, etc. has an `owner` label <br/>(except in e.g. `kube-system`) - enforce that each container has at least a `readinessProbe` healthcheck - How can we address that, and express these more complex *policies?* .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Admission control - The Kubernetes API server provides a generic mechanism called *admission control* - Admission controllers will examine each write request, and can: - approve/deny it (for *validating* admission controllers) - additionally *update* the object (for *mutating* admission controllers) - These admission controllers can be: - plug-ins built into the Kubernetes API server <br/>(selectively enabled/disabled by e.g. command-line flags) - webhooks registered dynamically with the Kubernetes API server .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## What's Kyverno? - Policy management solution for Kubernetes - Open source (https://github.com/kyverno/kyverno/) - Compatible with all clusters (doesn't require to reconfigure the control plane, enable feature gates...) - We don't endorse / support it in a particular way, but we think it's cool - It's not the only solution! (see e.g. [Open Policy Agent](https://www.openpolicyagent.org/docs/v0.12.2/kubernetes-admission-control/)) .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## What can Kyverno do? - *Validate* resource manifests (accept/deny depending on whether they conform to our policies) - *Mutate* resources when they get created or updated (to add/remove/change fields on the fly) - *Generate* additional resources when a resource gets created (e.g. when namespace is created, automatically add quotas and limits) - *Audit* existing resources (warn about resources that violate certain policies) .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## How does it do it? - Kyverno is implemented as a *controller* or *operator* - It typically runs as a Deployment on our cluster - Policies are defined as *custom resource definitions* - They are implemented with a set of *dynamic admission control webhooks* -- 🤔 -- - Let's unpack that! .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Custom resource definitions - When we install Kyverno, it will register new resource types: - Policy and ClusterPolicy (per-namespace and cluster-scope policies) - PolicyViolation and ClusterPolicyViolation (used in audit mode) - GenerateRequest (used internally when generating resources asynchronously) - We will be able to do e.g. `kubectl get policyviolations --all-namespaces` (to see policy violations across all namespaces) - Policies will be defined in YAML and registered/updated with e.g. `kubectl apply` .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Dynamic admission control webhooks - When we install Kyverno, it will register a few webhooks for its use (by creating ValidatingWebhookConfiguration and MutatingWebhookConfiguration resources) - All subsequent resource modifications are submitted to these webhooks (creations, updates, deletions) .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Controller - When we install Kyverno, it creates a Deployment (and therefore, a Pod) - That Pod runs the server used by the webhooks - It also runs a controller that will: - run optional checks in the background (and generate PolicyViolation objects) - process GenerateRequest objects asynchronously .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Kyverno in action - We're going to install Kyverno on our cluster - Then, we will use it to implement a few policies .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- class: extra-details ## Kyverno versions - We're going to use version 1.2 - Version 1.3.0-rc came out in November 2020 - It introduces a few changes (e.g. PolicyViolations are now PolicyReports) - Expect this to change in the near future! .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Installing Kyverno - Kyverno can be installed with a (big) YAML manifest - ... or with Helm charts (which allows to customize a few things) .exercise[ - Install Kyverno: ```bash kubectl apply -f https://raw.githubusercontent.com/kyverno/kyverno\ /v1.2.1/definitions/release/install.yaml ``` ] .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Kyverno policies in a nutshell - Which resources does it *select?* - can specify resources to *match* and/or *exclude* - can specify *kinds* and/or *selector* and/or users/roles doing the action - Which operation should be done? - validate, mutate, or generate - For validation, whether it should *enforce* or *audit* failures - Operation details (what exactly to validate, mutate, or generate) .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Immutable primary colors, take 1 - Our pods can have an optional `color` label - If the label exists, it *must* be `red`, `green`, or `blue` - One possible approach: - *match* all pods that have a `color` label that is not `red`, `green`, or `blue` - *deny* these pods - We could also *match* all pods, then *deny* with a condition .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Testing without the policy - First, let's create a pod with an "invalid" label (while we still can!) - We will use this later .exercise[ - Create a pod: ```bash kubectl run test-color-0 --image=nginx ``` - Apply a color label: ```bash kubectl label pod test-color-0 color=purple ``` ] .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Our first Kyverno policy .small[ ```yaml apiVersion: kyverno.io/v1 kind: ClusterPolicy metadata: name: pod-color-policy-1 spec: validationFailureAction: enforce rules: - name: ensure-pod-color-is-valid match: resources: kinds: - Pod selector: matchExpressions: - key: color operator: Exists - key: color operator: NotIn values: [ red, green, blue ] validate: message: "If it exists, the label color must be red, green, or blue." deny: {} ``` ] .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Load and try the policy .exercise[ - Load the policy: ```bash kubectl apply -f ~/container.training/k8s/kyverno-pod-color-1.yaml ``` - Create a pod: ```bash kubectl run test-color-1 --image=nginx ``` - Try to apply a few color labels: ```bash kubectl label pod test-color-1 color=purple kubectl label pod test-color-1 color=red kubectl label pod test-color-1 color- ``` ] .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Immutable primary colors, take 2 - New rule: once a `color` label has been added, it cannot be changed (i.e. if `color=red`, we can't change it to `color=blue`) - Our approach: - *match* all pods - *deny* these pods if their `color` label has changed - "Old" and "new" versions of the pod can be referenced through `{{ request.oldObject }}` and `{{ request.object }}` - Our label is available through `{{ request.object.metadata.labels.color }}` - Again, other approaches are possible! .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Our second Kyverno policy .small[ ```yaml apiVersion: kyverno.io/v1 kind: ClusterPolicy metadata: name: pod-color-policy-2 spec: validationFailureAction: enforce background: false rules: - name: prevent-color-change match: resources: kinds: - Pod validate: message: "Once label color has been added, it cannot be changed." deny: conditions: - key: "{{ request.oldObject.metadata.labels.color }}" operator: NotEqual value: "{{ request.object.metadata.labels.color }}" ``` ] .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Load and try the policy .exercise[ - Load the policy: ```bash kubectl apply -f ~/container.training/k8s/kyverno-pod-color-2.yaml ``` - Create a pod: ```bash kubectl run test-color-2 --image=nginx ``` - Try to apply a few color labels: ```bash kubectl label pod test-color-2 color=purple kubectl label pod test-color-2 color=red kubectl label pod test-color-2 color=blue --overwrite ``` ] .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## `background` - What is this `background: false` option, and why do we need it? -- - Admission controllers are only invoked when we change an object - Existing objects are not affected (e.g. if we have a pod with `color=pink` *before* installing our policy) - Kyvero can also run checks in the background, and report violations (we'll see later how they are reported) - `background: false` disables that -- - Alright, but ... *why* do we need it? .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Accessing `AdmissionRequest` context - In this specific policy, we want to prevent an *update* (as opposed to a mere *create* operation) - We want to compare the *old* and *new* version (to check if a specific label was removed) - The `AdmissionRequest` object has `object` and `oldObject` fields (the `AdmissionRequest` object is the thing that gets submitted to the webhook) - Kyverno lets us access the `AdmissionRequest` object (and in particular, `{{ request.object }}` and `{{ request.oldObject }}`) -- - Alright, but ... what's the link with `background: false`? .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## `{{ request }}` - The `{{ request }}` context is only available when there is an `AdmissionRequest` - When a resource is "at rest", there is no `{{ request }}` (and no old/new) - Therefore, a policy that uses `{{ request }}` cannot validate existing objects (it can only be used when an object is actually created/updated/deleted) .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Immutable primary colors, take 3 - New rule: once a `color` label has been added, it cannot be removed - Our approach: - *match* all pods that *do not* have a `color` label - *deny* these pods if they had a `color` label before - "before" can be referenced through `{{ request.oldObject }}` - Again, other approaches are possible! .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Our third Kyverno policy .small[ ```yaml apiVersion: kyverno.io/v1 kind: ClusterPolicy metadata: name: pod-color-policy-3 spec: validationFailureAction: enforce background: false rules: - name: prevent-color-removal match: resources: kinds: - Pod selector: matchExpressions: - key: color operator: DoesNotExist validate: message: "Once label color has been added, it cannot be removed." deny: conditions: - key: "{{ request.oldObject.metadata.labels.color }}" operator: NotIn value: [] ``` ] .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Load and try the policy .exercise[ - Load the policy: ```bash kubectl apply -f ~/container.training/k8s/kyverno-pod-color-3.yaml ``` - Create a pod: ```bash kubectl run test-color-3 --image=nginx ``` - Try to apply a few color labels: ```bash kubectl label pod test-color-3 color=purple kubectl label pod test-color-3 color=red kubectl label pod test-color-3 color- ``` ] .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Background checks - What about the `test-color-0` pod that we create initially? (remember: we did set `color=purple`) - Kyverno generated a ClusterPolicyViolation to indicate it .exercise[ - Check that the pod still an "invalid" color: ```bash kubectl get pods -L color ``` - List ClusterPolicyViolations: ```bash kubectl get clusterpolicyviolations kubectl get cpolv ``` ] .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Generating objects - When we create a Namespace, we also want to automatically create: - a LimitRange (to set default CPU and RAM requests and limits) - a ResourceQuota (to limit the resources used by the namespace) - a NetworkPolicy (to isolate the namespace) - We can do that with a Kyverno policy with a *generate* action (it is mutually exclusive with the *validate* action) .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Overview - The *generate* action must specify: - the `kind` of resource to generate - the `name` of the resource to generate - its `namespace`, when applicable - *either* a `data` structure, to be used to populate the resource - *or* a `clone` reference, to copy an existing resource Note: the `apiVersion` field appears to be optional. .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## In practice - We will use the policy [k8s/kyverno-namespace-setup.yaml](https://github.com/jpetazzo/container.training/tree/master/k8s/kyverno-namespace-setup.yaml) - We need to generate 3 resources, so we have 3 rules in the policy - Excerpt: ```yaml generate: kind: LimitRange name: default-limitrange namespace: "{{request.object.metadata.name}}" data: spec: limits: ``` - Note that we have to specify the `namespace` (and we infer it from the name of the resource being created, i.e. the Namespace) .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Lifecycle - After generated objects have been created, we can change them (Kyverno won't update them) - Except if we use `clone` together with the `synchronize` flag (in that case, Kyverno will watch the cloned resource) - This is convenient for e.g. ConfigMaps shared between Namespaces - Objects are generated only at *creation* (not when updating an old object) .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Asynchronous creation - Kyverno creates resources asynchronously (by creating a GenerateRequest resource first) - This is useful when the resource cannot be created (because of permissions or dependency issues) - Kyverno will periodically loop through the pending GenerateRequests - Once the ressource is created, the GenerateRequest is marked as Completed .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Footprint - 5 CRDs: 4 user-facing, 1 internal (GenerateRequest) - 5 webhooks - 1 Service, 1 Deployment, 1 ConfigMap - Internal resources (GenerateRequest) "parked" in a Namespace - Kyverno packs a lot of features in a small footprint .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Strengths - Kyverno is very easy to install (it's harder to get easier than one `kubectl apply -f`) - The setup of the webhooks is fully automated (including certificate generation) - It offers both namespaced and cluster-scope policies (same thing for the policy violations) - The policy language leverages existing constructs (e.g. `matchExpressions`) .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- ## Caveats - By default, the webhook failure policy is `Ignore` (meaning that there is a potential to evade policies if we can DOS the webhook) - Advanced policies (with conditionals) have unique, exotic syntax: ```yaml spec: =(volumes): =(hostPath): path: "!/var/run/docker.sock" ``` - The `{{ request }}` context is powerful, but difficult to validate (Kyverno can't know ahead of time how it will be populated) - Policy validation is difficult .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- class: extra-details ## Pods created by controllers - When e.g. a ReplicaSet or DaemonSet creates a pod, it "owns" it (the ReplicaSet or DaemonSet is listed in the Pod's `.metadata.ownerReferences`) - Kyverno treats these Pods differently - If my understanding of the code is correct (big *if*): - it skips validation for "owned" Pods - instead, it validates their controllers - this way, Kyverno can report errors on the controller instead of the pod - This can be a bit confusing when testing policies on such pods! ??? :EN:- Policy Management with Kyverno :FR:- Gestion de *policies* avec Kyverno .debug[[k8s/kyverno.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/kyverno.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/train-of-containers-1.jpg)] --- name: toc-an-elasticsearch-operator class: title An ElasticSearch Operator .nav[ [Previous part](#toc-policy-management-with-kyverno) | [Back to table of contents](#toc-part-11) | [Next part](#toc-finalizers) ] .debug[(automatically generated title slide)] --- # An ElasticSearch Operator - We will install [Elastic Cloud on Kubernetes](https://www.elastic.co/guide/en/cloud-on-k8s/current/k8s-quickstart.html), an ElasticSearch operator - This operator requires PersistentVolumes - We will install Rancher's [local path storage provisioner](https://github.com/rancher/local-path-provisioner) to automatically create these - Then, we will create an ElasticSearch resource - The operator will detect that resource and provision the cluster - We will integrate that ElasticSearch cluster with other resources (Kibana, Filebeat, Cerebro ...) .debug[[k8s/eck.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/eck.md)] --- ## Installing a Persistent Volume provisioner (This step can be skipped if you already have a dynamic volume provisioner.) - This provisioner creates Persistent Volumes backed by `hostPath` (local directories on our nodes) - It doesn't require anything special ... - ... But losing a node = losing the volumes on that node! .exercise[ - Install the local path storage provisioner: ```bash kubectl apply -f ~/container.training/k8s/local-path-storage.yaml ``` ] .debug[[k8s/eck.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/eck.md)] --- ## Making sure we have a default StorageClass - The ElasticSearch operator will create StatefulSets - These StatefulSets will instantiate PersistentVolumeClaims - These PVCs need to be explicitly associated with a StorageClass - Or we need to tag a StorageClass to be used as the default one .exercise[ - List StorageClasses: ```bash kubectl get storageclasses ``` ] We should see the `local-path` StorageClass. .debug[[k8s/eck.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/eck.md)] --- ## Setting a default StorageClass - This is done by adding an annotation to the StorageClass: `storageclass.kubernetes.io/is-default-class: true` .exercise[ - Tag the StorageClass so that it's the default one: ```bash kubectl annotate storageclass local-path \ storageclass.kubernetes.io/is-default-class=true ``` - Check the result: ```bash kubectl get storageclasses ``` ] Now, the StorageClass should have `(default)` next to its name. .debug[[k8s/eck.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/eck.md)] --- ## Install the ElasticSearch operator - The operator provides: - a few CustomResourceDefinitions - a Namespace for its other resources - a ValidatingWebhookConfiguration for type checking - a StatefulSet for its controller and webhook code - a ServiceAccount, ClusterRole, ClusterRoleBinding for permissions - All these resources are grouped in a convenient YAML file .exercise[ - Install the operator: ```bash kubectl apply -f ~/container.training/k8s/eck-operator.yaml ``` ] .debug[[k8s/eck.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/eck.md)] --- ## Check our new custom resources - Let's see which CRDs were created .exercise[ - List all CRDs: ```bash kubectl get crds ``` ] This operator supports ElasticSearch, but also Kibana and APM. Cool! .debug[[k8s/eck.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/eck.md)] --- ## Create the `eck-demo` namespace - For clarity, we will create everything in a new namespace, `eck-demo` - This namespace is hard-coded in the YAML files that we are going to use - We need to create that namespace .exercise[ - Create the `eck-demo` namespace: ```bash kubectl create namespace eck-demo ``` - Switch to that namespace: ```bash kns eck-demo ``` ] .debug[[k8s/eck.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/eck.md)] --- class: extra-details ## Can we use a different namespace? Yes, but then we need to update all the YAML manifests that we are going to apply in the next slides. The `eck-demo` namespace is hard-coded in these YAML manifests. Why? Because when defining a ClusterRoleBinding that references a ServiceAccount, we have to indicate in which namespace the ServiceAccount is located. .debug[[k8s/eck.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/eck.md)] --- ## Create an ElasticSearch resource - We can now create a resource with `kind: ElasticSearch` - The YAML for that resource will specify all the desired parameters: - how many nodes we want - image to use - add-ons (kibana, cerebro, ...) - whether to use TLS or not - etc. .exercise[ - Create our ElasticSearch cluster: ```bash kubectl apply -f ~/container.training/k8s/eck-elasticsearch.yaml ``` ] .debug[[k8s/eck.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/eck.md)] --- ## Operator in action - Over the next minutes, the operator will create our ES cluster - It will report our cluster status through the CRD .exercise[ - Check the logs of the operator: ```bash stern --namespace=elastic-system operator ``` <!-- ```wait elastic-operator-0``` ```tmux split-pane -v``` ---> - Watch the status of the cluster through the CRD: ```bash kubectl get es -w ``` <!-- ```longwait green``` ```key ^C``` ```key ^D``` ```key ^C``` --> ] .debug[[k8s/eck.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/eck.md)] --- ## Connecting to our cluster - It's not easy to use the ElasticSearch API from the shell - But let's check at least if ElasticSearch is up! .exercise[ - Get the ClusterIP of our ES instance: ```bash kubectl get services ``` - Issue a request with `curl`: ```bash curl http://`CLUSTERIP`:9200 ``` ] We get an authentication error. Our cluster is protected! .debug[[k8s/eck.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/eck.md)] --- ## Obtaining the credentials - The operator creates a user named `elastic` - It generates a random password and stores it in a Secret .exercise[ - Extract the password: ```bash kubectl get secret demo-es-elastic-user \ -o go-template="{{ .data.elastic | base64decode }} " ``` - Use it to connect to the API: ```bash curl -u elastic:`PASSWORD` http://`CLUSTERIP`:9200 ``` ] We should see a JSON payload with the `"You Know, for Search"` tagline. .debug[[k8s/eck.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/eck.md)] --- ## Sending data to the cluster - Let's send some data to our brand new ElasticSearch cluster! - We'll deploy a filebeat DaemonSet to collect node logs .exercise[ - Deploy filebeat: ```bash kubectl apply -f ~/container.training/k8s/eck-filebeat.yaml ``` - Wait until some pods are up: ```bash watch kubectl get pods -l k8s-app=filebeat ``` <!-- ```wait Running``` ```key ^C``` --> - Check that a filebeat index was created: ```bash curl -u elastic:`PASSWORD` http://`CLUSTERIP`:9200/_cat/indices ``` ] .debug[[k8s/eck.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/eck.md)] --- ## Deploying an instance of Kibana - Kibana can visualize the logs injected by filebeat - The ECK operator can also manage Kibana - Let's give it a try! .exercise[ - Deploy a Kibana instance: ```bash kubectl apply -f ~/container.training/k8s/eck-kibana.yaml ``` - Wait for it to be ready: ```bash kubectl get kibana -w ``` <!-- ```longwait green``` ```key ^C``` --> ] .debug[[k8s/eck.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/eck.md)] --- ## Connecting to Kibana - Kibana is automatically set up to conect to ElasticSearch (this is arranged by the YAML that we're using) - However, it will ask for authentication - It's using the same user/password as ElasticSearch .exercise[ - Get the NodePort allocated to Kibana: ```bash kubectl get services ``` - Connect to it with a web browser - Use the same user/password as before ] .debug[[k8s/eck.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/eck.md)] --- ## Setting up Kibana After the Kibana UI loads, we need to click around a bit .exercise[ - Pick "explore on my own" - Click on Use Elasticsearch data / Connect to your Elasticsearch index" - Enter `filebeat-*` for the index pattern and click "Next step" - Select `@timestamp` as time filter field name - Click on "discover" (the small icon looking like a compass on the left bar) - Play around! ] .debug[[k8s/eck.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/eck.md)] --- ## Scaling up the cluster - At this point, we have only one node - We are going to scale up - But first, we'll deploy Cerebro, an UI for ElasticSearch - This will let us see the state of the cluster, how indexes are sharded, etc. .debug[[k8s/eck.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/eck.md)] --- ## Deploying Cerebro - Cerebro is stateless, so it's fairly easy to deploy (one Deployment + one Service) - However, it needs the address and credentials for ElasticSearch - We prepared yet another manifest for that! .exercise[ - Deploy Cerebro: ```bash kubectl apply -f ~/container.training/k8s/eck-cerebro.yaml ``` - Lookup the NodePort number and connect to it: ```bash kubectl get services ``` ] .debug[[k8s/eck.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/eck.md)] --- ## Scaling up the cluster - We can see on Cerebro that the cluster is "yellow" (because our index is not replicated) - Let's change that! .exercise[ - Edit the ElasticSearch cluster manifest: ```bash kubectl edit es demo ``` - Find the field `count: 1` and change it to 3 - Save and quit <!-- ```wait Please edit``` ```keys /count:``` ```key ^J``` ```keys $r3:x``` ```key ^J``` --> ] ??? :EN:- Deploying ElasticSearch with ECK :FR:- Déployer ElasticSearch avec ECK .debug[[k8s/eck.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/eck.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/train-of-containers-2.jpg)] --- name: toc-finalizers class: title Finalizers .nav[ [Previous part](#toc-an-elasticsearch-operator) | [Back to table of contents](#toc-part-11) | [Next part](#toc-owners-and-dependents) ] .debug[(automatically generated title slide)] --- # Finalizers - Sometimes, we.red[¹] want to prevent a resource from being deleted: - perhaps it's "precious" (holds important data) - perhaps other resources depend on it (and should be deleted first) - perhaps we need to perform some clean up before it's deleted - *Finalizers* are a way to do that! .footnote[.red[¹]The "we" in that sentence generally stands for a controller. <br/>(We can also use finalizers directly ourselves, but it's not very common.)] .debug[[k8s/finalizers.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/finalizers.md)] --- ## Examples - Prevent deletion of a PersistentVolumeClaim which is used by a Pod - Prevent deletion of a PersistentVolume which is bound to a PersistentVolumeClaim - Prevent deletion of a Namespace that still contains objects - When a LoadBalancer Service is deleted, make sure that the corresponding external resource (e.g. NLB, GLB, etc.) gets deleted.red[¹] - When a CRD gets deleted, make sure that all the associated resources get deleted.red[²] .footnote[.red[¹²]Finalizers are not the only solution for these use-cases.] .debug[[k8s/finalizers.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/finalizers.md)] --- ## How do they work? - Each resource can have list of `finalizers` in its `metadata`, e.g.: ```yaml kind: PersistentVolumeClaim apiVersion: v1 metadata: name: my-pvc annotations: ... finalizers: - kubernetes.io/pvc-protection ``` - If we try to delete an resource that has at least one finalizer: - the resource is *not* deleted - instead, its `deletionTimestamp` is set to the current time - we are merely *marking the resource for deletion* .debug[[k8s/finalizers.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/finalizers.md)] --- ## What happens next? - The controller that added the finalizer is supposed to: - watch for resources with a `deletionTimestamp` - execute necessary clean-up actions - then remove the finalizer - The resource is deleted once all the finalizers have been removed (there is no timeout, so this could take forever) - Until then, the resource can be used normally (but no further finalizer can be *added* to the resource) .debug[[k8s/finalizers.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/finalizers.md)] --- ## Finalizers in review Let's review the examples mentioned earlier. For each of them, we'll see if there are other (perhaps better) options. .debug[[k8s/finalizers.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/finalizers.md)] --- ## Volume finalizer - Kubernetes applies the following finalizers: - `kubernetes.io/pvc-protection` on PersistentVolumeClaims - `kubernetes.io/pv-protection` on PersistentVolumes - This prevents removing them when they are in use - Implementation detail: the finalizer is present *even when the resource is not in use* - When the resource is ~~deleted~~ marked for deletion, the controller will check if the finalizer can be removed (Perhaps to avoid race conditions?) .debug[[k8s/finalizers.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/finalizers.md)] --- ## Namespace finalizer - Kubernetes applies a finalizer named `kubernetes` - It prevents removing the namespace if it still contains objects - *Can we remove the namespace anyway?* - remove the finalizer - delete the namespace - force deletion - It *seems to works* but, in fact, the objects in the namespace still exist (and they will re-appear if we re-create the namespace) See [this blog post](https://www.openshift.com/blog/the-hidden-dangers-of-terminating-namespaces) for more details about this. .debug[[k8s/finalizers.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/finalizers.md)] --- ## LoadBalancer finalizer - Scenario: We run a custom controller to implement provisioning of LoadBalancer Services. When a Service with type=LoadBalancer is deleted, we want to make sure that the corresponding external resources are properly deleted. - Rationale for using a finalizer: Normally, we would watch and observe the deletion of the Service; but if the Service is deleted while our controller is down, we could "miss" the deletion and forget to clean up the external resource. The finalizer ensures that we will "see" the deletion and clean up the external resource. .debug[[k8s/finalizers.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/finalizers.md)] --- ## Counterpoint - We could also: - Tag the external resources <br/>(to indicate which Kubernetes Service they correspond to) - Periodically reconcile them against Kubernetes resources - If a Kubernetes resource does no longer exist, delete the external resource - This doesn't have to be a *pre-delete* hook (unless we store important information in the Service, e.g. as annotations) .debug[[k8s/finalizers.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/finalizers.md)] --- ## CRD finalizer - Scenario: We have a CRD that represents a PostgreSQL cluster. It provisions StatefulSets, Deployments, Services, Secrets, ConfigMaps. When the CRD is deleted, we want to delete all these resources. - Rationale for using a finalizer: Same as previously; we could observe the CRD, but if it is deleted while the controller isn't running, we would miss the deletion, and the other resources would keep running. .debug[[k8s/finalizers.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/finalizers.md)] --- ## Counterpoint - We could use the same technique as described before (tag the resources with e.g. annotations, to associate them with the CRD) - Even better: we could use `ownerReferences` (this feature is *specifically* designed for that use-case!) .debug[[k8s/finalizers.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/finalizers.md)] --- ## CRD finalizer (take two) - Scenario: We have a CRD that represents a PostgreSQL cluster. It provisions StatefulSets, Deployments, Services, Secrets, ConfigMaps. When the CRD is deleted, we want to delete all these resources. We also want to store a final backup of the database. We also want to update final usage metrics (e.g. for billing purposes). - Rationale for using a finalizer: We need to take some actions *before* the resources get deleted, not *after*. .debug[[k8s/finalizers.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/finalizers.md)] --- ## Wrapping up - Finalizers are a great way to: - prevent deletion of a resource that is still in use - have a "guaranteed" pre-delete hook - They can also be (ab)used for other purposes - Code spelunking exercise: *check where finalizers are used in the Kubernetes code base and why!* ??? :EN:- Using "finalizers" to manage resource lifecycle :FR:- Gérer le cycle de vie des ressources avec les *finalizers* .debug[[k8s/finalizers.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/finalizers.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/two-containers-on-a-truck.jpg)] --- name: toc-owners-and-dependents class: title Owners and dependents .nav[ [Previous part](#toc-finalizers) | [Back to table of contents](#toc-part-11) | [Next part](#toc-building-our-own-cluster) ] .debug[(automatically generated title slide)] --- # Owners and dependents - Some objects are created by other objects (example: pods created by replica sets, themselves created by deployments) - When an *owner* object is deleted, its *dependents* are deleted (this is the default behavior; it can be changed) - We can delete a dependent directly if we want (but generally, the owner will recreate another right away) - An object can have multiple owners .debug[[k8s/owners-and-dependents.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/owners-and-dependents.md)] --- ## Finding out the owners of an object - The owners are recorded in the field `ownerReferences` in the `metadata` block .exercise[ - Let's create a deployment running `nginx`: ```bash kubectl create deployment yanginx --image=nginx ``` - Scale it to a few replicas: ```bash kubectl scale deployment yanginx --replicas=3 ``` - Once it's up, check the corresponding pods: ```bash kubectl get pods -l app=yanginx -o yaml | head -n 25 ``` ] These pods are owned by a ReplicaSet named yanginx-xxxxxxxxxx. .debug[[k8s/owners-and-dependents.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/owners-and-dependents.md)] --- ## Listing objects with their owners - This is a good opportunity to try the `custom-columns` output! .exercise[ - Show all pods with their owners: ```bash kubectl get pod -o custom-columns=\ NAME:.metadata.name,\ OWNER-KIND:.metadata.ownerReferences[0].kind,\ OWNER-NAME:.metadata.ownerReferences[0].name ``` ] Note: the `custom-columns` option should be one long option (without spaces), so the lines should not be indented (otherwise the indentation will insert spaces). .debug[[k8s/owners-and-dependents.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/owners-and-dependents.md)] --- ## Deletion policy - When deleting an object through the API, three policies are available: - foreground (API call returns after all dependents are deleted) - background (API call returns immediately; dependents are scheduled for deletion) - orphan (the dependents are not deleted) - When deleting an object with `kubectl`, this is selected with `--cascade`: - `--cascade=true` deletes all dependent objects (default) - `--cascade=false` orphans dependent objects .debug[[k8s/owners-and-dependents.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/owners-and-dependents.md)] --- ## What happens when an object is deleted - It is removed from the list of owners of its dependents - If, for one of these dependents, the list of owners becomes empty ... - if the policy is "orphan", the object stays - otherwise, the object is deleted .debug[[k8s/owners-and-dependents.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/owners-and-dependents.md)] --- ## Orphaning pods - We are going to delete the Deployment and Replica Set that we created - ... without deleting the corresponding pods! .exercise[ - Delete the Deployment: ```bash kubectl delete deployment -l app=yanginx --cascade=false ``` - Delete the Replica Set: ```bash kubectl delete replicaset -l app=yanginx --cascade=false ``` - Check that the pods are still here: ```bash kubectl get pods ``` ] .debug[[k8s/owners-and-dependents.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/owners-and-dependents.md)] --- class: extra-details ## When and why would we have orphans? - If we remove an owner and explicitly instruct the API to orphan dependents (like on the previous slide) - If we change the labels on a dependent, so that it's not selected anymore (e.g. change the `app: yanginx` in the pods of the previous example) - If a deployment tool that we're using does these things for us - If there is a serious problem within API machinery or other components (i.e. "this should not happen") .debug[[k8s/owners-and-dependents.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/owners-and-dependents.md)] --- ## Finding orphan objects - We're going to output all pods in JSON format - Then we will use `jq` to keep only the ones *without* an owner - And we will display their name .exercise[ - List all pods that *do not* have an owner: ```bash kubectl get pod -o json | jq -r " .items[] | select(.metadata.ownerReferences|not) | .metadata.name" ``` ] .debug[[k8s/owners-and-dependents.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/owners-and-dependents.md)] --- ## Deleting orphan pods - Now that we can list orphan pods, deleting them is easy .exercise[ - Add `| xargs kubectl delete pod` to the previous command: ```bash kubectl get pod -o json | jq -r " .items[] | select(.metadata.ownerReferences|not) | .metadata.name" | xargs kubectl delete pod ``` ] As always, the [documentation](https://kubernetes.io/docs/concepts/workloads/controllers/garbage-collection/) has useful extra information and pointers. ??? :EN:- Owners and dependents :FR:- Liens de parenté entre les ressources .debug[[k8s/owners-and-dependents.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/owners-and-dependents.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/wall-of-containers.jpeg)] --- name: toc-building-our-own-cluster class: title Building our own cluster .nav[ [Previous part](#toc-owners-and-dependents) | [Back to table of contents](#toc-part-12) | [Next part](#toc-adding-nodes-to-the-cluster) ] .debug[(automatically generated title slide)] --- # Building our own cluster - Let's build our own cluster! *Perfection is attained not when there is nothing left to add, but when there is nothing left to take away. (Antoine de Saint-Exupery)* - Our goal is to build a minimal cluster allowing us to: - create a Deployment (with `kubectl create deployment`) - expose it with a Service - connect to that service - "Minimal" here means: - smaller number of components - smaller number of command-line flags - smaller number of configuration files .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Non-goals - For now, we don't care about security - For now, we don't care about scalability - For now, we don't care about high availability - All we care about is *simplicity* .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Our environment - We will use the machine indicated as `dmuc1` (this stands for "Dessine Moi Un Cluster" or "Draw Me A Sheep", <br/>in homage to Saint-Exupery's "The Little Prince") - This machine: - runs Ubuntu LTS - has Kubernetes, Docker, and etcd binaries installed - but nothing is running .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Checking our environment - Let's make sure we have everything we need first .exercise[ - Log into the `dmuc1` machine - Get root: ```bash sudo -i ``` - Check available versions: ```bash etcd -version kube-apiserver --version dockerd --version ``` ] .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## The plan 1. Start API server 2. Interact with it (create Deployment and Service) 3. See what's broken 4. Fix it and go back to step 2 until it works! .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Dealing with multiple processes - We are going to start many processes - Depending on what you're comfortable with, you can: - open multiple windows and multiple SSH connections - use a terminal multiplexer like screen or tmux - put processes in the background with `&` <br/>(warning: log output might get confusing to read!) .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Starting API server .exercise[ - Try to start the API server: ```bash kube-apiserver # It will fail with "--etcd-servers must be specified" ``` ] Since the API server stores everything in etcd, it cannot start without it. .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Starting etcd .exercise[ - Try to start etcd: ```bash etcd ``` ] Success! Note the last line of output: ``` serving insecure client requests on 127.0.0.1:2379, this is strongly discouraged! ``` *Sure, that's discouraged. But thanks for telling us the address!* .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Starting API server (for real) - Try again, passing the `--etcd-servers` argument - That argument should be a comma-separated list of URLs .exercise[ - Start API server: ```bash kube-apiserver --etcd-servers http://127.0.0.1:2379 ``` ] Success! .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Interacting with API server - Let's try a few "classic" commands .exercise[ - List nodes: ```bash kubectl get nodes ``` - List services: ```bash kubectl get services ``` ] We should get `No resources found.` and the `kubernetes` service, respectively. Note: the API server automatically created the `kubernetes` service entry. .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- class: extra-details ## What about `kubeconfig`? - We didn't need to create a `kubeconfig` file - By default, the API server is listening on `localhost:8080` (without requiring authentication) - By default, `kubectl` connects to `localhost:8080` (without providing authentication) .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Creating a Deployment - Let's run a web server! .exercise[ - Create a Deployment with NGINX: ```bash kubectl create deployment web --image=nginx ``` ] Success? .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Checking our Deployment status .exercise[ - Look at pods, deployments, etc.: ```bash kubectl get all ``` ] Our Deployment is in bad shape: ``` NAME READY UP-TO-DATE AVAILABLE AGE deployment.apps/web 0/1 0 0 2m26s ``` And, there is no ReplicaSet, and no Pod. .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## What's going on? - We stored the definition of our Deployment in etcd (through the API server) - But there is no *controller* to do the rest of the work - We need to start the *controller manager* .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Starting the controller manager .exercise[ - Try to start the controller manager: ```bash kube-controller-manager ``` ] The final error message is: ``` invalid configuration: no configuration has been provided ``` But the logs include another useful piece of information: ``` Neither --kubeconfig nor --master was specified. Using the inClusterConfig. This might not work. ``` .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Reminder: everyone talks to API server - The controller manager needs to connect to the API server - It *does not* have a convenient `localhost:8080` default - We can pass the connection information in two ways: - `--master` and a host:port combination (easy) - `--kubeconfig` and a `kubeconfig` file - For simplicity, we'll use the first option .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Starting the controller manager (for real) .exercise[ - Start the controller manager: ```bash kube-controller-manager --master http://localhost:8080 ``` ] Success! .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Checking our Deployment status .exercise[ - Check all our resources again: ```bash kubectl get all ``` ] We now have a ReplicaSet. But we still don't have a Pod. .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## What's going on? In the controller manager logs, we should see something like this: ``` E0404 15:46:25.753376 22847 replica_set.go:450] Sync "default/web-5bc9bd5b8d" failed with `No API token found for service account "default"`, retry after the token is automatically created and added to the service account ``` - The service account `default` was automatically added to our Deployment (and to its pods) - The service account `default` exists - But it doesn't have an associated token (the token is a secret; creating it requires signature; therefore a CA) .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Solving the missing token issue There are many ways to solve that issue. We are going to list a few (to get an idea of what's happening behind the scenes). Of course, we don't need to perform *all* the solutions mentioned here. .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Option 1: disable service accounts - Restart the API server with `--disable-admission-plugins=ServiceAccount` - The API server will no longer add a service account automatically - Our pods will be created without a service account .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Option 2: do not mount the (missing) token - Add `automountServiceAccountToken: false` to the Deployment spec *or* - Add `automountServiceAccountToken: false` to the default ServiceAccount - The ReplicaSet controller will no longer create pods referencing the (missing) token .exercise[ - Programmatically change the `default` ServiceAccount: ```bash kubectl patch sa default -p "automountServiceAccountToken: false" ``` ] .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Option 3: set up service accounts properly - This is the most complex option! - Generate a key pair - Pass the private key to the controller manager (to generate and sign tokens) - Pass the public key to the API server (to verify these tokens) .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Continuing without service account token - Once we patch the default service account, the ReplicaSet can create a Pod .exercise[ - Check that we now have a pod: ```bash kubectl get all ``` ] Note: we might have to wait a bit for the ReplicaSet controller to retry. If we're impatient, we can restart the controller manager. .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## What's next? - Our pod exists, but it is in `Pending` state - Remember, we don't have a node so far (`kubectl get nodes` shows an empty list) - We need to: - start a container engine - start kubelet .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Starting a container engine - We're going to use Docker (because it's the default option) .exercise[ - Start the Docker Engine: ```bash dockerd ``` ] Success! Feel free to check that it actually works with e.g.: ```bash docker run alpine echo hello world ``` .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Starting kubelet - If we start kubelet without arguments, it *will* start - But it will not join the cluster! - It will start in *standalone* mode - Just like with the controller manager, we need to tell kubelet where the API server is - Alas, kubelet doesn't have a simple `--master` option - We have to use `--kubeconfig` - We need to write a `kubeconfig` file for kubelet .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Writing a kubeconfig file - We can copy/paste a bunch of YAML - Or we can generate the file with `kubectl` .exercise[ - Create the file `~/.kube/config` with `kubectl`: ```bash kubectl config \ set-cluster localhost --server http://localhost:8080 kubectl config \ set-context localhost --cluster localhost kubectl config \ use-context localhost ``` ] .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Our `~/.kube/config` file The file that we generated looks like the one below. That one has been slightly simplified (removing extraneous fields), but it is still valid. ```yaml apiVersion: v1 kind: Config current-context: localhost contexts: - name: localhost context: cluster: localhost clusters: - name: localhost cluster: server: http://localhost:8080 ``` .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Starting kubelet .exercise[ - Start kubelet with that kubeconfig file: ```bash kubelet --kubeconfig ~/.kube/config ``` ] Success! .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Looking at our 1-node cluster - Let's check that our node registered correctly .exercise[ - List the nodes in our cluster: ```bash kubectl get nodes ``` ] Our node should show up. Its name will be its hostname (it should be `dmuc1`). .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Are we there yet? - Let's check if our pod is running .exercise[ - List all resources: ```bash kubectl get all ``` ] -- Our pod is still `Pending`. 🤔 -- Which is normal: it needs to be *scheduled*. (i.e., something needs to decide which node it should go on.) .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Scheduling our pod - Why do we need a scheduling decision, since we have only one node? - The node might be full, unavailable; the pod might have constraints ... - The easiest way to schedule our pod is to start the scheduler (we could also schedule it manually) .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Starting the scheduler - The scheduler also needs to know how to connect to the API server - Just like for controller manager, we can use `--kubeconfig` or `--master` .exercise[ - Start the scheduler: ```bash kube-scheduler --master http://localhost:8080 ``` ] - Our pod should now start correctly .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Checking the status of our pod - Our pod will go through a short `ContainerCreating` phase - Then it will be `Running` .exercise[ - Check pod status: ```bash kubectl get pods ``` ] Success! .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- class: extra-details ## Scheduling a pod manually - We can schedule a pod in `Pending` state by creating a Binding, e.g.: ```bash kubectl create -f- <<EOF apiVersion: v1 kind: Binding metadata: name: name-of-the-pod target: apiVersion: v1 kind: Node name: name-of-the-node EOF ``` - This is actually how the scheduler works! - It watches pods, makes scheduling decisions, and creates Binding objects .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Connecting to our pod - Let's check that our pod correctly runs NGINX .exercise[ - Check our pod's IP address: ```bash kubectl get pods -o wide ``` - Send some HTTP request to the pod: ```bash curl `X.X.X.X` ``` ] We should see the `Welcome to nginx!` page. .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Exposing our Deployment - We can now create a Service associated with this Deployment .exercise[ - Expose the Deployment's port 80: ```bash kubectl expose deployment web --port=80 ``` - Check the Service's ClusterIP, and try connecting: ```bash kubectl get service web curl http://`X.X.X.X` ``` ] -- This won't work. We need kube-proxy to enable internal communication. .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Starting kube-proxy - kube-proxy also needs to connect to the API server - It can work with the `--master` flag (although that will be deprecated in the future) .exercise[ - Start kube-proxy: ```bash kube-proxy --master http://localhost:8080 ``` ] .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Connecting to our Service - Now that kube-proxy is running, we should be able to connect .exercise[ - Check the Service's ClusterIP again, and retry connecting: ```bash kubectl get service web curl http://`X.X.X.X` ``` ] Success! .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- class: extra-details ## How kube-proxy works - kube-proxy watches Service resources - When a Service is created or updated, kube-proxy creates iptables rules .exercise[ - Check out the `OUTPUT` chain in the `nat` table: ```bash iptables -t nat -L OUTPUT ``` - Traffic is sent to `KUBE-SERVICES`; check that too: ```bash iptables -t nat -L KUBE-SERVICES ``` ] For each Service, there is an entry in that chain. .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- class: extra-details ## Diving into iptables - The last command showed a chain named `KUBE-SVC-...` corresponding to our service .exercise[ - Check that `KUBE-SVC-...` chain: ```bash iptables -t nat -L `KUBE-SVC-...` ``` - It should show a jump to a `KUBE-SEP-...` chains; check it out too: ```bash iptables -t nat -L `KUBE-SEP-...` ``` ] This is a `DNAT` rule to rewrite the destination address of the connection to our pod. This is how kube-proxy works! .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- class: extra-details ## kube-router, IPVS - With recent versions of Kubernetes, it is possible to tell kube-proxy to use IPVS - IPVS is a more powerful load balancing framework (remember: iptables was primarily designed for firewalling, not load balancing!) - It is also possible to replace kube-proxy with kube-router - kube-router uses IPVS by default - kube-router can also perform other functions (e.g., we can use it as a CNI plugin to provide pod connectivity) .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- class: extra-details ## What about the `kubernetes` service? - If we try to connect, it won't work (by default, it should be `10.0.0.1`) - If we look at the Endpoints for this service, we will see one endpoint: `host-address:6443` - By default, the API server expects to be running directly on the nodes (it could be as a bare process, or in a container/pod using the host network) - ... And it expects to be listening on port 6443 with TLS ??? :EN:- Building our own cluster from scratch :FR:- Construire son cluster à la main .debug[[k8s/dmuc.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/dmuc.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/Container-Ship-Freighter-Navigation-Elbe-Romance-1782991.jpg)] --- name: toc-adding-nodes-to-the-cluster class: title Adding nodes to the cluster .nav[ [Previous part](#toc-building-our-own-cluster) | [Back to table of contents](#toc-part-12) | [Next part](#toc-the-container-network-interface) ] .debug[(automatically generated title slide)] --- # Adding nodes to the cluster - So far, our cluster has only 1 node - Let's see what it takes to add more nodes - We are going to use another set of machines: `kubenet` .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## The environment - We have 3 identical machines: `kubenet1`, `kubenet2`, `kubenet3` - The Docker Engine is installed (and running) on these machines - The Kubernetes binaries are installed, but nothing is running - We will use `kubenet1` to run the control plane .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## The plan - Start the control plane on `kubenet1` - Join the 3 nodes to the cluster - Deploy and scale a simple web server .exercise[ - Log into `kubenet1` ] .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## Running the control plane - We will use a Compose file to start the control plane components .exercise[ - Clone the repository containing the workshop materials: ```bash git clone https://github.com/barpilot/container.training ``` - Go to the `compose/simple-k8s-control-plane` directory: ```bash cd container.training/compose/simple-k8s-control-plane ``` - Start the control plane: ```bash docker-compose up ``` ] .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## Checking the control plane status - Before moving on, verify that the control plane works .exercise[ - Show control plane component statuses: ```bash kubectl get componentstatuses kubectl get cs ``` - Show the (empty) list of nodes: ```bash kubectl get nodes ``` ] .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- class: extra-details ## Differences from `dmuc` - Our new control plane listens on `0.0.0.0` instead of the default `127.0.0.1` - The ServiceAccount admission plugin is disabled .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## Joining the nodes - We need to generate a `kubeconfig` file for kubelet - This time, we need to put the public IP address of `kubenet1` (instead of `localhost` or `127.0.0.1`) .exercise[ - Generate the `kubeconfig` file: ```bash kubectl config set-cluster kubenet --server http://`X.X.X.X`:8080 kubectl config set-context kubenet --cluster kubenet kubectl config use-context kubenet cp ~/.kube/config ~/kubeconfig ``` ] .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## Distributing the `kubeconfig` file - We need that `kubeconfig` file on the other nodes, too .exercise[ - Copy `kubeconfig` to the other nodes: ```bash for N in 2 3; do scp ~/kubeconfig kubenet$N: done ``` ] .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## Starting kubelet - Reminder: kubelet needs to run as root; don't forget `sudo`! .exercise[ - Join the first node: ```bash sudo kubelet --kubeconfig ~/kubeconfig ``` - Open more terminals and join the other nodes to the cluster: ```bash ssh kubenet2 sudo kubelet --kubeconfig ~/kubeconfig ssh kubenet3 sudo kubelet --kubeconfig ~/kubeconfig ``` ] .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## Checking cluster status - We should now see all 3 nodes - At first, their `STATUS` will be `NotReady` - They will move to `Ready` state after approximately 10 seconds .exercise[ - Check the list of nodes: ```bash kubectl get nodes ``` ] .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## Deploy a web server - Let's create a Deployment and scale it (so that we have multiple pods on multiple nodes) .exercise[ - Create a Deployment running NGINX: ```bash kubectl create deployment web --image=nginx ``` - Scale it: ```bash kubectl scale deployment web --replicas=5 ``` ] .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## Check our pods - The pods will be scheduled on the nodes - The nodes will pull the `nginx` image, and start the pods - What are the IP addresses of our pods? .exercise[ - Check the IP addresses of our pods ```bash kubectl get pods -o wide ``` ] -- 🤔 Something's not right ... Some pods have the same IP address! .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## What's going on? - Without the `--network-plugin` flag, kubelet defaults to "no-op" networking - It lets the container engine use a default network (in that case, we end up with the default Docker bridge) - Our pods are running on independent, disconnected, host-local networks .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## What do we need to do? - On a normal cluster, kubelet is configured to set up pod networking with CNI plugins - This requires: - installing CNI plugins - writing CNI configuration files - running kubelet with `--network-plugin=cni` .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## Using network plugins - We need to set up a better network - Before diving into CNI, we will use the `kubenet` plugin - This plugin creates a `cbr0` bridge and connects the containers to that bridge - This plugin allocates IP addresses from a range: - either specified to kubelet (e.g. with `--pod-cidr`) - or stored in the node's `spec.podCIDR` field .footnote[See [here] for more details about this `kubenet` plugin.] [here]: https://kubernetes.io/docs/concepts/extend-kubernetes/compute-storage-net/network-plugins/#kubenet .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## What `kubenet` does and *does not* do - It allocates IP addresses to pods *locally* (each node has its own local subnet) - It connects the pods to a *local* bridge (pods on the same node can communicate together; not with other nodes) - It doesn't set up routing or tunneling (we get pods on separated networks; we need to connect them somehow) - It doesn't allocate subnets to nodes (this can be done manually, or by the controller manager) .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## Setting up routing or tunneling - *On each node*, we will add routes to the other nodes' pod network - Of course, this is not convenient or scalable! - We will see better techniques to do this; but for now, hang on! .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## Allocating subnets to nodes - There are multiple options: - passing the subnet to kubelet with the `--pod-cidr` flag - manually setting `spec.podCIDR` on each node - allocating node CIDRs automatically with the controller manager - The last option would be implemented by adding these flags to controller manager: ``` --allocate-node-cidrs=true --cluster-cidr=<cidr> ``` .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- class: extra-details ## The pod CIDR field is not mandatory - `kubenet` needs the pod CIDR, but other plugins don't need it (e.g. because they allocate addresses in multiple pools, or a single big one) - The pod CIDR field may eventually be deprecated and replaced by an annotation (see [kubernetes/kubernetes#57130](https://github.com/kubernetes/kubernetes/issues/57130)) .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## Restarting kubelet wih pod CIDR - We need to stop and restart all our kubelets - We will add the `--network-plugin` and `--pod-cidr` flags - We all have a "cluster number" (let's call that `C`) printed on your VM info card - We will use pod CIDR `10.C.N.0/24` (where `N` is the node number: 1, 2, 3) .exercise[ - Stop all the kubelets (Ctrl-C is fine) - Restart them all, adding `--network-plugin=kubenet --pod-cidr 10.C.N.0/24` ] .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## What happens to our pods? - When we stop (or kill) kubelet, the containers keep running - When kubelet starts again, it detects the containers .exercise[ - Check that our pods are still here: ```bash kubectl get pods -o wide ``` ] 🤔 But our pods still use local IP addresses! .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## Recreating the pods - The IP address of a pod cannot change - kubelet doesn't automatically kill/restart containers with "invalid" addresses <br/> (in fact, from kubelet's point of view, there is no such thing as an "invalid" address) - We must delete our pods and recreate them .exercise[ - Delete all the pods, and let the ReplicaSet recreate them: ```bash kubectl delete pods --all ``` - Wait for the pods to be up again: ```bash kubectl get pods -o wide -w ``` ] .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## Adding kube-proxy - Let's start kube-proxy to provide internal load balancing - Then see if we can create a Service and use it to contact our pods .exercise[ - Start kube-proxy: ```bash sudo kube-proxy --kubeconfig ~/.kube/config ``` - Expose our Deployment: ```bash kubectl expose deployment web --port=80 ``` ] .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## Test internal load balancing .exercise[ - Retrieve the ClusterIP address: ```bash kubectl get svc web ``` - Send a few requests to the ClusterIP address (with `curl`) ] -- Sometimes it works, sometimes it doesn't. Why? .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## Routing traffic - Our pods have new, distinct IP addresses - But they are on host-local, isolated networks - If we try to ping a pod on a different node, it won't work - kube-proxy merely rewrites the destination IP address - But we need that IP address to be reachable in the first place - How do we fix this? (hint: check the title of this slide!) .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## Important warning - The technique that we are about to use doesn't work everywhere - It only works if: - all the nodes are directly connected to each other (at layer 2) - the underlying network allows the IP addresses of our pods - If we are on physical machines connected by a switch: OK - If we are on virtual machines in a public cloud: NOT OK - on AWS, we need to disable "source and destination checks" on our instances - on OpenStack, we need to disable "port security" on our network ports .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## Routing basics - We need to tell *each* node: "The subnet 10.C.N.0/24 is located on node N" (for all values of N) - This is how we add a route on Linux: ```bash ip route add 10.C.N.0/24 via W.X.Y.Z ``` (where `W.X.Y.Z` is the internal IP address of node N) - We can see the internal IP addresses of our nodes with: ```bash kubectl get nodes -o wide ``` .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## Firewalling - By default, Docker prevents containers from using arbitrary IP addresses (by setting up iptables rules) - We need to allow our containers to use our pod CIDR - For simplicity, we will insert a blanket iptables rule allowing all traffic: `iptables -I FORWARD -j ACCEPT` - This has to be done on every node .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## Setting up routing .exercise[ - Create all the routes on all the nodes - Insert the iptables rule allowing traffic - Check that you can ping all the pods from one of the nodes - Check that you can `curl` the ClusterIP of the Service successfully ] .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- ## What's next? - We did a lot of manual operations: - allocating subnets to nodes - adding command-line flags to kubelet - updating the routing tables on our nodes - We want to automate all these steps - We want something that works on all networks ??? :EN:- Connecting nodes ands pods :FR:- Interconnecter les nœuds et les pods .debug[[k8s/multinode.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/multinode.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)] --- name: toc-the-container-network-interface class: title The Container Network Interface .nav[ [Previous part](#toc-adding-nodes-to-the-cluster) | [Back to table of contents](#toc-part-12) | [Next part](#toc-cni-internals) ] .debug[(automatically generated title slide)] --- # The Container Network Interface - Allows us to decouple network configuration from Kubernetes - Implemented by *plugins* - Plugins are executables that will be invoked by kubelet - Plugins are responsible for: - allocating IP addresses for containers - configuring the network for containers - Plugins can be combined and chained when it makes sense .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- ## Combining plugins - Interface could be created by e.g. `vlan` or `bridge` plugin - IP address could be allocated by e.g. `dhcp` or `host-local` plugin - Interface parameters (MTU, sysctls) could be tweaked by the `tuning` plugin The reference plugins are available [here]. Look in each plugin's directory for its documentation. [here]: https://github.com/containernetworking/plugins/tree/master/plugins .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- ## How does kubelet know which plugins to use? - The plugin (or list of plugins) is set in the CNI configuration - The CNI configuration is a *single file* in `/etc/cni/net.d` - If there are multiple files in that directory, the first one is used (in lexicographic order) - That path can be changed with the `--cni-conf-dir` flag of kubelet .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- ## CNI configuration in practice - When we set up the "pod network" (like Calico, Weave...) it ships a CNI configuration (and sometimes, custom CNI plugins) - Very often, that configuration (and plugins) is installed automatically (by a DaemonSet featuring an initContainer with hostPath volumes) - Examples: - Calico [CNI config](https://github.com/projectcalico/calico/blob/1372b56e3bfebe2b9c9cbf8105d6a14764f44159/v2.6/getting-started/kubernetes/installation/hosted/calico.yaml#L25) and [volume](https://github.com/projectcalico/calico/blob/1372b56e3bfebe2b9c9cbf8105d6a14764f44159/v2.6/getting-started/kubernetes/installation/hosted/calico.yaml#L219) - kube-router [CNI config](https://github.com/cloudnativelabs/kube-router/blob/c2f893f64fd60cf6d2b6d3fee7191266c0fc0fe5/daemonset/generic-kuberouter.yaml#L10) and [volume](https://github.com/cloudnativelabs/kube-router/blob/c2f893f64fd60cf6d2b6d3fee7191266c0fc0fe5/daemonset/generic-kuberouter.yaml#L73) .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- class: extra-details ## Conf vs conflist - There are two slightly different configuration formats - Basic configuration format: - holds configuration for a single plugin - typically has a `.conf` name suffix - has a `type` string field in the top-most structure - [examples](https://github.com/containernetworking/cni/blob/master/SPEC.md#example-configurations) - Configuration list format: - can hold configuration for multiple (chained) plugins - typically has a `.conflist` name suffix - has a `plugins` list field in the top-most structure - [examples](https://github.com/containernetworking/cni/blob/master/SPEC.md#network-configuration-lists) .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- class: extra-details ## How plugins are invoked - Parameters are given through environment variables, including: - CNI_COMMAND: desired operation (ADD, DEL, CHECK, or VERSION) - CNI_CONTAINERID: container ID - CNI_NETNS: path to network namespace file - CNI_IFNAME: what the network interface should be named - The network configuration must be provided to the plugin on stdin (this avoids race conditions that could happen by passing a file path) .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- ## In practice: kube-router - We are going to set up a new cluster - For this new cluster, we will use kube-router - kube-router will provide the "pod network" (connectivity with pods) - kube-router will also provide internal service connectivity (replacing kube-proxy) .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- ## How kube-router works - Very simple architecture - Does not introduce new CNI plugins (uses the `bridge` plugin, with `host-local` for IPAM) - Pod traffic is routed between nodes (no tunnel, no new protocol) - Internal service connectivity is implemented with IPVS - Can provide pod network and/or internal service connectivity - kube-router daemon runs on every node .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- ## What kube-router does - Connect to the API server - Obtain the local node's `podCIDR` - Inject it into the CNI configuration file (we'll use `/etc/cni/net.d/10-kuberouter.conflist`) - Obtain the addresses of all nodes - Establish a *full mesh* BGP peering with the other nodes - Exchange routes over BGP .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- class: extra-details ## What's BGP? - BGP (Border Gateway Protocol) is the protocol used between internet routers - It [scales](https://www.cidr-report.org/as2.0/) pretty [well](https://www.cidr-report.org/cgi-bin/plota?file=%2fvar%2fdata%2fbgp%2fas2.0%2fbgp-active%2etxt&descr=Active%20BGP%20entries%20%28FIB%29&ylabel=Active%20BGP%20entries%20%28FIB%29&with=step) (it is used to announce the 700k CIDR prefixes of the internet) - It is spoken by many hardware routers from many vendors - It also has many software implementations (Quagga, Bird, FRR...) - Experienced network folks generally know it (and appreciate it) - It also used by Calico (another popular network system for Kubernetes) - Using BGP allows us to interconnect our "pod network" with other systems .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- ## The plan - We'll work in a new cluster (named `kuberouter`) - We will run a simple control plane (like before) - ... But this time, the controller manager will allocate `podCIDR` subnets (so that we don't have to manually assign subnets to individual nodes) - We will create a DaemonSet for kube-router - We will join nodes to the cluster - The DaemonSet will automatically start a kube-router pod on each node .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- ## Logging into the new cluster .exercise[ - Log into node `kuberouter1` - Clone the workshop repository: ```bash git clone https://github.com/barpilot/container.training ``` - Move to this directory: ```bash cd container.training/compose/kube-router-k8s-control-plane ``` ] .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- class: extra-details ## Checking the CNI configuration - By default, kubelet gets the CNI configuration from `/etc/cni/net.d` .exercise[ - Check the content of `/etc/cni/net.d` ] (On most machines, at this point, `/etc/cni/net.d` doesn't even exist).) .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- ## Our control plane - We will use a Compose file to start the control plane - It is similar to the one we used with the `kubenet` cluster - The API server is started with `--allow-privileged` (because we will start kube-router in privileged pods) - The controller manager is started with extra flags too: `--allocate-node-cidrs` and `--cluster-cidr` - We need to edit the Compose file to set the Cluster CIDR .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- ## Starting the control plane - Our cluster CIDR will be `10.C.0.0/16` (where `C` is our cluster number) .exercise[ - Edit the Compose file to set the Cluster CIDR: ```bash vim docker-compose.yaml ``` - Start the control plane: ```bash docker-compose up ``` ] .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- ## The kube-router DaemonSet - In the same directory, there is a `kuberouter.yaml` file - It contains the definition for a DaemonSet and a ConfigMap - Before we load it, we also need to edit it - We need to indicate the address of the API server (because kube-router needs to connect to it to retrieve node information) .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- ## Creating the DaemonSet - The address of the API server will be `http://A.B.C.D:8080` (where `A.B.C.D` is the public address of `kuberouter1`, running the control plane) .exercise[ - Edit the YAML file to set the API server address: ```bash vim kuberouter.yaml ``` - Create the DaemonSet: ```bash kubectl create -f kuberouter.yaml ``` ] Note: the DaemonSet won't create any pods (yet) since there are no nodes (yet). .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- ## Generating the kubeconfig for kubelet - This is similar to what we did for the `kubenet` cluster .exercise[ - Generate the kubeconfig file (replacing `X.X.X.X` with the address of `kuberouter1`): ```bash kubectl config set-cluster cni --server http://`X.X.X.X`:8080 kubectl config set-context cni --cluster cni kubectl config use-context cni cp ~/.kube/config ~/kubeconfig ``` ] .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- ## Distributing kubeconfig - We need to copy that kubeconfig file to the other nodes .exercise[ - Copy `kubeconfig` to the other nodes: ```bash for N in 2 3; do scp ~/kubeconfig kuberouter$N: done ``` ] .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- ## Starting kubelet - We don't need the `--pod-cidr` option anymore (the controller manager will allocate these automatically) - We need to pass `--network-plugin=cni` .exercise[ - Join the first node: ```bash sudo kubelet --kubeconfig ~/kubeconfig --network-plugin=cni ``` - Open more terminals and join the other nodes: ```bash ssh kuberouter2 sudo kubelet --kubeconfig ~/kubeconfig --network-plugin=cni ssh kuberouter3 sudo kubelet --kubeconfig ~/kubeconfig --network-plugin=cni ``` ] .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- class: extra-details ## Checking the CNI configuration - At this point, kuberouter should have installed its CNI configuration (in `/etc/cni/net.d`) .exercise[ - Check the content of `/etc/cni/net.d` ] - There should be a file created by kuberouter - The file should contain the node's podCIDR .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- ## Setting up a test - Let's create a Deployment and expose it with a Service .exercise[ - Create a Deployment running a web server: ```bash kubectl create deployment web --image=jpetazzo/httpenv ``` - Scale it so that it spans multiple nodes: ```bash kubectl scale deployment web --replicas=5 ``` - Expose it with a Service: ```bash kubectl expose deployment web --port=8888 ``` ] .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- ## Checking that everything works .exercise[ - Get the ClusterIP address for the service: ```bash kubectl get svc web ``` - Send a few requests there: ```bash curl `X.X.X.X`:8888 ``` ] Note that if you send multiple requests, they are load-balanced in a round robin manner. This shows that we are using IPVS (vs. iptables, which picked random endpoints). .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- class: extra-details ## Troubleshooting - What if we need to check that everything is working properly? .exercise[ - Check the IP addresses of our pods: ```bash kubectl get pods -o wide ``` - Check our routing table: ```bash route -n ip route ``` ] We should see the local pod CIDR connected to `kube-bridge`, and the other nodes' pod CIDRs having individual routes, with each node being the gateway. .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- class: extra-details ## More troubleshooting - We can also look at the output of the kube-router pods (with `kubectl logs`) - kube-router also comes with a special shell that gives lots of useful info (we can access it with `kubectl exec`) - But with the current setup of the cluster, these options may not work! - Why? .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- class: extra-details ## Trying `kubectl logs` / `kubectl exec` .exercise[ - Try to show the logs of a kube-router pod: ```bash kubectl -n kube-system logs ds/kube-router ``` - Or try to exec into one of the kube-router pods: ```bash kubectl -n kube-system exec kube-router-xxxxx bash ``` ] These commands will give an error message that includes: ``` dial tcp: lookup kuberouterX on 127.0.0.11:53: no such host ``` What does that mean? .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- class: extra-details ## Internal name resolution - To execute these commands, the API server needs to connect to kubelet - By default, it creates a connection using the kubelet's name (e.g. `http://kuberouter1:...`) - This requires our nodes names to be in DNS - We can change that by setting a flag on the API server: `--kubelet-preferred-address-types=InternalIP` .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- class: extra-details ## Another way to check the logs - We can also ask the logs directly to the container engine - First, get the container ID, with `docker ps` or like this: ```bash CID=$(docker ps -q \ --filter label=io.kubernetes.pod.namespace=kube-system \ --filter label=io.kubernetes.container.name=kube-router) ``` - Then view the logs: ```bash docker logs $CID ``` .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- class: extra-details ## Other ways to distribute routing tables - We don't need kube-router and BGP to distribute routes - The list of nodes (and associated `podCIDR` subnets) is available through the API - This shell snippet generates the commands to add all required routes on a node: ```bash NODES=$(kubectl get nodes -o name | cut -d/ -f2) for DESTNODE in $NODES; do if [ "$DESTNODE" != "$HOSTNAME" ]; then echo $(kubectl get node $DESTNODE -o go-template=" route add -net {{.spec.podCIDR}} gw {{(index .status.addresses 0).address}}") fi done ``` - This could be useful for embedded platforms with very limited resources (or lab environments for learning purposes) ??? :EN:- Configuring CNI plugins :FR:- Configurer des plugins CNI .debug[[k8s/cni.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/aerial-view-of-containers.jpg)] --- name: toc-cni-internals class: title CNI internals .nav[ [Previous part](#toc-the-container-network-interface) | [Back to table of contents](#toc-part-12) | [Next part](#toc-api-server-availability) ] .debug[(automatically generated title slide)] --- # CNI internals - Kubelet looks for a CNI configuration file (by default, in `/etc/cni/net.d`) - Note: if we have multiple files, the first one will be used (in lexicographic order) - If no configuration can be found, kubelet holds off on creating containers (except if they are using `hostNetwork`) - Let's see how exactly plugins are invoked! .debug[[k8s/cni-internals.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni-internals.md)] --- ## General principle - A plugin is an executable program - It is invoked with by kubelet to set up / tear down networking for a container - It doesn't take any command-line argument - However, it uses environment variables to know what to do, which container, etc. - It reads JSON on stdin, and writes back JSON on stdout - There will generally be multiple plugins invoked in a row (at least IPAM + network setup; possibly more) .debug[[k8s/cni-internals.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni-internals.md)] --- ## Environment variables - `CNI_COMMAND`: `ADD`, `DEL`, `CHECK`, or `VERSION` - `CNI_CONTAINERID`: opaque identifier (container ID of the "sandbox", i.e. the container running the `pause` image) - `CNI_NETNS`: path to network namespace pseudo-file (e.g. `/var/run/netns/cni-0376f625-29b5-7a21-6c45-6a973b3224e5`) - `CNI_IFNAME`: interface name, usually `eth0` - `CNI_PATH`: path(s) with plugin executables (e.g. `/opt/cni/bin`) - `CNI_ARGS`: "extra arguments" (see next slide) .debug[[k8s/cni-internals.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni-internals.md)] --- ## `CNI_ARGS` - Extra key/value pair arguments passed by "the user" - "The user", here, is "kubelet" (or in an abstract way, "Kubernetes") - This is used to pass the pod name and namespace to the CNI plugin - Example: ``` IgnoreUnknown=1 K8S_POD_NAMESPACE=default K8S_POD_NAME=web-96d5df5c8-jcn72 K8S_POD_INFRA_CONTAINER_ID=016493dbff152641d334d9828dab6136c1ff... ``` Note that technically, it's a `;`-separated list, so it really looks like this: ``` CNI_ARGS=IgnoreUnknown=1;K8S_POD_NAMESPACE=default;K8S_POD_NAME=web-96d... ``` .debug[[k8s/cni-internals.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni-internals.md)] --- ## JSON in, JSON out - The plugin reads its configuration on stdin - It writes back results in JSON (e.g. allocated address, routes, DNS...) ⚠️ "Plugin configuration" is not always the same as "CNI configuration"! .debug[[k8s/cni-internals.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni-internals.md)] --- ## Conf vs Conflist - The CNI configuration can be a single plugin configuration - it will then contain a `type` field in the top-most structure - it will be passed "as-is" - It can also be a "conflist", containing a chain of plugins (it will then contain a `plugins` field in the top-most structure) - Plugins are then invoked in order (reverse order for `DEL` action) - In that case, the input of the plugin is not the whole configuration (see details on next slide) .debug[[k8s/cni-internals.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni-internals.md)] --- ## List of plugins - When invoking a plugin in a list, the JSON input will be: - the configuration of the plugin - augmented with `name` (matching the conf list `name`) - augmented with `prevResult` (which will be the output of the previous plugin) - Conceptually, a plugin (generally the first one) will do the "main setup" - Other plugins can do tuning / refinement (firewalling, traffic shaping...) .debug[[k8s/cni-internals.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni-internals.md)] --- ## Analyzing plugins - Let's see what goes in and out of our CNI plugins! - We will create a fake plugin that: - saves its environment and input - executes the real plugin with the saved input - saves the plugin output - passes the saved output .debug[[k8s/cni-internals.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni-internals.md)] --- ## Our fake plugin ```bash #!/bin/sh PLUGIN=$(basename $0) cat > /tmp/cni.$$.$PLUGIN.in env | sort > /tmp/cni.$$.$PLUGIN.env echo "PPID=$PPID, $(readlink /proc/$PPID/exe)" > /tmp/cni.$$.$PLUGIN.parent $0.real < /tmp/cni.$$.$PLUGIN.in > /tmp/cni.$$.$PLUGIN.out EXITSTATUS=$? cat /tmp/cni.$$.$PLUGIN.out exit $EXITSTATUS ``` Save this script as `/opt/cni/bin/debug` and make it executable. .debug[[k8s/cni-internals.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni-internals.md)] --- ## Substituting the fake plugin - For each plugin that we want to instrument: - rename the plugin from e.g. `ptp` to `ptp.real` - symlink `ptp` to our `debug` plugin - There is no need to change the CNI configuration or restart kubelet .debug[[k8s/cni-internals.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni-internals.md)] --- ## Create some pods and looks at the results - Create a pod - For each instrumented plugin, there will be files in `/tmp`: `cni.PID.pluginname.in` (JSON input) `cni.PID.pluginname.env` (environment variables) `cni.PID.pluginname.parent` (parent process information) `cni.PID.pluginname.out` (JSON output) ❓️ What is calling our plugins? ??? :EN:- Deep dive into CNI internals :FR:- La Container Network Interface (CNI) en détails .debug[[k8s/cni-internals.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cni-internals.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/blue-containers.jpg)] --- name: toc-api-server-availability class: title API server availability .nav[ [Previous part](#toc-cni-internals) | [Back to table of contents](#toc-part-12) | [Next part](#toc-static-pods) ] .debug[(automatically generated title slide)] --- # API server availability - When we set up a node, we need the address of the API server: - for kubelet - for kube-proxy - sometimes for the pod network system (like kube-router) - How do we ensure the availability of that endpoint? (what if the node running the API server goes down?) .debug[[k8s/apilb.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/apilb.md)] --- ## Option 1: external load balancer - Set up an external load balancer - Point kubelet (and other components) to that load balancer - Put the node(s) running the API server behind that load balancer - Update the load balancer if/when an API server node needs to be replaced - On cloud infrastructures, some mechanisms provide automation for this (e.g. on AWS, an Elastic Load Balancer + Auto Scaling Group) - [Example in Kubernetes The Hard Way](https://github.com/kelseyhightower/kubernetes-the-hard-way/blob/master/docs/08-bootstrapping-kubernetes-controllers.md#the-kubernetes-frontend-load-balancer) .debug[[k8s/apilb.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/apilb.md)] --- ## Option 2: local load balancer - Set up a load balancer (like NGINX, HAProxy...) on *each* node - Configure that load balancer to send traffic to the API server node(s) - Point kubelet (and other components) to `localhost` - Update the load balancer configuration when API server nodes are updated .debug[[k8s/apilb.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/apilb.md)] --- ## Updating the local load balancer config - Distribute the updated configuration (push) - Or regularly check for updates (pull) - The latter requires an external, highly available store (it could be an object store, an HTTP server, or even DNS...) - Updates can be facilitated by a DaemonSet (but remember that it can't be used when installing a new node!) .debug[[k8s/apilb.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/apilb.md)] --- ## Option 3: DNS records - Put all the API server nodes behind a round-robin DNS - Point kubelet (and other components) to that name - Update the records when needed - Note: this option is not officially supported (but since kubelet supports reconnection anyway, it *should* work) .debug[[k8s/apilb.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/apilb.md)] --- ## Option 4: .................... - Many managed clusters expose a high-availability API endpoint (and you don't have to worry about it) - You can also use HA mechanisms that you're familiar with (e.g. virtual IPs) - Tunnels are also fine (e.g. [k3s](https://k3s.io/) uses a tunnel to allow each node to contact the API server) ??? :EN:- Ensuring API server availability :FR:- Assurer la disponibilité du serveur API .debug[[k8s/apilb.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/apilb.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/chinook-helicopter-container.jpg)] --- name: toc-static-pods class: title Static pods .nav[ [Previous part](#toc-api-server-availability) | [Back to table of contents](#toc-part-12) | [Next part](#toc-upgrading-clusters) ] .debug[(automatically generated title slide)] --- # Static pods - Hosting the Kubernetes control plane on Kubernetes has advantages: - we can use Kubernetes' replication and scaling features for the control plane - we can leverage rolling updates to upgrade the control plane - However, there is a catch: - deploying on Kubernetes requires the API to be available - the API won't be available until the control plane is deployed - How can we get out of that chicken-and-egg problem? .debug[[k8s/staticpods.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/staticpods.md)] --- ## A possible approach - Since each component of the control plane can be replicated... - We could set up the control plane outside of the cluster - Then, once the cluster is fully operational, create replicas running on the cluster - Finally, remove the replicas that are running outside of the cluster *What could possibly go wrong?* .debug[[k8s/staticpods.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/staticpods.md)] --- ## Sawing off the branch you're sitting on - What if anything goes wrong? (During the setup or at a later point) - Worst case scenario, we might need to: - set up a new control plane (outside of the cluster) - restore a backup from the old control plane - move the new control plane to the cluster (again) - This doesn't sound like a great experience .debug[[k8s/staticpods.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/staticpods.md)] --- ## Static pods to the rescue - Pods are started by kubelet (an agent running on every node) - To know which pods it should run, the kubelet queries the API server - The kubelet can also get a list of *static pods* from: - a directory containing one (or multiple) *manifests*, and/or - a URL (serving a *manifest*) - These "manifests" are basically YAML definitions (As produced by `kubectl get pod my-little-pod -o yaml`) .debug[[k8s/staticpods.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/staticpods.md)] --- ## Static pods are dynamic - Kubelet will periodically reload the manifests - It will start/stop pods accordingly (i.e. it is not necessary to restart the kubelet after updating the manifests) - When connected to the Kubernetes API, the kubelet will create *mirror pods* - Mirror pods are copies of the static pods (so they can be seen with e.g. `kubectl get pods`) .debug[[k8s/staticpods.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/staticpods.md)] --- ## Bootstrapping a cluster with static pods - We can run control plane components with these static pods - They can start without requiring access to the API server - Once they are up and running, the API becomes available - These pods are then visible through the API (We cannot upgrade them from the API, though) *This is how kubeadm has initialized our clusters.* .debug[[k8s/staticpods.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/staticpods.md)] --- ## Static pods vs normal pods - The API only gives us read-only access to static pods - We can `kubectl delete` a static pod... ...But the kubelet will re-mirror it immediately - Static pods can be selected just like other pods (So they can receive service traffic) - A service can select a mixture of static and other pods .debug[[k8s/staticpods.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/staticpods.md)] --- ## From static pods to normal pods - Once the control plane is up and running, it can be used to create normal pods - We can then set up a copy of the control plane in normal pods - Then the static pods can be removed - The scheduler and the controller manager use leader election (Only one is active at a time; removing an instance is seamless) - Each instance of the API server adds itself to the `kubernetes` service - Etcd will typically require more work! .debug[[k8s/staticpods.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/staticpods.md)] --- ## From normal pods back to static pods - Alright, but what if the control plane is down and we need to fix it? - We restart it using static pods! - This can be done automatically with the [Pod Checkpointer] - The Pod Checkpointer automatically generates manifests of running pods - The manifests are used to restart these pods if API contact is lost (More details in the [Pod Checkpointer] documentation page) - This technique is used by [bootkube] [Pod Checkpointer]: https://github.com/kubernetes-incubator/bootkube/blob/master/cmd/checkpoint/README.md [bootkube]: https://github.com/kubernetes-incubator/bootkube .debug[[k8s/staticpods.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/staticpods.md)] --- ## Where should the control plane run? *Is it better to run the control plane in static pods, or normal pods?* - If I'm a *user* of the cluster: I don't care, it makes no difference to me - What if I'm an *admin*, i.e. the person who installs, upgrades, repairs... the cluster? - If I'm using a managed Kubernetes cluster (AKS, EKS, GKE...) it's not my problem (I'm not the one setting up and managing the control plane) - If I already picked a tool (kubeadm, kops...) to set up my cluster, the tool decides for me - What if I haven't picked a tool yet, or if I'm installing from scratch? - static pods = easier to set up, easier to troubleshoot, less risk of outage - normal pods = easier to upgrade, easier to move (if nodes need to be shut down) .debug[[k8s/staticpods.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/staticpods.md)] --- ## Static pods in action - On our clusters, the `staticPodPath` is `/etc/kubernetes/manifests` .exercise[ - Have a look at this directory: ```bash ls -l /etc/kubernetes/manifests ``` ] We should see YAML files corresponding to the pods of the control plane. .debug[[k8s/staticpods.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/staticpods.md)] --- class: static-pods-exercise ## Running a static pod - We are going to add a pod manifest to the directory, and kubelet will run it .exercise[ - Copy a manifest to the directory: ```bash sudo cp ~/container.training/k8s/just-a-pod.yaml /etc/kubernetes/manifests ``` - Check that it's running: ```bash kubectl get pods ``` ] The output should include a pod named `hello-node1`. .debug[[k8s/staticpods.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/staticpods.md)] --- class: static-pods-exercise ## Remarks In the manifest, the pod was named `hello`. ```yaml apiVersion: v1 kind: Pod metadata: name: hello namespace: default spec: containers: - name: hello image: nginx ``` The `-node1` suffix was added automatically by kubelet. If we delete the pod (with `kubectl delete`), it will be recreated immediately. To delete the pod, we need to delete (or move) the manifest file. ??? :EN:- Static pods :FR:- Les *static pods* .debug[[k8s/staticpods.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/staticpods.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/container-cranes.jpg)] --- name: toc-upgrading-clusters class: title Upgrading clusters .nav[ [Previous part](#toc-static-pods) | [Back to table of contents](#toc-part-13) | [Next part](#toc-backing-up-clusters) ] .debug[(automatically generated title slide)] --- # Upgrading clusters - It's *recommended* to run consistent versions across a cluster (mostly to have feature parity and latest security updates) - It's not *mandatory* (otherwise, cluster upgrades would be a nightmare!) - Components can be upgraded one at a time without problems <!-- ##VERSION## --> .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Checking what we're running - It's easy to check the version for the API server .exercise[ - Log into node `test1` - Check the version of kubectl and of the API server: ```bash kubectl version ``` ] - In a HA setup with multiple API servers, they can have different versions - Running the command above multiple times can return different values .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Node versions - It's also easy to check the version of kubelet .exercise[ - Check node versions (includes kubelet, kernel, container engine): ```bash kubectl get nodes -o wide ``` ] - Different nodes can run different kubelet versions - Different nodes can run different kernel versions - Different nodes can run different container engines .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Control plane versions - If the control plane is self-hosted (running in pods), we can check it .exercise[ - Show image versions for all pods in `kube-system` namespace: ```bash kubectl --namespace=kube-system get pods -o json \ | jq -r ' .items[] | [.spec.nodeName, .metadata.name] + (.spec.containers[].image | split(":")) | @tsv ' \ | column -t ``` ] .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## What version are we running anyway? - When I say, "I'm running Kubernetes 1.15", is that the version of: - kubectl - API server - kubelet - controller manager - something else? .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Other versions that are important - etcd - kube-dns or CoreDNS - CNI plugin(s) - Network controller, network policy controller - Container engine - Linux kernel .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## General guidelines - To update a component, use whatever was used to install it - If it's a distro package, update that distro package - If it's a container or pod, update that container or pod - If you used configuration management, update with that .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Know where your binaries come from - Sometimes, we need to upgrade *quickly* (when a vulnerability is announced and patched) - If we are using an installer, we should: - make sure it's using upstream packages - or make sure that whatever packages it uses are current - make sure we can tell it to pin specific component versions .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Important questions - Should we upgrade the control plane before or after the kubelets? - Within the control plane, should we upgrade the API server first or last? - How often should we upgrade? - How long are versions maintained? - All the answers are in [the documentation about version skew policy](https://kubernetes.io/docs/setup/release/version-skew-policy/)! - Let's review the key elements together ... .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Kubernetes uses semantic versioning - Kubernetes versions look like MAJOR.MINOR.PATCH; e.g. in 1.17.2: - MAJOR = 1 - MINOR = 17 - PATCH = 2 - It's always possible to mix and match different PATCH releases (e.g. 1.16.1 and 1.16.6 are compatible) - It is recommended to run the latest PATCH release (but it's mandatory only when there is a security advisory) .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Version skew - API server must be more recent than its clients (kubelet and control plane) - ... Which means it must always be upgraded first - All components support a difference of one¹ MINOR version - This allows live upgrades (since we can mix e.g. 1.15 and 1.16) - It also means that going from 1.14 to 1.16 requires going through 1.15 .footnote[¹Except kubelet, which can be up to two MINOR behind API server, and kubectl, which can be one MINOR ahead or behind API server.] .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Release cycle - There is a new PATCH relese whenever necessary (every few weeks, or "ASAP" when there is a security vulnerability) - There is a new MINOR release every 3 months (approximately) - At any given time, three MINOR releases are maintained - ... Which means that MINOR releases are maintained approximately 9 months - We should expect to upgrade at least every 3 months (on average) .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## In practice - We are going to update a few cluster components - We will change the kubelet version on one node - We will change the version of the API server - We will work with cluster `test` (nodes `test1`, `test2`, `test3`) .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Updating the API server - This cluster has been deployed with kubeadm - The control plane runs in *static pods* - These pods are started automatically by kubelet (even when kubelet can't contact the API server) - They are defined in YAML files in `/etc/kubernetes/manifests` (this path is set by a kubelet command-line flag) - kubelet automatically updates the pods when the files are changed .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Changing the API server version - We will edit the YAML file to use a different image version .exercise[ - Log into node `test1` - Check API server version: ```bash kubectl version ``` - Edit the API server pod manifest: ```bash sudo vim /etc/kubernetes/manifests/kube-apiserver.yaml ``` - Look for the `image:` line, and update it to e.g. `v1.16.0` ] .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Checking what we've done - The API server will be briefly unavailable while kubelet restarts it .exercise[ - Check the API server version: ```bash kubectl version ``` ] .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Was that a good idea? -- **No!** -- - Remember the guideline we gave earlier: *To update a component, use whatever was used to install it.* - This control plane was deployed with kubeadm - We should use kubeadm to upgrade it! .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Updating the whole control plane - Let's make it right, and use kubeadm to upgrade the entire control plane (note: this is possible only because the cluster was installed with kubeadm) .exercise[ - Check what will be upgraded: ```bash sudo kubeadm upgrade plan ``` ] Note 1: kubeadm thinks that our cluster is running 1.16.0. <br/>It is confused by our manual upgrade of the API server! Note 2: kubeadm itself is still version 1.15.9. <br/>It doesn't know how to upgrade do 1.16.X. .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Upgrading kubeadm - First things first: we need to upgrade kubeadm .exercise[ - Upgrade kubeadm: ``` sudo apt install kubeadm ``` - Check what kubeadm tells us: ``` sudo kubeadm upgrade plan ``` ] Problem: kubeadm doesn't know know how to handle upgrades from version 1.15. This is because we installed version 1.17 (or even later). We need to install kubeadm version 1.16.X. .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Downgrading kubeadm - We need to go back to version 1.16.X (e.g. 1.16.6) .exercise[ - View available versions for package `kubeadm`: ```bash apt show kubeadm -a | grep ^Version | grep 1.16 ``` - Downgrade kubeadm: ``` sudo apt install kubeadm=1.16.6-00 ``` - Check what kubeadm tells us: ``` sudo kubeadm upgrade plan ``` ] kubeadm should now agree to upgrade to 1.16.6. .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Upgrading the cluster with kubeadm - Ideally, we should revert our `image:` change (so that kubeadm executes the right migration steps) - Or we can try the upgrade anyway .exercise[ - Perform the upgrade: ```bash sudo kubeadm upgrade apply v1.16.6 ``` ] .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Updating kubelet - These nodes have been installed using the official Kubernetes packages - We can therefore use `apt` or `apt-get` .exercise[ - Log into node `test3` - View available versions for package `kubelet`: ```bash apt show kubelet -a | grep ^Version ``` - Upgrade kubelet: ```bash sudo apt install kubelet=1.16.6-00 ``` ] .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Checking what we've done .exercise[ - Log into node `test1` - Check node versions: ```bash kubectl get nodes -o wide ``` - Create a deployment and scale it to make sure that the node still works ] .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Was that a good idea? -- **Almost!** -- - Yes, kubelet was installed with distribution packages - However, kubeadm took care of configuring kubelet (when doing `kubeadm join ...`) - We were supposed to run a special command *before* upgrading kubelet! - That command should be executed on each node - It will download the kubelet configuration generated by kubeadm .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Upgrading kubelet the right way - We need to upgrade kubeadm, upgrade kubelet config, then upgrade kubelet (after upgrading the control plane) .exercise[ - Download the configuration on each node, and upgrade kubelet: ```bash for N in 1 2 3; do ssh test$N " sudo apt install kubeadm=1.16.6-00 && sudo kubeadm upgrade node && sudo apt install kubelet=1.16.6-00" done ``` ] .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- ## Checking what we've done - All our nodes should now be updated to version 1.16.6 .exercise[ - Check nodes versions: ```bash kubectl get nodes -o wide ``` ] .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- class: extra-details ## Skipping versions - This example worked because we went from 1.15 to 1.16 - If you are upgrading from e.g. 1.14, you will have to go through 1.15 first - This means upgrading kubeadm to 1.15.X, then using it to upgrade the cluster - Then upgrading kubeadm to 1.16.X, etc. - **Make sure to read the release notes before upgrading!** ??? :EN:- Best practices for cluster upgrades :EN:- Example: upgrading a kubeadm cluster :FR:- Bonnes pratiques pour la mise à jour des clusters :FR:- Exemple : mettre à jour un cluster kubeadm .debug[[k8s/cluster-upgrade.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-upgrade.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/container-housing.jpg)] --- name: toc-backing-up-clusters class: title Backing up clusters .nav[ [Previous part](#toc-upgrading-clusters) | [Back to table of contents](#toc-part-13) | [Next part](#toc-the-cloud-controller-manager) ] .debug[(automatically generated title slide)] --- # Backing up clusters - Backups can have multiple purposes: - disaster recovery (servers or storage are destroyed or unreachable) - error recovery (human or process has altered or corrupted data) - cloning environments (for testing, validation...) - Let's see the strategies and tools available with Kubernetes! .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## Important - Kubernetes helps us with disaster recovery (it gives us replication primitives) - Kubernetes helps us clone / replicate environments (all resources can be described with manifests) - Kubernetes *does not* help us with error recovery - We still need to back up/snapshot our data: - with database backups (mysqldump, pgdump, etc.) - and/or snapshots at the storage layer - and/or traditional full disk backups .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## In a perfect world ... - The deployment of our Kubernetes clusters is automated (recreating a cluster takes less than a minute of human time) - All the resources (Deployments, Services...) on our clusters are under version control (never use `kubectl run`; always apply YAML files coming from a repository) - Stateful components are either: - stored on systems with regular snapshots - backed up regularly to an external, durable storage - outside of Kubernetes .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## Kubernetes cluster deployment - If our deployment system isn't fully automated, it should at least be documented - Litmus test: how long does it take to deploy a cluster... - for a senior engineer? - for a new hire? - Does it require external intervention? (e.g. provisioning servers, signing TLS certs...) .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## Plan B - Full machine backups of the control plane can help - If the control plane is in pods (or containers), pay attention to storage drivers (if the backup mechanism is not container-aware, the backups can take way more resources than they should, or even be unusable!) - If the previous sentence worries you: **automate the deployment of your clusters!** .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## Managing our Kubernetes resources - Ideal scenario: - never create a resource directly on a cluster - push to a code repository - a special branch (`production` or even `master`) gets automatically deployed - Some folks call this "GitOps" (it's the logical evolution of configuration management and infrastructure as code) .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## GitOps in theory - What do we keep in version control? - For very simple scenarios: source code, Dockerfiles, scripts - For real applications: add resources (as YAML files) - For applications deployed multiple times: Helm, Kustomize... (staging and production count as "multiple times") .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## GitOps tooling - Various tools exist (Weave Flux, GitKube...) - These tools are still very young - You still need to write YAML for all your resources - There is no tool to: - list *all* resources in a namespace - get resource YAML in a canonical form - diff YAML descriptions with current state .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## GitOps in practice - Start describing your resources with YAML - Leverage a tool like Kustomize or Helm - Make sure that you can easily deploy to a new namespace (or even better: to a new cluster) - When tooling matures, you will be ready .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## Plan B - What if we can't describe everything with YAML? - What if we manually create resources and forget to commit them to source control? - What about global resources, that don't live in a namespace? - How can we be sure that we saved *everything*? .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## Backing up etcd - All objects are saved in etcd - etcd data should be relatively small (and therefore, quick and easy to back up) - Two options to back up etcd: - snapshot the data directory - use `etcdctl snapshot` .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## Making an etcd snapshot - The basic command is simple: ```bash etcdctl snapshot save <filename> ``` - But we also need to specify: - an environment variable to specify that we want etcdctl v3 - the address of the server to back up - the path to the key, certificate, and CA certificate <br/>(if our etcd uses TLS certificates) .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## Snapshotting etcd on kubeadm - The following command will work on clusters deployed with kubeadm (and maybe others) - It should be executed on a master node ```bash docker run --rm --net host -v $PWD:/vol \ -v /etc/kubernetes/pki/etcd:/etc/kubernetes/pki/etcd:ro \ -e ETCDCTL_API=3 k8s.gcr.io/etcd:3.3.10 \ etcdctl --endpoints=https://[127.0.0.1]:2379 \ --cacert=/etc/kubernetes/pki/etcd/ca.crt \ --cert=/etc/kubernetes/pki/etcd/healthcheck-client.crt \ --key=/etc/kubernetes/pki/etcd/healthcheck-client.key \ snapshot save /vol/snapshot ``` - It will create a file named `snapshot` in the current directory .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## How can we remember all these flags? - Older versions of kubeadm did add a healthcheck probe with all these flags - That healthcheck probe was calling `etcdctl` with all the right flags - With recent versions of kubeadm, we're on our own! - Exercise: write the YAML for a batch job to perform the backup (how will you access the key and certificate required to connect?) .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## Restoring an etcd snapshot - ~~Execute exactly the same command, but replacing `save` with `restore`~~ (Believe it or not, doing that will *not* do anything useful!) - The `restore` command does *not* load a snapshot into a running etcd server - The `restore` command creates a new data directory from the snapshot (it's an offline operation; it doesn't interact with an etcd server) - It will create a new data directory in a temporary container (leaving the running etcd node untouched) .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## When using kubeadm 1. Create a new data directory from the snapshot: ```bash sudo rm -rf /var/lib/etcd docker run --rm -v /var/lib:/var/lib -v $PWD:/vol \ -e ETCDCTL_API=3 k8s.gcr.io/etcd:3.3.10 \ etcdctl snapshot restore /vol/snapshot --data-dir=/var/lib/etcd ``` 2. Provision the control plane, using that data directory: ```bash sudo kubeadm init \ --ignore-preflight-errors=DirAvailable--var-lib-etcd ``` 3. Rejoin the other nodes .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## The fine print - This only saves etcd state - It **does not** save persistent volumes and local node data - Some critical components (like the pod network) might need to be reset - As a result, our pods might have to be recreated, too - If we have proper liveness checks, this should happen automatically .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## More information about etcd backups - [Kubernetes documentation](https://kubernetes.io/docs/tasks/administer-cluster/configure-upgrade-etcd/#built-in-snapshot) about etcd backups - [etcd documentation](https://coreos.com/etcd/docs/latest/op-guide/recovery.html#snapshotting-the-keyspace) about snapshots and restore - [A good blog post by elastisys](https://elastisys.com/2018/12/10/backup-kubernetes-how-and-why/) explaining how to restore a snapshot - [Another good blog post by consol labs](https://labs.consol.de/kubernetes/2018/05/25/kubeadm-backup.html) on the same topic .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## Don't forget ... - Also back up the TLS information (at the very least: CA key and cert; API server key and cert) - With clusters provisioned by kubeadm, this is in `/etc/kubernetes/pki` - If you don't: - you will still be able to restore etcd state and bring everything back up - you will need to redistribute user certificates .warning[**TLS information is highly sensitive! <br/>Anyone who has it has full access to your cluster!**] .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## Stateful services - It's totally fine to keep your production databases outside of Kubernetes *Especially if you have only one database server!* - Feel free to put development and staging databases on Kubernetes (as long as they don't hold important data) - Using Kubernetes for stateful services makes sense if you have *many* (because then you can leverage Kubernetes automation) .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## Snapshotting persistent volumes - Option 1: snapshot volumes out of band (with the API/CLI/GUI of our SAN/cloud/...) - Option 2: storage system integration (e.g. [Portworx](https://docs.portworx.com/portworx-install-with-kubernetes/storage-operations/create-snapshots/) can [create snapshots through annotations](https://docs.portworx.com/portworx-install-with-kubernetes/storage-operations/create-snapshots/snaps-annotations/#taking-periodic-snapshots-on-a-running-pod)) - Option 3: [snapshots through Kubernetes API](https://kubernetes.io/docs/concepts/storage/volume-snapshots/) (Generally available since Kuberentes 1.20 for a number of [CSI](https://kubernetes.io/blog/2019/01/15/container-storage-interface-ga/) volume plugins : GCE, OpenSDS, Ceph, Portworx, etc) .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- ## More backup tools - [Stash](https://appscode.com/products/stash/) back up Kubernetes persistent volumes - [ReShifter](https://github.com/mhausenblas/reshifter) cluster state management - ~~Heptio Ark~~ [Velero](https://github.com/heptio/velero) full cluster backup - [kube-backup](https://github.com/pieterlange/kube-backup) simple scripts to save resource YAML to a git repository - [bivac](https://github.com/camptocamp/bivac) Backup Interface for Volumes Attached to Containers ??? :EN:- Backing up clusters :FR:- Politiques de sauvegarde .debug[[k8s/cluster-backup.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cluster-backup.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/containers-by-the-water.jpg)] --- name: toc-the-cloud-controller-manager class: title The Cloud Controller Manager .nav[ [Previous part](#toc-backing-up-clusters) | [Back to table of contents](#toc-part-13) | [Next part](#toc-git-based-workflows) ] .debug[(automatically generated title slide)] --- # The Cloud Controller Manager - Kubernetes has many features that are cloud-specific (e.g. providing cloud load balancers when a Service of type LoadBalancer is created) - These features were initially implemented in API server and controller manager - Since Kubernetes 1.6, these features are available through a separate process: the *Cloud Controller Manager* - The CCM is optional, but if we run in a cloud, we probably want it! .debug[[k8s/cloud-controller-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cloud-controller-manager.md)] --- ## Cloud Controller Manager duties - Creating and updating cloud load balancers - Configuring routing tables in the cloud network (specific to GCE) - Updating node labels to indicate region, zone, instance type... - Obtain node name, internal and external addresses from cloud metadata service - Deleting nodes from Kubernetes when they're deleted in the cloud - Managing *some* volumes (e.g. ELBs, AzureDisks...) (Eventually, volumes will be managed by the Container Storage Interface) .debug[[k8s/cloud-controller-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cloud-controller-manager.md)] --- ## In-tree vs. out-of-tree - A number of cloud providers are supported "in-tree" (in the main kubernetes/kubernetes repository on GitHub) - More cloud providers are supported "out-of-tree" (with code in different repositories) - There is an [ongoing effort](https://github.com/kubernetes/kubernetes/tree/master/pkg/cloudprovider) to move everything to out-of-tree providers .debug[[k8s/cloud-controller-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cloud-controller-manager.md)] --- ## In-tree providers The following providers are actively maintained: - Amazon Web Services - Azure - Google Compute Engine - IBM Cloud - OpenStack - VMware vSphere These ones are less actively maintained: - Apache CloudStack - oVirt - VMware Photon .debug[[k8s/cloud-controller-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cloud-controller-manager.md)] --- ## Out-of-tree providers The list includes the following providers: - DigitalOcean - keepalived (not exactly a cloud; provides VIPs for load balancers) - Linode - Oracle Cloud Infrastructure (And possibly others; there is no central registry for these.) .debug[[k8s/cloud-controller-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cloud-controller-manager.md)] --- ## Audience questions - What kind of clouds are you using/planning to use? - What kind of details would you like to see in this section? - Would you appreciate details on clouds that you don't / won't use? .debug[[k8s/cloud-controller-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cloud-controller-manager.md)] --- ## Cloud Controller Manager in practice - Write a configuration file (typically `/etc/kubernetes/cloud.conf`) - Run the CCM process (on self-hosted clusters, this can be a DaemonSet selecting the control plane nodes) - Start kubelet with `--cloud-provider=external` - When using managed clusters, this is done automatically - There is very little documentation on writing the configuration file (except for OpenStack) .debug[[k8s/cloud-controller-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cloud-controller-manager.md)] --- ## Bootstrapping challenges - When a node joins the cluster, it needs to obtain a signed TLS certificate - That certificate must contain the node's addresses - These addresses are provided by the Cloud Controller Manager (at least the external address) - To get these addresses, the node needs to communicate with the control plane - ...Which means joining the cluster (The problem didn't occur when cloud-specific code was running in kubelet: kubelet could obtain the required information directly from the cloud provider's metadata service.) .debug[[k8s/cloud-controller-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cloud-controller-manager.md)] --- ## More information about CCM - CCM configuration and operation is highly specific to each cloud provider (which is why this section remains very generic) - The Kubernetes documentation has *some* information: - [architecture and diagrams](https://kubernetes.io/docs/concepts/architecture/cloud-controller/) - [configuration](https://kubernetes.io/docs/concepts/cluster-administration/cloud-providers/) (mainly for OpenStack) - [deployment](https://kubernetes.io/docs/tasks/administer-cluster/running-cloud-controller/) ??? :EN:- The Cloud Controller Manager :FR:- Le *Cloud Controller Manager* .debug[[k8s/cloud-controller-manager.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/cloud-controller-manager.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/distillery-containers.jpg)] --- name: toc-git-based-workflows class: title Git-based workflows .nav[ [Previous part](#toc-the-cloud-controller-manager) | [Back to table of contents](#toc-part-13) | [Next part](#toc-next-steps) ] .debug[(automatically generated title slide)] --- # Git-based workflows - Deploying with `kubectl` has downsides: - we don't know *who* deployed *what* and *when* - there is no audit trail (except the API server logs) - there is no easy way to undo most operations - there is no review/approval process (like for code reviews) - We have all these things for *code*, though - Can we manage cluster state like we manage our source code? .debug[[k8s/gitworkflows.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitworkflows.md)] --- ## Reminder: Kubernetes is *declarative* - All we do is create/change resources - These resources have a perfect YAML representation - All we do is manipulating these YAML representations (`kubectl run` generates a YAML file that gets applied) - We can store these YAML representations in a code repository - We can version that code repository and maintain it with best practices - define which branch(es) can go to qa/staging/production - control who can push to which branches - have formal review processes, pull requests ... .debug[[k8s/gitworkflows.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitworkflows.md)] --- ## Enabling git-based workflows - There are a few tools out there to help us do that - We'll see demos of two of them: [Flux] and [Gitkube] - There are *many* other tools, some of them with even more features - There are also *many* integrations with popular CI/CD systems (e.g.: GitLab, Jenkins, ...) [Flux]: https://www.weave.works/oss/flux/ [Gitkube]: https://gitkube.sh/ .debug[[k8s/gitworkflows.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitworkflows.md)] --- ## Flux overview - We put our Kubernetes resources as YAML files in a git repository - Flux polls that repository regularly (every 5 minutes by default) - The resources described by the YAML files are created/updated automatically - Changes are made by updating the code in the repository .debug[[k8s/gitworkflows.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitworkflows.md)] --- ## Preparing a repository for Flux - We need a repository with Kubernetes YAML files - I have one: https://github.com/jpetazzo/kubercoins - Fork it to your GitHub account - Create a new branch in your fork; e.g. `prod` (e.g. by adding a line in the README through the GitHub web UI) - This is the branch that we are going to use for deployment .debug[[k8s/gitworkflows.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitworkflows.md)] --- ## Setting up Flux - Clone the Flux repository: ``` git clone https://github.com/fluxcd/flux ``` - Edit `deploy/flux-deployment.yaml` - Change the `--git-url` and `--git-branch` parameters: ```yaml - --git-url=git@github.com:your-git-username/kubercoins - --git-branch=prod ``` - Apply all the YAML: ``` kubectl apply -f deploy/ ``` .debug[[k8s/gitworkflows.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitworkflows.md)] --- ## Allowing Flux to access the repository - When it starts, Flux generates an SSH key - Display that key: ``` kubectl logs deployment/flux | grep identity ``` - Then add that key to the repository, giving it **write** access (some Flux features require write access) - After a minute or so, DockerCoins will be deployed to the current namespace .debug[[k8s/gitworkflows.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitworkflows.md)] --- ## Making changes - Make changes (on the `prod` branch), e.g. change `replicas` in `worker` - After a few minutes, the changes will be picked up by Flux and applied .debug[[k8s/gitworkflows.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitworkflows.md)] --- ## Other features - Flux can keep a list of all the tags of all the images we're running - The `fluxctl` tool can show us if we're running the latest images - We can also "automate" a resource (i.e. automatically deploy new images) - And much more! .debug[[k8s/gitworkflows.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitworkflows.md)] --- ## Gitkube overview - We put our Kubernetes resources as YAML files in a git repository - Gitkube is a git server (or "git remote") - After making changes to the repository, we push to Gitkube - Gitkube applies the resources to the cluster .debug[[k8s/gitworkflows.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitworkflows.md)] --- ## Setting up Gitkube - Install the CLI: ``` sudo curl -L -o /usr/local/bin/gitkube \ https://github.com/hasura/gitkube/releases/download/v0.2.1/gitkube_linux_amd64 sudo chmod +x /usr/local/bin/gitkube ``` - Install Gitkube on the cluster: ``` gitkube install --expose ClusterIP ``` .debug[[k8s/gitworkflows.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitworkflows.md)] --- ## Creating a Remote - Gitkube provides a new type of API resource: *Remote* (this is using a mechanism called Custom Resource Definitions or CRD) - Create and apply a YAML file containing the following manifest: ```yaml apiVersion: gitkube.sh/v1alpha1 kind: Remote metadata: name: example spec: authorizedKeys: - `ssh-rsa AAA...` manifests: path: "." ``` (replace the `ssh-rsa AAA...` section with the content of `~/.ssh/id_rsa.pub`) .debug[[k8s/gitworkflows.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitworkflows.md)] --- ## Pushing to our remote - Get the `gitkubed` IP address: ``` kubectl -n kube-system get svc gitkubed IP=$(kubectl -n kube-system get svc gitkubed -o json | jq -r .spec.clusterIP) ``` - Get ourselves a sample repository with resource YAML files: ``` git clone git://github.com/jpetazzo/kubercoins cd kubercoins ``` - Add the remote and push to it: ``` git remote add k8s ssh://default-example@$IP/~/git/default-example git push k8s master ``` .debug[[k8s/gitworkflows.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitworkflows.md)] --- ## Making changes - Edit a local file - Commit - Push! - Make sure that you push to the `k8s` remote .debug[[k8s/gitworkflows.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitworkflows.md)] --- ## Other features - Gitkube can also build container images for us (see the [documentation](https://github.com/hasura/gitkube/blob/master/docs/remote.md) for more details) - Gitkube can also deploy Helm charts (instead of raw YAML files) ??? :EN:- GitOps :FR:- GitOps .debug[[k8s/gitworkflows.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/gitworkflows.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/lots-of-containers.jpg)] --- name: toc-next-steps class: title Next steps .nav[ [Previous part](#toc-git-based-workflows) | [Back to table of contents](#toc-part-14) | [Next part](#toc-last-words) ] .debug[(automatically generated title slide)] --- # Next steps *Alright, how do I get started and containerize my apps?* -- Suggested containerization checklist: .checklist[ - write a Dockerfile for one service in one app - write Dockerfiles for the other (buildable) services - write a Compose file for that whole app - make sure that devs are empowered to run the app in containers - set up automated builds of container images from the code repo - set up a CI pipeline using these container images - set up a CD pipeline (for staging/QA) using these images ] And *then* it is time to look at orchestration! .debug[[k8s/whatsnext.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/whatsnext.md)] --- ## Options for our first production cluster - Get a managed cluster from a major cloud provider (AKS, EKS, GKE...) (price: $, difficulty: medium) - Hire someone to deploy it for us (price: $$, difficulty: easy) - Do it ourselves (price: $-$$$, difficulty: hard) .debug[[k8s/whatsnext.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/whatsnext.md)] --- ## One big cluster vs. multiple small ones - Yes, it is possible to have prod+dev in a single cluster (and implement good isolation and security with RBAC, network policies...) - But it is not a good idea to do that for our first deployment - Start with a production cluster + at least a test cluster - Implement and check RBAC and isolation on the test cluster (e.g. deploy multiple test versions side-by-side) - Make sure that all our devs have usable dev clusters (whether it's a local minikube or a full-blown multi-node cluster) .debug[[k8s/whatsnext.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/whatsnext.md)] --- ## Namespaces - Namespaces let you run multiple identical stacks side by side - Two namespaces (e.g. `blue` and `green`) can each have their own `redis` service - Each of the two `redis` services has its own `ClusterIP` - CoreDNS creates two entries, mapping to these two `ClusterIP` addresses: `redis.blue.svc.cluster.local` and `redis.green.svc.cluster.local` - Pods in the `blue` namespace get a *search suffix* of `blue.svc.cluster.local` - As a result, resolving `redis` from a pod in the `blue` namespace yields the "local" `redis` .warning[This does not provide *isolation*! That would be the job of network policies.] .debug[[k8s/whatsnext.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/whatsnext.md)] --- ## Relevant sections - [Namespaces](kube-selfpaced.yml.html#toc-namespaces) - [Network Policies](kube-selfpaced.yml.html#toc-network-policies) - [Role-Based Access Control](kube-selfpaced.yml.html#toc-authentication-and-authorization) (covers permissions model, user and service accounts management ...) .debug[[k8s/whatsnext.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/whatsnext.md)] --- ## Stateful services (databases etc.) - As a first step, it is wiser to keep stateful services *outside* of the cluster - Exposing them to pods can be done with multiple solutions: - `ExternalName` services <br/> (`redis.blue.svc.cluster.local` will be a `CNAME` record) - `ClusterIP` services with explicit `Endpoints` <br/> (instead of letting Kubernetes generate the endpoints from a selector) - Ambassador services <br/> (application-level proxies that can provide credentials injection and more) .debug[[k8s/whatsnext.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/whatsnext.md)] --- ## Stateful services (second take) - If we want to host stateful services on Kubernetes, we can use: - a storage provider - persistent volumes, persistent volume claims - stateful sets - Good questions to ask: - what's the *operational cost* of running this service ourselves? - what do we gain by deploying this stateful service on Kubernetes? - Relevant sections: [Volumes](kube-selfpaced.yml.html#toc-volumes) | [Stateful Sets](kube-selfpaced.yml.html#toc-stateful-sets) | [Persistent Volumes](kube-selfpaced.yml.html#toc-highly-available-persistent-volumes) - Excellent [blog post](http://www.databasesoup.com/2018/07/should-i-run-postgres-on-kubernetes.html) tackling the question: “Should I run Postgres on Kubernetes?” .debug[[k8s/whatsnext.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/whatsnext.md)] --- ## HTTP traffic handling - *Services* are layer 4 constructs - HTTP is a layer 7 protocol - It is handled by *ingresses* (a different resource kind) - *Ingresses* allow: - virtual host routing - session stickiness - URI mapping - and much more! - [This section](kube-selfpaced.yml.html#toc-exposing-http-services-with-ingress-resources) shows how to expose multiple HTTP apps using [Træfik](https://docs.traefik.io/user-guide/kubernetes/) .debug[[k8s/whatsnext.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/whatsnext.md)] --- ## Logging - Logging is delegated to the container engine - Logs are exposed through the API - Logs are also accessible through local files (`/var/log/containers`) - Log shipping to a central platform is usually done through these files (e.g. with an agent bind-mounting the log directory) - [This section](kube-selfpaced.yml.html#toc-centralized-logging) shows how to do that with [Fluentd](https://docs.fluentd.org/v0.12/articles/kubernetes-fluentd) and the EFK stack .debug[[k8s/whatsnext.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/whatsnext.md)] --- ## Metrics - The kubelet embeds [cAdvisor](https://github.com/google/cadvisor), which exposes container metrics (cAdvisor might be separated in the future for more flexibility) - It is a good idea to start with [Prometheus](https://prometheus.io/) (even if you end up using something else) - Starting from Kubernetes 1.8, we can use the [Metrics API](https://kubernetes.io/docs/tasks/debug-application-cluster/core-metrics-pipeline/) - [Heapster](https://github.com/kubernetes/heapster) was a popular add-on (but is being [deprecated](https://github.com/kubernetes/heapster/blob/master/docs/deprecation.md) starting with Kubernetes 1.11) .debug[[k8s/whatsnext.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/whatsnext.md)] --- ## Managing the configuration of our applications - Two constructs are particularly useful: secrets and config maps - They allow to expose arbitrary information to our containers - **Avoid** storing configuration in container images (There are some exceptions to that rule, but it's generally a Bad Idea) - **Never** store sensitive information in container images (It's the container equivalent of the password on a post-it note on your screen) - [This section](kube-selfpaced.yml.html#toc-managing-configuration) shows how to manage app config with config maps (among others) .debug[[k8s/whatsnext.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/whatsnext.md)] --- ## Managing stack deployments - Applications are made of many resources (Deployments, Services, and much more) - We need to automate the creation / update / management of these resources - There is no "absolute best" tool or method; it depends on: - the size and complexity of our stack(s) - how often we change it (i.e. add/remove components) - the size and skills of our team .debug[[k8s/whatsnext.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/whatsnext.md)] --- ## A few tools to manage stacks - Shell scripts invoking `kubectl` - YAML resource manifests committed to a repo - [Kustomize](https://github.com/kubernetes-sigs/kustomize) (YAML manifests + patches applied on top) - [Helm](https://github.com/kubernetes/helm) (YAML manifests + templating engine) - [Spinnaker](https://www.spinnaker.io/) (Netflix' CD platform) - [Brigade](https://brigade.sh/) (event-driven scripting; no YAML) .debug[[k8s/whatsnext.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/whatsnext.md)] --- ## Cluster federation -- ![Star Trek Federation](images/startrek-federation.jpg) -- Sorry Star Trek fans, this is not the federation you're looking for! -- (If I add "Your cluster is in another federation" I might get a 3rd fandom wincing!) .debug[[k8s/whatsnext.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/whatsnext.md)] --- ## Cluster federation - Kubernetes master operation relies on etcd - etcd uses the [Raft](https://raft.github.io/) protocol - Raft recommends low latency between nodes - What if our cluster spreads to multiple regions? -- - Break it down in local clusters - Regroup them in a *cluster federation* - Synchronize resources across clusters - Discover resources across clusters .debug[[k8s/whatsnext.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/whatsnext.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/plastic-containers.JPG)] --- name: toc-last-words class: title Last words .nav[ [Previous part](#toc-next-steps) | [Back to table of contents](#toc-part-14) | [Next part](#toc-links-and-resources) ] .debug[(automatically generated title slide)] --- # Last words - Congratulations! - We learned a lot about Kubernetes, its internals, its advanced concepts -- - That was just the easy part - The hard challenges will revolve around *culture* and *people* -- - ... What does that mean? .debug[[k8s/lastwords.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/lastwords.md)] --- ## Running an app involves many steps - Write the app - Tests, QA ... - Ship *something* (more on that later) - Provision resources (e.g. VMs, clusters) - Deploy the *something* on the resources - Manage, maintain, monitor the resources - Manage, maintain, monitor the app - And much more .debug[[k8s/lastwords.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/lastwords.md)] --- ## Who does what? - The old "devs vs ops" division has changed - In some organizations, "ops" are now called "SRE" or "platform" teams (and they have very different sets of skills) - Do you know which team is responsible for each item on the list on the previous page? - Acknowledge that a lot of tasks are outsourced (e.g. if we add "buy/rack/provision machines" in that list) .debug[[k8s/lastwords.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/lastwords.md)] --- ## What do we ship? - Some organizations embrace "you build it, you run it" - When "build" and "run" are owned by different teams, where's the line? - What does the "build" team ship to the "run" team? - Let's see a few options, and what they imply .debug[[k8s/lastwords.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/lastwords.md)] --- ## Shipping code - Team "build" ships code (hopefully in a repository, identified by a commit hash) - Team "run" containerizes that code ✔️ no extra work for developers ❌ very little advantage of using containers .debug[[k8s/lastwords.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/lastwords.md)] --- ## Shipping container images - Team "build" ships container images (hopefully built automatically from a source repository) - Team "run" uses theses images to create e.g. Kubernetes resources ✔️ universal artefact (support all languages uniformly) ✔️ easy to start a single component (good for monoliths) ❌ complex applications will require a lot of extra work ❌ adding/removing components in the stack also requires extra work ❌ complex applications will run very differently between dev and prod .debug[[k8s/lastwords.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/lastwords.md)] --- ## Shipping Compose files (Or another kind of dev-centric manifest) - Team "build" ships a manifest that works on a single node (as well as images, or ways to build them) - Team "run" adapts that manifest to work on a cluster ✔️ all teams can start the stack in a reliable, deterministic manner ❌ adding/removing components still requires *some* work (but less than before) ❌ there will be *some* differences between dev and prod .debug[[k8s/lastwords.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/lastwords.md)] --- ## Shipping Kubernetes manifests - Team "build" ships ready-to-run manifests (YAML, Helm charts, Kustomize ...) - Team "run" adjusts some parameters and monitors the application ✔️ parity between dev and prod environments ✔️ "run" team can focus on SLAs, SLOs, and overall quality ❌ requires *a lot* of extra work (and new skills) from the "build" team ❌ Kubernetes is not a very convenient development platform (at least, not yet) .debug[[k8s/lastwords.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/lastwords.md)] --- ## What's the right answer? - It depends on our teams - existing skills (do they know how to do it?) - availability (do they have the time to do it?) - potential skills (can they learn to do it?) - It depends on our culture - owning "run" often implies being on call - do we reward on-call duty without encouraging hero syndrome? - do we give people resources (time, money) to learn? .debug[[k8s/lastwords.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/lastwords.md)] --- class: extra-details ## Tools to develop on Kubernetes *If we decide to make Kubernetes the primary development platform, here are a few tools that can help us.* - Docker Desktop - Draft - Minikube - Skaffold - Tilt - ... .debug[[k8s/lastwords.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/lastwords.md)] --- ## Where do we run? - Managed vs. self-hosted - Cloud vs. on-premises - If cloud: public vs. private - Which vendor/distribution to pick? - Which versions/features to enable? .debug[[k8s/lastwords.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/lastwords.md)] --- ## Developer experience *These questions constitute a quick "smoke test" for our strategy:* - How do we on-board a new developer? - What do they need to install to get a dev stack? - How does a code change make it from dev to prod? - How does someone add a component to a stack? .debug[[k8s/lastwords.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/lastwords.md)] --- ## Some guidelines - Start small - Outsource what we don't know - Start simple, and stay simple as long as possible (try to stay away from complex features that we don't need) - Automate (regularly check that we can successfully redeploy by following scripts) - Transfer knowledge (make sure everyone is on the same page/level) - Iterate! .debug[[k8s/lastwords.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/lastwords.md)] --- class: pic .interstitial[![Image separating from the next part](https://gallant-turing-d0d520.netlify.com/containers/train-of-containers-1.jpg)] --- name: toc-links-and-resources class: title Links and resources .nav[ [Previous part](#toc-last-words) | [Back to table of contents](#toc-part-14) | [Next part](#toc-) ] .debug[(automatically generated title slide)] --- # Links and resources All things Kubernetes: - [Kubernetes Community](https://kubernetes.io/community/) - Slack, Google Groups, meetups - [Kubernetes on StackOverflow](https://stackoverflow.com/questions/tagged/kubernetes) - [Play With Kubernetes Hands-On Labs](https://medium.com/@marcosnils/introducing-pwk-play-with-k8s-159fcfeb787b) All things Docker: - [Docker documentation](http://docs.docker.com/) - [Docker Hub](https://hub.docker.com) - [Docker on StackOverflow](https://stackoverflow.com/questions/tagged/docker) - [Play With Docker Hands-On Labs](http://training.play-with-docker.com/) Everything else: - [Local meetups](https://www.meetup.com/) .footnote[These slides (and future updates) are on → http://container.training/] .debug[[k8s/links.md](https://github.com/barpilot/container.training/tree/master/slides/k8s/links.md)] --- class: title, self-paced Thank you! .debug[[shared/thankyou.md](https://github.com/barpilot/container.training/tree/master/slides/shared/thankyou.md)] --- class: title, in-person That's all, folks! <br/> Questions? ![end](images/end.jpg) .debug[[shared/thankyou.md](https://github.com/barpilot/container.training/tree/master/slides/shared/thankyou.md)]