class: title, self-paced Getting started with<br/>Kubernetes and<br/>container orchestration<br/> .nav[*Self-paced version*] .debug[ ``` ``` These slides have been built from commit: c42f2ec [shared/title.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/shared/title.md)] --- class: title, in-person Getting started with<br/>Kubernetes and<br/>container orchestration<br/><br/></br> .footnote[ **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[.](https://www.youtube.com/watch?v=h16zyxiwDLY)*<br/> *Thank you!* **Slides: http://gotochgo2019.container.training/** ] .debug[[shared/title.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/shared/title.md)] --- ## Intros - Hello! We are: - .emoji[👷🏻‍♀️] AJ ([@s0ulshake](https://twitter.com/s0ulshake), Travis CI) - .emoji[🐳] Jérôme ([@jpetazzo](https://twitter.com/jpetazzo), Enix SAS) - The workshop will run from 9am to 4pm - There will be a lunch break at noon (And coffee breaks at 10:30am and 2:30pm) - Feel free to interrupt for questions at any time - *Especially when you see full screen container pictures!* - Live feedback, questions, help: [Gitter](https://gitter.im/jpetazzo/workshop-20190428) .debug[[logistics.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/logistics.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/jpetazzo/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/intro.md)] --- ## About these slides - All the content is available in a public GitHub repository: https://github.com/jpetazzo/container.training - You can get updated "builds" of the slides there: http://container.training/ <!-- .exercise[ ```open https://github.com/jpetazzo/container.training``` ```open http://container.training/``` ] --> -- - Typos? Mistakes? Questions? Feel free to hover over the bottom of the slide ... .footnote[.emoji[👇] 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/jpetazzo/container.training/tree/master/slides/common/about-slides.md``` ] --> .debug[[shared/about-slides.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/shared/about-slides.md)] --- name: toc-chapter-1 ## Chapter 1 - [Pre-requirements](#toc-pre-requirements) - [Our sample application](#toc-our-sample-application) - [Kubernetes concepts](#toc-kubernetes-concepts) - [Declarative vs imperative](#toc-declarative-vs-imperative) - [Kubernetes network model](#toc-kubernetes-network-model) .debug[(auto-generated TOC)] --- name: toc-chapter-2 ## Chapter 2 - [First contact with `kubectl`](#toc-first-contact-with-kubectl) - [Setting up Kubernetes](#toc-setting-up-kubernetes) - [Running our first containers on Kubernetes](#toc-running-our-first-containers-on-kubernetes) - [Exposing containers](#toc-exposing-containers) .debug[(auto-generated TOC)] --- name: toc-chapter-3 ## Chapter 3 - [Shipping images with a registry](#toc-shipping-images-with-a-registry) - [Running our application on Kubernetes](#toc-running-our-application-on-kubernetes) - [Scaling our demo app](#toc-scaling-our-demo-app) - [Daemon sets](#toc-daemon-sets) - [Labels and selectors](#toc-labels-and-selectors) .debug[(auto-generated TOC)] --- name: toc-chapter-4 ## Chapter 4 - [Rolling updates](#toc-rolling-updates) - [Namespaces](#toc-namespaces) - [Network policies](#toc-network-policies) - [Authentication and authorization (bonus)](#toc-authentication-and-authorization-bonus) .debug[(auto-generated TOC)] --- name: toc-chapter-5 ## Chapter 5 - [Next steps](#toc-next-steps) - [Links and resources](#toc-links-and-resources) .debug[(auto-generated TOC)] .debug[[shared/toc.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/shared/toc.md)] --- class: pic .interstitial[] --- name: toc-pre-requirements class: title Pre-requirements .nav[ [Previous section](#toc-) | [Back to table of contents](#toc-chapter-1) | [Next section](#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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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 http://gotochgo2019.container.training/ to view these slides - Join the chat room: [Gitter](https://gitter.im/jpetazzo/workshop-20190428) <!-- ```open http://gotochgo2019.container.training/``` --> ] .debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/shared/prereqs.md)] --- class: in-person ## Where are we going to run our containers? .debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/shared/prereqs.md)] --- class: in-person, pic  .debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/shared/prereqs.md)] --- class: in-person ## Why don't we run containers locally? - Installing that 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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/shared/prereqs.md)] --- class: in-person ## Connecting to our lab environment .exercise[ - Log into the first VM (`node1`) with your SSH client <!-- ```bash for N in $(awk '/\Wnode/{print $2}' /etc/hosts); do ssh -o StrictHostKeyChecking=no $N true done ``` ```bash if which kubectl; then kubectl get deploy,ds -o name | xargs -rn1 kubectl delete kubectl get all -o name | grep -v service/kubernetes | xargs -rn1 kubectl delete --ignore-not-found=true kubectl -n kube-system get deploy,svc -o name | grep -v dns | xargs -rn1 kubectl -n kube-system delete fi ``` --> - Check that you can SSH (without password) to `node2`: ```bash ssh node2 ``` - Type `exit` or `^D` to come back to `node1` <!-- ```bash exit``` --> ] If anything goes wrong — ask for help! .debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/shared/prereqs.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/jpetazzo/container.training/tree/master/prepare-vms)) Bigger setup effort; ideal for group training .debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/shared/prereqs.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/prereqs.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/shared/prereqs.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 checkout/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/prereqs.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/shared/prereqs.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/prereqs.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/shared/prereqs.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/prereqs.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/shared/prereqs.md)] --- ## Versions installed - Kubernetes 1.14.0 - Docker Engine 18.09.3 - Docker Compose 1.21.1 <!-- ##VERSION## --> .exercise[ - Check all installed versions: ```bash kubectl version docker version docker-compose -v ``` ] .debug[[k8s/versions-k8s.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/versions-k8s.md)] --- class: extra-details ## Kubernetes and Docker compatibility - Kubernetes 1.14 validates Docker Engine versions [up to 18.09](https://github.com/kubernetes/kubernetes/blob/master/CHANGELOG-1.14.md#external-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-1.13.md#external-dependencies) - Is this 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 (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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/versions-k8s.md)] --- class: pic .interstitial[] --- name: toc-our-sample-application class: title Our sample application .nav[ [Previous section](#toc-pre-requirements) | [Back to table of contents](#toc-chapter-1) | [Next section](#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/jpetazzo/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/shared/sampleapp.md)] --- ## What's this application? -- - It is a DockerCoin miner! .emoji[💰🐳📦🚢] -- - 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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/blob/master/dockercoins/docker-compose.yml) .debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/shared/sampleapp.md)] --- class: pic  .debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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 FQDN 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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/blob/8279a3bce9398f7c1a53bdd95187c53eda4e6435/dockercoins/worker/worker.py#L17 )) .debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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 did require "links" sections - 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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training - The application is in the [dockercoins]( https://github.com/jpetazzo/container.training/tree/master/dockercoins) subdirectory - The Compose file ([docker-compose.yml]( https://github.com/jpetazzo/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/jpetazzo/container.training/blob/master/dockercoins/hasher/) directory, `rng` is in the [rng](https://github.com/jpetazzo/container.training/blob/master/dockercoins/rng/) directory, etc.) .debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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` <!-- ```keys ^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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/shared/composedown.md)] --- class: pic .interstitial[] --- name: toc-kubernetes-concepts class: title Kubernetes concepts .nav[ [Previous section](#toc-our-sample-application) | [Back to table of contents](#toc-chapter-1) | [Next section](#toc-declarative-vs-imperative) ] .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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/concepts-k8s.md)] --- ## Other things that Kubernetes can do for us - Basic autoscaling - Blue/green deployment, canary deployment - Long running services, but also batch (one-off) jobs - Overcommit our cluster and *evict* low-priority jobs - Run services with *stateful* data (databases etc.) - Fine-grained access control defining *what* can be done by *whom* on *which* resources - Integrating third party services (*service catalog*) - Automating complex tasks (*operators*) .debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/concepts-k8s.md)] --- ## Kubernetes architecture .debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/concepts-k8s.md)] --- class: pic  .debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/concepts-k8s.md)] --- class: pic  .debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/concepts-k8s.md)] --- ## 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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/concepts-k8s.md)] --- ## 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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/concepts-k8s.md)] --- ## Do we need to run Docker at all? No! -- - By default, Kubernetes uses the Docker Engine to run containers - We could also use `rkt` ("Rocket") from CoreOS - Or leverage other pluggable runtimes through the *Container Runtime Interface* (like CRI-O, or containerd) .debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/concepts-k8s.md)] --- ## 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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/concepts-k8s.md)] --- ## 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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/concepts-k8s.md)] --- ## Kubernetes resources - The Kubernetes API defines a lot of objects called *resources* - These resources are organized by type, or `Kind` (in the API) - 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) - namespace (more-or-less isolated group of things) - secret (bundle of sensitive data to be passed to a container) And much more! - We can see the full list by running `kubectl api-resources` (In Kubernetes 1.10 and prior, the command to list API resources was `kubectl get`) .debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/concepts-k8s.md)] --- class: pic  .debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/concepts-k8s.md)] --- class: pic  .debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/concepts-k8s.md)] --- ## Credits - The first diagram is courtesy of Weave Works - a *pod* can have multiple containers working together - IP addresses are associated with *pods*, not with individual containers - The second 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! Both diagrams used with permission. .debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/concepts-k8s.md)] --- class: pic .interstitial[] --- name: toc-declarative-vs-imperative class: title Declarative vs imperative .nav[ [Previous section](#toc-kubernetes-concepts) | [Back to table of contents](#toc-chapter-1) | [Next section](#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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/shared/declarative.md)] --- ## Declarative vs imperative in Kubernetes - Virtually everything we create in Kubernetes is created from a *spec* - 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 .debug[[k8s/declarative.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/declarative.md)] --- class: pic .interstitial[] --- name: toc-kubernetes-network-model class: title Kubernetes network model .nav[ [Previous section](#toc-declarative-vs-imperative) | [Back to table of contents](#toc-chapter-1) | [Next section](#toc-first-contact-with-kubectl) ] .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) - Kubernetes doesn't mandate any particular implementation .debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubenet.md)] --- ## Kubernetes network model: the good - Everything can reach everything - No address translation - No port translation - No new protocol - Pods cannot move from a node to another and keep their IP address - 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/jpetazzo/container.training/tree/gotochgo2019/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 (15 are listed in the Kubernetes documentation) - Pods have level 3 (IP) connectivity, but *services* are level 4 (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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubenet.md)] --- ## The Container Network Interface (CNI) - The CNI has a well-defined [specification](https://github.com/containernetworking/cni/blob/master/SPEC.md#network-configuration) for network plugins - When a pod is created, Kubernetes delegates the network setup to CNI plugins - Typically, a CNI plugin 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. - Using multiple plugins can be done with "meta-plugins" like CNI-Genie or Multus - Not all CNI plugins are equal (e.g. they don't all implement network policies, which are required to isolate pods) .debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubenet.md)] --- class: pic .interstitial[] --- name: toc-first-contact-with-kubectl class: title First contact with `kubectl` .nav[ [Previous section](#toc-kubernetes-network-model) | [Back to table of contents](#toc-chapter-2) | [Next section](#toc-setting-up-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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlget.md)] --- ## 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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlget.md)] --- ## Introspection vs. documentation - We can access the same information by reading the [API documentation](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.14/) - 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/jpetazzo/container.training/tree/gotochgo2019/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 names - `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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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 error that we see is expected: the Kubernetes API requires authentication. .debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlget.md)] --- class: pic .interstitial[] --- name: toc-setting-up-kubernetes class: title Setting up Kubernetes .nav[ [Previous section](#toc-first-contact-with-kubectl) | [Back to table of contents](#toc-chapter-2) | [Next section](#toc-running-our-first-containers-on-kubernetes) ] .debug[(automatically generated title slide)] --- # Setting up Kubernetes - How did we set up these Kubernetes clusters that we're using? -- <!-- ##VERSION## --> - 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) <br/> (that step is just one `kubectl apply` command; discussed later) 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/jpetazzo/container.training/blob/master/prepare-vms/README.md) for more details .debug[[k8s/setup-k8s.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/setup-k8s.md)] --- ## `kubeadm` drawbacks - Doesn't set up Docker or any other container engine - Doesn't set up the overlay network - Doesn't set up multi-master (no high availability) -- (At least ... not yet! Though it's [experimental in 1.12](https://kubernetes.io/docs/setup/independent/high-availability/).) -- - "It's still twice as many steps as setting up a Swarm cluster 😕" -- Jérôme .debug[[k8s/setup-k8s.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/setup-k8s.md)] --- ## Other deployment options - [AKS](https://azure.microsoft.com/services/kubernetes-service/): managed Kubernetes on Azure - [GKE](https://cloud.google.com/kubernetes-engine/): managed Kubernetes on Google Cloud - [EKS](https://aws.amazon.com/eks/), [eksctl](https://eksctl.io/): managed Kubernetes on AWS - [kops](https://github.com/kubernetes/kops): customizable deployments on AWS, Digital Ocean, GCE (beta), vSphere (alpha) - [minikube](https://kubernetes.io/docs/setup/minikube/), [kubespawn](https://github.com/kinvolk/kube-spawn), [Docker Desktop](https://docs.docker.com/docker-for-mac/kubernetes/): for local development - [kubicorn](https://github.com/kubicorn/kubicorn), the [Cluster API](https://blogs.vmware.com/cloudnative/2019/03/14/what-and-why-of-cluster-api/): deploy your clusters declaratively, "the Kubernetes way" .debug[[k8s/setup-k8s.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/setup-k8s.md)] --- ## Even more deployment options - If you like Ansible: [kubespray](https://github.com/kubernetes-incubator/kubespray) - If you like Terraform: [typhoon](https://github.com/poseidon/typhoon) - If you like Terraform and Puppet: [tarmak](https://github.com/jetstack/tarmak) - You can also learn how to install every component manually, with the excellent tutorial [Kubernetes The Hard Way](https://github.com/kelseyhightower/kubernetes-the-hard-way) *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.* - There are also many commercial options available! - For a longer list, check the Kubernetes documentation: <br/> it has a great guide to [pick the right solution](https://kubernetes.io/docs/setup/pick-right-solution/) to set up Kubernetes. .debug[[k8s/setup-k8s.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/setup-k8s.md)] --- class: pic .interstitial[] --- name: toc-running-our-first-containers-on-kubernetes class: title Running our first containers on Kubernetes .nav[ [Previous section](#toc-setting-up-kubernetes) | [Back to table of contents](#toc-chapter-2) | [Next section](#toc-exposing-containers) ] .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 - Then we are going to start additional copies of the pod .debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlrun.md)] --- ## Starting a simple pod with `kubectl run` - We need to specify at least a *name* and the image we want to use .exercise[ - Let's ping `1.1.1.1`, Cloudflare's [public DNS resolver](https://blog.cloudflare.com/announcing-1111/): ```bash kubectl run pingpong --image alpine ping 1.1.1.1 ``` <!-- ```hide kubectl wait deploy/pingpong --for condition=available``` --> ] -- (Starting with Kubernetes 1.12, we get a message telling us that `kubectl run` is deprecated. Let's ignore it for now.) .debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlrun.md)] --- ## Behind the scenes of `kubectl run` - Let's look at the resources that were created by `kubectl run` .exercise[ - List most resource types: ```bash kubectl get all ``` ] -- We should see the following things: - `deployment.apps/pingpong` (the *deployment* that we just created) - `replicaset.apps/pingpong-xxxxxxxxxx` (a *replica set* created by the deployment) - `pod/pingpong-xxxxxxxxxx-yyyyy` (a *pod* created by the replica set) Note: as of 1.10.1, resource types are displayed in more detail. .debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlrun.md)] --- ## What are these different things? - A *deployment* is a high-level construct - allows scaling, rolling updates, rollbacks - multiple deployments can be used together to implement a [canary deployment](https://kubernetes.io/docs/concepts/cluster-administration/manage-deployment/#canary-deployments) - delegates pods management to *replica sets* - A *replica set* is a low-level construct - makes sure that a given number of identical pods are running - allows scaling - rarely used directly - A *replication controller* is the (deprecated) predecessor of a replica set .debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlrun.md)] --- ## Our `pingpong` deployment - `kubectl run` created a *deployment*, `deployment.apps/pingpong` ``` NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE deployment.apps/pingpong 1 1 1 1 10m ``` - That deployment created a *replica set*, `replicaset.apps/pingpong-xxxxxxxxxx` ``` NAME DESIRED CURRENT READY AGE replicaset.apps/pingpong-7c8bbcd9bc 1 1 1 10m ``` - That replica set created a *pod*, `pod/pingpong-xxxxxxxxxx-yyyyy` ``` NAME READY STATUS RESTARTS AGE pod/pingpong-7c8bbcd9bc-6c9qz 1/1 Running 0 10m ``` - We'll see later how these folks play together for: - scaling, high availability, rolling updates .debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlrun.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/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlrun.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 ``` <!-- ```wait seq=3``` ```keys ^C``` --> ] .debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlrun.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 ``` ] Note: what if we tried to scale `replicaset.apps/pingpong-xxxxxxxxxx`? We could! But the *deployment* would notice it right away, and scale back to the initial level. .debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlrun.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, list pods, and keep watching them: ```bash kubectl get pods -w ``` <!-- ```wait Running``` ```keys ^C``` ```hide kubectl wait deploy pingpong --for condition=available``` ```keys kubectl delete pod ping``` ```copypaste pong-..........-.....``` --> - Destroy a pod: ``` kubectl delete pod pingpong-xxxxxxxxxx-yyyyy ``` ] .debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlrun.md)] --- ## What if we wanted something different? - What if we wanted to start a "one-shot" container that *doesn't* get restarted? - We could use `kubectl run --restart=OnFailure` or `kubectl run --restart=Never` - These commands would create *jobs* or *pods* instead of *deployments* - Under the hood, `kubectl run` invokes "generators" to create resource descriptions - We could also write these resource descriptions ourselves (typically in YAML), <br/>and create them on the cluster with `kubectl apply -f` (discussed later) - With `kubectl run --schedule=...`, we can also create *cronjobs* .debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlrun.md)] --- ## What about that deprecation warning? - As we can see from the previous slide, `kubectl run` can do many things - The exact type of resource created is not obvious - To make things more explicit, it is better to use `kubectl create`: - `kubectl create deployment` to create a deployment - `kubectl create job` to create a job - `kubectl create cronjob` to run a job periodically <br/>(since Kubernetes 1.14) - Eventually, `kubectl run` will be used only to start one-shot pods (see https://github.com/kubernetes/kubernetes/pull/68132) .debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlrun.md)] --- ## Various ways of creating resources - `kubectl run` - easy way to get started - versatile - `kubectl create <resource>` - explicit, but lacks some features - can't create a CronJob before Kubernetes 1.14 - can't pass command-line arguments to deployments - `kubectl create -f foo.yaml` or `kubectl apply -f foo.yaml` - all features are available - requires writing YAML .debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlrun.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* - A selector is a logic expression using *labels* - Conveniently, when you `kubectl run somename`, the associated objects have a `run=somename` label .exercise[ - View the last line of log from all pods with the `run=pingpong` label: ```bash kubectl logs -l run=pingpong --tail 1 ``` ] .debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlrun.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 run=pingpong --tail 1 -f ``` <!-- ```wait seq=``` ```keys ^C``` --> ] *Note: combining `-l` and `-f` is only possible since Kubernetes 1.14!* *Let's try to understand why ...* .debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlrun.md)] --- ### 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 run=pingpong --tail 1 -f ``` ] 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/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlrun.md)] --- ## 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/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlrun.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/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlrun.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.* .debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlrun.md)] --- ## Aren't we flooding 1.1.1.1? - If you're wondering this, good question! - Don't worry, though: *APNIC's research group held the IP addresses 1.1.1.1 and 1.0.0.1. While the addresses were valid, so many people had entered them into various random systems that they were continuously overwhelmed by a flood of garbage traffic. APNIC wanted to study this garbage traffic but any time they'd tried to announce the IPs, the flood would overwhelm any conventional network.* (Source: https://blog.cloudflare.com/announcing-1111/) - It's very unlikely that our concerted pings manage to produce even a modest blip at Cloudflare's NOC! .debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlrun.md)] --- class: pic .interstitial[] --- name: toc-exposing-containers class: title Exposing containers .nav[ [Previous section](#toc-running-our-first-containers-on-kubernetes) | [Back to table of contents](#toc-chapter-2) | [Next section](#toc-shipping-images-with-a-registry) ] .debug[(automatically generated title slide)] --- # Exposing containers - `kubectl expose` creates a *service* for existing pods - A *service* is a stable address for a pod (or a bunch of pods) - If we want to connect to our pod(s), we need to create a *service* - Once a service is created, CoreDNS will allow us to resolve it by name (i.e. after creating service `hello`, the name `hello` will resolve to something) - There are different types of services, detailed on the following slides: `ClusterIP`, `NodePort`, `LoadBalancer`, `ExternalName` .debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlexpose.md)] --- ## Basic service types - `ClusterIP` (default type) - a virtual IP address is allocated for the service (in an internal, private range) - 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 - `NodePort` - a port is allocated for the service (by default, in the 30000-32768 range) - that port is made available *on all our nodes* and anybody can connect to it - our code must be changed to connect to that new port number These service types are always available. Under the hood: `kube-proxy` is using a userland proxy and a bunch of `iptables` rules. .debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlexpose.md)] --- ## More service types - `LoadBalancer` - an external load balancer is allocated for the service - the load balancer is configured accordingly <br/>(e.g.: a `NodePort` service is created, and the load balancer sends traffic to that port) - available only when the underlying infrastructure provides some "load balancer as a service" <br/>(e.g. AWS, Azure, GCE, OpenStack...) - `ExternalName` - the DNS entry managed by CoreDNS will just be a `CNAME` to a provided record - no port, no IP address, no nothing else is allocated .debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlexpose.md)] --- ## Creating a deployment for our HTTP server - We *could* do `kubectl run httpenv --image=jpetazzo/httpenv` ... - But since `kubectl run` is being deprecated, let's see how to use `kubectl create` instead .exercise[ - In another window, watch the pods (to see when they will be created): ```bash kubectl get pods -w ``` <!-- ```keys ^C``` --> - 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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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 - Running services with arbitrary port (or port ranges) requires hacks (e.g. host networking mode) .debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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=$(kubectl get svc httpenv -o go-template --template '{{ .spec.clusterIP }}') ``` <!-- ```hide kubectl wait deploy httpenv --for condition=available``` --> - Send a few requests: ```bash curl http://$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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlexpose.md)] --- class: extra-details ## If we don't need a load balancer - 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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlexpose.md)] --- class: extra-details ## 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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/kubectlexpose.md)] --- class: pic .interstitial[] --- name: toc-shipping-images-with-a-registry class: title Shipping images with a registry .nav[ [Previous section](#toc-exposing-containers) | [Back to table of contents](#toc-chapter-3) | [Next section](#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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/shippingimages.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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/buildshiprun-dockerhub.md)] --- ## Setting `$REGISTRY` and `$TAG` - In the upcoming exercises and labs, we use a couple of environment variables: - `$REGISTRY` as a prefix to all image names - `$TAG` as the image version tag - For example, the worker image is `$REGISTRY/worker:$TAG` - If you copy-paste the commands in these exercises: **make sure that you set `$REGISTRY` and `$TAG` first!** - For example: ``` export REGISTRY=dockercoins TAG=v0.1 ``` (this will expand `$REGISTRY/worker:$TAG` to `dockercoins/worker:v0.1`) .debug[[k8s/buildshiprun-dockerhub.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/buildshiprun-dockerhub.md)] --- class: pic .interstitial[] --- name: toc-running-our-application-on-kubernetes class: title Running our application on Kubernetes .nav[ [Previous section](#toc-shipping-images-with-a-registry) | [Back to table of contents](#toc-chapter-3) | [Next section](#toc-scaling-our-demo-app) ] .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 for SERVICE in hasher rng webui worker; do kubectl create deployment $SERVICE --image=$REGISTRY/$SERVICE:$TAG done ``` ] .debug[[k8s/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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``` ```keys ^C``` --> ] -- We should now see the `worker`, well, working happily. .debug[[k8s/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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!* .debug[[k8s/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/ourapponkube.md)] --- class: pic .interstitial[] --- name: toc-scaling-our-demo-app class: title Scaling our demo app .nav[ [Previous section](#toc-running-our-application-on-kubernetes) | [Back to table of contents](#toc-chapter-3) | [Next section](#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:  - The loop is done in the worker; perhaps we could try adding more workers? .debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/scalingdockercoins.md)] --- ## Adding another worker - All we have to do is scale 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``` ```keys ^C``` ```wait AVAILABLE``` ```keys ^C``` --> - 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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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 ``` ] -- 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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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? .debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/shared/hastyconclusions.md)] --- class: pic .interstitial[] --- name: toc-daemon-sets class: title Daemon sets .nav[ [Previous section](#toc-scaling-our-demo-app) | [Back to table of contents](#toc-chapter-3) | [Next section](#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 - What if we just scale up `deploy/rng` to the number of nodes? - 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 - Instead of a `deployment`, we will use a `daemonset` .debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/daemonset.md)] --- ## Creating a daemon set <!-- ##VERSION## --> - Unfortunately, as of Kubernetes 1.14, 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/jpetazzo/container.training/tree/gotochgo2019/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 --export >rng.yml ``` - Edit `rng.yml` ] Note: `--export` will remove "cluster-specific" information, i.e.: - namespace (so that the resource is not tied to a specific namespace) - status and creation timestamp (useless when creating a new resource) - resourceVersion and uid (these would cause... *interesting* problems) .debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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``` ```keys ^J``` ```keys cwDaemonSet``` ```keys ^[``` ] ```keys :wq``` ```keys ^J``` --> - Save, quit - Try to create our new resource: ``` kubectl apply -f rng.yml ``` ] -- We all knew this couldn't be that easy, right! .debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/daemonset.md)] --- class: pic .interstitial[] --- name: toc-labels-and-selectors class: title Labels and selectors .nav[ [Previous section](#toc-daemon-sets) | [Back to table of contents](#toc-chapter-3) | [Next section](#toc-rolling-updates) ] .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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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 "diseappears" for its parent, which re-creates another pod to replace it .debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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. `enabled=yes`) .debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/daemonset.md)] --- ## Complex selectors - If a selector specifies multiple labels, they are understood as a logical *AND* (In other words: the pods must match all the labels) - Kubernetes has support for advanced, set-based selectors (But these cannot be used with services, at least not yet!) .debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/daemonset.md)] --- ## The plan 1. Add the label `enabled=yes` to all our `rng` pods 2. Update the selector for the `rng` service to also include `enabled=yes` 3. Toggle traffic to a pod by manually adding/removing the `enabled` 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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/daemonset.md)] --- ## Adding labels to pods - We want to add the label `enabled=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 `enabled=yes` to all pods that have `app=rng`: ```bash kubectl label pods -l app=rng enabled=yes ``` ] .debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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 `enabled: yes` to its selector: ```bash kubectl edit service rng ``` <!-- ```wait Please edit the object below``` ```keys /app: rng``` ```keys ^J``` ```keys noenabled: yes``` ```keys ^[``` ] ```keys :wq``` ```keys ^J``` --> ] -- ... And then we get *the weirdest error ever.* Why? .debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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 `enabled: "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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/daemonset.md)] --- ## Updating the service selector, take 2 .exercise[ - Update the service to add `enabled: "yes"` to its selector: ```bash kubectl edit service rng ``` <!-- ```wait Please edit the object below``` ```keys /app: rng``` ```keys ^J``` ```keys noenabled: "yes"``` ```keys ^[``` ] ```keys :wq``` ```keys ^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/jpetazzo/container.training/tree/gotochgo2019/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 `enabled` 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/jpetazzo/container.training/tree/gotochgo2019/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) - In another window, remove the label from the pod: ```bash kubectl label pod -l app=rng,pod-template-hash enabled- ``` (The stream of HTTP logs should stop immediately) ] 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/jpetazzo/container.training/tree/gotochgo2019/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 `enabled=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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/daemonset.md)] --- class: pic .interstitial[] --- name: toc-rolling-updates class: title Rolling updates .nav[ [Previous section](#toc-labels-and-selectors) | [Back to table of contents](#toc-chapter-4) | [Next section](#toc-namespaces) ] .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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/rollout.md)] --- ## Rolling updates - With rolling updates, when a resource is updated, it happens progressively - 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 to rollback to the previous version <br/>(if the update fails or is unsatisfactory in any way) .debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/rollout.md)] --- ## Building a new version of the `worker` service .exercise[ - Go to the `stack` directory: ```bash cd ~/container.training/stacks ``` - Edit `dockercoins/worker/worker.py`; update the first `sleep` line to sleep 1 second - Build a new tag and push it to the registry: ```bash #export REGISTRY=localhost:3xxxx export TAG=v0.2 docker-compose -f dockercoins.yml build docker-compose -f dockercoins.yml push ``` ] .debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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``` ```keys ^C``` --> - Update `worker` either with `kubectl edit`, or by running: ```bash kubectl set image deploy worker worker=$REGISTRY/worker:$TAG ``` ] -- That rollout should be pretty quick. What shows in the web UI? .debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/rollout.md)] --- ## Give it some time - At first, it looks like nothing is happening (the graph remains at the same level) - According to `kubectl get deploy -w`, the `deployment` was updated really quickly - But `kubectl get pods -w` tells a different story - The old `pods` are still here, and they stay in `Terminating` state for a while - Eventually, they are terminated; and then the graph decreases significantly - This delay is due to the fact that our worker doesn't handle signals - Kubernetes sends a "polite" shutdown request to the worker, which ignores it - After a grace period, Kubernetes gets impatient and kills the container (The grace period is 30 seconds, but [can be changed](https://kubernetes.io/docs/concepts/workloads/pods/pod/#termination-of-pods) if needed) .debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/rollout.md)] --- ## Rolling out something invalid - What happens if we make a mistake? .exercise[ - Update `worker` by specifying a non-existent image: ```bash export TAG=v0.3 kubectl set image deploy worker worker=$REGISTRY/worker:$TAG ``` - Check what's going on: ```bash kubectl rollout status deploy worker ``` <!-- ```wait Waiting for deployment``` ```keys ^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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/rollout.md)] --- ## Checking the dashboard during the bad rollout If you haven't deployed the Kubernetes dashboard earlier, just skip this slide. .exercise[ - Check which port the dashboard is on: ```bash kubectl -n kube-system get svc socat ``` ] Note the `3xxxx` port. .exercise[ - Connect to http://oneofournodes:3xxxx/ <!-- ```open https://node1:3xxxx/``` --> ] -- - We have failures in Deployments, Pods, and Replica Sets .debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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[ <!-- ```keys ^C ``` --> - Cancel the deployment and wait for the dust to settle down: ```bash kubectl rollout undo deploy worker kubectl rollout status deploy worker ``` ] .debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/rollout.md)] --- ## 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: $REGISTRY/worker:v0.1 strategy: rollingUpdate: maxUnavailable: 0 maxSurge: 1 minReadySeconds: 10 ``` ] .debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/rollout.md)] --- ## 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: $REGISTRY/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" ``` ] ] .debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/rollout.md)] --- class: pic .interstitial[] --- name: toc-namespaces class: title Namespaces .nav[ [Previous section](#toc-rolling-updates) | [Back to table of contents](#toc-chapter-4) | [Next section](#toc-network-policies) ] .debug[(automatically generated title slide)] --- # Namespaces - We would like to deploy multiple copies of our demo app - But we cannot have two resources with the same name ... Or can we ? -- - We cannot have two resources *of the same type* with the same name (But it's OK to have a `rng` service, a `rng` deployment, and a `rng` daemon set!) -- - We cannot have two resources of the same type with the same name *in the same namespace* (But it's OK to have e.g. two `rng` services in different namespaces!) -- - In other words: **the tuple *(type, name, namespace)* needs to be unique** (In the resource YAML, the type is called `Kind`) .debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/namespaces.md)] --- ## Creating namespaces - Creating a namespace is done with the `kubectl create namespace` command: ```bash kubectl create namespace blue ``` - We can also get fancy and use a very minimal YAML snippet, e.g.: ```bash kubectl apply -f- <<EOF apiVersion: v1 kind: Namespace metadata: name: blue EOF ``` - The two methods above are identical - If we are using a tool like Helm, it will create namespaces automatically .debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/namespaces.md)] --- ## Using our new namespace - Let's check that we are in our new namespace .exercise[ - Verify that the new context is empty: ```bash kubectl get all ``` ] .debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/namespaces.md)] --- ## Deploying another copy of DockerCoins - We will use YAML definitions from a GitHub repository: https://github.com/jpetazzo/kubercoins .exercise[ - Clone kubercoins: ```bash https://github.com/jpetazzo/kubercoins ``` - Deploy it: ```bash kubectl apply -f kubercoins ``` ] Note how `kubectl apply` can work on a whole directory! .debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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, check the next slide! .debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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's a simple shell script available from https://github.com/jonmosco/kube-ps1 - On our clusters, `kube-ps1` is installed and included in `PS1`: ``` [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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/namespaces.md)] --- class: pic .interstitial[] --- name: toc-network-policies class: title Network policies .nav[ [Previous section](#toc-namespaces) | [Back to table of contents](#toc-chapter-4) | [Next section](#toc-authentication-and-authorization-bonus) ] .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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/netpol.md)] --- ## Running our test web server .exercise[ - Let's use the `nginx` image: ```bash kubectl create deployment testweb --image=nginx ``` - 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/jpetazzo/container.training/tree/gotochgo2019/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 ``` ] The `curl` command should now time out. .debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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` fields 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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/netpol.md)] --- ## Further resources - 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: - [NetworkPolicy](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.12/#networkpolicy-v1-networking-k8s-io) - [NetworkPolicySpec](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.12/#networkpolicyspec-v1-networking-k8s-io) - [NetworkPolicyIngressRule](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.12/#networkpolicyingressrule-v1-networking-k8s-io) - etc. - And 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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/netpol.md)] --- class: pic .interstitial[] --- name: toc-authentication-and-authorization-bonus class: title Authentication and authorization (bonus) .nav[ [Previous section](#toc-network-policies) | [Back to table of contents](#toc-chapter-4) | [Next section](#toc-next-steps) ] .debug[(automatically generated title slide)] --- # Authentication and authorization (bonus) *And first, a little refresher!* - 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/jpetazzo/container.training/tree/gotochgo2019/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; it'll be the job of *authorizers* .debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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 a HTTP header) - Authentication proxy (sitting in front of the API and setting trusted headers) .debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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 given membership 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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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 cannot easily suspend a user's access - There are workarounds, but they are very inconvenient: - issue short-lived certificates (e.g. 24 hours) and regenerate them often - re-create the CA and re-issue all certificates in case of compromise - grant permissions to individual users, not groups <br/> (and remove all permissions to a compromised user) - Until this is fixed, we probably want to use other methods .debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/authn-authz.md)] --- class: extra-details ## Listing service accounts .exercise[ - The resource name is `serviceaccount` or `sa` in 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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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 $SECRET -o json \ | jq -r .data.token | openssl base64 -d -A) ``` ] .debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/authn-authz.md)] --- class: extra-details ## Using the token - Let's send a request to the API, without and with the token .exercise[ - Find the ClusterIP for the `kubernetes` service: ```bash kubectl get svc kubernetes API=$(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/jpetazzo/container.training/tree/gotochgo2019/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 right, so we can't do nothin' - Let's change that! .debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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 to: - specify actions on cluster-wide objects (like nodes) - operate across all namespaces - We can create Role and RoleBinding resources within a namespaces - ClusterRole and ClusterRoleBinding resources are global .debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/authn-authz.md)] --- ## Pods and service accounts - A pod can be associated to a service account - by default, it is associated to the `default` service account - as we've seen earlier, this service account has no permission anyway - The associated token is exposed into 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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/authn-authz.md)] --- ## In practice - We are going to create a service account - We will use an existing 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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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` <!-- ```keys ^D``` --> ] .debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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` to 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/jpetazzo/container.training/tree/gotochgo2019/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 an action - Here is a proof-of-concept tool by Aqua Security, doing exactly that: https://github.com/aquasecurity/kubectl-who-can - This is one way to install it: ```bash docker run --rm -v /usr/local/bin:/go/bin golang \ go get -v github.com/aquasecurity/kubectl-who-can ``` - This is one way to use it: ```bash kubectl-who-can create pods ``` .debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/authn-authz.md)] --- class: pic .interstitial[] --- name: toc-next-steps class: title Next steps .nav[ [Previous section](#toc-authentication-and-authorization-bonus) | [Back to table of contents](#toc-chapter-5) | [Next section](#toc-links-and-resources) ] .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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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) .debug[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/whatsnext.md)] --- ## Managing stack deployments - The best deployment tool will vary, depending on: - the size and complexity of your stack(s) - how often you change it (i.e. add/remove components) - the size and skills of your team - A few examples: - shell scripts invoking `kubectl` - YAML resources descriptions committed to a repo - [Helm](https://github.com/kubernetes/helm) (~package manager) - [Spinnaker](https://www.spinnaker.io/) (Netflix' CD platform) - [Brigade](https://brigade.sh/) (event-driven scripting; no YAML) .debug[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/whatsnext.md)] --- ## Cluster federation --  -- 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/jpetazzo/container.training/tree/gotochgo2019/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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/whatsnext.md)] --- ## Developer experience *We've put this last, but it's pretty important!* - How do you 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/whatsnext.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/whatsnext.md)] --- class: pic .interstitial[] --- name: toc-links-and-resources class: title Links and resources .nav[ [Previous section](#toc-next-steps) | [Back to table of contents](#toc-chapter-5) | [Next section](#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/jpetazzo/container.training/tree/gotochgo2019/slides/k8s/links.md)] --- class: title, self-paced Thank you! .debug[[shared/thankyou.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/shared/thankyou.md)] --- class: title, in-person That's all, folks! <br/> Questions?  .debug[[shared/thankyou.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/shared/thankyou.md)] --- ## Final words - You can find more content on http://container.training/ (More slides, videos, dates of upcoming workshops and tutorials...) - This workshop is also available as longer training sessions: - [two-day Kubernetes bootstrap](https://tinyshellscript.com/kubernetes-bootstrap.html) - [four-day Kubernetes administrator training](https://tinyshellscript.com/kubernetes-ops-week.html) - If you want me to train your team: [contact me!](https://docs.google.com/forms/d/e/1FAIpQLScm2evHMvRU8C5ZK59l8FGsLY_Kkup9P_GHgjfByUMyMpMmDA/viewform) .footnote[*Thank you!*] .debug[[shared/thankyou.md](https://github.com/jpetazzo/container.training/tree/gotochgo2019/slides/shared/thankyou.md)]