Advanced Kubernetes Training

```

These slides have been built from commit: bf88262

[shared/title.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/title.md)]
---

Advanced Kubernetes Training

**Slides: http://2020-01-zr.container.training/**
]

.debug[[shared/title.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/title.md)]
---
## Intros

- Hello! We are:

- .emoji[👷🏻‍♀️] AJ ([@s0ulshake](https://twitter.com/s0ulshake), Tiny Shell Script LLC)

- .emoji[🐳] Jérôme ([@jpetazzo](https://twitter.com/jpetazzo), Ardan Labs LLC)

- The workshop will run from 9:15 to 16:30

- There will be a lunch break around noon

(And coffee breaks!)

- 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/training-20200121-telaviv)

.debug[[logistics.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/slides/k8s/intro.md)]
---

## 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:
 you will be given specific instructions to access your cluster

- If you are doing this on your own:
 the first chapter will give you various options to get your own cluster

.debug[[k8s/intro.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/

- 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.]

.debug[[shared/about-slides.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/about-slides.md)]
---

## 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/2020-01-zr/slides/shared/about-slides.md)]
---

## Chapter 1

- [Pre-requirements](#toc-pre-requirements)

- [Our sample application](#toc-our-sample-application)

- [Kubernetes concepts](#toc-kubernetes-concepts)

- [First contact with `kubectl`](#toc-first-contact-with-kubectl)

## Chapter 2

- [Running our first containers on Kubernetes](#toc-running-our-first-containers-on-kubernetes)

- [Accessing logs from the CLI](#toc-accessing-logs-from-the-cli)

- [Declarative vs imperative](#toc-declarative-vs-imperative)

- [Kubernetes network model](#toc-kubernetes-network-model)

- [Exposing containers](#toc-exposing-containers)

## Chapter 3

- [Shipping images with a registry](#toc-shipping-images-with-a-registry)

- [Running our application on Kubernetes](#toc-running-our-application-on-kubernetes)

- [Deploying with YAML](#toc-deploying-with-yaml)

- [Scaling our demo app](#toc-scaling-our-demo-app)

- [Daemon sets](#toc-daemon-sets)

- [Labels and selectors](#toc-labels-and-selectors)

## Chapter 4

- [Rolling updates](#toc-rolling-updates)

- [Healthchecks](#toc-healthchecks)

- [Recording deployment actions](#toc-recording-deployment-actions)

## Chapter 5

- [Namespaces](#toc-namespaces)

- [Exposing HTTP services with Ingress resources](#toc-exposing-http-services-with-ingress-resources)

- [Controlling a Kubernetes cluster remotely](#toc-controlling-a-kubernetes-cluster-remotely)

- [Accessing internal services](#toc-accessing-internal-services)

## Chapter 6

- [Centralized logging](#toc-centralized-logging)

- [Collecting metrics with Prometheus](#toc-collecting-metrics-with-prometheus)

## Chapter 7

- [Volumes](#toc-volumes)

- [Managing configuration](#toc-managing-configuration)

## Chapter 8

- [Extending the Kubernetes API](#toc-extending-the-kubernetes-api)

- [Operators](#toc-operators)

## Chapter 9

- [Building our own cluster](#toc-building-our-own-cluster)

## Chapter 10

- [Resource Limits](#toc-resource-limits)

- [Defining min, max, and default resources](#toc-defining-min-max-and-default-resources)

- [Namespace quotas](#toc-namespace-quotas)

- [Limiting resources in practice](#toc-limiting-resources-in-practice)

- [Checking pod and node resource usage](#toc-checking-pod-and-node-resource-usage)

- [Cluster sizing](#toc-cluster-sizing)

- [The Horizontal Pod Autoscaler](#toc-the-horizontal-pod-autoscaler)

## Chapter 11

- [Stateful sets](#toc-stateful-sets)

- [Running a Consul cluster](#toc-running-a-consul-cluster)

- [Local Persistent Volumes](#toc-local-persistent-volumes)

- [Highly available Persistent Volumes](#toc-highly-available-persistent-volumes)

## Chapter 12

- [(Extra content: security)](#toc-extra-content-security)

- [Network policies](#toc-network-policies)

- [Authentication and authorization](#toc-authentication-and-authorization)

- [Securing the control plane](#toc-securing-the-control-plane)

## Chapter 13

- [(Extra content: templatization & packaging)](#toc-extra-content-templatization--packaging)

- [Kustomize](#toc-kustomize)

- [Managing stacks with Helm](#toc-managing-stacks-with-helm)

- [Creating Helm charts](#toc-creating-helm-charts)

## Chapter 14

- [(Extra content: user management)](#toc-extra-content-user-management)

- [The CSR API](#toc-the-csr-api)

- [OpenID Connect](#toc-openid-connect)

- [Pod Security Policies](#toc-pod-security-policies)

## Chapter 15

- [What's next?](#toc-whats-next)

- [Links and resources](#toc-links-and-resources)

.debug[[shared/toc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/toc.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Pre-requirements

.nav[
[Previous section](#toc-)
|
[Back to table of contents](#toc-chapter-1)
|
[Next section](#toc-our-sample-application)
]

---
# 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
 
 (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/2020-01-zr/slides/shared/prereqs.md)]
---

*Tell me and I forget.*
 
*Teach me and I remember.*
 
*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/2020-01-zr/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

- This is the stuff you're supposed to do!

- Go to http://2020-01-zr.container.training/ to view these slides

- Join the chat room: [Gitter](https://gitter.im/jpetazzo/training-20200121-telaviv)

]

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/prereqs.md)]
---

## Navigating slides

- Use arrows to move to next/previous slide

(up, down, left, right, page up, page down)

- Type a slide number + ENTER to go to that slide

- The slide number is also visible in the URL bar

(e.g. .../#123 for slide 123)

- Slides will remain online so you can review them later if needed

- You can download the slides using that URL:

http://2020-01-zr.container.training/slides.zip

(then open the file `kube.yml.html`)

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/prereqs.md)]
---

## Where are we going to run our containers?

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/prereqs.md)]
---

![You get a cluster](images/you-get-a-cluster.jpg)

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/prereqs.md)]
---

## 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/2020-01-zr/slides/shared/prereqs.md)]
---

## Why don't we run containers locally?

- Installing this stuff can be hard on some machines

(32 bits CPU or OS... Laptops without administrator access... etc.)

- *"The whole team downloaded all these container images from the WiFi!
 ... 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/2020-01-zr/slides/shared/prereqs.md)]
---

## 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/2020-01-zr/slides/shared/prereqs.md)]
---

## What is this Mosh thing?

*You don't have to use Mosh or even know about it to follow along.
 
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/2020-01-zr/slides/shared/prereqs.md)]
---

## 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/2020-01-zr/slides/shared/prereqs.md)]
---
## WebSSH

- The virtual machines are also accessible via WebSSH

- This can be useful if:

- you can't install an SSH client on your machine

- SSH connections are blocked (by firewall or local policy)

- To use WebSSH, connect to the IP address of the remote VM on port 1080

(each machine runs a WebSSH server)

- Then provide the login and password indicated on your card

.debug[[shared/webssh.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/webssh.md)]
---

## Good to know

- WebSSH uses WebSocket

- If you're having connections issues, try to disable your HTTP proxy

(many HTTP proxies can't handle WebSocket properly)

- Most keyboard shortcuts should work, except Ctrl-W

(as it is hardwired by the browser to "close this tab")

.debug[[shared/webssh.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/webssh.md)]
---
class: in-person

## Connecting to our lab environment

- Log into the first VM (`node1`) with your SSH client:
  ```bash
  ssh `user`@`A.B.C.D`
  ```

(Replace `user` and `A.B.C.D` with the user and IP address provided to you)

<!--
```bash
for N in $(awk '/\Wnode/{print $2}' /etc/hosts); do
 ssh -o StrictHostKeyChecking=no $N true
done
```

```bash
### FIXME find a way to reset the cluster, maybe?
```
-->

]

You should see a prompt looking like this:
```
[A.B.C.D] (...) user@node1 ~
$
```
If anything goes wrong — ask for help!

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/connecting.md)]
---

## Doing or re-doing the workshop on your own?

- Use something like
  [Play-With-Docker](http://play-with-docker.com/) or
  [Play-With-Kubernetes](https://training.play-with-kubernetes.com/)

Zero setup effort; but environment are short-lived and
  might have limited resources

- Create your own cluster (local or cloud VMs)

Small setup effort; small cost; flexible environments

- Create a bunch of clusters for you and your friends
    ([instructions](https://github.com/jpetazzo/container.training/tree/master/prepare-vms))

Bigger setup effort; ideal for group training

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/connecting.md)]
---

## For a consistent Kubernetes experience ...

- If you are using your own Kubernetes cluster, you can use [shpod](https://github.com/jpetazzo/shpod)

- `shpod` provides a shell running in a pod on your own cluster

- It comes with many tools pre-installed (helm, stern...)

- These tools are used in many exercises in these slides

- `shpod` also gives you completion and a fancy prompt

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/connecting.md)]
---

## Get your own Docker nodes

- If you already have some Docker nodes: great!

- If not: let's get some thanks to Play-With-Docker

- Go to http://www.play-with-docker.com/

- Log in

- Create your first node

]

You will need a Docker ID to use Play-With-Docker.

(Creating a Docker ID is free.)

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/connecting.md)]
---

## We will (mostly) interact with node1 only

*These remarks apply only when using multiple nodes, of course.*

- Unless instructed, **all commands must be run from the first VM, `node1`**

- We will only check out/copy the code on `node1`

- During normal operations, we do not need access to the other nodes

- If we had to troubleshoot issues, we would use a combination of:

- SSH (to access system logs, daemon status...)

- Docker API (to check running containers and container engine status)

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/connecting.md)]
---

## Terminals

Once in a while, the instructions will say:
 "Open a new terminal."

There are multiple ways to do this:

- create a new window or tab on your machine, and SSH into the VM;

- use screen or tmux on the VM and open a new window from there.

You are welcome to use the method that you feel the most comfortable with.

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/connecting.md)]
---

## Tmux cheatsheet

[Tmux](https://en.wikipedia.org/wiki/Tmux) is a terminal multiplexer like `screen`.

*You don't have to use it or even know about it to follow along.
 
But some of us like to use it to switch between terminals.
 
It has been preinstalled on your workshop nodes.*

- Ctrl-b c → creates a new window
- Ctrl-b n → go to next window
- Ctrl-b p → go to previous window
- Ctrl-b " → split window top/bottom
- Ctrl-b % → split window left/right
- Ctrl-b Alt-1 → rearrange windows in columns
- Ctrl-b Alt-2 → rearrange windows in rows
- Ctrl-b arrows → navigate to other windows
- Ctrl-b d → detach session
- tmux attach → reattach to session

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/connecting.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Our sample application

.nav[
[Previous section](#toc-pre-requirements)
|
[Back to table of contents](#toc-chapter-1)
|
[Next section](#toc-kubernetes-concepts)
]

---
# 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

- 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/2020-01-zr/slides/shared/sampleapp.md)]
---

## Downloading and running the application

Let's start this before we look around, as downloading will take a little time...

- 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
  ```

]

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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/slides/shared/sampleapp.md)]
---

![Diagram showing the 5 containers of the applications](images/dockercoins-diagram.svg)

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/sampleapp.md)]
---

## Service discovery in container-land

How does each service find out the address of the other ones?

- We do not hard-code IP addresses in the code

- We do not hard-code FQDNs in the code, either

- We just connect to a service name, and container-magic does the rest

(And by container-magic, we mean "a crafty, dynamic, embedded DNS server")

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/slides/shared/sampleapp.md)]
---

## Links, naming, and service discovery

- Containers can have network aliases (resolvable through DNS)

- Compose file version 2+ makes each container reachable through its service name

- Compose file version 1 required "links" sections to accomplish this

- Network aliases are automatically namespaced

- you can have multiple apps declaring and using a service named `database`

- containers in the blue app will resolve `database` to the IP of the blue database

- containers in the green app will resolve `database` to the IP of the green database

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/sampleapp.md)]
---

## Show me the code!

- You can check the GitHub repository with all the materials of this workshop:
 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/2020-01-zr/slides/shared/sampleapp.md)]
---

## 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/2020-01-zr/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/2020-01-zr/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

- 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

]

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/2020-01-zr/slides/shared/sampleapp.md)]
---

## 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/2020-01-zr/slides/shared/sampleapp.md)]
---

## 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?

- The app actually has a constant, steady speed: 3.33 hashes/second
 
 (which corresponds to 1 hash every 0.3 seconds, for *reasons*)

- Yes, and?

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/sampleapp.md)]
---

## 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?

- "I'm clearly incapable of writing good frontend code!" 😀 — Jérôme

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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

- Stop the application by hitting `^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/2020-01-zr/slides/shared/sampleapp.md)]
---
## Clean up

- Before moving on, let's remove those containers

- Tell Compose to remove everything:
  ```bash
  docker-compose down
  ```

]

.debug[[shared/composedown.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/shared/composedown.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Kubernetes concepts

.nav[
[Previous section](#toc-our-sample-application)
|
[Back to table of contents](#toc-chapter-1)
|
[Next section](#toc-first-contact-with-kubectl)
]

---
# 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/2020-01-zr/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/2020-01-zr/slides/k8s/concepts-k8s.md)]
---

## Other things that Kubernetes can do for us

- Autoscaling

(straightforward on CPU; more complex on other metrics)

- Ressource management and scheduling

(reserve CPU/RAM for containers; placement constraints)

- Advanced rollout patterns

(blue/green deployment, canary deployment)

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/concepts-k8s.md)]
---

## More things that Kubernetes can do for us

- Batch jobs

(one-off; parallel; also cron-style periodic execution)

- Fine-grained access control

(defining *what* can be done by *whom* on *which* resources)

- Stateful services

(databases, message queues, etc.)

- Automating complex tasks with *operators*

(e.g. database replication, failover, etc.)

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/concepts-k8s.md)]
---

## Kubernetes architecture

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/concepts-k8s.md)]
---

![haha only kidding](images/k8s-arch1.png)

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/slides/k8s/concepts-k8s.md)]
---

![that one is more like the real thing](images/k8s-arch2.png)

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/slides/k8s/concepts-k8s.md)]
---

![One of the best Kubernetes architecture diagrams available](images/k8s-arch4-thanks-luxas.png)

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/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/2020-01-zr/slides/k8s/concepts-k8s.md)]
---

## How many nodes should a cluster have?

- There is no particular constraint

(no need to have an odd number of nodes for quorum)

- A cluster can have zero node

(but then it won't be able to start any pods)

- For testing and development, having a single node is fine

- For production, make sure that you have extra capacity

(so that your workload still fits if you lose a node or a group of nodes)

- Kubernetes is tested with [up to 5000 nodes](https://kubernetes.io/docs/setup/best-practices/cluster-large/)

(however, running a cluster of that size requires a lot of tuning)

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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 can leverage other pluggable runtimes through the *Container Runtime Interface*

- <del>We could also use `rkt` ("Rocket") from CoreOS</del> (deprecated)

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/concepts-k8s.md)]
---

## Some runtimes available through CRI

- [containerd](https://github.com/containerd/containerd/blob/master/README.md)

- maintained by Docker, IBM, and community
  - used by Docker Engine, microk8s, k3s, GKE; also standalone
  - comes with its own CLI, `ctr`

- [CRI-O](https://github.com/cri-o/cri-o/blob/master/README.md):

- maintained by Red Hat, SUSE, and community
  - used by OpenShift and Kubic
  - designed specifically as a minimal runtime for Kubernetes

- [And more](https://kubernetes.io/docs/setup/production-environment/container-runtimes/)

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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
 
 (and get diagnosed with NIH¹ syndrome)

- Docker is still the most stable container engine today
 
 (but other options are maturing very quickly)

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/slides/k8s/concepts-k8s.md)]
---

## Interacting with Kubernetes

- We will interact with our Kubernetes cluster through the Kubernetes API

- The Kubernetes API is (mostly) RESTful

- It allows us to create, read, update, delete *resources*

- A few common resource types are:

- node (a machine — physical or virtual — in our cluster)

- pod (group of containers running together on a node)

- service (stable network endpoint to connect to one or multiple containers)

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/concepts-k8s.md)]
---

![Node, pod, container](images/k8s-arch3-thanks-weave.png)

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/concepts-k8s.md)]
---

## Scaling

- How would we scale the pod shown on the previous slide?

- **Do** create additional pods

- each pod can be on a different node

- each pod will have its own IP address

- **Do not** add more NGINX containers in the pod

- all the NGINX containers would be on the same node

- they would all have the same IP address
 (resulting in `Address alreading in use` errors)

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/concepts-k8s.md)]
---

## Together or separate

- Should we put e.g. a web application server and a cache together?
 
 ("cache" being something like e.g. Memcached or Redis)

- Putting them **in the same pod** means:

- they have to be scaled together

- they can communicate very efficiently over `localhost`

- Putting them **in different pods** means:

- they can be scaled separately

- they must communicate over remote IP addresses
 (incurring more latency, lower performance)

- Both scenarios can make sense, depending on our goals

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/concepts-k8s.md)]
---

## Credits

- The first diagram is courtesy of Lucas Käldström, in [this presentation](https://speakerdeck.com/luxas/kubeadm-cluster-creation-internals-from-self-hosting-to-upgradability-and-ha)

- it's one of the best Kubernetes architecture diagrams available!

- The second diagram is courtesy of Weave Works

- a *pod* can have multiple containers working together

- IP addresses are associated with *pods*, not with individual containers

Both diagrams used with permission.

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/concepts-k8s.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

First contact with `kubectl`

.nav[
[Previous section](#toc-kubernetes-concepts)
|
[Back to table of contents](#toc-chapter-1)
|
[Next section](#toc-running-our-first-containers-on-kubernetes)
]

---
# 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/2020-01-zr/slides/k8s/kubectlget.md)]
---

## `kubectl` is the new SSH

- We often start managing servers with SSH

(installing packages, troubleshooting ...)

- At scale, it becomes tedious, repetitive, error-prone

- Instead, we use config management, central logging, etc.

- In many cases, we still need SSH:

- as the underlying access method (e.g. Ansible)

- to debug tricky scenarios

- to inspect and poke at things

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlget.md)]
---

## The parallel with `kubectl`

- We often start managing Kubernetes clusters with `kubectl`

(deploying applications, troubleshooting ...)

- At scale (with many applications or clusters), it becomes tedious, repetitive, error-prone

- Instead, we use automated pipelines, observability tooling, etc.

- In many cases, we still need `kubectl`:

- to debug tricky scenarios

- to inspect and poke at things

- The Kubernetes API is always the underlying access method

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlget.md)]
---

## `kubectl get`

- Let's look at our `Node` resources with `kubectl get`!

- 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/2020-01-zr/slides/k8s/kubectlget.md)]
---

## Obtaining machine-readable output

- `kubectl get` can output JSON, YAML, or be directly formatted

- 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/2020-01-zr/slides/k8s/kubectlget.md)]
---

## (Ab)using `kubectl` and `jq`

- It's super easy to build custom reports

- 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/2020-01-zr/slides/k8s/kubectlget.md)]
---

## Exploring types and definitions

- We can list all available resource types by running `kubectl api-resources`
 
 (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/2020-01-zr/slides/k8s/kubectlget.md)]
---

## Introspection vs. documentation

- We can access the same information by reading the [API documentation](https://kubernetes.io/docs/reference/#api-reference)

- The API documentation is usually easier to read, but:

- it won't show custom types (like Custom Resource Definitions)

- we need to make sure that we look at the correct version

- `kubectl api-resources` and `kubectl explain` perform *introspection*

(they communicate with the API server and obtain the exact type definitions)

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlget.md)]
---

## Type names

- The most common resource names have three forms:

- singular (e.g. `node`, `service`, `deployment`)

- plural (e.g. `nodes`, `services`, `deployments`)

- short (e.g. `no`, `svc`, `deploy`)

- Some resources do not have a short name

- `Endpoints` only have a plural form

(because even a single `Endpoints` resource is actually a list of endpoints)

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/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

- 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/2020-01-zr/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)

- 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/2020-01-zr/slides/k8s/kubectlget.md)]
---

## Namespaces

- Namespaces allow us to segregate resources

- 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/2020-01-zr/slides/k8s/kubectlget.md)]
---

## Accessing namespaces

- By default, `kubectl` uses the `default` namespace

- We can see resources in all namespaces with `--all-namespaces`

- 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/2020-01-zr/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/2020-01-zr/slides/k8s/kubectlget.md)]
---

## Scoping another namespace

- We can also look at a different namespace (other than `default`)

- 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/2020-01-zr/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/2020-01-zr/slides/k8s/kubectlget.md)]
---

## What about `kube-public`?

- 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/2020-01-zr/slides/k8s/kubectlget.md)]
---

## Exploring `kube-public`

- The only interesting object in `kube-public` is a ConfigMap named `cluster-info`

- 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/2020-01-zr/slides/k8s/kubectlget.md)]
---

## 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)

- 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/2020-01-zr/slides/k8s/kubectlget.md)]
---

## Retrieving `kubeconfig`

- We can easily extract the `kubeconfig` file from this ConfigMap

- 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/2020-01-zr/slides/k8s/kubectlget.md)]
---

## 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/2020-01-zr/slides/k8s/kubectlget.md)]
---

## Services

- A *service* is a stable endpoint to connect to "something"

(In the initial proposal, they were called "portals")

- 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/2020-01-zr/slides/k8s/kubectlget.md)]
---

## ClusterIP services

- A `ClusterIP` service is internal, available from the cluster only

- This is useful for introspection from within containers

- Try to connect to the API:
  ```bash
  curl -k https://`10.96.0.1`
  ```

- `-k` is used to skip certificate verification

- Make sure to replace 10.96.0.1 with the CLUSTER-IP shown by `kubectl get svc`

]

The command above should either time out, or show an authentication error. Why?

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlget.md)]
---

## Time out

- Connections to ClusterIP services only work *from within the cluster*

- If we are outside the cluster, the `curl` command will probably time out

(Because the IP address, e.g. 10.96.0.1, isn't routed properly outside the cluster)

- This is the case with most "real" Kubernetes clusters

- To try the connection from within the cluster, we can use [shpod](https://github.com/jpetazzo/shpod)

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlget.md)]
---

## Authentication error

This is what we should see when connecting from within the cluster:
```json
$ curl -k https://10.96.0.1
{
  "kind": "Status",
  "apiVersion": "v1",
  "metadata": {

},
  "status": "Failure",
  "message": "forbidden: User \"system:anonymous\" cannot get path \"/\"",
  "reason": "Forbidden",
  "details": {

},
  "code": 403
}
```

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlget.md)]
---

## Explanations

- We can see `kind`, `apiVersion`, `metadata`

- These are typical of a Kubernetes API reply

- Because we *are* talking to the Kubernetes API

- The Kubernetes API tells us "Forbidden"

(because it requires authentication)

- The Kubernetes API is reachable from within the cluster

(many apps integrating with Kubernetes will use this)

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlget.md)]
---

## DNS integration

- Each service also gets a DNS record

- The Kubernetes DNS resolver is available *from within pods*

(and sometimes, from within nodes, depending on configuration)

- Code running in pods can connect to services using their name

(e.g. https://kubernetes/...)

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlget.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Running our first containers on Kubernetes

.nav[
[Previous section](#toc-first-contact-with-kubectl)
|
[Back to table of contents](#toc-chapter-2)
|
[Next section](#toc-accessing-logs-from-the-cli)
]

---
# 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/2020-01-zr/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

- Let's ping the address of `localhost`, the loopback interface:
  ```bash
  kubectl run pingpong --image alpine ping 127.0.0.1
  ```

]

(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/2020-01-zr/slides/k8s/kubectlrun.md)]
---

## Behind the scenes of `kubectl run`

- Let's look at the resources that were created by `kubectl run`

- 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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/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!)

- View the result of our `ping` command:
  ```bash
  kubectl logs deploy/pingpong
  ```

]

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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

- View the latest logs of our `ping` command:
  ```bash
  kubectl logs deploy/pingpong --tail 1 --follow
  ```

- Leave that command running, so that we can keep an eye on these logs

]

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlrun.md)]
---

## Scaling our application

- We can create additional copies of our container (I mean, our pod) with `kubectl scale`

- 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/2020-01-zr/slides/k8s/kubectlrun.md)]
---

## Log streaming

- Let's look again at the output of `kubectl logs`

(the one we started before scaling up)

- `kubectl logs` shows us one line per second

- We could expect 3 lines per second

(since we should now have 3 pods running `ping`)

- Let's try to figure out what's happening!

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlrun.md)]
---

## Streaming logs of multiple pods

- What happens if we restart `kubectl logs`?

- Interrupt `kubectl logs` (with Ctrl-C)

- Restart it:
  ```bash
  kubectl logs deploy/pingpong --tail 1 --follow
  ```

]

`kubectl logs` will warn us that multiple pods were found, and that it's showing us only one of them.

Let's leave `kubectl logs` running while we keep exploring.

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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?

- In a separate window, watch the list of pods:
  ```bash
  watch kubectl get pods
  ```

- Destroy the pod currently shown by `kubectl logs`:
  ```
  kubectl delete pod pingpong-xxxxxxxxxx-yyyyy
  ```

]

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlrun.md)]
---

## What happened?

- `kubectl delete pod` terminates the pod gracefully

(sending it the TERM signal and waiting for it to shutdown)

- As soon as the pod is in "Terminating" state, the Replica Set replaces it

- But we can still see the output of the "Terminating" pod in `kubectl logs`

- Until 30 seconds later, when the grace period expires

- The pod is then killed, and `kubectl logs` exits

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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),
 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/2020-01-zr/slides/k8s/kubectlrun.md)]
---

## Scheduling periodic background work

- A Cron Job is a job that will be executed at specific intervals

(the name comes from the traditional cronjobs executed by the UNIX crond)

- It requires a *schedule*, represented as five space-separated fields:

- minute [0,59]
  - hour [0,23]
  - day of the month [1,31]
  - month of the year [1,12]
  - day of the week ([0,6] with 0=Sunday)

- `*` means "all valid values"; `/N` means "every N"

- Example: `*/3 * * * *` means "every three minutes"

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlrun.md)]
---

## Creating a Cron Job

- Let's create a simple job to be executed every three minutes

- Cron Jobs need to terminate, otherwise they'd run forever

- Create the Cron Job:
  ```bash
  kubectl run every3mins --schedule="*/3 * * * *" --restart=OnFailure \
      --image=alpine sleep 10
  ```

- Check the resource that was created:
  ```bash
  kubectl get cronjobs
  ```

]

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlrun.md)]
---

## Cron Jobs in action

- At the specified schedule, the Cron Job will create a Job

- The Job will create a Pod

- The Job will make sure that the Pod completes

(re-creating another one if it fails, for instance if its node fails)

- Check the Jobs that are created:
  ```bash
  kubectl get jobs
  ```

]

(It will take a few minutes before the first job is scheduled.)

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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
 (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/2020-01-zr/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/2020-01-zr/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

- 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/2020-01-zr/slides/k8s/kubectlrun.md)]
---

### Streaming logs of multiple pods

- Can we stream the logs of all our `pingpong` pods?

- Combine `-l` and `-f` flags:
  ```bash
  kubectl logs -l run=pingpong --tail 1 -f
  ```

]

*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/2020-01-zr/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

- 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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/slides/k8s/kubectlrun.md)]
---

## `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/2020-01-zr/slides/k8s/kubectlrun.md)]
---

## Party tricks involving IP addresses

- It is possible to specify an IP address with less than 4 bytes

(example: `127.1`)

- Zeroes are then inserted in the middle

- As a result, `127.1` expands to `127.0.0.1`

- So we can `ping 127.1` to ping `localhost`!

(See [this blog post](https://ma.ttias.be/theres-more-than-one-way-to-write-an-ip-address/
) for more details.)

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlrun.md)]
---

## More party tricks with IP addresses

- We can also ping `1.1`

- `1.1` will expand to `1.0.0.1`

- This is one of the addresses of Cloudflare's
  [public DNS resolver](https://blog.cloudflare.com/announcing-1111/)

- This is a quick way to check connectivity

(if we can reach 1.1, we probably have internet access)

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlrun.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Accessing logs from the CLI

.nav[
[Previous section](#toc-running-our-first-containers-on-kubernetes)
|
[Back to table of contents](#toc-chapter-2)
|
[Next section](#toc-declarative-vs-imperative)
]

---
# Accessing logs from the CLI

- The `kubectl logs` command has limitations:

- it cannot stream logs from multiple pods at a time

- when showing logs from multiple pods, it mixes them all together

- We are going to see how to do it better

.debug[[k8s/logs-cli.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/logs-cli.md)]
---

## Doing it manually

- We *could* (if we were so inclined) write a program or script that would:

- take a selector as an argument

- enumerate all pods matching that selector (with `kubectl get -l ...`)

- fork one `kubectl logs --follow ...` command per container

- annotate the logs (the output of each `kubectl logs ...` process) with their origin

- preserve ordering by using `kubectl logs --timestamps ...` and merge the output

- We *could* do it, but thankfully, others did it for us already!

.debug[[k8s/logs-cli.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/logs-cli.md)]
---

## Stern

[Stern](https://github.com/wercker/stern) is an open source project
by [Wercker](http://www.wercker.com/).

From the README:

*Stern allows you to tail multiple pods on Kubernetes and multiple containers within the pod. Each result is color coded for quicker debugging.*

*The query is a regular expression so the pod name can easily be filtered and you don't need to specify the exact id (for instance omitting the deployment id). If a pod is deleted it gets removed from tail and if a new pod is added it automatically gets tailed.*

Exactly what we need!

.debug[[k8s/logs-cli.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/logs-cli.md)]
---

## Installing Stern

- Run `stern` (without arguments) to check if it's installed:

```
  $ stern
  Tail multiple pods and containers from Kubernetes

Usage:
    stern pod-query [flags]
  ```

- If it is not installed, the easiest method is to download a [binary release](https://github.com/wercker/stern/releases)

- The following commands will install Stern on a Linux Intel 64 bit machine:
  ```bash
  sudo curl -L -o /usr/local/bin/stern \
       https://github.com/wercker/stern/releases/download/1.11.0/stern_linux_amd64
  sudo chmod +x /usr/local/bin/stern
  ```

- On OS X, just `brew install stern`

.debug[[k8s/logs-cli.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/logs-cli.md)]
---

## Using Stern

- There are two ways to specify the pods whose logs we want to see:

- `-l` followed by a selector expression (like with many `kubectl` commands)

- with a "pod query," i.e. a regex used to match pod names

- These two ways can be combined if necessary

- View the logs for all the pingpong containers:
  ```bash
  stern pingpong
  ```

]

.debug[[k8s/logs-cli.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/logs-cli.md)]
---

## Stern convenient options

- The `--tail N` flag shows the last `N` lines for each container

(Instead of showing the logs since the creation of the container)

- The `-t` / `--timestamps` flag shows timestamps

- The `--all-namespaces` flag is self-explanatory

- View what's up with the `weave` system containers:
  ```bash
  stern --tail 1 --timestamps --all-namespaces weave
  ```

]

.debug[[k8s/logs-cli.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/logs-cli.md)]
---

## Using Stern with a selector

- When specifying a selector, we can omit the value for a label

- This will match all objects having that label (regardless of the value)

- Everything created with `kubectl run` has a label `run`

- We can use that property to view the logs of all the pods created with `kubectl run`

- Similarly, everything created with `kubectl create deployment` has a label `app`

- View the logs for all the things started with `kubectl run`:
  ```bash
  stern -l run
  ```

]

.debug[[k8s/logs-cli.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/logs-cli.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Declarative vs imperative

.nav[
[Previous section](#toc-accessing-logs-from-the-cli)
|
[Back to table of contents](#toc-chapter-2)
|
[Next section](#toc-kubernetes-network-model)
]

---
# 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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/slides/shared/declarative.md)]
---
## Declarative vs imperative in Kubernetes

- With Kubernetes, we cannot say: "run this container"

- All we can do is write a *spec* and push it to the API server

(by creating a resource like e.g. a Pod or a Deployment)

- The API server will validate that spec (and reject it if it's invalid)

- Then it will store it in etcd

- A *controller* will "notice" that spec and act upon it

.debug[[k8s/declarative.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/declarative.md)]
---

## Reconciling state

- Watch for the `spec` fields in the YAML files later!

- The *spec* describes *how we want the thing to be*

- Kubernetes will *reconcile* the current state with the spec
 (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/2020-01-zr/slides/k8s/declarative.md)]
---
## 19,000 words

They say, "a picture is worth one thousand words."

The following 19 slides show what really happens when we run:

```bash
kubectl run web --image=nginx --replicas=3
```

.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/01.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/02.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/03.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/04.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/05.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/06.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/07.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/08.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/09.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/10.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/11.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/12.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/13.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/14.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/15.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/16.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/17.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/18.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/19.svg)

.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/deploymentslideshow.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Kubernetes network model

.nav[
[Previous section](#toc-declarative-vs-imperative)
|
[Back to table of contents](#toc-chapter-2)
|
[Next section](#toc-exposing-containers)
]

---
# Kubernetes network model

- TL,DR:

*Our cluster (nodes and pods) is one big flat IP network.*

- In detail:

- all nodes must be able to reach each other, without NAT

- all pods must be able to reach each other, without NAT

- pods and nodes must be able to reach each other, without NAT

- each pod is aware of its IP address (no NAT)

- pod IP addresses are assigned by the network implementation

- Kubernetes doesn't mandate any particular implementation

.debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubenet.md)]
---

## Kubernetes network model: the good

- Everything can reach everything

- No address translation

- No port translation

- No new protocol

- The network implementation can decide how to allocate addresses

- IP addresses don't have to be "portable" from a node to another

(We can use e.g. a subnet per node and use a simple routed topology)

- The specification is simple enough to allow many various implementations

.debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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 (TCP or UDP)

(Services map to a single UDP or TCP port; no port ranges or arbitrary IP packets)

- `kube-proxy` is on the data path when connecting to a pod or container,
 and it's not particularly fast (relies on userland proxying or iptables)

.debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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
 (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/2020-01-zr/slides/k8s/kubenet.md)]
---

## The Container Network Interface (CNI)

- Most Kubernetes clusters use CNI "plugins" to implement networking

- When a pod is created, Kubernetes delegates the network setup to these plugins

(it can be a single plugin, or a combination of plugins, each doing one task)

- Typically, CNI plugins will:

- allocate an IP address (by calling an IPAM plugin)

- add a network interface into the pod's network namespace

- configure the interface as well as required routes etc.

.debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubenet.md)]
---

## Multiple moving parts

- The "pod-to-pod network" or "pod network":

- provides communication between pods and nodes

- is generally implemented with CNI plugins

- The "pod-to-service network":

- provides internal communication and load balancing

- is generally implemented with kube-proxy (or e.g. kube-router)

- Network policies:

- provide firewalling and isolation

- can be bundled with the "pod network" or provided by another component

.debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubenet.md)]
---

## Even more moving parts

- Inbound traffic can be handled by multiple components:

- something like kube-proxy or kube-router (for NodePort services)

- load balancers (ideally, connected to the pod network)

- It is possible to use multiple pod networks in parallel

(with "meta-plugins" like CNI-Genie or Multus)

- Some solutions can fill multiple roles

(e.g. kube-router can be set up to provide the pod network and/or network policies and/or replace kube-proxy)

.debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubenet.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Exposing containers

.nav[
[Previous section](#toc-kubernetes-network-model)
|
[Back to table of contents](#toc-chapter-2)
|
[Next section](#toc-shipping-images-with-a-registry)
]

---
# 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`

- HTTP services can also use `Ingress` resources (more on that later)

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlexpose.md)]
---

## `ClusterIP`

- It's the default service type

- A virtual IP address is allocated for the service

(in an internal, private range; e.g. 10.96.0.0/12)

- This IP address is reachable only from within the cluster (nodes and pods)

- Our code can connect to the service using the original port number

- Perfect for internal communication, within the cluster

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlexpose.md)]
---

## `LoadBalancer`

- An external load balancer is allocated for the service

(typically a cloud load balancer, e.g. ELB on AWS, GLB on GCE ...)

- This is available only when the underlying infrastructure provides some kind of
  "load balancer as a service"

- Each service of that type will typically cost a little bit of money

(e.g. a few cents per hour on AWS or GCE)

- Ideally, traffic would flow directly from the load balancer to the pods

- In practice, it will often flow through a `NodePort` first

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlexpose.md)]
---

## `NodePort`

- A port number is allocated for the service

(by default, in the 30000-32767 range)

- That port is made available *on all our nodes* and anybody can connect to it

(we can connect to any node on that port to reach the service)

- Our code needs to be changed to connect to that new port number

- Under the hood: `kube-proxy` sets up a bunch of `iptables` rules on our nodes

- Sometimes, it's the only available option for external traffic

(e.g. most clusters deployed with kubeadm or on-premises)

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlexpose.md)]
---

## `ExternalName`

- No load balancer (internal or external) is created

- Only a DNS entry gets added to the DNS managed by Kubernetes

- That DNS entry will just be a `CNAME` to a provided record

Example:
```bash
kubectl create service externalname k8s --external-name kubernetes.io
```
*Creates a CNAME `k8s` pointing to `kubernetes.io`*

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/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

- In another window, watch the pods (to see when they are created):
  ```bash
  kubectl get pods -w
  ```

- 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/2020-01-zr/slides/k8s/kubectlexpose.md)]
---

## Exposing our deployment

- We'll create a default `ClusterIP` service

- 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/2020-01-zr/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/2020-01-zr/slides/k8s/kubectlexpose.md)]
---

## Testing our service

- We will now send a few HTTP requests to our pods

- 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 }}')
  ```

- 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/2020-01-zr/slides/k8s/kubectlexpose.md)]
---

## 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/2020-01-zr/slides/k8s/kubectlexpose.md)]
---

## 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/2020-01-zr/slides/k8s/kubectlexpose.md)]
---

## 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

- 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/2020-01-zr/slides/k8s/kubectlexpose.md)]
---

## 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/2020-01-zr/slides/k8s/kubectlexpose.md)]
---

## `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/2020-01-zr/slides/k8s/kubectlexpose.md)]
---

## `ExternalIP`

- When creating a servivce, we can also specify an `ExternalIP`

(this is not a type, but an extra attribute to the service)

- It will make the service availableon this IP address

(if the IP address belongs to a node of the cluster)

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlexpose.md)]
---

## `Ingress`

- Ingresses are another type (kind) of resource

- They are specifically for HTTP services

(not TCP or UDP)

- They can also handle TLS certificates, URL rewriting ...

- They require an *Ingress Controller* to function

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kubectlexpose.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

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)
]

---
# 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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/slides/k8s/shippingimages.md)]
---

## Building on the fly

- Some services can build images on the fly from a repository

- Example: [ctr.run](https://ctr.run/)

- Use ctr.run to automatically build a container image and run it:
  ```bash
  docker run ctr.run/github.com/jpetazzo/container.training/dockercoins/hasher
  ```

]

There might be a long pause before the first layer is pulled,
because the API behind `docker pull` doesn't allow to stream build logs, and there is no feedback during the build.

It is possible to view the build logs by setting up an account on [ctr.run](https://ctr.run/).
.debug[[k8s/shippingimages.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/slides/k8s/buildshiprun-dockerhub.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

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-deploying-with-yaml)
]

---
# Running our application on Kubernetes

- We can now deploy our code (as well as a redis instance)

- Deploy `redis`:
  ```bash
  kubectl create deployment redis --image=redis
  ```

- Deploy everything else:
  ```bash
  kubectl create deployment hasher --image=dockercoins/hasher:v0.1
  kubectl create deployment rng --image=dockercoins/rng:v0.1
  kubectl create deployment webui --image=dockercoins/webui:v0.1
  kubectl create deployment worker --image=dockercoins/worker:v0.1
  ```

]

.debug[[k8s/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ourapponkube.md)]
---

## Deploying other images

- If we wanted to deploy images from another registry ...

- ... Or with a different tag ...

- ... We could use the following snippet:

```bash
  REGISTRY=dockercoins
  TAG=v0.1
  for SERVICE in hasher rng webui worker; do
    kubectl create deployment $SERVICE --image=$REGISTRY/$SERVICE:$TAG
  done
```

.debug[[k8s/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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)

- 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/2020-01-zr/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

- 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/2020-01-zr/slides/k8s/ourapponkube.md)]
---

## Is this working yet?

- The `worker` has an infinite loop, that retries 10 seconds after an error

- Stream the worker's logs:
  ```bash
  kubectl logs deploy/worker --follow
  ```

(Give it about 10 seconds to recover)

]

We should now see the `worker`, well, working happily.

.debug[[k8s/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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)

- 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/2020-01-zr/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

- Open the web UI in your browser (http://node-ip-address: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/2020-01-zr/slides/k8s/ourapponkube.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Deploying with YAML

.nav[
[Previous section](#toc-running-our-application-on-kubernetes)
|
[Back to table of contents](#toc-chapter-3)
|
[Next section](#toc-scaling-our-demo-app)
]

---
# Deploying with YAML

- So far, we created resources with the following commands:

- `kubectl run`

- `kubectl create deployment`

- `kubectl expose`

- We can also create resources directly with YAML manifests

.debug[[k8s/yamldeploy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/yamldeploy.md)]
---

## `kubectl apply` vs `create`

- `kubectl create -f whatever.yaml`

- creates resources if they don't exist

- if resources already exist, don't alter them
 (and display error message)

- `kubectl apply -f whatever.yaml`

- creates resources if they don't exist

- if resources already exist, update them
 (to match the definition provided by the YAML file)

- stores the manifest as an *annotation* in the resource

.debug[[k8s/yamldeploy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/yamldeploy.md)]
---

## Creating multiple resources

- The manifest can contain multiple resources separated by `---`

```yaml
 kind: ...
 apiVersion: ...
 metadata: ...
   name: ...
 ...
 ---
 kind: ...
 apiVersion: ...
 metadata: ...
   name: ...
 ...
```

.debug[[k8s/yamldeploy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/yamldeploy.md)]
---

## Creating multiple resources

- The manifest can also contain a list of resources

```yaml
 apiVersion: v1
 kind: List
 items:
 - kind: ...
   apiVersion: ...
   ...
 - kind: ...
   apiVersion: ...
   ...
```

.debug[[k8s/yamldeploy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/yamldeploy.md)]
---

## Deploying dockercoins with YAML

- We provide a YAML manifest with all the resources for Dockercoins

(Deployments and Services)

- We can use it if we need to deploy or redeploy Dockercoins

- Deploy or redeploy Dockercoins:
  ```bash
  kubectl apply -f ~/container.training/k8s/dockercoins.yaml
  ```

]

(If we deployed Dockercoins earlier, we will see warning messages,
because the resources that we created lack the necessary annotation.
We can safely ignore them.)

.debug[[k8s/yamldeploy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/yamldeploy.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Scaling our demo app

.nav[
[Previous section](#toc-deploying-with-yaml)
|
[Back to table of contents](#toc-chapter-3)
|
[Next section](#toc-daemon-sets)
]

---
# Scaling our demo app

- Our ultimate goal is to get more DockerCoins

(i.e. increase the number of loops per second shown on the web UI)

- Let's look at the architecture again:

![DockerCoins architecture](images/dockercoins-diagram.svg)

- The loop is done in the worker;
  perhaps we could try adding more workers?

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/scalingdockercoins.md)]
---

## Adding another worker

- All we have to do is scale the `worker` Deployment

- Open a new terminal to keep an eye on our pods:
  ```bash
  kubectl get pods -w
  ```

- 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/2020-01-zr/slides/k8s/scalingdockercoins.md)]
---

## Adding more workers

- If 2 workers give us 2x speed, what about 3 workers?

- 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/2020-01-zr/slides/k8s/scalingdockercoins.md)]
---

## Adding even more workers

- Let's see if 10 workers give us 10x speed!

- 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/2020-01-zr/slides/k8s/scalingdockercoins.md)]
---

## 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/2020-01-zr/slides/k8s/scalingdockercoins.md)]
---

## 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
 
 (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
 
 (e.g. the web UI check interval is client-side JavaScript)

- Sometimes, between two web UI counter measurements,
 
 the workers are able to update the counter *twice*

- During that cycle, the instant speed will appear to be much bigger
 
 (but it will be compensated by lower instant speed before and after)

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/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/2020-01-zr/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

- 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/2020-01-zr/slides/k8s/scalingdockercoins.md)]
---

## Checking `hasher` and `rng` response times

- 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/2020-01-zr/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...
 
...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/2020-01-zr/slides/shared/hastyconclusions.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Daemon sets

.nav[
[Previous section](#toc-scaling-our-demo-app)
|
[Back to table of contents](#toc-chapter-3)
|
[Next section](#toc-labels-and-selectors)
]

---
# Daemon sets

- We want to scale `rng` in a way that is different from how we scaled `worker`

- We want one (and exactly one) instance of `rng` per node

- We *do not want* two instances of `rng` on the same node

- We will do that with a *daemon set*

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/daemonset.md)]
---

## Why not a deployment?

- Can't we just do `kubectl scale deployment rng --replicas=...`?

- Nothing guarantees that the `rng` containers will be distributed evenly

- If we add nodes later, they will not automatically run a copy of `rng`

- If we remove (or reboot) a node, one `rng` container will restart elsewhere

(and we will end up with two instances `rng` on the same node)

- By contrast, a daemon set will start one pod per node and keep it that way

(as nodes are added or removed)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/slides/k8s/daemonset.md)]
---

## Creating a daemon set

- Unfortunately, as of Kubernetes 1.15, 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/2020-01-zr/slides/k8s/daemonset.md)]
---

## Creating the YAML file for our daemon set

- Let's start with the YAML file for the current `rng` resource

- Dump the `rng` resource in YAML:
  ```bash
  kubectl get deploy/rng -o yaml >rng.yml
  ```

- Edit `rng.yml`

]

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/daemonset.md)]
---

## "Casting" a resource to another

- What if we just changed the `kind` field?

(It can't be that easy, right?)

- Change `kind: Deployment` to `kind: DaemonSet`

- Save, quit

- Try to create our new resource:
  ```bash
  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/2020-01-zr/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/2020-01-zr/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`

- 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/2020-01-zr/slides/k8s/daemonset.md)]
---

## Checking what we've done

- Did we transform our `deployment` into a `daemonset`?

- 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.
 
(The pod corresponding to the *deployment* still exists.)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/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/).)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/slides/k8s/daemonset.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Labels and selectors

.nav[
[Previous section](#toc-daemon-sets)
|
[Back to table of contents](#toc-chapter-3)
|
[Next section](#toc-rolling-updates)
]

---

# 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

- 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/2020-01-zr/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

- 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/2020-01-zr/slides/k8s/daemonset.md)]
---

## Where do labels come from?

- When we create a deployment with `kubectl create deployment rng`,
 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

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/slides/k8s/daemonset.md)]
---

## Selectors for replica sets and daemon sets

- The "mission" of a replica set is:

"Make sure that there is the right number of pods matching this spec!"

- The "mission" of a daemon set is:

"Make sure that there is a pod matching this spec on each node!"

- *In fact,* replica sets and daemon sets do not check pod specifications

- They merely have a *selector*, and they look for pods matching that selector

- Yes, we can fool them by manually creating pods with the "right" labels

- Bottom line: if we remove our `app=rng` label ...

... The pod "disappears" for its parent, which re-creates another pod to replace it

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/daemonset.md)]
---

## 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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/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

- 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/2020-01-zr/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)

- Update the service to add `enabled: yes` to its selector:
  ```bash
  kubectl edit service rng
  ```

]

... And then we get *the weirdest error ever.* Why?

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/slides/k8s/daemonset.md)]
---

## Updating the service selector, take 2

- Update the YAML manifest of the service

- Add `enabled: "yes"` to its selector

]

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/2020-01-zr/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/2020-01-zr/slides/k8s/daemonset.md)]
---

## Removing a pod from the load balancer

- 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/2020-01-zr/slides/k8s/daemonset.md)]
---

## 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/2020-01-zr/slides/k8s/daemonset.md)]
---

## 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/2020-01-zr/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/2020-01-zr/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
 
 (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/2020-01-zr/slides/k8s/daemonset.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Rolling updates

.nav[
[Previous section](#toc-labels-and-selectors)
|
[Back to table of contents](#toc-chapter-4)
|
[Next section](#toc-healthchecks)
]

---
# 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/2020-01-zr/slides/k8s/rollout.md)]
---

## Rolling updates

- With rolling updates, when a Deployment is updated, it happens progressively

- The Deployment controls multiple Replica Sets

- Each Replica Set is a group of identical Pods

(with the same image, arguments, parameters ...)

- During the rolling update, we have at least two Replica Sets:

- the "new" set (corresponding to the "target" version)

- at least one "old" set

- We can have multiple "old" sets

(if we start another update before the first one is done)

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/rollout.md)]
---

## Update strategy

- Two parameters determine the pace of the rollout: `maxUnavailable` and `maxSurge`

- They can be specified in absolute number of pods, or percentage of the `replicas` count

- At any given time ...

- there will always be at least `replicas`-`maxUnavailable` pods available

- there will never be more than `replicas`+`maxSurge` pods in total

- there will therefore be up to `maxUnavailable`+`maxSurge` pods being updated

- We have the possibility of rolling back to the previous version
 (if the update fails or is unsatisfactory in any way)

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/rollout.md)]
---

## Checking current rollout parameters

- Recall how we build custom reports with `kubectl` and `jq`:

- 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/2020-01-zr/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/2020-01-zr/slides/k8s/rollout.md)]
---

## Rolling out the new `worker` service

- 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
  ```

- Update `worker` either with `kubectl edit`, or by running:
  ```bash
  kubectl set image deploy worker worker=dockercoins/worker:v0.2
  ```

]

That rollout should be pretty quick. What shows in the web UI?

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/slides/k8s/rollout.md)]
---

## Rolling out something invalid

- What happens if we make a mistake?

- Update `worker` by specifying a non-existent image:
  ```bash
  kubectl set image deploy worker worker=dockercoins/worker:v0.3
  ```

- Check what's going on:
  ```bash
  kubectl rollout status deploy worker
  ```

]

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/2020-01-zr/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,
 
 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/2020-01-zr/slides/k8s/rollout.md)]
---

## 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/2020-01-zr/slides/k8s/rollout.md)]
---

## Checking the dashboard during the bad rollout

If you didn't deploy the Kubernetes dashboard earlier, just skip this slide.

- Connect to the dashboard that we deployed earlier

- Check that we have failures in Deployments, Pods, and Replica Sets

- Can we see the reason for the failure?

]

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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

- Cancel the deployment and wait for the dust to settle:
  ```bash
  kubectl rollout undo deploy worker
  kubectl rollout status deploy worker
  ```

]

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/rollout.md)]
---

## Rolling back to an older version

- We reverted to `v0.2`

- But this version still has a performance problem

- How can we get back to the previous version?

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/rollout.md)]
---

## Multiple "undos"

- What happens if we try `kubectl rollout undo` again?

- Try it:
  ```bash
  kubectl rollout undo deployment worker
  ```

- Check the web UI, the list of pods ...

]

🤔 That didn't work.

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/rollout.md)]
---

## Multiple "undos" don't work

- If we see successive versions as a stack:

- `kubectl rollout undo` doesn't "pop" the last element from the stack

- it copies the N-1th element to the top

- Multiple "undos" just swap back and forth between the last two versions!

- Go back to v0.2 again:
  ```bash
  kubectl rollout undo deployment worker
  ```

]

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/rollout.md)]
---

## In this specific scenario

- Our version numbers are easy to guess

- What if we had used git hashes?

- What if we had changed other parameters in the Pod spec?

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/rollout.md)]
---

## Listing versions

- We can list successive versions of a Deployment with `kubectl rollout history`

- Look at our successive versions:
  ```bash
  kubectl rollout history deployment worker
  ```

]

We don't see *all* revisions.

We might see something like 1, 4, 5.

(Depending on how many "undos" we did before.)

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/rollout.md)]
---

## Explaining deployment revisions

- These revisions correspond to our Replica Sets

- This information is stored in the Replica Set annotations

- Check the annotations for our replica sets:
  ```bash
  kubectl describe replicasets -l app=worker | grep -A3 ^Annotations
  ```

]

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/rollout.md)]
---

## What about the missing revisions?

- The missing revisions are stored in another annotation:

`deployment.kubernetes.io/revision-history`

- These are not shown in `kubectl rollout history`

- We could easily reconstruct the full list with a script

(if we wanted to!)

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/rollout.md)]
---

## Rolling back to an older version

- `kubectl rollout undo` can work with a revision number

- Roll back to the "known good" deployment version:
  ```bash
  kubectl rollout undo deployment worker --to-revision=1
  ```

- Check the web UI or the list of pods

]

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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: dockercoins/worker:v0.1
  strategy:
    rollingUpdate:
      maxUnavailable: 0
      maxSurge: 1
  minReadySeconds: 10
```
]

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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

- Apply all our changes and wait for them to take effect:
  ```bash
  kubectl patch deployment worker -p "
    spec:
      template:
        spec:
          containers:
          - name: worker
            image: dockercoins/worker:v0.1
      strategy:
        rollingUpdate:
          maxUnavailable: 0
          maxSurge: 1
      minReadySeconds: 10
    "
  kubectl rollout status deployment worker
  kubectl get deploy -o json worker |
          jq "{name:.metadata.name} + .spec.strategy.rollingUpdate"
  ```
  ]

]

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/rollout.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Healthchecks

.nav[
[Previous section](#toc-rolling-updates)
|
[Back to table of contents](#toc-chapter-4)
|
[Next section](#toc-recording-deployment-actions)
]

---
# Healthchecks

- Kubernetes provides two kinds of healthchecks: liveness and readiness

- Healthchecks are *probes* that apply to *containers* (not to pods)

- Each container can have two (optional) probes:

- liveness = is this container dead or alive?

- readiness = is this container ready to serve traffic?

- Different probes are available (HTTP, TCP, program execution)

- Let's see the difference and how to use them!

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks.md)]
---

## Liveness probe

- Indicates if the container is dead or alive

- A dead container cannot come back to life

- If the liveness probe fails, the container is killed

(to make really sure that it's really dead; no zombies or undeads!)

- What happens next depends on the pod's `restartPolicy`:

- `Never`: the container is not restarted

- `OnFailure` or `Always`: the container is restarted

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks.md)]
---

## When to use a liveness probe

- To indicate failures that can't be recovered

- deadlocks (causing all requests to time out)

- internal corruption (causing all requests to error)

- Anything where our incident response would be "just restart/reboot it"

- Otherwise we just restart our pods for no reason, creating useless load

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks.md)]
---

## Readiness probe

- Indicates if the container is ready to serve traffic

- If a container becomes "unready" it might be ready again soon

- If the readiness probe fails:

- the container is *not* killed

- if the pod is a member of a service, it is temporarily removed

- it is re-added as soon as the readiness probe passes again

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks.md)]
---

## When to use a readiness probe

- To indicate failure due to an external cause

- database is down or unreachable

- mandatory auth or other backend service unavailable

- To indicate temporary failure or unavailability

- application can only service *N* parallel connections

- runtime is busy doing garbage collection or initial data load

- For processes that take a long time to start

(more on that later)

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks.md)]
---

## Dependencies

- If a web server depends on a database to function, and the database is down:

- the web server's liveness probe should succeed

- the web server's readiness probe should fail

- Same thing for any hard dependency (without which the container can't work)

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks.md)]
---

## Timing and thresholds

- Probes are executed at intervals of `periodSeconds` (default: 10)

- The timeout for a probe is set with `timeoutSeconds` (default: 1)

- A probe is considered successful after `successThreshold` successes (default: 1)

- A probe is considered failing after `failureThreshold` failures (default: 3)

- A probe can have an `initialDelaySeconds` parameter (default: 0)

- Kubernetes will wait that amount of time before running the probe for the first time

(this is important to avoid killing services that take a long time to start)

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks.md)]
---

## Startup probe

- Kubernetes 1.16 introduces a third type of probe: `startupProbe`

(it is in `alpha` in Kubernetes 1.16)

- It can be used to indicate "container not ready *yet*"

- process is still starting

- loading external data, priming caches

- Before Kubernetes 1.16, we had to use the `initialDelaySeconds` parameter

(available for both liveness and readiness probes)

- `initialDelaySeconds` is a rigid delay (always wait X before running probes)

- `startupProbe` works better when a container start time can vary a lot

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks.md)]
---

## Different types of probes

- HTTP request

- specify URL of the request (and optional headers)

- any status code between 200 and 399 indicates success

- TCP connection

- the probe succeeds if the TCP port is open

- arbitrary exec

- a command is executed in the container

- exit status of zero indicates success

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks.md)]
---

## Benefits of using probes

- Rolling updates proceed when containers are *actually ready*

(as opposed to merely started)

- Containers in a broken state get killed and restarted

(instead of serving errors or timeouts)

- Unavailable backends get removed from load balancer rotation

(thus improving response times across the board)

- If a probe is not defined, it's as if there was an "always successful" probe

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks.md)]
---

## Example: HTTP probe

Here is a pod template for the `rng` web service of the DockerCoins app:

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: rng-with-liveness
spec:
  containers:
  - name: rng
    image: dockercoins/rng:v0.1
    livenessProbe:
      httpGet:
        path: /
        port: 80
      initialDelaySeconds: 10
      periodSeconds: 1
```

If the backend serves an error, or takes longer than 1s, 3 times in a row, it gets killed.

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks.md)]
---

## Example: exec probe

Here is a pod template for a Redis server:

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: redis-with-liveness
spec:
  containers:
  - name: redis
    image: redis
    livenessProbe:
      exec:
        command: ["redis-cli", "ping"]
```

If the Redis process becomes unresponsive, it will be killed.

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks.md)]
---

## Questions to ask before adding healthchecks

- Do we want liveness, readiness, both?

(sometimes, we can use the same check, but with different failure thresholds)

- Do we have existing HTTP endpoints that we can use?

- Do we need to add new endpoints, or perhaps use something else?

- Are our healthchecks likely to use resources and/or slow down the app?

- Do they depend on additional services?

(this can be particularly tricky, see next slide)

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks.md)]
---

## Healthchecks and dependencies

- Liveness checks should not be influenced by the state of external services

- All checks should reply quickly (by default, less than 1 second)

- Otherwise, they are considered to fail

- This might require to check the health of dependencies asynchronously

(e.g. if a database or API might be healthy but still take more than
  1 second to reply, we should check the status asynchronously and report
  a cached status)

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks.md)]
---

## Healthchecks for workers

(In that context, worker = process that doesn't accept connections)

- Readiness isn't useful

(because workers aren't backends for a service)

- Liveness may help us restart a broken worker, but how can we check it?

- Embedding an HTTP server is a (potentially expensive) option

- Using a "lease" file can be relatively easy:

- touch a file during each iteration of the main loop

- check the timestamp of that file from an exec probe

- Writing logs (and checking them from the probe) also works

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks.md)]
---
## Adding healthchecks to an app

- Let's add healthchecks to DockerCoins!

- We will examine the questions of the previous slide

- Then we will review each component individually to add healthchecks

.debug[[k8s/healthchecks-more.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks-more.md)]
---

## Liveness, readiness, or both?

- To answer that question, we need to see the app run for a while

- Do we get temporary, recoverable glitches?

→ then use readiness

- Or do we get hard lock-ups requiring a restart?

→ then use liveness

- In the case of DockerCoins, we don't know yet!

- Let's pick liveness

.debug[[k8s/healthchecks-more.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks-more.md)]
---

## Do we have HTTP endpoints that we can use?

- Each of the 3 web services (hasher, rng, webui) has a trivial route on `/`

- These routes:

- don't seem to perform anything complex or expensive

- don't seem to call other services

- Perfect!

(See next slides for individual details)

.debug[[k8s/healthchecks-more.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks-more.md)]
---

- [hasher.rb](https://github.com/jpetazzo/container.training/blob/master/dockercoins/hasher/hasher.rb)
  ```ruby
    get '/' do
      "HASHER running on #{Socket.gethostname}\n"
    end
  ```

- [rng.py](https://github.com/jpetazzo/container.training/blob/master/dockercoins/rng/rng.py)
  ```python
    @app.route("/")
    def index():
      return "RNG running on {}\n".format(hostname)
  ```

- [webui.js](https://github.com/jpetazzo/container.training/blob/master/dockercoins/webui/webui.js)
  ```javascript
    app.get('/', function (req, res) {
      res.redirect('/index.html');
    });
  ```

.debug[[k8s/healthchecks-more.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks-more.md)]
---

## Running DockerCoins

- We will run DockerCoins in a new, separate namespace

- We will use a set of YAML manifests and pre-built images

- We will add our new liveness probe to the YAML of the `rng` DaemonSet

- Then, we will deploy the application

.debug[[k8s/healthchecks-more.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks-more.md)]
---

## Creating a new namespace

- This will make sure that we don't collide / conflict with previous exercises

- Create the yellow namespace:
  ```bash
  kubectl create namespace yellow
  ```

- Switch to that namespace:
  ```bash
  kns yellow
  ```

]

.debug[[k8s/healthchecks-more.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks-more.md)]
---

## Retrieving DockerCoins manifests

- All the manifests that we need are on a convenient repository:

https://github.com/jpetazzo/kubercoins

- Clone that repository:
  ```bash
  cd ~
  git clone https://github.com/jpetazzo/kubercoins
  ```

- Change directory to the repository:
  ```bash
  cd kubercoins
  ```

]

.debug[[k8s/healthchecks-more.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks-more.md)]
---

## A simple HTTP liveness probe

This is what our liveness probe should look like:

```yaml
containers:
- name: ...
image: ...
livenessProbe:
  httpGet:
    path: /
    port: 80
  initialDelaySeconds: 30
  periodSeconds: 5
```

This will give 30 seconds to the service to start. (Way more than necessary!)
 
It will run the probe every 5 seconds.
 
It will use the default timeout (1 second).
 
It will use the default failure threshold (3 failed attempts = dead).
 
It will use the default success threshold (1 successful attempt = alive).

.debug[[k8s/healthchecks-more.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks-more.md)]
---

## Adding the liveness probe

- Let's add the liveness probe, then deploy DockerCoins

- Edit `rng-daemonset.yaml` and add the liveness probe
  ```bash
  vim rng-daemonset.yaml
  ```

- Load the YAML for all the resources of DockerCoins:
  ```bash
  kubectl apply -f .
  ```

]

.debug[[k8s/healthchecks-more.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks-more.md)]
---

## Testing the liveness probe

- The rng service needs 100ms to process a request

(because it is single-threaded and sleeps 0.1s in each request)

- The probe timeout is set to 1 second

- If we send more than 10 requests per second per backend, it will break

- Let's generate traffic and see what happens!

- Get the ClusterIP address of the rng service:
  ```bash
  kubectl get svc rng
  ```

]

.debug[[k8s/healthchecks-more.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks-more.md)]
---

## Monitoring the rng service

- Each command below will show us what's happening on a different level

- In one window, monitor cluster events:
  ```bash
  kubectl get events -w
  ```

- In another window, monitor the response time of rng:
 ```bash
 httping `<ClusterIP>`
 ```

- In another window, monitor pods status:
  ```bash
  kubectl get pods -w
  ```

]

.debug[[k8s/healthchecks-more.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks-more.md)]
---

## Generating traffic

- Let's use `ab` to send concurrent requests to rng

- In yet another window, generate traffic:
 ```bash
 ab -c 10 -n 1000 http://`<ClusterIP>`/1
 ```

- Experiment with higher values of `-c` and see what happens

]

- The `-c` parameter indicates the number of concurrent requests

- The final `/1` is important to generate actual traffic

(otherwise we would use the ping endpoint, which doesn't sleep 0.1s per request)

.debug[[k8s/healthchecks-more.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks-more.md)]
---

## Discussion

- Above a given threshold, the liveness probe starts failing

(about 10 concurrent requests per backend should be plenty enough)

- When the liveness probe fails 3 times in a row, the container is restarted

- During the restart, there is *less* capacity available

- ... Meaning that the other backends are likely to timeout as well

- ... Eventually causing all backends to be restarted

- ... And each fresh backend gets restarted, too

- This goes on until the load goes down, or we add capacity

*This wouldn't be a good healthcheck in a real application!*

.debug[[k8s/healthchecks-more.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks-more.md)]
---

## Better healthchecks

- We need to make sure that the healthcheck doesn't trip when
  performance degrades due to external pressure

- Using a readiness check would have fewer effects

(but it would still be an imperfect solution)

- A possible combination:

- readiness check with a short timeout / low failure threshold

- liveness check with a longer timeout / higher failure threshold

.debug[[k8s/healthchecks-more.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks-more.md)]
---

## Healthchecks for redis

- A liveness probe is enough

(it's not useful to remove a backend from rotation when it's the only one)

- We could use an exec probe running `redis-cli ping`

.debug[[k8s/healthchecks-more.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks-more.md)]
---

## Exec probes and zombies

- When using exec probes, we should make sure that we have a *zombie reaper*

🤔🧐🧟 Wait, what?

- When a process terminates, its parent must call `wait()`/`waitpid()`

(this is how the parent process retrieves the child's exit status)

- In the meantime, the process is in *zombie* state

(the process state will show as `Z` in `ps`, `top` ...)

- When a process is killed, its children are *orphaned* and attached to PID 1

- PID 1 has the responsibility of *reaping* these processes when they terminate

- OK, but how does that affect us?

.debug[[k8s/healthchecks-more.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks-more.md)]
---

## PID 1 in containers

- On ordinary systems, PID 1 (`/sbin/init`) has logic to reap processes

- In containers, PID 1 is typically our application process

(e.g. Apache, the JVM, NGINX, Redis ...)

- These *do not* take care of reaping orphans

- If we use exec probes, we need to add a process reaper

- We can add [tini](https://github.com/krallin/tini) to our images

- Or [share the PID namespace between containers of a pod](https://kubernetes.io/docs/tasks/configure-pod-container/share-process-namespace/)

(and have gcr.io/pause take care of the reaping)

- Discussion of this in [Video - 10 Ways to Shoot Yourself in the Foot with Kubernetes, #9 Will Surprise You](https://www.youtube.com/watch?v=QKI-JRs2RIE)

.debug[[k8s/healthchecks-more.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/healthchecks-more.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Recording deployment actions

.nav[
[Previous section](#toc-healthchecks)
|
[Back to table of contents](#toc-chapter-4)
|
[Next section](#toc-namespaces)
]

---
# Recording deployment actions

- Some commands that modify a Deployment accept an optional `--record` flag

(Example: `kubectl set image deployment worker worker=alpine --record`)

- That flag will store the command line in the Deployment

(Technically, using the annotation `kubernetes.io/change-cause`)

- It gets copied to the corresponding ReplicaSet

(Allowing to keep track of which command created or promoted this ReplicaSet)

- We can view this information with `kubectl rollout history`

.debug[[k8s/record.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/record.md)]
---

## Using `--record`

- Let's make a couple of changes to a Deployment and record them

- Roll back `worker` to image version 0.1:
  ```bash
  kubectl set image deployment worker worker=dockercoins/worker:v0.1 --record
  ```

- Promote it to version 0.2 again:
  ```bash
  kubectl set image deployment worker worker=dockercoins/worker:v0.2 --record
  ```

- View the change history:
  ```bash
  kubectl rollout history deployment worker
  ```

]

.debug[[k8s/record.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/record.md)]
---

## Pitfall #1: forgetting `--record`

- What happens if we don't specify `--record`?

- Promote `worker` to image version 0.3:
  ```bash
  kubectl set image deployment worker worker=dockercoins/worker:v0.3
  ```

- View the change history:
  ```bash
  kubectl rollout history deployment worker
  ```

]

It recorded version 0.2 instead of 0.3! Why?

.debug[[k8s/record.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/record.md)]
---

## How `--record` really works

- `kubectl` adds the annotation `kubernetes.io/change-cause` to the Deployment

- The Deployment controller copies that annotation to the ReplicaSet

- `kubectl rollout history` shows the ReplicaSets' annotations

- If we don't specify `--record`, the annotation is not updated

- The previous value of that annotation is copied to the new ReplicaSet

- In that case, the ReplicaSet annotation does not reflect reality!

.debug[[k8s/record.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/record.md)]
---

## Pitfall #2: recording `scale` commands

- What happens if we use `kubectl scale --record`?

- Check the current history:
  ```bash
  kubectl rollout history deployment worker
  ```

- Scale the deployment:
  ```bash
  kubectl scale deployment worker --replicas=3 --record
  ```

- Check the change history again:
  ```bash
  kubectl rollout history deployment worker
  ```

]

The last entry in the history was overwritten by the `scale` command! Why?

.debug[[k8s/record.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/record.md)]
---

## Actions that don't create a new ReplicaSet

- The `scale` command updates the Deployment definition

- But it doesn't create a new ReplicaSet

- Using the `--record` flag sets the annotation like before

- The annotation gets copied to the existing ReplicaSet

- This overwrites the previous annotation that was there

- In that case, we lose the previous change cause!

.debug[[k8s/record.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/record.md)]
---

## Updating the annotation directly

- Let's see what happens if we set the annotation manually

- Annotate the Deployment:
  ```bash
  kubectl annotate deployment worker kubernetes.io/change-cause="Just for fun"
  ```

- Check that our annotation shows up in the change history:
  ```bash
  kubectl rollout history deployment worker
  ```

]

Our annotation shows up (and overwrote whatever was there before).

.debug[[k8s/record.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/record.md)]
---

## Using change cause

- It sounds like a good idea to use `--record`, but:

*"Incorrect documentation is often worse than no documentation."*
 
 (Bertrand Meyer)

- If we use `--record` once, we need to either:

- use it every single time after that

- or clear the Deployment annotation after using `--record`
 
 (subsequent changes will show up with a `<none>` change cause)

- A safer way is to set it through our tooling

.debug[[k8s/record.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/record.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Namespaces

.nav[
[Previous section](#toc-recording-deployment-actions)
|
[Back to table of contents](#toc-chapter-5)
|
[Next section](#toc-exposing-http-services-with-ingress-resources)
]

---
# Namespaces

- We would like to deploy another copy of DockerCoins on our cluster

- We could rename all our deployments and services:

hasher → hasher2, redis → redis2, rng → rng2, etc.

- That would require updating the code

- There has to be a better way!

- As hinted by the title of this section, we will use *namespaces*

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/namespaces.md)]
---

## Identifying a resource

- We cannot have two resources with the same name

(or can we...?)

- We cannot have two resources *of the same kind* with the same name

(but it's OK to have an `rng` service, an `rng` deployment, and an `rng` daemon set)

- We cannot have two resources of the same kind with the same name *in the same namespace*

(but it's OK to have e.g. two `rng` services in different namespaces)

- Except for resources that exist at the *cluster scope*

(these do not belong to a namespace)

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/namespaces.md)]
---

## Uniquely identifying a resource

- For *namespaced* resources:

the tuple *(kind, name, namespace)* needs to be unique

- For resources at the *cluster scope*:

the tuple *(kind, name)* needs to be unique

- List resource types again, and check the NAMESPACED column:
  ```bash
  kubectl api-resources
  ```

]

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/slides/k8s/namespaces.md)]
---

## Creating namespaces

- Let's see two identical methods to create a namespace

- We can use `kubectl create namespace`:
  ```bash
  kubectl create namespace blue
  ```

- Or we can construct a very minimal YAML snippet:
 ```bash
	kubectl apply -f- <<EOF
	apiVersion: v1
	kind: Namespace
	metadata:
	 name: blue
	EOF
 ```

]

- Some tools like Helm will create namespaces automatically when needed

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/slides/k8s/namespaces.md)]
---

## Viewing existing contexts

- On our training environments, at this point, there should be only one context

- 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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/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`

- 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/2020-01-zr/slides/k8s/namespaces.md)]
---

## Using our new namespace

- Let's check that we are in our new namespace, then deploy a new copy of Dockercoins

- Verify that the new context is empty:
  ```bash
  kubectl get all
  ```

]

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/namespaces.md)]
---

## Deploying DockerCoins with YAML files

- The GitHub repository `jpetazzo/kubercoins` contains everything we need!

- Clone the kubercoins repository:
  ```bash
  cd ~
  git clone https://github.com/jpetazzo/kubercoins
  ```

- Create all the DockerCoins resources:
  ```bash
  kubectl create -f kubercoins
  ```

]

If the argument behind `-f` is a directory, all the files in that directory are processed.

The subdirectories are *not* processed, unless we also add the `-R` flag.

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/namespaces.md)]
---

## Viewing the deployed app

- Let's see if this worked correctly!

- Retrieve the port number allocated to the `webui` service:
  ```bash
  kubectl get svc webui
  ```

- Point our browser to http://X.X.X.X:3xxxx

]

If the graph shows up but stays at zero, give it a minute or two!

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/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/2020-01-zr/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

- 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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/slides/k8s/namespaces.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Exposing HTTP services with Ingress resources

.nav[
[Previous section](#toc-namespaces)
|
[Back to table of contents](#toc-chapter-5)
|
[Next section](#toc-controlling-a-kubernetes-cluster-remotely)
]

---
# Exposing HTTP services with Ingress resources

- *Services* give us a way to access a pod or a set of pods

- Services can be exposed to the outside world:

- with type `NodePort` (on a port >30000)

- with type `LoadBalancer` (allocating an external load balancer)

- What about HTTP services?

- how can we expose `webui`, `rng`, `hasher`?

- the Kubernetes dashboard?

- a new version of `webui`?

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Exposing HTTP services

- If we use `NodePort` services, clients have to specify port numbers

(i.e. http://xxxxx:31234 instead of just http://xxxxx)

- `LoadBalancer` services are nice, but:

- they are not available in all environments

- they often carry an additional cost (e.g. they provision an ELB)

- they require one extra step for DNS integration
 
 (waiting for the `LoadBalancer` to be provisioned; then adding it to DNS)

- We could build our own reverse proxy

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Building a custom reverse proxy

- There are many options available:

Apache, HAProxy, Hipache, NGINX, Traefik, ...

(look at [jpetazzo/aiguillage](https://github.com/jpetazzo/aiguillage) for a minimal reverse proxy configuration using NGINX)

- Most of these options require us to update/edit configuration files after each change

- Some of them can pick up virtual hosts and backends from a configuration store

- Wouldn't it be nice if this configuration could be managed with the Kubernetes API?

- Enter.red[¹] *Ingress* resources!

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Ingress resources

- Kubernetes API resource (`kubectl get ingress`/`ingresses`/`ing`)

- Designed to expose HTTP services

- Basic features:

- load balancing
  - SSL termination
  - name-based virtual hosting

- Can also route to different services depending on:

- URI path (e.g. `/api`→`api-service`, `/static`→`assets-service`)
  - Client headers, including cookies (for A/B testing, canary deployment...)
  - and more!

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Principle of operation

- Step 1: deploy an *ingress controller*

- ingress controller = load balancer + control loop

- the control loop watches over ingress resources, and configures the LB accordingly

- Step 2: set up DNS

- associate DNS entries with the load balancer address

- Step 3: create *ingress resources*

- the ingress controller picks up these resources and configures the LB

- Step 4: profit!

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Ingress in action

- We will deploy the Traefik ingress controller

- this is an arbitrary choice

- maybe motivated by the fact that Traefik releases are named after cheeses

- For DNS, we will use [nip.io](http://nip.io/)

- `*.1.2.3.4.nip.io` resolves to `1.2.3.4`

- We will create ingress resources for various HTTP services

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Deploying pods listening on port 80

- We want our ingress load balancer to be available on port 80

- We could do that with a `LoadBalancer` service

... but it requires support from the underlying infrastructure

- We could use pods specifying `hostPort: 80`

... but with most CNI plugins, this [doesn't work or requires additional setup](https://github.com/kubernetes/kubernetes/issues/23920)

- We could use a `NodePort` service

... but that requires [changing the `--service-node-port-range` flag in the API server](https://kubernetes.io/docs/reference/command-line-tools-reference/kube-apiserver/)

- Last resort: the `hostNetwork` mode

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Without `hostNetwork`

- Normally, each pod gets its own *network namespace*

(sometimes called sandbox or network sandbox)

- An IP address is assigned to the pod

- This IP address is routed/connected to the cluster network

- All containers of that pod are sharing that network namespace

(and therefore using the same IP address)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## With `hostNetwork: true`

- No network namespace gets created

- The pod is using the network namespace of the host

- It "sees" (and can use) the interfaces (and IP addresses) of the host

- The pod can receive outside traffic directly, on any port

- Downside: with most network plugins, network policies won't work for that pod

- most network policies work at the IP address level

- filtering that pod = filtering traffic from the node

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Running Traefik

- The [Traefik documentation](https://docs.traefik.io/user-guide/kubernetes/#deploy-trfik-using-a-deployment-or-daemonset) tells us to pick between Deployment and Daemon Set

- We are going to use a Daemon Set so that each node can accept connections

- We will do two minor changes to the [YAML provided by Traefik](https://github.com/containous/traefik/blob/v1.7/examples/k8s/traefik-ds.yaml):

- enable `hostNetwork`

- add a *toleration* so that Traefik also runs on `node1`

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Taints and tolerations

- A *taint* is an attribute added to a node

- It prevents pods from running on the node

- ... Unless they have a matching *toleration*

- When deploying with `kubeadm`:

- a taint is placed on the node dedicated to the control plane

- the pods running the control plane have a matching toleration

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Checking taints on our nodes

- Check our nodes specs:
  ```bash
  kubectl get node node1 -o json | jq .spec
  kubectl get node node2 -o json | jq .spec
  ```

]

We should see a result only for `node1` (the one with the control plane):

```json
  "taints": [
    {
      "effect": "NoSchedule",
      "key": "node-role.kubernetes.io/master"
    }
  ]
```

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Understanding a taint

- The `key` can be interpreted as:

- a reservation for a special set of pods
 
 (here, this means "this node is reserved for the control plane")

- an error condition on the node
 
 (for instance: "disk full," do not start new pods here!)

- The `effect` can be:

- `NoSchedule` (don't run new pods here)

- `PreferNoSchedule` (try not to run new pods here)

- `NoExecute` (don't run new pods and evict running pods)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Checking tolerations on the control plane

- Check tolerations for CoreDNS:
  ```bash
  kubectl -n kube-system get deployments coredns -o json |
          jq .spec.template.spec.tolerations
  ```

]

The result should include:
```json
  {
    "effect": "NoSchedule",
    "key": "node-role.kubernetes.io/master"
  }
```

It means: "bypass the exact taint that we saw earlier on `node1`."

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Special tolerations

- Check tolerations on `kube-proxy`:
  ```bash
  kubectl -n kube-system get ds kube-proxy -o json | 
          jq .spec.template.spec.tolerations
  ```

]

The result should include:
```json
  {
    "operator": "Exists"
  }
```

This one is a special case that means "ignore all taints and run anyway."

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Running Traefik on our cluster

- We provide a YAML file (`k8s/traefik.yaml`) which is essentially the sum of:

- [Traefik's Daemon Set resources](https://github.com/containous/traefik/blob/v1.7/examples/k8s/traefik-ds.yaml) (patched with `hostNetwork` and tolerations)

- [Traefik's RBAC rules](https://github.com/containous/traefik/blob/v1.7/examples/k8s/traefik-rbac.yaml) allowing it to watch necessary API objects

- Apply the YAML:
  ```bash
  kubectl apply -f ~/container.training/k8s/traefik.yaml
  ```

]

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Checking that Traefik runs correctly

- If Traefik started correctly, we now have a web server listening on each node

- Check that Traefik is serving 80/tcp:
  ```bash
  curl localhost
  ```

]

We should get a `404 page not found` error.

This is normal: we haven't provided any ingress rule yet.

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Setting up DNS

- To make our lives easier, we will use [nip.io](http://nip.io)

- Check out `http://cheddar.A.B.C.D.nip.io`

(replacing A.B.C.D with the IP address of `node1`)

- We should get the same `404 page not found` error

(meaning that our DNS is "set up properly", so to speak!)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Traefik web UI

- Traefik provides a web dashboard

- With the current install method, it's listening on port 8080

- Go to `http://node1:8080` (replacing `node1` with its IP address)

]

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Setting up host-based routing ingress rules

- We are going to use `errm/cheese` images

(there are [3 tags available](https://hub.docker.com/r/errm/cheese/tags/): wensleydale, cheddar, stilton)

- These images contain a simple static HTTP server sending a picture of cheese

- We will run 3 deployments (one for each cheese)

- We will create 3 services (one for each deployment)

- Then we will create 3 ingress rules (one for each service)

- We will route `<name-of-cheese>.A.B.C.D.nip.io` to the corresponding deployment

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Running cheesy web servers

- Run all three deployments:
  ```bash
  kubectl create deployment cheddar --image=errm/cheese:cheddar
  kubectl create deployment stilton --image=errm/cheese:stilton
  kubectl create deployment wensleydale --image=errm/cheese:wensleydale
  ```

- Create a service for each of them:
  ```bash
  kubectl expose deployment cheddar --port=80
  kubectl expose deployment stilton --port=80
  kubectl expose deployment wensleydale --port=80
  ```

]

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## What does an ingress resource look like?

Here is a minimal host-based ingress resource:

```yaml
apiVersion: networking.k8s.io/v1beta1
kind: Ingress
metadata:
  name: cheddar
spec:
  rules:
  - host: cheddar.`A.B.C.D`.nip.io
    http:
      paths:
      - path: /
        backend:
          serviceName: cheddar
          servicePort: 80

```

(It is in `k8s/ingress.yaml`.)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Creating our first ingress resources

- Edit the file `~/container.training/k8s/ingress.yaml`

- Replace A.B.C.D with the IP address of `node1`

- Apply the file

- Open http://cheddar.A.B.C.D.nip.io

]

(An image of a piece of cheese should show up.)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Creating the other ingress resources

- Edit the file `~/container.training/k8s/ingress.yaml`

- Replace `cheddar` with `stilton` (in `name`, `host`, `serviceName`)

- Apply the file

- Check that `stilton.A.B.C.D.nip.io` works correctly

- Repeat for `wensleydale`

]

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Using multiple ingress controllers

- You can have multiple ingress controllers active simultaneously

(e.g. Traefik and NGINX)

- You can even have multiple instances of the same controller

(e.g. one for internal, another for external traffic)

- The `kubernetes.io/ingress.class` annotation can be used to tell which one to use

- It's OK if multiple ingress controllers configure the same resource

(it just means that the service will be accessible through multiple paths)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Ingress: the good

- The traffic flows directly from the ingress load balancer to the backends

- it doesn't need to go through the `ClusterIP`

- in fact, we don't even need a `ClusterIP` (we can use a headless service)

- The load balancer can be outside of Kubernetes

(as long as it has access to the cluster subnet)

- This allows the use of external (hardware, physical machines...) load balancers

- Annotations can encode special features

(rate-limiting, A/B testing, session stickiness, etc.)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Ingress: the bad

- Aforementioned "special features" are not standardized yet

- Some controllers will support them; some won't

- Even relatively common features (stripping a path prefix) can differ:

- [traefik.ingress.kubernetes.io/rule-type: PathPrefixStrip](https://docs.traefik.io/user-guide/kubernetes/#path-based-routing)

- [ingress.kubernetes.io/rewrite-target: /](https://github.com/kubernetes/contrib/tree/master/ingress/controllers/nginx/examples/rewrite)

- This should eventually stabilize

(remember that ingresses are currently `apiVersion: networking.k8s.io/v1beta1`)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## A special feature in action

- We're going to see how to implement *canary releases* with Traefik

- This feature is available on multiple ingress controllers

- ... But it is configured very differently on each of them

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Canary releases

- A *canary release* (or canary launch or canary deployment) is a release that will process only a small fraction of the workload

- After deploying the canary, we compare its metrics to the normal release

- If the metrics look good, the canary will progressively receive more traffic

(until it gets 100% and becomes the new normal release)

- If the metrics aren't good, the canary is automatically removed

- When we deploy a bad release, only a tiny fraction of traffic is affected

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Various ways to implement canary

- Example 1: canary for a microservice

- 1% of all requests (sampled randomly) are sent to the canary
  - the remaining 99% are sent to the normal release

- Example 2: canary for a web app

- 1% of users are sent to the canary web site
  - the remaining 99% are sent to the normal release

- Example 3: canary for shipping physical goods

- 1% of orders are shipped with the canary process
  - the reamining 99% are shipped with the normal process

- We're going to implement example 1 (per-request routing)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Canary releases with Traefik

- We need to deploy the canary and expose it with a separate service

- Then, in the Ingress resource, we need:

- multiple `paths` entries (one for each service, canary and normal)

- an extra annotation indicating the weight of each service

- If we want, we can send requests to more than 2 services

- Let's send requests to our 3 cheesy services!

- Create the resource shown on the next slide

]

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## The Ingress resource

.small[
```yaml
apiVersion: networking.k8s.io/v1beta1
kind: Ingress
metadata:
  name: cheeseplate
  annotations:
    traefik.ingress.kubernetes.io/service-weights: |
      cheddar: 50%
      wensleydale: 25%
      stilton: 25%
spec:
  rules:
  - host: cheeseplate.`A.B.C.D`.nip.io
    http:
      paths:
      - path: /
        backend:
          serviceName: cheddar
          servicePort: 80
      - path: /
        backend:
          serviceName: wensledale
          servicePort: 80
      - path: /
        backend:
          serviceName: stilton
          servicePort: 80
```
]

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Testing the canary

- Let's check the percentage of requests going to each service

- Continuously send HTTP requests to the new ingress:
  ```bash
    while sleep 0.1; do
      curl -s http://cheeseplate.A.B.C.D.nip.io/
    done
  ```

]

We should see a 50/25/25 request mix.

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Load balancing fairness

Note: if we use odd request ratios, the load balancing algorithm might appear to be broken on a small scale (when sending a small number of requests), but on a large scale (with many requests) it will be fair.

For instance, with a 11%/89% ratio, we can see 79 requests going to the 89%-weighted service, and then requests alternating between the two services; then 79 requests again, etc.

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## Other ingress controllers

*Just to illustrate how different things are ...*

- With the NGINX ingress controller:

- define two ingress ressources
 
 (specifying rules with the same host+path)

- add `nginx.ingress.kubernetes.io/canary` annotations on each

- With Linkerd2:

- define two services

- define an extra service for the weighted aggregate of the two

- define a TrafficSplit (this is a CRD introduced by the SMI spec)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

## We need more than that

What we saw is just one of the multiple building blocks that we need to achieve a canary release.

We also need:

- metrics (latency, performance ...) for our releases

- automation to alter canary weights

(increase canary weight if metrics look good; decrease otherwise)

- a mechanism to manage the lifecycle of the canary releases

(create them, promote them, delete them ...)

For inspiration, check [flagger by Weave](https://github.com/weaveworks/flagger).

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/ingress.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Controlling a Kubernetes cluster remotely

.nav[
[Previous section](#toc-exposing-http-services-with-ingress-resources)
|
[Back to table of contents](#toc-chapter-5)
|
[Next section](#toc-accessing-internal-services)
]

---
# Controlling a Kubernetes cluster remotely

- `kubectl` can be used either on cluster instances or outside the cluster

- Here, we are going to use `kubectl` from our local machine

.debug[[k8s/localkubeconfig.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/localkubeconfig.md)]
---

## Requirements

- `kubectl` is officially available on Linux, macOS, Windows

(and unofficially anywhere we can build and run Go binaries)

- You may skip these exercises if you are following along from:

- a tablet or phone

- a web-based terminal

- an environment where you can't install and run new binaries

.debug[[k8s/localkubeconfig.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/localkubeconfig.md)]
---

## Installing `kubectl`

- If you already have `kubectl` on your local machine, you can skip this

- Download the `kubectl` binary from one of these links:

[Linux](https://storage.googleapis.com/kubernetes-release/release/v1.15.3/bin/linux/amd64/kubectl)
  |
  [macOS](https://storage.googleapis.com/kubernetes-release/release/v1.15.3/bin/darwin/amd64/kubectl)
  |
  [Windows](https://storage.googleapis.com/kubernetes-release/release/v1.15.3/bin/windows/amd64/kubectl.exe)

- On Linux and macOS, make the binary executable with `chmod +x kubectl`

(And remember to run it with `./kubectl` or move it to your `$PATH`)

]

Note: if you are following along with a different platform (e.g. Linux on an architecture different from amd64, or with a phone or tablet), installing `kubectl` might be more complicated (or even impossible) so feel free to skip this section.

.debug[[k8s/localkubeconfig.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/localkubeconfig.md)]
---

## Testing `kubectl`

- Check that `kubectl` works correctly

(before even trying to connect to a remote cluster!)

- Ask `kubectl` to show its version number:
  ```bash
  kubectl version --client
  ```

]

The output should look like this:
```
Client Version: version.Info{Major:"1", Minor:"15", GitVersion:"v1.15.0",
GitCommit:"e8462b5b5dc2584fdcd18e6bcfe9f1e4d970a529", GitTreeState:"clean",
BuildDate:"2019-06-19T16:40:16Z", GoVersion:"go1.12.5", Compiler:"gc",
Platform:"darwin/amd64"}
```

.debug[[k8s/localkubeconfig.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/localkubeconfig.md)]
---

## Preserving the existing `~/.kube/config`

- If you already have a `~/.kube/config` file, rename it

(we are going to overwrite it in the following slides!)

- If you never used `kubectl` on your machine before: nothing to do!

- Make a copy of `~/.kube/config`; if you are using macOS or Linux, you can do:
  ```bash
  cp ~/.kube/config ~/.kube/config.before.training
  ```

- If you are using Windows, you will need to adapt this command

]

.debug[[k8s/localkubeconfig.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/localkubeconfig.md)]
---

## Copying the configuration file from `node1`

- The `~/.kube/config` file that is on `node1` contains all the credentials we need

- Let's copy it over!

- Copy the file from `node1`; if you are using macOS or Linux, you can do:
  ```
  scp `USER`@`X.X.X.X`:.kube/config ~/.kube/config
  # Make sure to replace X.X.X.X with the IP address of node1,
  # and USER with the user name used to log into node1!
  ```

- If you are using Windows, adapt these instructions to your SSH client

]

.debug[[k8s/localkubeconfig.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/localkubeconfig.md)]
---

## Updating the server address

- There is a good chance that we need to update the server address

- To know if it is necessary, run `kubectl config view`

- Look for the `server:` address:

- if it matches the public IP address of `node1`, you're good!

- if it is anything else (especially a private IP address), update it!

- To update the server address, run:
  ```bash
  kubectl config set-cluster kubernetes --server=https://`X.X.X.X`:6443
  # Make sure to replace X.X.X.X with the IP address of node1!
  ```

.debug[[k8s/localkubeconfig.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/localkubeconfig.md)]
---

## What if we get a certificate error?

- Generally, the Kubernetes API uses a certificate that is valid for:

- `kubernetes`
  - `kubernetes.default`
  - `kubernetes.default.svc`
  - `kubernetes.default.svc.cluster.local`
  - the ClusterIP address of the `kubernetes` service
  - the hostname of the node hosting the control plane (e.g. `node1`)
  - the IP address of the node hosting the control plane

- On most clouds, the IP address of the node is an internal IP address

- ... And we are going to connect over the external IP address

- ... And that external IP address was not used when creating the certificate!

.debug[[k8s/localkubeconfig.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/localkubeconfig.md)]
---

## Working around the certificate error

- We need to tell `kubectl` to skip TLS verification

(only do this with testing clusters, never in production!)

- The following command will do the trick:
  ```bash
  kubectl config set-cluster kubernetes --insecure-skip-tls-verify
  ```

.debug[[k8s/localkubeconfig.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/localkubeconfig.md)]
---

## Checking that we can connect to the cluster

- We can now run a couple of trivial commands to check that all is well

- Check the versions of the local client and remote server:
  ```bash
  kubectl version
  ```

- View the nodes of the cluster:
  ```bash
  kubectl get nodes
  ```

]

We can now utilize the cluster exactly as if we're logged into a node, except that it's remote.

.debug[[k8s/localkubeconfig.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/localkubeconfig.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Accessing internal services

.nav[
[Previous section](#toc-controlling-a-kubernetes-cluster-remotely)
|
[Back to table of contents](#toc-chapter-5)
|
[Next section](#toc-centralized-logging)
]

---
# Accessing internal services

- When we are logged in on a cluster node, we can access internal services

(by virtue of the Kubernetes network model: all nodes can reach all pods and services)

- When we are accessing a remote cluster, things are different

(generally, our local machine won't have access to the cluster's internal subnet)

- How can we temporarily access a service without exposing it to everyone?

- `kubectl proxy`: gives us access to the API, which includes a proxy for HTTP resources

- `kubectl port-forward`: allows forwarding of TCP ports to arbitrary pods, services, ...

.debug[[k8s/accessinternal.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/accessinternal.md)]
---

## Suspension of disbelief

The exercises in this section assume that we have set up `kubectl` on our
local machine in order to access a remote cluster.

We will therefore show how to access services and pods of the remote cluster,
from our local machine.

You can also run these exercises directly on the cluster (if you haven't
installed and set up `kubectl` locally).

Running commands locally will be less useful
(since you could access services and pods directly),
but keep in mind that these commands will work anywhere as long as you have
installed and set up `kubectl` to communicate with your cluster.

.debug[[k8s/accessinternal.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/accessinternal.md)]
---

## `kubectl proxy` in theory

- Running `kubectl proxy` gives us access to the entire Kubernetes API

- The API includes routes to proxy HTTP traffic

- These routes look like the following:

`/api/v1/namespaces/<namespace>/services/<service>/proxy`

- We just add the URI to the end of the request, for instance:

`/api/v1/namespaces/<namespace>/services/<service>/proxy/index.html`

- We can access `services` and `pods` this way

.debug[[k8s/accessinternal.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/accessinternal.md)]
---

## `kubectl proxy` in practice

- Let's access the `webui` service through `kubectl proxy`

- Run an API proxy in the background:
  ```bash
  kubectl proxy &
  ```

- Access the `webui` service:
  ```bash
  curl localhost:8001/api/v1/namespaces/default/services/webui/proxy/index.html
  ```

- Terminate the proxy:
  ```bash
  kill %1
  ```

]

.debug[[k8s/accessinternal.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/accessinternal.md)]
---

## `kubectl port-forward` in theory

- What if we want to access a TCP service?

- We can use `kubectl port-forward` instead

- It will create a TCP relay to forward connections to a specific port

(of a pod, service, deployment...)

- The syntax is:

`kubectl port-forward service/name_of_service local_port:remote_port`

- If only one port number is specified, it is used for both local and remote ports

.debug[[k8s/accessinternal.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/accessinternal.md)]
---

## `kubectl port-forward` in practice

- Let's access our remote Redis server

- Forward connections from local port 10000 to remote port 6379:
  ```bash
  kubectl port-forward svc/redis 10000:6379 &
  ```

- Connect to the Redis server:
  ```bash
  telnet localhost 10000
  ```

- Issue a few commands, e.g. `INFO server` then `QUIT`

- Terminate the port forwarder:
  ```bash
  kill %1
  ```

]

.debug[[k8s/accessinternal.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/accessinternal.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Centralized logging

.nav[
[Previous section](#toc-accessing-internal-services)
|
[Back to table of contents](#toc-chapter-6)
|
[Next section](#toc-collecting-metrics-with-prometheus)
]

---
# Centralized logging

- Using `kubectl` or `stern` is simple; but it has drawbacks:

- when a node goes down, its logs are not available anymore

- we can only dump or stream logs; we want to search/index/count...

- We want to send all our logs to a single place

- We want to parse them (e.g. for HTTP logs) and index them

- We want a nice web dashboard

- We are going to deploy an EFK stack

.debug[[k8s/logs-centralized.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/logs-centralized.md)]
---

## What is EFK?

- EFK is three components:

- ElasticSearch (to store and index log entries)

- Fluentd (to get container logs, process them, and put them in ElasticSearch)

- Kibana (to view/search log entries with a nice UI)

- The only component that we need to access from outside the cluster will be Kibana

.debug[[k8s/logs-centralized.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/logs-centralized.md)]
---

## Deploying EFK on our cluster

- We are going to use a YAML file describing all the required resources

- Load the YAML file into our cluster:
  ```bash
  kubectl apply -f ~/container.training/k8s/efk.yaml
  ```

]

If we [look at the YAML file](https://github.com/jpetazzo/container.training/blob/master/k8s/efk.yaml), we see that
it creates a daemon set, two deployments, two services,
and a few roles and role bindings (to give fluentd the required permissions).

.debug[[k8s/logs-centralized.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/logs-centralized.md)]
---

## The itinerary of a log line (before Fluentd)

- A container writes a line on stdout or stderr

- Both are typically piped to the container engine (Docker or otherwise)

- The container engine reads the line, and sends it to a logging driver

- The timestamp and stream (stdout or stderr) is added to the log line

- With the default configuration for Kubernetes, the line is written to a JSON file

(`/var/log/containers/pod-name_namespace_container-id.log`)

- That file is read when we invoke `kubectl logs`; we can access it directly too

.debug[[k8s/logs-centralized.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/logs-centralized.md)]
---

## The itinerary of a log line (with Fluentd)

- Fluentd runs on each node (thanks to a daemon set)

- It bind-mounts `/var/log/containers` from the host (to access these files)

- It continuously scans this directory for new files; reads them; parses them

- Each log line becomes a JSON object, fully annotated with extra information:
 container id, pod name, Kubernetes labels...

- These JSON objects are stored in ElasticSearch

- ElasticSearch indexes the JSON objects

- We can access the logs through Kibana (and perform searches, counts, etc.)

.debug[[k8s/logs-centralized.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/logs-centralized.md)]
---

## Accessing Kibana

- Kibana offers a web interface that is relatively straightforward

- Let's check it out!

- Check which `NodePort` was allocated to Kibana:
  ```bash
  kubectl get svc kibana
  ```

- With our web browser, connect to Kibana

]

.debug[[k8s/logs-centralized.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/logs-centralized.md)]
---

## Using Kibana

*Note: this is not a Kibana workshop! So this section is deliberately very terse.*

- The first time you connect to Kibana, you must "configure an index pattern"

- Just use the one that is suggested, `@timestamp`.red[*]

- Then click "Discover" (in the top-left corner)

- You should see container logs

- Advice: in the left column, select a few fields to display, e.g.:

`kubernetes.host`, `kubernetes.pod_name`, `stream`, `log`

.red[*]If you don't see `@timestamp`, it's probably because no logs exist yet.
 Wait a bit, and double-check the logging pipeline!

.debug[[k8s/logs-centralized.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/logs-centralized.md)]
---

## Caveat emptor

We are using EFK because it is relatively straightforward
to deploy on Kubernetes, without having to redeploy or reconfigure
our cluster. But it doesn't mean that it will always be the best
option for your use-case. If you are running Kubernetes in the
cloud, you might consider using the cloud provider's logging
infrastructure (if it can be integrated with Kubernetes).

The deployment method that we will use here has been simplified:
there is only one ElasticSearch node. In a real deployment, you
might use a cluster, both for performance and reliability reasons.
But this is outside of the scope of this chapter.

The YAML file that we used creates all the resources in the
`default` namespace, for simplicity. In a real scenario, you will
create the resources in the `kube-system` namespace or in a dedicated namespace.

.debug[[k8s/logs-centralized.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/logs-centralized.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Collecting metrics with Prometheus

.nav[
[Previous section](#toc-centralized-logging)
|
[Back to table of contents](#toc-chapter-6)
|
[Next section](#toc-volumes)
]

---
# Collecting metrics with Prometheus

- Prometheus is an open-source monitoring system including:

- multiple *service discovery* backends to figure out which metrics to collect

- a *scraper* to collect these metrics

- an efficient *time series database* to store these metrics

- a specific query language (PromQL) to query these time series

- an *alert manager* to notify us according to metrics values or trends

- We are going to use it to collect and query some metrics on our Kubernetes cluster

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Why Prometheus?

- We don't endorse Prometheus more or less than any other system

- It's relatively well integrated within the cloud-native ecosystem

- It can be self-hosted (this is useful for tutorials like this)

- It can be used for deployments of varying complexity:

- one binary and 10 lines of configuration to get started

- all the way to thousands of nodes and millions of metrics

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Exposing metrics to Prometheus

- Prometheus obtains metrics and their values by querying *exporters*

- An exporter serves metrics over HTTP, in plain text

- This is what the *node exporter* looks like:

http://demo.robustperception.io:9100/metrics

- Prometheus itself exposes its own internal metrics, too:

http://demo.robustperception.io:9090/metrics

- If you want to expose custom metrics to Prometheus:

- serve a text page like these, and you're good to go

- libraries are available in various languages to help with quantiles etc.

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## How Prometheus gets these metrics

- The *Prometheus server* will *scrape* URLs like these at regular intervals

(by default: every minute; can be more/less frequent)

- The list of URLs to scrape (the *scrape targets*) is defined in configuration

.footnote[Worried about the overhead of parsing a text format?
 
Check this [comparison](https://github.com/RichiH/OpenMetrics/blob/master/markdown/protobuf_vs_text.md) of the text format with the (now deprecated) protobuf format!]

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Defining scrape targets

This is maybe the simplest configuration file for Prometheus:
```yaml
scrape_configs:
  - job_name: 'prometheus'
    static_configs:
      - targets: ['localhost:9090']
```

- In this configuration, Prometheus collects its own internal metrics

- A typical configuration file will have multiple `scrape_configs`

- In this configuration, the list of targets is fixed

- A typical configuration file will use dynamic service discovery

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Service discovery

This configuration file will leverage existing DNS `A` records:
```yaml
scrape_configs:
  - ...
  - job_name: 'node'
    dns_sd_configs:
      - names: ['api-backends.dc-paris-2.enix.io']
        type: 'A'
        port: 9100
```

- In this configuration, Prometheus resolves the provided name(s)

(here, `api-backends.dc-paris-2.enix.io`)

- Each resulting IP address is added as a target on port 9100

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Dynamic service discovery

- In the DNS example, the names are re-resolved at regular intervals

- As DNS records are created/updated/removed, scrape targets change as well

- Existing data (previously collected metrics) is not deleted

- Other service discovery backends work in a similar fashion

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Other service discovery mechanisms

- Prometheus can connect to e.g. a cloud API to list instances

- Or to the Kubernetes API to list nodes, pods, services ...

- Or a service like Consul, Zookeeper, etcd, to list applications

- The resulting configurations files are *way more complex*

(but don't worry, we won't need to write them ourselves)

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Time series database

- We could wonder, "why do we need a specialized database?"

- One metrics data point = metrics ID + timestamp + value

- With a classic SQL or noSQL data store, that's at least 160 bits of data + indexes

- Prometheus is way more efficient, without sacrificing performance

(it will even be gentler on the I/O subsystem since it needs to write less)

- Would you like to know more? Check this video:

[Storage in Prometheus 2.0](https://www.youtube.com/watch?v=C4YV-9CrawA) by [Goutham V](https://twitter.com/putadent) at DC17EU

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Checking if Prometheus is installed

- Before trying to install Prometheus, let's check if it's already there

- Look for services with a label `app=prometheus` across all namespaces:
  ```bash
  kubectl get services --selector=app=prometheus --all-namespaces
  ```

]

If we see a `NodePort` service called `prometheus-server`, we're good!

(We can then skip to "Connecting to the Prometheus web UI".)

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Running Prometheus on our cluster

We need to:

- Run the Prometheus server in a pod

(using e.g. a Deployment to ensure that it keeps running)

- Expose the Prometheus server web UI (e.g. with a NodePort)

- Run the *node exporter* on each node (with a Daemon Set)

- Set up a Service Account so that Prometheus can query the Kubernetes API

- Configure the Prometheus server

(storing the configuration in a Config Map for easy updates)

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Helm charts to the rescue

- To make our lives easier, we are going to use a Helm chart

- The Helm chart will take care of all the steps explained above

(including some extra features that we don't need, but won't hurt)

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Step 1: install Helm

- If we already installed Helm earlier, this command won't break anything

- Install the Helm CLI:
  ```bash
  curl https://raw.githubusercontent.com/kubernetes/helm/master/scripts/get-helm-3 \
  | bash
  ```

]

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Step 2: add the `stable` repo

- This will add the repository containing the chart for Prometheus

- This command is idempotent

(it won't break anything if the repository was already added)

- Add the repository:
  ```bash
  helm repo add stable https://kubernetes-charts.storage.googleapis.com/
  ```

]

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Step 3: install Prometheus

- The following command, just like the previous ones, is idempotent

(it won't error out if Prometheus is already installed)

- Install Prometheus on our cluster:
  ```bash
    helm upgrade prometheus stable/prometheus \
        --install \
        --namespace kube-system \
        --set server.service.type=NodePort \
        --set server.service.nodePort=30090 \
        --set server.persistentVolume.enabled=false \
        --set alertmanager.enabled=false
  ```

]

Curious about all these flags? They're explained in the next slide.

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Explaining all the Helm flags

- `helm upgrade prometheus` → upgrade release "prometheus" to the latest version...

(a "release" is a unique name given to an app deployed with Helm)

- `stable/prometheus` → ... of the chart `prometheus` in repo `stable`

- `--install` → if the app doesn't exist, create it

- `--namespace kube-system` → put it in that specific namespace

- And set the following *values* when rendering the chart's templates:

- `server.service.type=NodePort` → expose the Prometheus server with a NodePort
  - `server.service.nodePort=30090` → set the specific NodePort number to use
  - `server.persistentVolume.enabled=false` → do not use a PersistentVolumeClaim
  - `alertmanager.enabled=false` → disable the alert manager entirely

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Connecting to the Prometheus web UI

- Let's connect to the web UI and see what we can do

- Figure out the NodePort that was allocated to the Prometheus server:
  ```bash
  kubectl get svc --all-namespaces | grep prometheus-server
  ```

- With your browser, connect to that port

]

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Querying some metrics

- This is easy... if you are familiar with PromQL

- Click on "Graph", and in "expression", paste the following:
  ```
    sum by (instance) (
      irate(
        container_cpu_usage_seconds_total{
          pod_name=~"worker.*"
          }[5m]
      )
    )
  ```

]

- Click on the blue "Execute" button and on the "Graph" tab just below

- We see the cumulated CPU usage of worker pods for each node
 
 (if we just deployed Prometheus, there won't be much data to see, though)

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Getting started with PromQL

- We can't learn PromQL in just 5 minutes

- But we can cover the basics to get an idea of what is possible

(and have some keywords and pointers)

- We are going to break down the query above

(building it one step at a time)

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Graphing one metric across all tags

This query will show us CPU usage across all containers:
```
container_cpu_usage_seconds_total
```

- The suffix of the metrics name tells us:

- the unit (seconds of CPU)

- that it's the total used since the container creation

- Since it's a "total," it is an increasing quantity

(we need to compute the derivative if we want e.g. CPU % over time)

- We see that the metrics retrieved have *tags* attached to them

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Selecting metrics with tags

This query will show us only metrics for worker containers:
```
container_cpu_usage_seconds_total{pod_name=~"worker.*"}
```

- The `=~` operator allows regex matching

- We select all the pods with a name starting with `worker`

(it would be better to use labels to select pods; more on that later)

- The result is a smaller set of containers

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Transforming counters in rates

This query will show us CPU usage % instead of total seconds used:
```
100*irate(container_cpu_usage_seconds_total{pod_name=~"worker.*"}[5m])
```

- The [`irate`](https://prometheus.io/docs/prometheus/latest/querying/functions/#irate) operator computes the "per-second instant rate of increase"

- `rate` is similar but allows decreasing counters and negative values

- with `irate`, if a counter goes back to zero, we don't get a negative spike

- The `[5m]` tells how far to look back if there is a gap in the data

- And we multiply with `100*` to get CPU % usage

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Aggregation operators

This query sums the CPU usage per node:
```
sum by (instance) (
  irate(container_cpu_usage_seconds_total{pod_name=~"worker.*"}[5m])
)
```

- `instance` corresponds to the node on which the container is running

- `sum by (instance) (...)` computes the sum for each instance

- Note: all the other tags are collapsed

(in other words, the resulting graph only shows the `instance` tag)

- PromQL supports many more [aggregation operators](https://prometheus.io/docs/prometheus/latest/querying/operators/#aggregation-operators)

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## What kind of metrics can we collect?

- Node metrics (related to physical or virtual machines)

- Container metrics (resource usage per container)

- Databases, message queues, load balancers, ...

(check out this [list of exporters](https://prometheus.io/docs/instrumenting/exporters/)!)

- Instrumentation (=deluxe `printf` for our code)

- Business metrics (customers served, revenue, ...)

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Node metrics

- CPU, RAM, disk usage on the whole node

- Total number of processes running, and their states

- Number of open files, sockets, and their states

- I/O activity (disk, network), per operation or volume

- Physical/hardware (when applicable): temperature, fan speed...

- ...and much more!

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Container metrics

- Similar to node metrics, but not totally identical

- RAM breakdown will be different

- active vs inactive memory
  - some memory is *shared* between containers, and specially accounted for

- I/O activity is also harder to track

- async writes can cause deferred "charges"
  - some page-ins are also shared between containers

For details about container metrics, see:
 
http://jpetazzo.github.io/2013/10/08/docker-containers-metrics/

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Application metrics

- Arbitrary metrics related to your application and business

- System performance: request latency, error rate...

- Volume information: number of rows in database, message queue size...

- Business data: inventory, items sold, revenue...

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Detecting scrape targets

- Prometheus can leverage Kubernetes service discovery

(with proper configuration)

- Services or pods can be annotated with:

- `prometheus.io/scrape: true` to enable scraping
  - `prometheus.io/port: 9090` to indicate the port number
  - `prometheus.io/path: /metrics` to indicate the URI (`/metrics` by default)

- Prometheus will detect and scrape these (without needing a restart or reload)

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Querying labels

- What if we want to get metrics for containers belonging to a pod tagged `worker`?

- The cAdvisor exporter does not give us Kubernetes labels

- Kubernetes labels are exposed through another exporter

- We can see Kubernetes labels through metrics `kube_pod_labels`

(each container appears as a time series with constant value of `1`)

- Prometheus *kind of* supports "joins" between time series

- But only if the names of the tags match exactly

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## Unfortunately ...

- The cAdvisor exporter uses tag `pod_name` for the name of a pod

- The Kubernetes service endpoints exporter uses tag `pod` instead

- See [this blog post](https://www.robustperception.io/exposing-the-software-version-to-prometheus) or [this other one](https://www.weave.works/blog/aggregating-pod-resource-cpu-memory-usage-arbitrary-labels-prometheus/) to see how to perform "joins"

- Alas, Prometheus cannot "join" time series with different labels

(see [Prometheus issue #2204](https://github.com/prometheus/prometheus/issues/2204) for the rationale)

- There is a workaround involving relabeling, but it's "not cheap"

- see [this comment](https://github.com/prometheus/prometheus/issues/2204#issuecomment-261515520) for an overview

- or [this blog post](https://5pi.de/2017/11/09/use-prometheus-vector-matching-to-get-kubernetes-utilization-across-any-pod-label/) for a complete description of the process

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

## In practice

- Grafana is a beautiful (and useful) frontend to display all kinds of graphs

- Not everyone needs to know Prometheus, PromQL, Grafana, etc.

- But in a team, it is valuable to have at least one person who know them

- That person can set up queries and dashboards for the rest of the team

- It's a little bit like knowing how to optimize SQL queries, Dockerfiles...

Don't panic if you don't know these tools!

...But make sure at least one person in your team is on it 💯

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/prometheus.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Volumes

.nav[
[Previous section](#toc-collecting-metrics-with-prometheus)
|
[Back to table of contents](#toc-chapter-7)
|
[Next section](#toc-managing-configuration)
]

---
# Volumes

- Volumes are special directories that are mounted in containers

- Volumes can have many different purposes:

- share files and directories between containers running on the same machine

- share files and directories between containers and their host

- centralize configuration information in Kubernetes and expose it to containers

- manage credentials and secrets and expose them securely to containers

- store persistent data for stateful services

- access storage systems (like Ceph, EBS, NFS, Portworx, and many others)

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## Kubernetes volumes vs. Docker volumes

- Kubernetes and Docker volumes are very similar

(the [Kubernetes documentation](https://kubernetes.io/docs/concepts/storage/volumes/) says otherwise ...
 
 but it refers to Docker 1.7, which was released in 2015!)

- Docker volumes allow us to share data between containers running on the same host

- Kubernetes volumes allow us to share data between containers in the same pod

- Both Docker and Kubernetes volumes enable access to storage systems

- Kubernetes volumes are also used to expose configuration and secrets

- Docker has specific concepts for configuration and secrets
 
 (but under the hood, the technical implementation is similar)

- If you're not familiar with Docker volumes, you can safely ignore this slide!

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## Volumes ≠ Persistent Volumes

- Volumes and Persistent Volumes are related, but very different!

- *Volumes*:

- appear in Pod specifications (see next slide)

- do not exist as API resources (**cannot** do `kubectl get volumes`)

- *Persistent Volumes*:

- are API resources (**can** do `kubectl get persistentvolumes`)

- correspond to concrete volumes (e.g. on a SAN, EBS, etc.)

- cannot be associated with a Pod directly; but through a Persistent Volume Claim

- won't be discussed further in this section

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## Adding a volume to a Pod

- We will start with the simplest Pod manifest we can find

- We will add a volume to that Pod manifest

- We will mount that volume in a container in the Pod

- By default, this volume will be an `emptyDir`

(an empty directory)

- It will "shadow" the directory where it's mounted

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## Our basic Pod

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: nginx-without-volume
spec:
  containers:
  - name: nginx
    image: nginx
```

This is a MVP! (Minimum Viable Pod😉)

It runs a single NGINX container.

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## Trying the basic pod

- Create the Pod:
  ```bash
  kubectl create -f ~/container.training/k8s/nginx-1-without-volume.yaml
  ```

- Get its IP address:
  ```bash
  IPADDR=$(kubectl get pod nginx-without-volume -o jsonpath={.status.podIP})
  ```

- Send a request with curl:
  ```bash
  curl $IPADDR
  ```

]

(We should see the "Welcome to NGINX" page.)

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## Adding a volume

- We need to add the volume in two places:

- at the Pod level (to declare the volume)

- at the container level (to mount the volume)

- We will declare a volume named `www`

- No type is specified, so it will default to `emptyDir`

(as the name implies, it will be initialized as an empty directory at pod creation)

- In that pod, there is also a container named `nginx`

- That container mounts the volume `www` to path `/usr/share/nginx/html/`

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## The Pod with a volume

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: nginx-with-volume
spec:
  volumes:
  - name: www
  containers:
  - name: nginx
    image: nginx
    volumeMounts:
    - name: www
      mountPath: /usr/share/nginx/html/
```

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## Trying the Pod with a volume

- Create the Pod:
  ```bash
  kubectl create -f ~/container.training/k8s/nginx-2-with-volume.yaml
  ```

- Get its IP address:
  ```bash
  IPADDR=$(kubectl get pod nginx-with-volume -o jsonpath={.status.podIP})
  ```

- Send a request with curl:
  ```bash
  curl $IPADDR
  ```

]

(We should now see a "403 Forbidden" error page.)

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## Populating the volume with another container

- Let's add another container to the Pod

- Let's mount the volume in *both* containers

- That container will populate the volume with static files

- NGINX will then serve these static files

- To populate the volume, we will clone the Spoon-Knife repository

- this repository is https://github.com/octocat/Spoon-Knife

- it's very popular (more than 100K stars!)

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## Sharing a volume between two containers

.small[
```yaml
apiVersion: v1
kind: Pod
metadata:
  name: nginx-with-git
spec:
  volumes:
  - name: www
  containers:
  - name: nginx
    image: nginx
    volumeMounts:
    - name: www
      mountPath: /usr/share/nginx/html/
  - name: git
    image: alpine
    command: [ "sh", "-c", "apk add --no-cache git && git clone https://github.com/octocat/Spoon-Knife /www" ]
    volumeMounts:
    - name: www
      mountPath: /www/
  restartPolicy: OnFailure
```
]

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## Sharing a volume, explained

- We added another container to the pod

- That container mounts the `www` volume on a different path (`/www`)

- It uses the `alpine` image

- When started, it installs `git` and clones the `octocat/Spoon-Knife` repository

(that repository contains a tiny HTML website)

- As a result, NGINX now serves this website

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## Trying the shared volume

- This one will be time-sensitive!

- We need to catch the Pod IP address *as soon as it's created*

- Then send a request to it *as fast as possible*

- Watch the pods (so that we can catch the Pod IP address)
  ```bash
  kubectl get pods -o wide --watch
  ```

]

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## Shared volume in action

- Create the pod:
  ```bash
  kubectl create -f ~/container.training/k8s/nginx-3-with-git.yaml
  ```

- As soon as we see its IP address, access it:
  ```bash
  curl $IP
  ```

- A few seconds later, the state of the pod will change; access it again:
  ```bash
  curl $IP
  ```

]

The first time, we should see "403 Forbidden".

The second time, we should see the HTML file from the Spoon-Knife repository.

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## Explanations

- Both containers are started at the same time

- NGINX starts very quickly

(it can serve requests immediately)

- But at this point, the volume is empty

(NGINX serves "403 Forbidden")

- The other containers installs git and clones the repository

(this takes a bit longer)

- When the other container is done, the volume holds the repository

(NGINX serves the HTML file)

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## The devil is in the details

- The default `restartPolicy` is `Always`

- This would cause our `git` container to run again ... and again ... and again

(with an exponential back-off delay, as explained [in the documentation](https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle/#restart-policy))

- That's why we specified `restartPolicy: OnFailure`

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## Inconsistencies

- There is a short period of time during which the website is not available

(because the `git` container hasn't done its job yet)

- With a bigger website, we could get inconsistent results

(where only a part of the content is ready)

- In real applications, this could cause incorrect results

- How can we avoid that?

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## Init Containers

- We can define containers that should execute *before* the main ones

- They will be executed in order

(instead of in parallel)

- They must all succeed before the main containers are started

- This is *exactly* what we need here!

- Let's see one in action

.footnote[See [Init Containers](https://kubernetes.io/docs/concepts/workloads/pods/init-containers/) documentation for all the details.]

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## Defining Init Containers

.small[
```yaml
apiVersion: v1
kind: Pod
metadata:
  name: nginx-with-init
spec:
  volumes:
  - name: www
  containers:
  - name: nginx
    image: nginx
    volumeMounts:
    - name: www
      mountPath: /usr/share/nginx/html/
  initContainers:
  - name: git
    image: alpine
    command: [ "sh", "-c", "apk add --no-cache git && git clone https://github.com/octocat/Spoon-Knife /www" ]
    volumeMounts:
    - name: www
      mountPath: /www/
```
]

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## Trying the init container

- Repeat the same operation as earlier

(try to send HTTP requests as soon as the pod comes up)

]

- This time, instead of "403 Forbidden" we get a "connection refused"

- NGINX doesn't start until the git container has done its job

- We never get inconsistent results

(a "half-ready" container)

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## Other uses of init containers

- Load content

- Generate configuration (or certificates)

- Database migrations

- Waiting for other services to be up

(to avoid flurry of connection errors in main container)

- etc.

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

## Volume lifecycle

- The lifecycle of a volume is linked to the pod's lifecycle

- This means that a volume is created when the pod is created

- This is mostly relevant for `emptyDir` volumes

(other volumes, like remote storage, are not "created" but rather "attached" )

- A volume survives across container restarts

- A volume is destroyed (or, for remote storage, detached) when the pod is destroyed

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/volumes.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Managing configuration

.nav[
[Previous section](#toc-volumes)
|
[Back to table of contents](#toc-chapter-7)
|
[Next section](#toc-extending-the-kubernetes-api)
]

---
# Managing configuration

- Some applications need to be configured (obviously!)

- There are many ways for our code to pick up configuration:

- command-line arguments

- environment variables

- configuration files

- configuration servers (getting configuration from a database, an API...)

- ... and more (because programmers can be very creative!)

- How can we do these things with containers and Kubernetes?

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Passing configuration to containers

- There are many ways to pass configuration to code running in a container:

- baking it into a custom image

- command-line arguments

- environment variables

- injecting configuration files

- exposing it over the Kubernetes API

- configuration servers

- Let's review these different strategies!

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Baking custom images

- Put the configuration in the image

(it can be in a configuration file, but also `ENV` or `CMD` actions)

- It's easy! It's simple!

- Unfortunately, it also has downsides:

- multiplication of images

- different images for dev, staging, prod ...

- minor reconfigurations require a whole build/push/pull cycle

- Avoid doing it unless you don't have the time to figure out other options

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Command-line arguments

- Pass options to `args` array in the container specification

- Example ([source](https://github.com/coreos/pods/blob/master/kubernetes.yaml#L29)): 
  ```yaml
      args: 
        - "--data-dir=/var/lib/etcd"
        - "--advertise-client-urls=http://127.0.0.1:2379"
        - "--listen-client-urls=http://127.0.0.1:2379"
        - "--listen-peer-urls=http://127.0.0.1:2380"
        - "--name=etcd"
  ```

- The options can be passed directly to the program that we run ...

... or to a wrapper script that will use them to e.g. generate a config file

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Command-line arguments, pros & cons

- Works great when options are passed directly to the running program

(otherwise, a wrapper script can work around the issue)

- Works great when there aren't too many parameters

(to avoid a 20-lines `args` array)

- Requires documentation and/or understanding of the underlying program

("which parameters and flags do I need, again?")

- Well-suited for mandatory parameters (without default values)

- Not ideal when we need to pass a real configuration file anyway

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Environment variables

- Pass options through the `env` map in the container specification

- Example:
  ```yaml
      env:
      - name: ADMIN_PORT
        value: "8080"
      - name: ADMIN_AUTH
        value: Basic
      - name: ADMIN_CRED
        value: "admin:0pensesame!"
  ```

🤔 Why this weird `{name: xxx, value: yyy}` scheme? It will be revealed soon!

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## The downward API

- In the previous example, environment variables have fixed values

- We can also use a mechanism called the *downward API*

- The downward API allows exposing pod or container information

- either through special files (we won't show that for now)

- or through environment variables

- The value of these environment variables is computed when the container is started

- Remember: environment variables won't (can't) change after container start

- Let's see a few concrete examples!

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Exposing the pod's namespace

```yaml
    - name: MY_POD_NAMESPACE
      valueFrom:
        fieldRef:
          fieldPath: metadata.namespace
```

- Useful to generate FQDN of services

(in some contexts, a short name is not enough)

- For instance, the two commands should be equivalent:
  ```
  curl api-backend
  curl api-backend.$MY_POD_NAMESPACE.svc.cluster.local
  ```

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Exposing the pod's IP address

```yaml
    - name: MY_POD_IP
      valueFrom:
        fieldRef:
          fieldPath: status.podIP
```

- Useful if we need to know our IP address

(we could also read it from `eth0`, but this is more solid)

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Exposing the container's resource limits

```yaml
    - name: MY_MEM_LIMIT
      valueFrom:
        resourceFieldRef:
          containerName: test-container
          resource: limits.memory
```

- Useful for runtimes where memory is garbage collected

- Example: the JVM

(the memory available to the JVM should be set with the `-Xmx ` flag)

- Best practice: set a memory limit, and pass it to the runtime

- Note: recent versions of the JVM can do this automatically

(see [JDK-8146115](https://bugs.java.com/bugdatabase/view_bug.do?bug_id=JDK-8146115))
  and
  [this blog post](https://very-serio.us/2017/12/05/running-jvms-in-kubernetes/)
  for detailed examples)

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## More about the downward API

- [This documentation page](https://kubernetes.io/docs/tasks/inject-data-application/environment-variable-expose-pod-information/) tells more about these environment variables

- And [this one](https://kubernetes.io/docs/tasks/inject-data-application/downward-api-volume-expose-pod-information/) explains the other way to use the downward API

(through files that get created in the container filesystem)

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Environment variables, pros and cons

- Works great when the running program expects these variables

- Works great for optional parameters with reasonable defaults

(since the container image can provide these defaults)

- Sort of auto-documented

(we can see which environment variables are defined in the image, and their values)

- Can be (ab)used with longer values ...

- ... You *can* put an entire Tomcat configuration file in an environment ...

- ... But *should* you?

(Do it if you really need to, we're not judging! But we'll see better ways.)

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Injecting configuration files

- Sometimes, there is no way around it: we need to inject a full config file

- Kubernetes provides a mechanism for that purpose: `configmaps`

- A configmap is a Kubernetes resource that exists in a namespace

- Conceptually, it's a key/value map

(values are arbitrary strings)

- We can think about them in (at least) two different ways:

- as holding entire configuration file(s)

- as holding individual configuration parameters

*Note: to hold sensitive information, we can use "Secrets", which
are another type of resource behaving very much like configmaps.
We'll cover them just after!*

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Configmaps storing entire files

- In this case, each key/value pair corresponds to a configuration file

- Key = name of the file

- Value = content of the file

- There can be one key/value pair, or as many as necessary

(for complex apps with multiple configuration files)

- Examples:
  ```
  # Create a configmap with a single key, "app.conf"
  kubectl create configmap my-app-config --from-file=app.conf
  # Create a configmap with a single key, "app.conf" but another file
  kubectl create configmap my-app-config --from-file=app.conf=app-prod.conf
  # Create a configmap with multiple keys (one per file in the config.d directory)
  kubectl create configmap my-app-config --from-file=config.d/
  ```

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Configmaps storing individual parameters

- In this case, each key/value pair corresponds to a parameter

- Key = name of the parameter

- Value = value of the parameter

- Examples:
  ```
  # Create a configmap with two keys
  kubectl create cm my-app-config \
      --from-literal=foreground=red \
      --from-literal=background=blue
  
  # Create a configmap from a file containing key=val pairs
  kubectl create cm my-app-config \
      --from-env-file=app.conf
  ```

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Exposing configmaps to containers

- Configmaps can be exposed as plain files in the filesystem of a container

- this is achieved by declaring a volume and mounting it in the container

- this is particularly effective for configmaps containing whole files

- Configmaps can be exposed as environment variables in the container

- this is achieved with the downward API

- this is particularly effective for configmaps containing individual parameters

- Let's see how to do both!

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Passing a configuration file with a configmap

- We will start a load balancer powered by HAProxy

- We will use the [official `haproxy` image](https://hub.docker.com/_/haproxy/)

- It expects to find its configuration in `/usr/local/etc/haproxy/haproxy.cfg`

- We will provide a simple HAproxy configuration, `k8s/haproxy.cfg`

- It listens on port 80, and load balances connections between IBM and Google

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Creating the configmap

- Go to the `k8s` directory in the repository:
  ```bash
  cd ~/container.training/k8s
  ```

- Create a configmap named `haproxy` and holding the configuration file:
  ```bash
  kubectl create configmap haproxy --from-file=haproxy.cfg
  ```

- Check what our configmap looks like:
  ```bash
  kubectl get configmap haproxy -o yaml
  ```

]

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Using the configmap

We are going to use the following pod definition:

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: haproxy
spec:
  volumes:
  - name: config
    configMap:
      name: haproxy
  containers:
  - name: haproxy
    image: haproxy
    volumeMounts:
    - name: config
      mountPath: /usr/local/etc/haproxy/
```

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Using the configmap

- The resource definition from the previous slide is in `k8s/haproxy.yaml`

- Create the HAProxy pod:
  ```bash
  kubectl apply -f ~/container.training/k8s/haproxy.yaml
  ```

- Check the IP address allocated to the pod:
  ```bash
  kubectl get pod haproxy -o wide
  IP=$(kubectl get pod haproxy -o json | jq -r .status.podIP)
  ```

]

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Testing our load balancer

- The load balancer will send:

- half of the connections to Google

- the other half to IBM

- Access the load balancer a few times:
  ```bash
  curl $IP
  curl $IP
  curl $IP
  ```

]

We should see connections served by Google, and others served by IBM.
 
(Each server sends us a redirect page. Look at the URL that they send us to!)

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Exposing configmaps with the downward API

- We are going to run a Docker registry on a custom port

- By default, the registry listens on port 5000

- This can be changed by setting environment variable `REGISTRY_HTTP_ADDR`

- We are going to store the port number in a configmap

- Then we will expose that configmap as a container environment variable

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Creating the configmap

- Our configmap will have a single key, `http.addr`:
  ```bash
  kubectl create configmap registry --from-literal=http.addr=0.0.0.0:80
  ```

- Check our configmap:
  ```bash
  kubectl get configmap registry -o yaml
  ```

]

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Using the configmap

We are going to use the following pod definition:

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: registry
spec:
  containers:
  - name: registry
    image: registry
    env:
    - name: REGISTRY_HTTP_ADDR
      valueFrom:
        configMapKeyRef:
          name: registry
          key: http.addr
```

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Using the configmap

- The resource definition from the previous slide is in `k8s/registry.yaml`

- Create the registry pod:
  ```bash
  kubectl apply -f ~/container.training/k8s/registry.yaml
  ```

- Check the IP address allocated to the pod:
  ```bash
  kubectl get pod registry -o wide
  IP=$(kubectl get pod registry -o json | jq -r .status.podIP)
  ```

- Confirm that the registry is available on port 80:
  ```bash
  curl $IP/v2/_catalog
  ```

]

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Passwords, tokens, sensitive information

- For sensitive information, there is another special resource: *Secrets*

- Secrets and Configmaps work almost the same way

(we'll expose the differences on the next slide)

- The *intent* is different, though:

*"You should use secrets for things which are actually secret like API keys, 
  credentials, etc., and use config map for not-secret configuration data."*

*"In the future there will likely be some differentiators for secrets like rotation or support for backing the secret API w/ HSMs, etc."*

(Source: [the author of both features](https://stackoverflow.com/a/36925553/580281
))

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

## Differences between configmaps and secrets
 
- Secrets are base64-encoded when shown with `kubectl get secrets -o yaml`

- keep in mind that this is just *encoding*, not *encryption*

- it is very easy to [automatically extract and decode secrets](https://medium.com/@mveritym/decoding-kubernetes-secrets-60deed7a96a3)

- [Secrets can be encrypted at rest](https://kubernetes.io/docs/tasks/administer-cluster/encrypt-data/)

- With RBAC, we can authorize a user to access configmaps, but not secrets

(since they are two different kinds of resources)

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/configuration.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Extending the Kubernetes API

.nav[
[Previous section](#toc-managing-configuration)
|
[Back to table of contents](#toc-chapter-8)
|
[Next section](#toc-operators)
]

---
# Extending the Kubernetes API

There are multiple ways to extend the Kubernetes API.

We are going to cover:

- Custom Resource Definitions (CRDs)

- Admission Webhooks

.debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/extending-api.md)]
---

## Revisiting the API server

- The Kubernetes API server is a central point of the control plane

(everything connects to it: controller manager, scheduler, kubelets)

- Almost everything in Kubernetes is materialized by a resource

- Resources have a type (or "kind")

(similar to strongly typed languages)

- We can see existing types with `kubectl api-resources`

- We can list resources of a given type with `kubectl get <type>`

.debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/extending-api.md)]
---

## Creating new types

- We can create new types with Custom Resource Definitions (CRDs)

- CRDs are created dynamically

(without recompiling or restarting the API server)

- CRDs themselves are resources:

- we can create a new type with `kubectl create` and some YAML

- we can see all our custom types with `kubectl get crds`

- After we create a CRD, the new type works just like built-in types

.debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/extending-api.md)]
---

## What can we do with CRDs?

There are many possibilities!

- *Operators* encapsulate complex sets of resources

(e.g.: a PostgreSQL replicated cluster; an etcd cluster...
 
 see [awesome operators](https://github.com/operator-framework/awesome-operators) and
 [OperatorHub](https://operatorhub.io/) to find more)

- Custom use-cases like [gitkube](https://gitkube.sh/)

- creates a new custom type, `Remote`, exposing a git+ssh server

- deploy by pushing YAML or Helm charts to that remote

- Replacing built-in types with CRDs

(see [this lightning talk by Tim Hockin](https://www.youtube.com/watch?v=ji0FWzFwNhA&index=2&list=PLj6h78yzYM2PZf9eA7bhWnIh_mK1vyOfU))

.debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/extending-api.md)]
---

## Little details

- By default, CRDs are not *validated*

(we can put anything we want in the `spec`)

- When creating a CRD, we can pass an OpenAPI v3 schema (BETA!)

(which will then be used to validate resources)

- Generally, when creating a CRD, we also want to run a *controller*

(otherwise nothing will happen when we create resources of that type)

- The controller will typically *watch* our custom resources

(and take action when they are created/updated)

*
Examples:
[YAML to install the gitkube CRD](https://storage.googleapis.com/gitkube/gitkube-setup-stable.yaml),
[YAML to install a redis operator CRD](https://github.com/amaizfinance/redis-operator/blob/master/deploy/crds/k8s_v1alpha1_redis_crd.yaml)
*

.debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/extending-api.md)]
---

## Service catalog

- *Service catalog* is another extension mechanism

- It's not extending the Kubernetes API strictly speaking

(but it still provides new features!)

- It doesn't create new types; it uses:

- ClusterServiceBroker
  - ClusterServiceClass
  - ClusterServicePlan
  - ServiceInstance
  - ServiceBinding

- It uses the Open service broker API

.debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/extending-api.md)]
---

## Admission controllers

- When a Pod is created, it is associated with a ServiceAccount

(even if we did not specify one explicitly)

- That ServiceAccount was added on the fly by an *admission controller*

(specifically, a *mutating admission controller*)

- Admission controllers sit on the API request path

(see the cool diagram on next slide, courtesy of Banzai Cloud)

.debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/extending-api.md)]
---

![API request lifecycle](images/api-request-lifecycle.png)

.debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/extending-api.md)]
---

## Admission controllers

- *Validating* admission controllers can accept/reject the API call

- *Mutating* admission controllers can modify the API request payload

- Both types can also trigger additional actions

(e.g. automatically create a Namespace if it doesn't exist)

- There are a number of built-in admission controllers

(see [documentation](https://kubernetes.io/docs/reference/access-authn-authz/admission-controllers/#what-does-each-admission-controller-do) for a list)

- But we can also define our own!

.debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/extending-api.md)]
---

## Admission Webhooks

- We can setup *admission webhooks* to extend the behavior of the API server

- The API server will submit incoming API requests to these webhooks

- These webhooks can be *validating* or *mutating*

- Webhooks can be set up dynamically (without restarting the API server)

- To setup a dynamic admission webhook, we create a special resource:

a `ValidatingWebhookConfiguration` or a `MutatingWebhookConfiguration`

- These resources are created and managed like other resources

(i.e. `kubectl create`, `kubectl get`...)

.debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/extending-api.md)]
---

## Webhook Configuration

- A ValidatingWebhookConfiguration or MutatingWebhookConfiguration contains:

- the address of the webhook

- the authentication information to use with the webhook

- a list of rules

- The rules indicate for which objects and actions the webhook is triggered

(to avoid e.g. triggering webhooks when setting up webhooks)

.debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/extending-api.md)]
---

## (Ab)using the API server

- If we need to store something "safely" (as in: in etcd), we can use CRDs

- This gives us primitives to read/write/list objects (and optionally validate them)

- The Kubernetes API server can run on its own

(without the scheduler, controller manager, and kubelets)

- By loading CRDs, we can have it manage totally different objects

(unrelated to containers, clusters, etc.)

.debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/extending-api.md)]
---

## Documentation

- [Custom Resource Definitions: when to use them](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/)

- [Custom Resources Definitions: how to use them](https://kubernetes.io/docs/tasks/access-kubernetes-api/custom-resources/custom-resource-definitions/)

- [Service Catalog](https://kubernetes.io/docs/concepts/extend-kubernetes/service-catalog/)

- [Built-in Admission Controllers](https://kubernetes.io/docs/reference/access-authn-authz/admission-controllers/)

- [Dynamic Admission Controllers](https://kubernetes.io/docs/reference/access-authn-authz/extensible-admission-controllers/)

.debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/extending-api.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Operators

.nav[
[Previous section](#toc-extending-the-kubernetes-api)
|
[Back to table of contents](#toc-chapter-8)
|
[Next section](#toc-building-our-own-cluster)
]

---
# Operators

- Operators are one of the many ways to extend Kubernetes

- We will define operators

- We will see how they work

- We will install a specific operator (for ElasticSearch)

- We will use it to provision an ElasticSearch cluster

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## What are operators?

*An operator represents **human operational knowledge in software,**
 
to reliably manage an application.
— [CoreOS](https://coreos.com/blog/introducing-operators.html)*

Examples:

- Deploying and configuring replication with MySQL, PostgreSQL ...

- Setting up Elasticsearch, Kafka, RabbitMQ, Zookeeper ...

- Reacting to failures when intervention is needed

- Scaling up and down these systems

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## What are they made from?

- Operators combine two things:

- Custom Resource Definitions

- controller code watching the corresponding resources and acting upon them

- A given operator can define one or multiple CRDs

- The controller code (control loop) typically runs within the cluster

(running as a Deployment with 1 replica is a common scenario)

- But it could also run elsewhere

(nothing mandates that the code run on the cluster, as long as it has API access)

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Why use operators?

- Kubernetes gives us Deployments, StatefulSets, Services ...

- These mechanisms give us building blocks to deploy applications

- They work great for services that are made of *N* identical containers

(like stateless ones)

- They also work great for some stateful applications like Consul, etcd ...

(with the help of highly persistent volumes)

- They're not enough for complex services:

- where different containers have different roles

- where extra steps have to be taken when scaling or replacing containers

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Use-cases for operators

- Systems with primary/secondary replication

Examples: MariaDB, MySQL, PostgreSQL, Redis ...

- Systems where different groups of nodes have different roles

Examples: ElasticSearch, MongoDB ...

- Systems with complex dependencies (that are themselves managed with operators)

Examples: Flink or Kafka, which both depend on Zookeeper

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## More use-cases

- Representing and managing external resources

(Example: [AWS Service Operator](https://operatorhub.io/operator/alpha/aws-service-operator.v0.0.1))

- Managing complex cluster add-ons

(Example: [Istio operator](https://operatorhub.io/operator/beta/istio-operator.0.1.6))

- Deploying and managing our applications' lifecycles

(more on that later)

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## How operators work

- An operator creates one or more CRDs

(i.e., it creates new "Kinds" of resources on our cluster)

- The operator also runs a *controller* that will watch its resources

- Each time we create/update/delete a resource, the controller is notified

(we could write our own cheap controller with `kubectl get --watch`)

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## One operator in action

- We will install [Elastic Cloud on Kubernetes](https://www.elastic.co/guide/en/cloud-on-k8s/current/k8s-quickstart.html), an ElasticSearch operator

- This operator requires PersistentVolumes

- We will install Rancher's [local path storage provisioner](https://github.com/rancher/local-path-provisioner) to automatically create these

- Then, we will create an ElasticSearch resource

- The operator will detect that resource and provision the cluster

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Installing a Persistent Volume provisioner

(This step can be skipped if you already have a dynamic volume provisioner.)

- This provisioner creates Persistent Volumes backed by `hostPath`

(local directories on our nodes)

- It doesn't require anything special ...

- ... But losing a node = losing the volumes on that node!

- Install the local path storage provisioner:
  ```bash
  kubectl apply -f ~/container.training/k8s/local-path-storage.yaml
  ```

]

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Making sure we have a default StorageClass

- The ElasticSearch operator will create StatefulSets

- These StatefulSets will instantiate PersistentVolumeClaims

- These PVCs need to be explicitly associated with a StorageClass

- Or we need to tag a StorageClass to be used as the default one

- List StorageClasses:
  ```bash
  kubectl get storageclasses
  ```

]

We should see the `local-path` StorageClass.

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Setting a default StorageClass

- This is done by adding an annotation to the StorageClass:

`storageclass.kubernetes.io/is-default-class: true`

- Tag the StorageClass so that it's the default one:
  ```bash
  kubectl annotate storageclass local-path \
            storageclass.kubernetes.io/is-default-class=true
  ```

- Check the result:
  ```bash
  kubectl get storageclasses
  ```

]

Now, the StorageClass should have `(default)` next to its name.

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Install the ElasticSearch operator

- The operator provides:

- a few CustomResourceDefinitions
  - a Namespace for its other resources
  - a ValidatingWebhookConfiguration for type checking
  - a StatefulSet for its controller and webhook code
  - a ServiceAccount, ClusterRole, ClusterRoleBinding for permissions

- All these resources are grouped in a convenient YAML file

- Install the operator:
  ```bash
  kubectl apply -f ~/container.training/k8s/eck-operator.yaml
  ```

]

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Check our new custom resources

- Let's see which CRDs were created

- List all CRDs:
  ```bash
  kubectl get crds
  ```

]

This operator supports ElasticSearch, but also Kibana and APM. Cool!

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Create the `eck-demo` namespace

- For clarity, we will create everything in a new namespace, `eck-demo`

- This namespace is hard-coded in the YAML files that we are going to use

- We need to create that namespace

- Create the `eck-demo` namespace:
  ```bash
  kubectl create namespace eck-demo
  ```

- Switch to that namespace:
  ```bash
  kns eck-demo
  ```

]

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Can we use a different namespace?

Yes, but then we need to update all the YAML manifests that we
are going to apply in the next slides.

The `eck-demo` namespace is hard-coded in these YAML manifests.

Why?

Because when defining a ClusterRoleBinding that references a
ServiceAccount, we have to indicate in which namespace the
ServiceAccount is located.

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Create an ElasticSearch resource

- We can now create a resource with `kind: ElasticSearch`

- The YAML for that resource will specify all the desired parameters:

- how many nodes we want
  - image to use
  - add-ons (kibana, cerebro, ...)
  - whether to use TLS or not
  - etc.

- Create our ElasticSearch cluster:
  ```bash
  kubectl apply -f ~/container.training/k8s/eck-elasticsearch.yaml
  ```

]

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Operator in action

- Over the next minutes, the operator will create our ES cluster

- It will report our cluster status through the CRD

- Check the logs of the operator:
  ```bash
  stern --namespace=elastic-system operator
  ```

- Watch the status of the cluster through the CRD:
  ```bash
  kubectl get es -w
  ```

]

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Connecting to our cluster

- It's not easy to use the ElasticSearch API from the shell

- But let's check at least if ElasticSearch is up!

- Get the ClusterIP of our ES instance:
  ```bash
  kubectl get services
  ```

- Issue a request with `curl`:
  ```bash
  curl http://`CLUSTERIP`:9200
  ```

]

We get an authentication error. Our cluster is protected!

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Obtaining the credentials

- The operator creates a user named `elastic`

- It generates a random password and stores it in a Secret

- Extract the password:
  ```bash
    kubectl get secret demo-es-elastic-user \
            -o go-template="{{ .data.elastic | base64decode }} "
  ```

- Use it to connect to the API:
  ```bash
  curl -u elastic:`PASSWORD` http://`CLUSTERIP`:9200
  ```

]

We should see a JSON payload with the `"You Know, for Search"` tagline.

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Sending data to the cluster

- Let's send some data to our brand new ElasticSearch cluster!

- We'll deploy a filebeat DaemonSet to collect node logs

- Deploy filebeat:
  ```bash
  kubectl apply -f ~/container.training/k8s/eck-filebeat.yaml
  ```

- Wait until some pods are up:
  ```bash
  watch kubectl get pods -l k8s-app=filebeat
  ```

- Check that a filebeat index was created:
  ```bash
  curl -u elastic:`PASSWORD` http://`CLUSTERIP`:9200/_cat/indices
  ```

]

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Deploying an instance of Kibana

- Kibana can visualize the logs injected by filebeat

- The ECK operator can also manage Kibana

- Let's give it a try!

- Deploy a Kibana instance:
  ```bash
  kubectl apply -f ~/container.training/k8s/eck-kibana.yaml
  ```

- Wait for it to be ready:
  ```bash
  kubectl get kibana -w
  ```

]

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Connecting to Kibana

- Kibana is automatically set up to conect to ElasticSearch

(this is arranged by the YAML that we're using)

- However, it will ask for authentication

- It's using the same user/password as ElasticSearch

- Get the NodePort allocated to Kibana:
  ```bash
  kubectl get services
  ```

- Connect to it with a web browser

- Use the same user/password as before

]

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Setting up Kibana

After the Kibana UI loads, we need to click around a bit

- Pick "explore on my own"

- Click on Use Elasticsearch data / Connect to your Elasticsearch index"

- Enter `filebeat-*` for the index pattern and click "Next step"

- Select `@timestamp` as time filter field name

- Click on "discover" (the small icon looking like a compass on the left bar)

- Play around!

]

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Scaling up the cluster

- At this point, we have only one node

- We are going to scale up

- But first, we'll deploy Cerebro, an UI for ElasticSearch

- This will let us see the state of the cluster, how indexes are sharded, etc.

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Deploying Cerebro

- Cerebro is stateless, so it's fairly easy to deploy

(one Deployment + one Service)

- However, it needs the address and credentials for ElasticSearch

- We prepared yet another manifest for that!

- Deploy Cerebro:
  ```bash
  kubectl apply -f ~/container.training/k8s/eck-cerebro.yaml
  ```

- Lookup the NodePort number and connect to it:
  ```bash
  kuebctl get services
  ```

]

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Scaling up the cluster

- We can see on Cerebro that the cluster is "yellow"

(because our index is not replicated)

- Let's change that!

- Edit the ElasticSearch cluster manifest:
  ```bash
  kubectl edit es demo
  ```

- Find the field `count: 1` and change it to 3

- Save and quit

]

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Deploying our apps with operators

- It is very simple to deploy with `kubectl run` / `kubectl expose`

- We can unlock more features by writing YAML and using `kubectl apply`

- Kustomize or Helm let us deploy in multiple environments

(and adjust/tweak parameters in each environment)

- We can also use an operator to deploy our application

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Pros and cons of deploying with operators

- The app definition and configuration is persisted in the Kubernetes API

- Multiple instances of the app can be manipulated with `kubectl get`

- We can add labels, annotations to the app instances

- Our controller can execute custom code for any lifecycle event

- However, we need to write this controller

- We need to be careful about changes

(what happens when the resource `spec` is updated?)

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---

## Operators are not magic

- Look at the ElasticSearch resource definition

(`~/container.training/k8s/eck-elasticsearch.yaml`)

- What should happen if we flip the TLS flag? Twice?

- What should happen if we add another group of nodes?

- What if we want different images or parameters for the different nodes?

*Operators can be very powerful.
 
But we need to know exactly the scenarios that they can handle.*

.debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators.md)]
---
## What does it take to write an operator?

- Writing a quick-and-dirty operator, or a POC/MVP, is easy

- Writing a robust operator is hard

- We will describe the general idea

- We will identify some of the associated challenges

- We will list a few tools that can help us

.debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators-design.md)]
---

## Top-down vs. bottom-up

- Both approaches are possible

- Let's see what they entail, and their respective pros and cons

.debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators-design.md)]
---

## Top-down approach

- Start with high-level design (see next slide)

- Pros:

- can yield cleaner design that will be more robust

- Cons:

- must be able to anticipate all the events that might happen

- design will be better only to the extent of what we anticipated

- hard to anticipate if we don't have production experience

.debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators-design.md)]
---

## High-level design

- What are we solving?

(e.g.: geographic databases backed by PostGIS with Redis caches)

- What are our use-cases, stories?

(e.g.: adding/resizing caches and read replicas; load balancing queries)

- What kind of outage do we want to address?

(e.g.: loss of individual node, pod, volume)

- What are our *non-features*, the things we don't want to address?

(e.g.: loss of datacenter/zone; differentiating between read and write queries;
 
 cache invalidation; upgrading to newer major versions of Redis, PostGIS, PostgreSQL)

.debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators-design.md)]
---

## Low-level design

- What Custom Resource Definitions do we need?

(one, many?)

- How will we store configuration information?

(part of the CRD spec fields, annotations, other?)

- Do we need to store state? If so, where?

- state that is small and doesn't change much can be stored via the Kubernetes API
 
 (e.g.: leader information, configuration, credentials)

- things that are big and/or change a lot should go elsewhere
 
 (e.g.: metrics, bigger configuration file like GeoIP)

.debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators-design.md)]
---

## What can we store via the Kubernetes API?

- The API server stores most Kubernetes resources in etcd

- Etcd is designed for reliability, not for performance

- If our storage needs exceed what etcd can offer, we need to use something else:

- either directly

- or by extending the API server
 (for instance by using the agregation layer, like [metrics server](https://github.com/kubernetes-incubator/metrics-server) does)

.debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators-design.md)]
---

## Bottom-up approach

- Start with existing Kubernetes resources (Deployment, Stateful Set...)

- Run the system in production

- Add scripts, automation, to facilitate day-to-day operations

- Turn the scripts into an operator

- Pros: simpler to get started; reflects actual use-cases

- Cons: can result in convoluted designs requiring extensive refactor

.debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators-design.md)]
---

## General idea

- Our operator will watch its CRDs *and associated resources*

- Drawing state diagrams and finite state automata helps a lot

- It's OK if some transitions lead to a big catch-all "human intervention"

- Over time, we will learn about new failure modes and add to these diagrams

- It's OK to start with CRD creation / deletion and prevent any modification

(that's the easy POC/MVP we were talking about)

- *Presentation* and *validation* will help our users

(more on that later)

.debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators-design.md)]
---

## Challenges

- Reacting to infrastructure disruption can seem hard at first

- Kubernetes gives us a lot of primitives to help:

- Pods and Persistent Volumes will *eventually* recover

- Stateful Sets give us easy ways to "add N copies" of a thing

- The real challenges come with configuration changes

(i.e., what to do when our users update our CRDs)

- Keep in mind that [some] of the [largest] cloud [outages] haven't been caused by [natural catastrophes], or even code bugs, but by configuration changes

[some]: https://www.datacenterdynamics.com/news/gcp-outage-mainone-leaked-google-cloudflare-ip-addresses-china-telecom/
[largest]: https://aws.amazon.com/message/41926/
[outages]: https://aws.amazon.com/message/65648/
[natural catastrophes]: https://www.datacenterknowledge.com/amazon/aws-says-it-s-never-seen-whole-data-center-go-down

.debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators-design.md)]
---

## Configuration changes

- It is helpful to analyze and understand how Kubernetes controllers work:

- watch resource for modifications

- compare desired state (CRD) and current state

- issue actions to converge state

- Configuration changes will probably require *another* state diagram or FSA

- Again, it's OK to have transitions labeled as "unsupported"

(i.e. reject some modifications because we can't execute them)

.debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators-design.md)]
---

## Tools

- CoreOS / RedHat Operator Framework

[GitHub](https://github.com/operator-framework)
  |
  [Blog](https://developers.redhat.com/blog/2018/12/18/introduction-to-the-kubernetes-operator-framework/)
  |
  [Intro talk](https://www.youtube.com/watch?v=8k_ayO1VRXE)
  |
  [Deep dive talk](https://www.youtube.com/watch?v=fu7ecA2rXmc)
  |
  [Simple example](https://medium.com/faun/writing-your-first-kubernetes-operator-8f3df4453234)

- Zalando Kubernetes Operator Pythonic Framework (KOPF)

[GitHub](https://github.com/zalando-incubator/kopf)
  |
  [Docs](https://kopf.readthedocs.io/)
  |
  [Step-by-step tutorial](https://kopf.readthedocs.io/en/stable/walkthrough/problem/)

- Mesosphere Kubernetes Universal Declarative Operator (KUDO)

[GitHub](https://github.com/kudobuilder/kudo)
  |
  [Blog](https://mesosphere.com/blog/announcing-maestro-a-declarative-no-code-approach-to-kubernetes-day-2-operators/)
  |
  [Docs](https://kudo.dev/)
  |
  [Zookeeper example](https://github.com/kudobuilder/frameworks/tree/master/repo/stable/zookeeper)

.debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators-design.md)]
---

## Validation

- By default, a CRD is "free form"

(we can put pretty much anything we want in it)

- When creating a CRD, we can provide an OpenAPI v3 schema
  ([Example](https://github.com/amaizfinance/redis-operator/blob/master/deploy/crds/k8s_v1alpha1_redis_crd.yaml#L34))

- The API server will then validate resources created/edited with this schema

- If we need a stronger validation, we can use a Validating Admission Webhook:

- run an [admission webhook server](https://kubernetes.io/docs/reference/access-authn-authz/extensible-admission-controllers/#write-an-admission-webhook-server) to receive validation requests

- register the webhook by creating a [ValidatingWebhookConfiguration](https://kubernetes.io/docs/reference/access-authn-authz/extensible-admission-controllers/#configure-admission-webhooks-on-the-fly)

- each time the API server receives a request matching the configuration,
 the request is sent to our server for validation

.debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators-design.md)]
---

## Presentation

- By default, `kubectl get mycustomresource` won't display much information

(just the name and age of each resource)

- When creating a CRD, we can specify additional columns to print
  ([Example](https://github.com/amaizfinance/redis-operator/blob/master/deploy/crds/k8s_v1alpha1_redis_crd.yaml#L6),
  [Docs](https://kubernetes.io/docs/tasks/access-kubernetes-api/custom-resources/custom-resource-definitions/#additional-printer-columns))

- By default, `kubectl describe mycustomresource` will also be generic

- `kubectl describe` can show events related to our custom resources

(for that, we need to create Event resources, and fill the `involvedObject` field)

- For scalable resources, we can define a `scale` sub-resource

- This will enable the use of `kubectl scale` and other scaling-related operations

.debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators-design.md)]
---

## About scaling

- It is possible to use the HPA (Horizontal Pod Autoscaler) with CRDs

- But it is not always desirable

- The HPA works very well for homogenous, stateless workloads

- For other workloads, your mileage may vary

- Some systems can scale across multiple dimensions

(for instance: increase number of replicas, or number of shards?)

- If autoscaling is desired, the operator will have to take complex decisions

(example: Zalando's Elasticsearch Operator ([Video](https://www.youtube.com/watch?v=lprE0J0kAq0)))

.debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators-design.md)]
---

## Versioning

- As our operator evolves over time, we may have to change the CRD

(add, remove, change fields)

- Like every other resource in Kubernetes, [custom resources are versioned](https://kubernetes.io/docs/tasks/access-kubernetes-api/custom-resources/custom-resource-definition-versioning/
)

- When creating a CRD, we need to specify a *list* of versions

- Versions can be marked as `stored` and/or `served`

.debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators-design.md)]
---

## Stored version

- Exactly one version has to be marked as the `stored` version

- As the name implies, it is the one that will be stored in etcd

- Resources in storage are never converted automatically

(we need to read and re-write them ourselves)

- Yes, this means that we can have different versions in etcd at any time

- Our code needs to handle all the versions that still exist in storage

.debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators-design.md)]
---

## Served versions

- By default, the Kubernetes API will serve resources "as-is"

(using their stored version)

- It will assume that all versions are compatible storage-wise

(i.e. that the spec and fields are compatible between versions)

- We can provide [conversion webhooks](https://kubernetes.io/docs/tasks/access-kubernetes-api/custom-resources/custom-resource-definition-versioning/#webhook-conversion) to "translate" requests

(the alternative is to upgrade all stored resources and stop serving old versions)

.debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators-design.md)]
---

## Operator reliability

- Remember that the operator itself must be resilient

(e.g.: the node running it can fail)

- Our operator must be able to restart and recover gracefully

- Do not store state locally

(unless we can reconstruct that state when we restart)

- As indicated earlier, we can use the Kubernetes API to store data:

- in the custom resources themselves

- in other resources' annotations

.debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators-design.md)]
---

## Beyond CRDs

- CRDs cannot use custom storage (e.g. for time series data)

- CRDs cannot support arbitrary subresources (like logs or exec for Pods)

- CRDs cannot support protobuf (for faster, more efficient communication)

- If we need these things, we can use the [aggregation layer](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/) instead

- The aggregation layer proxies all requests below a specific path to another server

(this is used e.g. by the metrics server)

- [This documentation page](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/#choosing-a-method-for-adding-custom-resources) compares the features of CRDs and API aggregation

.debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/operators-design.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Building our own cluster

.nav[
[Previous section](#toc-operators)
|
[Back to table of contents](#toc-chapter-9)
|
[Next section](#toc-resource-limits)
]

---
# Building our own cluster

- Let's build our own cluster!

*Perfection is attained not when there is nothing left to add, but when there is nothing left to take away. (Antoine de Saint-Exupery)*

- Our goal is to build a minimal cluster allowing us to:

- create a Deployment (with `kubectl run` or `kubectl create deployment`)
  - expose it with a Service
  - connect to that service

- "Minimal" here means:

- smaller number of components
  - smaller number of command-line flags
  - smaller number of configuration files

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Non-goals

- For now, we don't care about security

- For now, we don't care about scalability

- For now, we don't care about high availability

- All we care about is *simplicity*

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Our environment

- We will use the machine indicated as `dmuc1`

(this stands for "Dessine Moi Un Cluster" or "Draw Me A Sheep",
 in homage to Saint-Exupery's "The Little Prince")

- This machine:

- runs Ubuntu LTS

- has Kubernetes, Docker, and etcd binaries installed

- but nothing is running

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Checking our environment

- Let's make sure we have everything we need first

- Log into the `dmuc1` machine

- Get root:
  ```bash
  sudo -i
  ```

- Check available versions:
  ```bash
  etcd -version
  kube-apiserver --version
  dockerd --version
  ```

]

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## The plan

1. Start API server

2. Interact with it (create Deployment and Service)

3. See what's broken

4. Fix it and go back to step 2 until it works!

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Dealing with multiple processes

- We are going to start many processes

- Depending on what you're comfortable with, you can:

- open multiple windows and multiple SSH connections

- use a terminal multiplexer like screen or tmux

- put processes in the background with `&`
 (warning: log output might get confusing to read!)

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Starting API server

- Try to start the API server:
  ```bash
  kube-apiserver
  # It will fail with "--etcd-servers must be specified"
  ```

]

Since the API server stores everything in etcd,
it cannot start without it.

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Starting etcd

- Try to start etcd:
  ```bash
  etcd
  ```

]

Success!

Note the last line of output:
```
serving insecure client requests on 127.0.0.1:2379, this is strongly discouraged!
```

*Sure, that's discouraged. But thanks for telling us the address!*

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Starting API server (for real)

- Try again, passing the `--etcd-servers` argument

- That argument should be a comma-separated list of URLs

- Start API server:
  ```bash
  kube-apiserver --etcd-servers http://127.0.0.1:2379
  ```

]

Success!

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Interacting with API server

- Let's try a few "classic" commands

- List nodes:
  ```bash
  kubectl get nodes
  ```

- List services:
  ```bash
  kubectl get services
  ```

]

We should get `No resources found.` and the `kubernetes` service, respectively.

Note: the API server automatically created the `kubernetes` service entry.

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## What about `kubeconfig`?

- We didn't need to create a `kubeconfig` file

- By default, the API server is listening on `localhost:8080`

(without requiring authentication)

- By default, `kubectl` connects to `localhost:8080`

(without providing authentication)

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Creating a Deployment

- Let's run a web server!

- Create a Deployment with NGINX:
  ```bash
  kubectl create deployment web --image=nginx
  ```

]

Success?

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Checking our Deployment status

- Look at pods, deployments, etc.:
  ```bash
  kubectl get all
  ```

]

Our Deployment is in bad shape:
```
NAME                  READY   UP-TO-DATE   AVAILABLE   AGE
deployment.apps/web   0/1     0            0           2m26s
```

And, there is no ReplicaSet, and no Pod.

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## What's going on?

- We stored the definition of our Deployment in etcd

(through the API server)

- But there is no *controller* to do the rest of the work

- We need to start the *controller manager*

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Starting the controller manager

- Try to start the controller manager:
  ```bash
  kube-controller-manager
  ```

]

The final error message is:
```
invalid configuration: no configuration has been provided
```

But the logs include another useful piece of information:
```
Neither --kubeconfig nor --master was specified.
Using the inClusterConfig.  This might not work.
```

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Reminder: everyone talks to API server

- The controller manager needs to connect to the API server

- It *does not* have a convenient `localhost:8080` default

- We can pass the connection information in two ways:

- `--master` and a host:port combination (easy)

- `--kubeconfig` and a `kubeconfig` file

- For simplicity, we'll use the first option

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Starting the controller manager (for real)

- Start the controller manager:
  ```bash
  kube-controller-manager --master http://localhost:8080
  ```

]

Success!

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Checking our Deployment status

- Check all our resources again:
  ```bash
  kubectl get all
  ```

]

We now have a ReplicaSet.

But we still don't have a Pod.

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## What's going on?

In the controller manager logs, we should see something like this:
```
E0404 15:46:25.753376   22847 replica_set.go:450] Sync "default/web-5bc9bd5b8d"
failed with `No API token found for service account "default"`, retry after the
token is automatically created and added to the service account
```

- The service account `default` was automatically added to our Deployment

(and to its pods)

- The service account `default` exists

- But it doesn't have an associated token

(the token is a secret; creating it requires signature; therefore a CA)

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Solving the missing token issue

There are many ways to solve that issue.

We are going to list a few (to get an idea of what's happening behind the scenes).

Of course, we don't need to perform *all* the solutions mentioned here.

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Option 1: disable service accounts

- Restart the API server with
  `--disable-admission-plugins=ServiceAccount`

- The API server will no longer add a service account automatically

- Our pods will be created without a service account

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Option 2: do not mount the (missing) token

- Add `automountServiceAccountToken: false` to the Deployment spec

*or*

- Add `automountServiceAccountToken: false` to the default ServiceAccount

- The ReplicaSet controller will no longer create pods referencing the (missing) token

- Programmatically change the `default` ServiceAccount:
  ```bash
  kubectl patch sa default -p "automountServiceAccountToken: false"
  ```

]

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Option 3: set up service accounts properly

- This is the most complex option!

- Generate a key pair

- Pass the private key to the controller manager

(to generate and sign tokens)

- Pass the public key to the API server

(to verify these tokens)

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Continuing without service account token

- Once we patch the default service account, the ReplicaSet can create a Pod

- Check that we now have a pod:
  ```bash
  kubectl get all
  ```

]

Note: we might have to wait a bit for the ReplicaSet controller to retry.

If we're impatient, we can restart the controller manager.

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## What's next?

- Our pod exists, but it is in `Pending` state

- Remember, we don't have a node so far

(`kubectl get nodes` shows an empty list)

- We need to:

- start a container engine

- start kubelet

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Starting a container engine

- We're going to use Docker (because it's the default option)

- Start the Docker Engine:
  ```bash
  dockerd
  ```

]

Success!

Feel free to check that it actually works with e.g.:
```bash
docker run alpine echo hello world
```

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Starting kubelet

- If we start kubelet without arguments, it *will* start

- But it will not join the cluster!

- It will start in *standalone* mode

- Just like with the controller manager, we need to tell kubelet where the API server is

- Alas, kubelet doesn't have a simple `--master` option

- We have to use `--kubeconfig`

- We need to write a `kubeconfig` file for kubelet

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Writing a kubeconfig file

- We can copy/paste a bunch of YAML

- Or we can generate the file with `kubectl`

- Create the file `~/.kube/config` with `kubectl`:
  ```bash
    kubectl config \
            set-cluster localhost --server http://localhost:8080
    kubectl config \
            set-context localhost --cluster localhost
    kubectl config \
            use-context localhost
  ```

]

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Our `~/.kube/config` file

The file that we generated looks like the one below.

That one has been slightly simplified (removing extraneous fields), but it is still valid.

```yaml
apiVersion: v1
kind: Config
current-context: localhost
contexts:
- name: localhost
  context:
    cluster: localhost
clusters:
- name: localhost
  cluster:
    server: http://localhost:8080
```

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Starting kubelet

- Start kubelet with that kubeconfig file:
  ```bash
  kubelet --kubeconfig ~/.kube/config
  ```

]

Success!

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Looking at our 1-node cluster

- Let's check that our node registered correctly

- List the nodes in our cluster:
  ```bash
  kubectl get nodes
  ```

]

Our node should show up.

Its name will be its hostname (it should be `dmuc1`).

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Are we there yet?

- Let's check if our pod is running

- List all resources:
  ```bash
  kubectl get all
  ```

]

Our pod is still `Pending`. 🤔

Which is normal: it needs to be *scheduled*.

(i.e., something needs to decide which node it should go on.)

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Scheduling our pod

- Why do we need a scheduling decision, since we have only one node?

- The node might be full, unavailable; the pod might have constraints ...

- The easiest way to schedule our pod is to start the scheduler

(we could also schedule it manually)

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Starting the scheduler

- The scheduler also needs to know how to connect to the API server

- Just like for controller manager, we can use `--kubeconfig` or `--master`

- Start the scheduler:
  ```bash
  kube-scheduler --master http://localhost:8080
  ```

]

- Our pod should now start correctly

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Checking the status of our pod

- Our pod will go through a short `ContainerCreating` phase

- Then it will be `Running`

- Check pod status:
  ```bash
  kubectl get pods
  ```

]

Success!

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Scheduling a pod manually

- We can schedule a pod in `Pending` state by creating a Binding, e.g.:
 ```bash
 kubectl create -f- <<EOF
 apiVersion: v1
 kind: Binding
 metadata:
 name: name-of-the-pod
 target:
 apiVersion: v1
 kind: Node
 name: name-of-the-node
 EOF
 ```

- This is actually how the scheduler works!

- It watches pods, makes scheduling decisions, and creates Binding objects

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Connecting to our pod

- Let's check that our pod correctly runs NGINX

- Check our pod's IP address:
  ```bash
  kubectl get pods -o wide
  ```

- Send some HTTP request to the pod:
  ```bash
  curl `X.X.X.X`
  ```

]

We should see the `Welcome to nginx!` page.

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Exposing our Deployment

- We can now create a Service associated with this Deployment

- Expose the Deployment's port 80:
  ```bash
  kubectl expose deployment web --port=80
  ```

- Check the Service's ClusterIP, and try connecting:
  ```bash
  kubectl get service web
  curl http://`X.X.X.X`
  ```

]

This won't work. We need kube-proxy to enable internal communication.

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Starting kube-proxy

- kube-proxy also needs to connect to the API server

- It can work with the `--master` flag

(although that will be deprecated in the future)

- Start kube-proxy:
  ```bash
  kube-proxy --master http://localhost:8080
  ```

]

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Connecting to our Service

- Now that kube-proxy is running, we should be able to connect

- Check the Service's ClusterIP again, and retry connecting:
  ```bash
  kubectl get service web
  curl http://`X.X.X.X`
  ```

]

Success!

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## How kube-proxy works

- kube-proxy watches Service resources

- When a Service is created or updated, kube-proxy creates iptables rules

- Check out the `OUTPUT` chain in the `nat` table:
  ```bash
  iptables -t nat -L OUTPUT
  ```

- Traffic is sent to `KUBE-SERVICES`; check that too:
  ```bash
  iptables -t nat -L KUBE-SERVICES
  ```

]

For each Service, there is an entry in that chain.

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## Diving into iptables

- The last command showed a chain named `KUBE-SVC-...` corresponding to our service

- Check that `KUBE-SVC-...` chain:
  ```bash
  iptables -t nat -L `KUBE-SVC-...`
  ```

- It should show a jump to a `KUBE-SEP-...` chains; check it out too:
  ```bash
  iptables -t nat -L `KUBE-SEP-...`
  ```

]

This is a `DNAT` rule to rewrite the destination address of the connection to our pod.

This is how kube-proxy works!

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## kube-router, IPVS

- With recent versions of Kubernetes, it is possible to tell kube-proxy to use IPVS

- IPVS is a more powerful load balancing framework

(remember: iptables was primarily designed for firewalling, not load balancing!)

- It is also possible to replace kube-proxy with kube-router

- kube-router uses IPVS by default

- kube-router can also perform other functions

(e.g., we can use it as a CNI plugin to provide pod connectivity)

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

## What about the `kubernetes` service?

- If we try to connect, it won't work

(by default, it should be `10.0.0.1`)

- If we look at the Endpoints for this service, we will see one endpoint:

`host-address:6443`

- By default, the API server expects to be running directly on the nodes

(it could be as a bare process, or in a container/pod using the host network)

- ... And it expects to be listening on port 6443 with TLS

.debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/dmuc.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Resource Limits

.nav[
[Previous section](#toc-building-our-own-cluster)
|
[Back to table of contents](#toc-chapter-10)
|
[Next section](#toc-defining-min-max-and-default-resources)
]

---
# Resource Limits

- We can attach resource indications to our pods

(or rather: to the *containers* in our pods)

- We can specify *limits* and/or *requests*

- We can specify quantities of CPU and/or memory

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## CPU vs memory

- CPU is a *compressible resource*

(it can be preempted immediately without adverse effect)

- Memory is an *incompressible resource*

(it needs to be swapped out to be reclaimed; and this is costly)

- As a result, exceeding limits will have different consequences for CPU and memory

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Exceeding CPU limits

- CPU can be reclaimed instantaneously

(in fact, it is preempted hundreds of times per second, at each context switch)

- If a container uses too much CPU, it can be throttled

(it will be scheduled less often)

- The processes in that container will run slower

(or rather: they will not run faster)

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Exceeding memory limits

- Memory needs to be swapped out before being reclaimed

- "Swapping" means writing memory pages to disk, which is very slow

- On a classic system, a process that swaps can get 1000x slower

(because disk I/O is 1000x slower than memory I/O)

- Exceeding the memory limit (even by a small amount) can reduce performance *a lot*

- Kubernetes *does not support swap* (more on that later!)

- Exceeding the memory limit will cause the container to be killed

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Limits vs requests

- Limits are "hard limits" (they can't be exceeded)

- a container exceeding its memory limit is killed

- a container exceeding its CPU limit is throttled

- Requests are used for scheduling purposes

- a container using *less* than what it requested will never be killed or throttled

- the scheduler uses the requested sizes to determine placement

- the resources requested by all pods on a node will never exceed the node size

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Pod quality of service

Each pod is assigned a QoS class (visible in `status.qosClass`).

- If limits = requests:

- as long as the container uses less than the limit, it won't be affected

- if all containers in a pod have *(limits=requests)*, QoS is considered "Guaranteed"

- If requests < limits:

- as long as the container uses less than the request, it won't be affected

- otherwise, it might be killed/evicted if the node gets overloaded

- if at least one container has *(requests<limits)*, QoS is considered "Burstable"

- If a pod doesn't have any request nor limit, QoS is considered "BestEffort"

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Quality of service impact

- When a node is overloaded, BestEffort pods are killed first

- Then, Burstable pods that exceed their limits

- Burstable and Guaranteed pods below their limits are never killed

(except if their node fails)

- If we only use Guaranteed pods, no pod should ever be killed

(as long as they stay within their limits)

(Pod QoS is also explained in [this page](https://kubernetes.io/docs/tasks/configure-pod-container/quality-service-pod/) of the Kubernetes documentation and in [this blog post](https://medium.com/google-cloud/quality-of-service-class-qos-in-kubernetes-bb76a89eb2c6).)

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Where is my swap?

- The semantics of memory and swap limits on Linux cgroups are complex

- In particular, it's not possible to disable swap for a cgroup

(the closest option is to [reduce "swappiness"](https://unix.stackexchange.com/questions/77939/turning-off-swapping-for-only-one-process-with-cgroups))

- The architects of Kubernetes wanted to ensure that Guaranteed pods never swap

- The only solution was to disable swap entirely

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Alternative point of view

- Swap enables paging¹ of anonymous² memory

- Even when swap is disabled, Linux will still page memory for:

- executables, libraries

- mapped files

- Disabling swap *will reduce performance and available resources*

- For a good time, read [kubernetes/kubernetes#53533](https://github.com/kubernetes/kubernetes/issues/53533)

- Also read this [excellent blog post about swap](https://jvns.ca/blog/2017/02/17/mystery-swap/)

¹Paging: reading/writing memory pages from/to disk to reclaim physical memory

²Anonymous memory: memory that is not backed by files or blocks

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Enabling swap anyway

- If you don't care that pods are swapping, you can enable swap

- You will need to add the flag `--fail-swap-on=false` to kubelet

(otherwise, it won't start!)

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Specifying resources

- Resource requests are expressed at the *container* level

- CPU is expressed in "virtual CPUs"

(corresponding to the virtual CPUs offered by some cloud providers)

- CPU can be expressed with a decimal value, or even a "milli" suffix

(so 100m = 0.1)

- Memory is expressed in bytes

- Memory can be expressed with k, M, G, T, ki, Mi, Gi, Ti suffixes

(corresponding to 10^3, 10^6, 10^9, 10^12, 2^10, 2^20, 2^30, 2^40)

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Specifying resources in practice

This is what the spec of a Pod with resources will look like:

```yaml
containers:
- name: httpenv
  image: jpetazzo/httpenv
  resources:
    limits:
      memory: "100Mi"
      cpu: "100m"
    requests:
      memory: "100Mi"
      cpu: "10m"
```

This set of resources makes sure that this service won't be killed (as long as it stays below 100 MB of RAM), but allows its CPU usage to be throttled if necessary.

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Default values

- If we specify a limit without a request:

the request is set to the limit

- If we specify a request without a limit:

there will be no limit

(which means that the limit will be the size of the node)

- If we don't specify anything:

the request is zero and the limit is the size of the node

*Unless there are default values defined for our namespace!*

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## We need default resource values

- If we do not set resource values at all:

- the limit is "the size of the node"

- the request is zero

- This is generally *not* what we want

- a container without a limit can use up all the resources of a node

- if the request is zero, the scheduler can't make a smart placement decision

- To address this, we can set default values for resources

- This is done with a LimitRange object

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Defining min, max, and default resources

.nav[
[Previous section](#toc-resource-limits)
|
[Back to table of contents](#toc-chapter-10)
|
[Next section](#toc-namespace-quotas)
]

---

# Defining min, max, and default resources

- We can create LimitRange objects to indicate any combination of:

- min and/or max resources allowed per pod

- default resource *limits*

- default resource *requests*

- maximal burst ratio (*limit/request*)

- LimitRange objects are namespaced

- They apply to their namespace only

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## LimitRange example

```yaml
apiVersion: v1
kind: LimitRange
metadata:
  name: my-very-detailed-limitrange
spec:
  limits:
  - type: Container
    min:
      cpu: "100m"
    max:
      cpu: "2000m"
      memory: "1Gi"
    default:
      cpu: "500m"
      memory: "250Mi"
    defaultRequest:
      cpu: "500m"
```

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Example explanation

The YAML on the previous slide shows an example LimitRange object specifying very detailed limits on CPU usage,
and providing defaults on RAM usage.

Note the `type: Container` line: in the future,
it might also be possible to specify limits
per Pod, but it's not [officially documented yet](https://github.com/kubernetes/website/issues/9585).

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## LimitRange details

- LimitRange restrictions are enforced only when a Pod is created

(they don't apply retroactively)

- They don't prevent creation of e.g. an invalid Deployment or DaemonSet

(but the pods will not be created as long as the LimitRange is in effect)

- If there are multiple LimitRange restrictions, they all apply together

(which means that it's possible to specify conflicting LimitRanges,
 preventing any Pod from being created)

- If a LimitRange specifies a `max` for a resource but no `default`,
 that `max` value becomes the `default` limit too

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Namespace quotas

.nav[
[Previous section](#toc-defining-min-max-and-default-resources)
|
[Back to table of contents](#toc-chapter-10)
|
[Next section](#toc-limiting-resources-in-practice)
]

---

# Namespace quotas

- We can also set quotas per namespace

- Quotas apply to the total usage in a namespace

(e.g. total CPU limits of all pods in a given namespace)

- Quotas can apply to resource limits and/or requests

(like the CPU and memory limits that we saw earlier)

- Quotas can also apply to other resources:

- "extended" resources (like GPUs)

- storage size

- number of objects (number of pods, services...)

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Creating a quota for a namespace

- Quotas are enforced by creating a ResourceQuota object

- ResourceQuota objects are namespaced, and apply to their namespace only

- We can have multiple ResourceQuota objects in the same namespace

- The most restrictive values are used

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Limiting total CPU/memory usage

- The following YAML specifies an upper bound for *limits* and *requests*:
  ```yaml
    apiVersion: v1
    kind: ResourceQuota
    metadata:
      name: a-little-bit-of-compute
    spec:
      hard:
        requests.cpu: "10"
        requests.memory: 10Gi
        limits.cpu: "20"
        limits.memory: 20Gi
  ```

These quotas will apply to the namespace where the ResourceQuota is created.

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Limiting number of objects

- The following YAML specifies how many objects of specific types can be created:
  ```yaml
    apiVersion: v1
    kind: ResourceQuota
    metadata:
      name: quota-for-objects
    spec:
      hard:
        pods: 100
        services: 10
        secrets: 10
        configmaps: 10
        persistentvolumeclaims: 20
        services.nodeports: 0
        services.loadbalancers: 0
        count/roles.rbac.authorization.k8s.io: 10
  ```

(The `count/` syntax allows limiting arbitrary objects, including CRDs.)

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## YAML vs CLI

- Quotas can be created with a YAML definition

- ...Or with the `kubectl create quota` command

- Example:
  ```bash
  kubectl create quota my-resource-quota --hard=pods=300,limits.memory=300Gi
  ```

- With both YAML and CLI form, the values are always under the `hard` section

(there is no `soft` quota)

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Viewing current usage

When a ResourceQuota is created, we can see how much of it is used:

```
kubectl describe resourcequota my-resource-quota

Name:                            my-resource-quota
Namespace:                       default
Resource                         Used  Hard
--------                         ----  ----
pods                             12    100
services                         1     5
services.loadbalancers           0     0
services.nodeports               0     0
```

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Advanced quotas and PriorityClass

- Since Kubernetes 1.12, it is possible to create PriorityClass objects

- Pods can be assigned a PriorityClass

- Quotas can be linked to a PriorityClass

- This allows us to reserve resources for pods within a namespace

- For more details, check [this documentation page](https://kubernetes.io/docs/concepts/policy/resource-quotas/#resource-quota-per-priorityclass)

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Limiting resources in practice

.nav[
[Previous section](#toc-namespace-quotas)
|
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|
[Next section](#toc-checking-pod-and-node-resource-usage)
]

---

# Limiting resources in practice

- We have at least three mechanisms:

- requests and limits per Pod

- LimitRange per namespace

- ResourceQuota per namespace

- Let's see a simple recommendation to get started with resource limits

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Set a LimitRange

- In each namespace, create a LimitRange object

- Set a small default CPU request and CPU limit

(e.g. "100m")

- Set a default memory request and limit depending on your most common workload

- for Java, Ruby: start with "1G"

- for Go, Python, PHP, Node: start with "250M"

- Set upper bounds slightly below your expected node size

(80-90% of your node size, with at least a 500M memory buffer)

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Set a ResourceQuota

- In each namespace, create a ResourceQuota object

- Set generous CPU and memory limits

(e.g. half the cluster size if the cluster hosts multiple apps)

- Set generous objects limits

- these limits should not be here to constrain your users

- they should catch a runaway process creating many resources

- example: a custom controller creating many pods

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Observe, refine, iterate

- Observe the resource usage of your pods

(we will see how in the next chapter)

- Adjust individual pod limits

- If you see trends: adjust the LimitRange

(rather than adjusting every individual set of pod limits)

- Observe the resource usage of your namespaces

(with `kubectl describe resourcequota ...`)

- Rinse and repeat regularly

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

## Additional resources

- [A Practical Guide to Setting Kubernetes Requests and Limits](http://blog.kubecost.com/blog/requests-and-limits/)

- explains what requests and limits are

- provides guidelines to set requests and limits

- gives PromQL expressions to compute good values
 (our app needs to be running for a while)

- [Kube Resource Report](https://github.com/hjacobs/kube-resource-report/)

- generates web reports on resource usage

- [static demo](https://hjacobs.github.io/kube-resource-report/sample-report/output/index.html)
    |
    [live demo](https://kube-resource-report.demo.j-serv.de/applications.html)

.debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/resource-limits.md)]
---

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---

Checking pod and node resource usage

.nav[
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|
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|
[Next section](#toc-cluster-sizing)
]

---
# Checking pod and node resource usage

- Since Kubernetes 1.8, metrics are collected by the [resource metrics pipeline](https://kubernetes.io/docs/tasks/debug-application-cluster/resource-metrics-pipeline/)

- The resource metrics pipeline is:

- optional (Kubernetes can function without it)

- necessary for some features (like the Horizontal Pod Autoscaler)

- exposed through the Kubernetes API using the [aggregation layer](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/)

- usually implemented by the "metrics server"

.debug[[k8s/metrics-server.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/metrics-server.md)]
---

## How to know if the metrics server is running?

- The easiest way to know is to run `kubectl top`

- Check if the core metrics pipeline is available:
  ```bash
  kubectl top nodes
  ```

]

If it shows our nodes and their CPU and memory load, we're good!

.debug[[k8s/metrics-server.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/metrics-server.md)]
---

## Installing metrics server

- The metrics server doesn't have any particular requirements

(it doesn't need persistence, as it doesn't *store* metrics)

- It has its own repository, [kubernetes-incubator/metrics-server](https://github.com/kubernetes-incubator/metrics-server)

- The repository comes with [YAML files for deployment](https://github.com/kubernetes-incubator/metrics-server/tree/master/deploy/1.8%2B)

- These files may not work on some clusters

(e.g. if your node names are not in DNS)

- The container.training repository has a [metrics-server.yaml](https://github.com/jpetazzo/container.training/blob/master/k8s/metrics-server.yaml#L90) file to help with that

(we can `kubectl apply -f` that file if needed)

.debug[[k8s/metrics-server.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/metrics-server.md)]
---

## Showing container resource usage

- Once the metrics server is running, we can check container resource usage

- Show resource usage across all containers:
  ```bash
  kubectl top pods --containers --all-namespaces
  ```
]

- We can also use selectors (`-l app=...`)

.debug[[k8s/metrics-server.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/metrics-server.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Cluster sizing

.nav[
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|
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|
[Next section](#toc-the-horizontal-pod-autoscaler)
]

---
# Cluster sizing

- What happens when the cluster gets full?

- How can we scale up the cluster?

- Can we do it automatically?

- What are other methods to address capacity planning?

.debug[[k8s/cluster-sizing.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/cluster-sizing.md)]
---

## When are we out of resources?

- kubelet monitors node resources:

- memory

- node disk usage (typically the root filesystem of the node)

- image disk usage (where container images and RW layers are stored)

- For each resource, we can provide two thresholds:

- a hard threshold (if it's met, it provokes immediate action)

- a soft threshold (provokes action only after a grace period)

- Resource thresholds and grace periods are configurable

(by passing kubelet command-line flags)

.debug[[k8s/cluster-sizing.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/cluster-sizing.md)]
---

## What happens then?

- If disk usage is too high:

- kubelet will try to remove terminated pods

- then, it will try to *evict* pods

- If memory usage is too high:

- it will try to evict pods

- The node is marked as "under pressure"

- This temporarily prevents new pods from being scheduled on the node

.debug[[k8s/cluster-sizing.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/cluster-sizing.md)]
---

## Which pods get evicted?

- kubelet looks at the pods' QoS and PriorityClass

- First, pods with BestEffort QoS are considered

- Then, pods with Burstable QoS exceeding their *requests*

(but only if the exceeding resource is the one that is low on the node)

- Finally, pods with Guaranteed QoS, and Burstable pods within their requests

- Within each group, pods are sorted by PriorityClass

- If there are pods with the same PriorityClass, they are sorted by usage excess

(i.e. the pods whose usage exceeds their requests the most are evicted first)

.debug[[k8s/cluster-sizing.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/cluster-sizing.md)]
---

## Eviction of Guaranteed pods

- *Normally*, pods with Guaranteed QoS should not be evicted

- A chunk of resources is reserved for node processes (like kubelet)

- It is expected that these processes won't use more than this reservation

- If they do use more resources anyway, all bets are off!

- If this happens, kubelet must evict Guaranteed pods to preserve node stability

(or Burstable pods that are still within their requested usage)

.debug[[k8s/cluster-sizing.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/cluster-sizing.md)]
---

## What happens to evicted pods?

- The pod is terminated

- It is marked as `Failed` at the API level

- If the pod was created by a controller, the controller will recreate it

- The pod will be recreated on another node, *if there are resources available!*

- For more details about the eviction process, see:

- [this documentation page](https://kubernetes.io/docs/tasks/administer-cluster/out-of-resource/) about resource pressure and pod eviction,

- [this other documentation page](https://kubernetes.io/docs/concepts/configuration/pod-priority-preemption/) about pod priority and preemption.

.debug[[k8s/cluster-sizing.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/cluster-sizing.md)]
---

## What if there are no resources available?

- Sometimes, a pod cannot be scheduled anywhere:

- all the nodes are under pressure,

- or the pod requests more resources than are available

- The pod then remains in `Pending` state until the situation improves

.debug[[k8s/cluster-sizing.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/cluster-sizing.md)]
---

## Cluster scaling

- One way to improve the situation is to add new nodes

- This can be done automatically with the [Cluster Autoscaler](https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler)

- The autoscaler will automatically scale up:

- if there are pods that failed to be scheduled

- The autoscaler will automatically scale down:

- if nodes have a low utilization for an extended period of time

.debug[[k8s/cluster-sizing.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/cluster-sizing.md)]
---

## Restrictions, gotchas ...

- The Cluster Autoscaler only supports a few cloud infrastructures

(see [here](https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler/cloudprovider) for a list)

- The Cluster Autoscaler cannot scale down nodes that have pods using:

- local storage

- affinity/anti-affinity rules preventing them from being rescheduled

- a restrictive PodDisruptionBudget

.debug[[k8s/cluster-sizing.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/cluster-sizing.md)]
---

## Other way to do capacity planning

- "Running Kubernetes without nodes"

- Systems like [Virtual Kubelet](https://virtual-kubelet.io/) or Kiyot can run pods using on-demand resources

- Virtual Kubelet can leverage e.g. ACI or Fargate to run pods

- Kiyot runs pods in ad-hoc EC2 instances (1 instance per pod)

- Economic advantage (no wasted capacity)

- Security advantage (stronger isolation between pods)

Check [this blog post](http://jpetazzo.github.io/2019/02/13/running-kubernetes-without-nodes-with-kiyot/) for more details.

.debug[[k8s/cluster-sizing.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/cluster-sizing.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

The Horizontal Pod Autoscaler

.nav[
[Previous section](#toc-cluster-sizing)
|
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|
[Next section](#toc-stateful-sets)
]

---
# The Horizontal Pod Autoscaler

- What is the Horizontal Pod Autoscaler, or HPA?

- It is a controller that can perform *horizontal* scaling automatically

- Horizontal scaling = changing the number of replicas

(adding/removing pods)

- Vertical scaling = changing the size of individual replicas

(increasing/reducing CPU and RAM per pod)

- Cluster scaling = changing the size of the cluster

(adding/removing nodes)

.debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/horizontal-pod-autoscaler.md)]
---

## Principle of operation

- Each HPA resource (or "policy") specifies:

- which object to monitor and scale (e.g. a Deployment, ReplicaSet...)

- min/max scaling ranges (the max is a safety limit!)

- a target resource usage (e.g. the default is CPU=80%)

- The HPA continuously monitors the CPU usage for the related object

- It computes how many pods should be running:

`TargetNumOfPods = ceil(sum(CurrentPodsCPUUtilization) / Target)`

- It scales the related object up/down to this target number of pods

.debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/horizontal-pod-autoscaler.md)]
---

## Pre-requirements

- The metrics server needs to be running

(i.e. we need to be able to see pod metrics with `kubectl top pods`)

- The pods that we want to autoscale need to have resource requests

(because the target CPU% is not absolute, but relative to the request)

- The latter actually makes a lot of sense:

- if a Pod doesn't have a CPU request, it might be using 10% of CPU...

- ...but only because there is no CPU time available!

- this makes sure that we won't add pods to nodes that are already resource-starved

.debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/horizontal-pod-autoscaler.md)]
---

## Testing the HPA

- We will start a CPU-intensive web service

- We will send some traffic to that service

- We will create an HPA policy

- The HPA will automatically scale up the service for us

.debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/horizontal-pod-autoscaler.md)]
---

## A CPU-intensive web service

- Let's use `jpetazzo/busyhttp`

(it is a web server that will use 1s of CPU for each HTTP request)

- Deploy the web server:
  ```bash
  kubectl create deployment busyhttp --image=jpetazzo/busyhttp
  ```

- Expose it with a ClusterIP service:
  ```bash
  kubectl expose deployment busyhttp --port=80
  ```

- Get the ClusterIP allocated to the service:
  ```bash
  kubectl get svc busyhttp
  ```

]

.debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/horizontal-pod-autoscaler.md)]
---

## Monitor what's going on

- Let's start a bunch of commands to watch what is happening

- Monitor pod CPU usage:
  ```bash
  watch kubectl top pods -l app=busyhttp
  ```

- Monitor service latency:
  ```bash
  httping http://`$CLUSTERIP`/
  ```

- Monitor cluster events:
  ```bash
  kubectl get events -w
  ```

]

.debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/horizontal-pod-autoscaler.md)]
---

## Send traffic to the service

- We will use `ab` (Apache Bench) to send traffic

- Send a lot of requests to the service, with a concurrency level of 3:
  ```bash
  ab -c 3 -n 100000 http://`$CLUSTERIP`/
  ```

]

The latency (reported by `httping`) should increase above 3s.

The CPU utilization should increase to 100%.

(The server is single-threaded and won't go above 100%.)

.debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/horizontal-pod-autoscaler.md)]
---

## Create an HPA policy

- There is a helper command to do that for us: `kubectl autoscale`

- Create the HPA policy for the `busyhttp` deployment:
  ```bash
  kubectl autoscale deployment busyhttp --max=10
  ```

]

By default, it will assume a target of 80% CPU usage.

This can also be set with `--cpu-percent=`.

*The autoscaler doesn't seem to work. Why?*

.debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/horizontal-pod-autoscaler.md)]
---

## What did we miss?

- The events stream gives us a hint, but to be honest, it's not very clear:

`missing request for cpu`

- We forgot to specify a resource request for our Deployment!

- The HPA target is not an absolute CPU%

- It is relative to the CPU requested by the pod

.debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/horizontal-pod-autoscaler.md)]
---

## Adding a CPU request

- Let's edit the deployment and add a CPU request

- Since our server can use up to 1 core, let's request 1 core

- Edit the Deployment definition:
  ```bash
  kubectl edit deployment busyhttp
  ```

- In the `containers` list, add the following block:
  ```yaml
    resources:
      requests:
        cpu: "1"
  ```

]

.debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/horizontal-pod-autoscaler.md)]
---

## Results

- After saving and quitting, a rolling update happens

(if `ab` or `httping` exits, make sure to restart it)

- It will take a minute or two for the HPA to kick in:

- the HPA runs every 30 seconds by default

- it needs to gather metrics from the metrics server first

- If we scale further up (or down), the HPA will react after a few minutes:

- it won't scale up if it already scaled in the last 3 minutes

- it won't scale down if it already scaled in the last 5 minutes

.debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/horizontal-pod-autoscaler.md)]
---

## What about other metrics?

- The HPA in API group `autoscaling/v1` only supports CPU scaling

- The HPA in API group `autoscaling/v2beta2` supports metrics from various API groups:

- metrics.k8s.io, aka metrics server (per-Pod CPU and RAM)

- custom.metrics.k8s.io, custom metrics per Pod

- external.metrics.k8s.io, external metrics (not associated to Pods)

- Kubernetes doesn't implement any of these API groups

- Using these metrics requires [registering additional APIs](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/#support-for-metrics-apis)

- The metrics provided by metrics server are standard; everything else is custom

- For more details, see [this great blog post](https://medium.com/uptime-99/kubernetes-hpa-autoscaling-with-custom-and-external-metrics-da7f41ff7846) or [this talk](https://www.youtube.com/watch?v=gSiGFH4ZnS8)

.debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/horizontal-pod-autoscaler.md)]
---

## Cleanup

- Since `busyhttp` uses CPU cycles, let's stop it before moving on

- Delete the `busyhttp` Deployment:
  ```bash
  kubectl delete deployment busyhttp
  ```

]

.debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/horizontal-pod-autoscaler.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Stateful sets

.nav[
[Previous section](#toc-the-horizontal-pod-autoscaler)
|
[Back to table of contents](#toc-chapter-11)
|
[Next section](#toc-running-a-consul-cluster)
]

---
# Stateful sets

- Stateful sets are a type of resource in the Kubernetes API

(like pods, deployments, services...)

- They offer mechanisms to deploy scaled stateful applications

- At a first glance, they look like *deployments*:

- a stateful set defines a pod spec and a number of replicas *R*

- it will make sure that *R* copies of the pod are running

- that number can be changed while the stateful set is running

- updating the pod spec will cause a rolling update to happen

- But they also have some significant differences

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## Stateful sets unique features

- Pods in a stateful set are numbered (from 0 to *R-1*) and ordered

- They are started and updated in order (from 0 to *R-1*)

- A pod is started (or updated) only when the previous one is ready

- They are stopped in reverse order (from *R-1* to 0)

- Each pod know its identity (i.e. which number it is in the set)

- Each pod can discover the IP address of the others easily

- The pods can persist data on attached volumes

🤔 Wait a minute ... Can't we already attach volumes to pods and deployments?

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## Revisiting volumes

- [Volumes](https://kubernetes.io/docs/concepts/storage/volumes/) are used for many purposes:

- sharing data between containers in a pod

- exposing configuration information and secrets to containers

- accessing storage systems

- Let's see examples of the latter usage

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## Volumes types

- There are many [types of volumes](https://kubernetes.io/docs/concepts/storage/volumes/#types-of-volumes) available:

- public cloud storage (GCEPersistentDisk, AWSElasticBlockStore, AzureDisk...)

- private cloud storage (Cinder, VsphereVolume...)

- traditional storage systems (NFS, iSCSI, FC...)

- distributed storage (Ceph, Glusterfs, Portworx...)

- Using a persistent volume requires:

- creating the volume out-of-band (outside of the Kubernetes API)

- referencing the volume in the pod description, with all its parameters

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## Using a cloud volume

Here is a pod definition using an AWS EBS volume (that has to be created first):

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: pod-using-my-ebs-volume
spec:
  containers:
  - image: ...
    name: container-using-my-ebs-volume
    volumeMounts:
    - mountPath: /my-ebs
      name: my-ebs-volume
  volumes:
  - name: my-ebs-volume
    awsElasticBlockStore:
      volumeID: vol-049df61146c4d7901
      fsType: ext4
```

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## Using an NFS volume

Here is another example using a volume on an NFS server:

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: pod-using-my-nfs-volume
spec:
  containers:
  - image: ...
    name: container-using-my-nfs-volume
    volumeMounts:
    - mountPath: /my-nfs
      name: my-nfs-volume
  volumes:
  - name: my-nfs-volume
      nfs:
        server: 192.168.0.55
        path: "/exports/assets"
```

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## Shortcomings of volumes

- Their lifecycle (creation, deletion...) is managed outside of the Kubernetes API

(we can't just use `kubectl apply/create/delete/...` to manage them)

- If a Deployment uses a volume, all replicas end up using the same volume

- That volume must then support concurrent access

- some volumes do (e.g. NFS servers support multiple read/write access)

- some volumes support concurrent reads

- some volumes support concurrent access for colocated pods

- What we really need is a way for each replica to have its own volume

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## Persistent Volume Claims

- To abstract the different types of storage, a pod can use a special volume type

- This type is a *Persistent Volume Claim*

- A Persistent Volume Claim (PVC) is a resource type

(visible with `kubectl get persistentvolumeclaims` or `kubectl get pvc`)

- A PVC is not a volume; it is a *request for a volume*

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## Persistent Volume Claims in practice

- Using a Persistent Volume Claim is a two-step process:

- creating the claim

- using the claim in a pod (as if it were any other kind of volume)

- A PVC starts by being Unbound (without an associated volume)

- Once it is associated with a Persistent Volume, it becomes Bound

- A Pod referring an unbound PVC will not start

(but as soon as the PVC is bound, the Pod can start)

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## Binding PV and PVC

- A Kubernetes controller continuously watches PV and PVC objects

- When it notices an unbound PVC, it tries to find a satisfactory PV

("satisfactory" in terms of size and other characteristics; see next slide)

- If no PV fits the PVC, a PV can be created dynamically

(this requires to configure a *dynamic provisioner*, more on that later)

- Otherwise, the PVC remains unbound indefinitely

(until we manually create a PV or setup dynamic provisioning)

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## What's in a Persistent Volume Claim?

- At the very least, the claim should indicate:

- the size of the volume (e.g. "5 GiB")

- the access mode (e.g. "read-write by a single pod")

- Optionally, it can also specify a Storage Class

- The Storage Class indicates:

- which storage system to use (e.g. Portworx, EBS...)

- extra parameters for that storage system

e.g.: "replicate the data 3 times, and use SSD media"

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## What's a Storage Class?

- A Storage Class is yet another Kubernetes API resource

(visible with e.g. `kubectl get storageclass` or `kubectl get sc`)

- It indicates which *provisioner* to use

(which controller will create the actual volume)

- And arbitrary parameters for that provisioner

(replication levels, type of disk ... anything relevant!)

- Storage Classes are required if we want to use [dynamic provisioning](https://kubernetes.io/docs/concepts/storage/dynamic-provisioning/)

(but we can also create volumes manually, and ignore Storage Classes)

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## Defining a Persistent Volume Claim

Here is a minimal PVC:

```yaml
kind: PersistentVolumeClaim
apiVersion: v1
metadata:
   name: my-claim
spec:
   accessModes:
     - ReadWriteOnce
   resources:
     requests:
       storage: 1Gi
```

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## Using a Persistent Volume Claim

Here is a Pod definition like the ones shown earlier, but using a PVC:

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: pod-using-a-claim
spec:
  containers:
  - image: ...
    name: container-using-a-claim
    volumeMounts:
    - mountPath: /my-vol
      name: my-volume
  volumes:
  - name: my-volume
    persistentVolumeClaim:
      claimName: my-claim
```

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## Persistent Volume Claims and Stateful sets

- The pods in a stateful set can define a `volumeClaimTemplate`

- A `volumeClaimTemplate` will dynamically create one Persistent Volume Claim per pod

- Each pod will therefore have its own volume

- These volumes are numbered (like the pods)

- When updating the stateful set (e.g. image upgrade), each pod keeps its volume

- When pods get rescheduled (e.g. node failure), they keep their volume

(this requires a storage system that is not node-local)

- These volumes are not automatically deleted

(when the stateful set is scaled down or deleted)

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## Stateful set recap

- A Stateful sets manages a number of identical pods

(like a Deployment)

- These pods are numbered, and started/upgraded/stopped in a specific order

- These pods are aware of their number

(e.g., #0 can decide to be the primary, and #1 can be secondary)

- These pods can find the IP addresses of the other pods in the set

(through a *headless service*)

- These pods can each have their own persistent storage

(Deployments cannot do that)

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Running a Consul cluster

.nav[
[Previous section](#toc-stateful-sets)
|
[Back to table of contents](#toc-chapter-11)
|
[Next section](#toc-local-persistent-volumes)
]

---

# Running a Consul cluster

- Here is a good use-case for Stateful sets!

- We are going to deploy a Consul cluster with 3 nodes

- Consul is a highly-available key/value store

(like etcd or Zookeeper)

- One easy way to bootstrap a cluster is to tell each node:

- the addresses of other nodes

- how many nodes are expected (to know when quorum is reached)

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## Bootstrapping a Consul cluster

*After reading the Consul documentation carefully (and/or asking around),
we figure out the minimal command-line to run our Consul cluster.*

```
consul agent -data-dir=/consul/data -client=0.0.0.0 -server -ui \
       -bootstrap-expect=3 \
       -retry-join=`X.X.X.X` \
       -retry-join=`Y.Y.Y.Y`
```

- Replace X.X.X.X and Y.Y.Y.Y with the addresses of other nodes

- The same command-line can be used on all nodes (convenient!)

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## Cloud Auto-join

- Since version 1.4.0, Consul can use the Kubernetes API to find its peers

- This is called [Cloud Auto-join]

- Instead of passing an IP address, we need to pass a parameter like this:

```
  consul agent -retry-join "provider=k8s label_selector=\"app=consul\""
  ```

- Consul needs to be able to talk to the Kubernetes API

- We can provide a `kubeconfig` file

- If Consul runs in a pod, it will use the *service account* of the pod

[Cloud Auto-join]: https://www.consul.io/docs/agent/cloud-auto-join.html#kubernetes-k8s-

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## Setting up Cloud auto-join

- We need to create a service account for Consul

- We need to create a role that can `list` and `get` pods

- We need to bind that role to the service account

- And of course, we need to make sure that Consul pods use that service account

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## Putting it all together

- The file `k8s/consul.yaml` defines the required resources

(service account, cluster role, cluster role binding, service, stateful set)

- It has a few extra touches:

- a `podAntiAffinity` prevents two pods from running on the same node

- a `preStop` hook makes the pod leave the cluster when shutdown gracefully

This was inspired by this [excellent tutorial](https://github.com/kelseyhightower/consul-on-kubernetes) by Kelsey Hightower.
Some features from the original tutorial (TLS authentication between
nodes and encryption of gossip traffic) were removed for simplicity.

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## Running our Consul cluster

- We'll use the provided YAML file

- Create the stateful set and associated service:
  ```bash
  kubectl apply -f ~/container.training/k8s/consul.yaml
  ```

- Check the logs as the pods come up one after another:
  ```bash
  stern consul
  ```

- Check the health of the cluster:
  ```bash
  kubectl exec consul-0 consul members
  ```

]

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## Caveats

- We haven't used a `volumeClaimTemplate` here

- That's because we don't have a storage provider yet

(except if you're running this on your own and your cluster has one)

- What happens if we lose a pod?

- a new pod gets rescheduled (with an empty state)

- the new pod tries to connect to the two others

- it will be accepted (after 1-2 minutes of instability)

- and it will retrieve the data from the other pods

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

## Failure modes

- What happens if we lose two pods?

- manual repair will be required

- we will need to instruct the remaining one to act solo

- then rejoin new pods

- What happens if we lose three pods? (aka all of them)

- we lose all the data (ouch)

- If we run Consul without persistent storage, backups are a good idea!

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/statefulsets.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Local Persistent Volumes

.nav[
[Previous section](#toc-running-a-consul-cluster)
|
[Back to table of contents](#toc-chapter-11)
|
[Next section](#toc-highly-available-persistent-volumes)
]

---
# Local Persistent Volumes

- We want to run that Consul cluster *and* actually persist data

- But we don't have a distributed storage system

- We are going to use local volumes instead

(similar conceptually to `hostPath` volumes)

- We can use local volumes without installing extra plugins

- However, they are tied to a node

- If that node goes down, the volume becomes unavailable

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/local-persistent-volumes.md)]
---

## With or without dynamic provisioning

- We will deploy a Consul cluster *with* persistence

- That cluster's StatefulSet will create PVCs

- These PVCs will remain unbound¹, until we will create local volumes manually

(we will basically do the job of the dynamic provisioner)

- Then, we will see how to automate that with a dynamic provisioner

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/local-persistent-volumes.md)]
---

## If we have a dynamic provisioner ...

- The labs in this section assume that we *do not* have a dynamic provisioner

- If we do have one, we need to disable it

- Check if we have a dynamic provisioner:
  ```bash
  kubectl get storageclass
  ```

- If the output contains a line with `(default)`, run this command:
  ```bash
  kubectl annotate sc storageclass.kubernetes.io/is-default-class- --all
  ```

- Check again that it is no longer marked as `(default)`

]

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/local-persistent-volumes.md)]
---

## Deploying Consul

- We will use a slightly different YAML file

- The only differences between that file and the previous one are:

- `volumeClaimTemplate` defined in the Stateful Set spec

- the corresponding `volumeMounts` in the Pod spec

- the label `consul` has been changed to `persistentconsul`
 
 (to avoid conflicts with the other Stateful Set)

- Apply the persistent Consul YAML file:
  ```bash
  kubectl apply -f ~/container.training/k8s/persistent-consul.yaml
  ```

]

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/local-persistent-volumes.md)]
---

## Observing the situation

- Let's look at Persistent Volume Claims and Pods

- Check that we now have an unbound Persistent Volume Claim:
  ```bash
  kubectl get pvc
  ```

- We don't have any Persistent Volume:
  ```bash
  kubectl get pv
  ```

- The Pod `persistentconsul-0` is not scheduled yet:
  ```bash
  kubectl get pods -o wide
  ```

]

*Hint: leave these commands running with `-w` in different windows.*

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/local-persistent-volumes.md)]
---

## Explanations

- In a Stateful Set, the Pods are started one by one

- `persistentconsul-1` won't be created until `persistentconsul-0` is running

- `persistentconsul-0` has a dependency on an unbound Persistent Volume Claim

- The scheduler won't schedule the Pod until the PVC is bound

(because the PVC might be bound to a volume that is only available on a subset of nodes; for instance EBS are tied to an availability zone)

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/local-persistent-volumes.md)]
---

## Creating Persistent Volumes

- Let's create 3 local directories (`/mnt/consul`) on node2, node3, node4

- Then create 3 Persistent Volumes corresponding to these directories

- Create the local directories:
  ```bash
    for NODE in node2 node3 node4; do
      ssh $NODE sudo mkdir -p /mnt/consul
    done
  ```

- Create the PV objects:
  ```bash
  kubectl apply -f ~/container.training/k8s/volumes-for-consul.yaml
  ```

]

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/local-persistent-volumes.md)]
---

## Check our Consul cluster

- The PVs that we created will be automatically matched with the PVCs

- Once a PVC is bound, its pod can start normally

- Once the pod `persistentconsul-0` has started, `persistentconsul-1` can be created, etc.

- Eventually, our Consul cluster is up, and backend by "persistent" volumes

- Check that our Consul clusters has 3 members indeed:
  ```bash
  kubectl exec persistentconsul-0 consul members
  ```

]

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/local-persistent-volumes.md)]
---

## Devil is in the details (1/2)

- The size of the Persistent Volumes is bogus

(it is used when matching PVs and PVCs together, but there is no actual quota or limit)

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/local-persistent-volumes.md)]
---

## Devil is in the details (2/2)

- This specific example worked because we had exactly 1 free PV per node:

- if we had created multiple PVs per node ...

- we could have ended with two PVCs bound to PVs on the same node ...

- which would have required two pods to be on the same node ...

- which is forbidden by the anti-affinity constraints in the StatefulSet

- To avoid that, we need to associated the PVs with a Storage Class that has:
  ```yaml
  volumeBindingMode: WaitForFirstConsumer
  ```
  (this means that a PVC will be bound to a PV only after being used by a Pod)

- See [this blog post](https://kubernetes.io/blog/2018/04/13/local-persistent-volumes-beta/) for more details

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/local-persistent-volumes.md)]
---

## Bulk provisioning

- It's not practical to manually create directories and PVs for each app

- We *could* pre-provision a number of PVs across our fleet

- We could even automate that with a Daemon Set:

- creating a number of directories on each node

- creating the corresponding PV objects

- We also need to recycle volumes

- ... This can quickly get out of hand

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/local-persistent-volumes.md)]
---

## Dynamic provisioning

- We could also write our own provisioner, which would:

- watch the PVCs across all namespaces

- when a PVC is created, create a corresponding PV on a node

- Or we could use one of the dynamic provisioners for local persistent volumes

(for instance the [Rancher local path provisioner](https://github.com/rancher/local-path-provisioner))

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/local-persistent-volumes.md)]
---

## Strategies for local persistent volumes

- Remember, when a node goes down, the volumes on that node become unavailable

- High availability will require another layer of replication

(like what we've just seen with Consul; or primary/secondary; etc)

- Pre-provisioning PVs makes sense for machines with local storage

(e.g. cloud instance storage; or storage directly attached to a physical machine)

- Dynamic provisioning makes sense for large number of applications

(when we can't or won't dedicate a whole disk to a volume)

- It's possible to mix both (using distinct Storage Classes)

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/local-persistent-volumes.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Highly available Persistent Volumes

.nav[
[Previous section](#toc-local-persistent-volumes)
|
[Back to table of contents](#toc-chapter-11)
|
[Next section](#toc-extra-content-security)
]

---
# Highly available Persistent Volumes

- How can we achieve true durability?

- How can we store data that would survive the loss of a node?

- We need to use Persistent Volumes backed by highly available storage systems

- There are many ways to achieve that:

- leveraging our cloud's storage APIs

- using NAS/SAN systems or file servers

- distributed storage systems

- We are going to see one distributed storage system in action

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Our test scenario

- We will set up a distributed storage system on our cluster

- We will use it to deploy a SQL database (PostgreSQL)

- We will insert some test data in the database

- We will disrupt the node running the database

- We will see how it recovers

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Portworx

- Portworx is a *commercial* persistent storage solution for containers

- It works with Kubernetes, but also Mesos, Swarm ...

- It provides [hyper-converged](https://en.wikipedia.org/wiki/Hyper-converged_infrastructure) storage

(=storage is provided by regular compute nodes)

- We're going to use it here because it can be deployed on any Kubernetes cluster

(it doesn't require any particular infrastructure)

- We don't endorse or support Portworx in any particular way

(but we appreciate that it's super easy to install!)

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## A useful reminder

- We're installing Portworx because we need a storage system

- If you are using AKS, EKS, GKE ... you already have a storage system

(but you might want another one, e.g. to leverage local storage)

- If you have setup Kubernetes yourself, there are other solutions available too

- on premises, you can use a good old SAN/NAS

- on a private cloud like OpenStack, you can use e.g. Cinder

- everywhere, you can use other systems, e.g. Gluster, StorageOS

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Portworx requirements

- Kubernetes cluster ✔️

- Optional key/value store (etcd or Consul) ❌

- At least one available block device ❌

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## The key-value store

- In the current version of Portworx (1.4) it is recommended to use etcd or Consul

- But Portworx also has beta support for an embedded key/value store

- For simplicity, we are going to use the latter option

(but if we have deployed Consul or etcd, we can use that, too)

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## One available block device

- Block device = disk or partition on a disk

- We can see block devices with `lsblk`

(or `cat /proc/partitions` if we're old school like that!)

- If we don't have a spare disk or partition, we can use a *loop device*

- A loop device is a block device actually backed by a file

- These are frequently used to mount ISO (CD/DVD) images or VM disk images

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Setting up a loop device

- We are going to create a 10 GB (empty) file on each node

- Then make a loop device from it, to be used by Portworx

- Create a 10 GB file on each node:
  ```bash
  for N in $(seq 1 4); do ssh node$N sudo truncate --size 10G /portworx.blk; done
  ```
  (If SSH asks to confirm host keys, enter `yes` each time.)

- Associate the file to a loop device on each node:
  ```bash
  for N in $(seq 1 4); do ssh node$N sudo losetup /dev/loop4 /portworx.blk; done
  ```

]

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Installing Portworx

- To install Portworx, we need to go to https://install.portworx.com/

- This website will ask us a bunch of questions about our cluster

- Then, it will generate a YAML file that we should apply to our cluster

- Or, we can just apply that YAML file directly (it's in `k8s/portworx.yaml`)

- Install Portworx:
  ```bash
  kubectl apply -f ~/container.training/k8s/portworx.yaml
  ```

]

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Generating a custom YAML file

If you want to generate a YAML file tailored to your own needs, the easiest
way is to use https://install.portworx.com/.

FYI, this is how we obtained the YAML file used earlier:
```
KBVER=$(kubectl version -o json | jq -r .serverVersion.gitVersion)
BLKDEV=/dev/loop4
curl https://install.portworx.com/1.4/?kbver=$KBVER&b=true&s=$BLKDEV&c=px-workshop&stork=true&lh=true
```
If you want to use an external key/value store, add one of the following:
```
&k=etcd://`XXX`:2379
&k=consul://`XXX`:8500
```
... where `XXX` is the name or address of your etcd or Consul server.

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Waiting for Portworx to be ready

- The installation process will take a few minutes

- Check out the logs:
  ```bash
  stern -n kube-system portworx
  ```

- Wait until it gets quiet

(you should see `portworx service is healthy`, too)

]

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Dynamic provisioning of persistent volumes

- We are going to run PostgreSQL in a Stateful set

- The Stateful set will specify a `volumeClaimTemplate`

- That `volumeClaimTemplate` will create Persistent Volume Claims

- Kubernetes' [dynamic provisioning](https://kubernetes.io/docs/concepts/storage/dynamic-provisioning/) will satisfy these Persistent Volume Claims

(by creating Persistent Volumes and binding them to the claims)

- The Persistent Volumes are then available for the PostgreSQL pods

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Storage Classes

- It's possible that multiple storage systems are available

- Or, that a storage system offers multiple tiers of storage

(SSD vs. magnetic; mirrored or not; etc.)

- We need to tell Kubernetes *which* system and tier to use

- This is achieved by creating a Storage Class

- A `volumeClaimTemplate` can indicate which Storage Class to use

- It is also possible to mark a Storage Class as "default"

(it will be used if a `volumeClaimTemplate` doesn't specify one)

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Check our default Storage Class

- The YAML manifest applied earlier should define a default storage class

- Check that we have a default storage class:
  ```bash
  kubectl get storageclass
  ```

]

There should be a storage class showing as `portworx-replicated (default)`.

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Our default Storage Class

This is our Storage Class (in `k8s/storage-class.yaml`):

```yaml
kind: StorageClass
apiVersion: storage.k8s.io/v1beta1
metadata:
  name: portworx-replicated
  annotations:
    storageclass.kubernetes.io/is-default-class: "true"
provisioner: kubernetes.io/portworx-volume
parameters:
 repl: "2"
 priority_io: "high"
```

- It says "use Portworx to create volumes"

- It tells Portworx to "keep 2 replicas of these volumes"

- It marks the Storage Class as being the default one

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Our Postgres Stateful set

- The next slide shows `k8s/postgres.yaml`

- It defines a Stateful set

- With a `volumeClaimTemplate` requesting a 1 GB volume

- That volume will be mounted to `/var/lib/postgresql/data`

- There is another little detail: we enable the `stork` scheduler

- The `stork` scheduler is optional (it's specific to Portworx)

- It helps the Kubernetes scheduler to colocate the pod with its volume

(see [this blog post](https://portworx.com/stork-storage-orchestration-kubernetes/) for more details about that)

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

.small[
```yaml
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: postgres
spec:
  selector:
    matchLabels:
      app: postgres
  serviceName: postgres
  template:
    metadata:
      labels:
        app: postgres
    spec:
      schedulerName: stork
      containers:
      - name: postgres
        image: postgres:11
        volumeMounts:
        - mountPath: /var/lib/postgresql/data
          name: postgres
  volumeClaimTemplates:
  - metadata:
      name: postgres
    spec:
      accessModes: ["ReadWriteOnce"]
      resources:
        requests:
          storage: 1Gi
```
]

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Creating the Stateful set

- Before applying the YAML, watch what's going on with `kubectl get events -w`

- Apply that YAML:
  ```bash
  kubectl apply -f ~/container.training/k8s/postgres.yaml
  ```

]

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Testing our PostgreSQL pod

- We will use `kubectl exec` to get a shell in the pod

- Good to know: we need to use the `postgres` user in the pod

- Get a shell in the pod, as the `postgres` user:
  ```bash
  kubectl exec -ti postgres-0 su postgres
  ```

- Check that default databases have been created correctly:
  ```bash
  psql -l
  ```

]

(This should show us 3 lines: postgres, template0, and template1.)

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Inserting data in PostgreSQL

- We will create a database and populate it with `pgbench`

- Create a database named `demo`:
  ```bash
  createdb demo
  ```

- Populate it with `pgbench`:
  ```bash
  pgbench -i -s 10 demo
  ```

]

- The `-i` flag means "create tables"

- The `-s 10` flag means "create 10 x 100,000 rows"

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Checking how much data we have now

- The `pgbench` tool inserts rows in table `pgbench_accounts`

- Check that the `demo` base exists:
  ```bash
  psql -l
  ```

- Check how many rows we have in `pgbench_accounts`:
  ```bash
  psql demo -c "select count(*) from pgbench_accounts"
  ```

]

(We should see a count of 1,000,000 rows.)

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Find out which node is hosting the database

- We can find that information with `kubectl get pods -o wide`

- Check the node running the database:
  ```bash
  kubectl get pod postgres-0 -o wide
  ```

]

We are going to disrupt that node.

By "disrupt" we mean: "disconnect it from the network".

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Disconnect the node

- We will use `iptables` to block all traffic exiting the node

(except SSH traffic, so we can repair the node later if needed)

- SSH to the node to disrupt:
  ```bash
  ssh `nodeX`
  ```

- Allow SSH traffic leaving the node, but block all other traffic:
  ```bash
  sudo iptables -I OUTPUT -p tcp --sport 22 -j ACCEPT
  sudo iptables -I OUTPUT 2 -j DROP
  ```

]

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Check that the node is disconnected

- Check that the node can't communicate with other nodes:
  ```
  ping node1
  ```

- Logout to go back on `node1`

- Watch the events unfolding with `kubectl get events -w` and `kubectl get pods -w`

]

- It will take some time for Kubernetes to mark the node as unhealthy

- Then it will attempt to reschedule the pod to another node

- In about a minute, our pod should be up and running again

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Check that our data is still available

- We are going to reconnect to the (new) pod and check

- Get a shell on the pod:
  ```bash
  kubectl exec -ti postgres-0 su postgres
  ```

- Check the number of rows in the `pgbench_accounts` table:
  ```bash
  psql demo -c "select count(*) from pgbench_accounts"
  ```

]

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Double-check that the pod has really moved

- Just to make sure the system is not bluffing!

- Look at which node the pod is now running on
  ```bash
  kubectl get pod postgres-0 -o wide
  ```

]

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Re-enable the node

- Let's fix the node that we disconnected from the network

- SSH to the node:
  ```bash
  ssh `nodeX`
  ```

- Remove the iptables rule blocking traffic:
  ```bash
  sudo iptables -D OUTPUT 2
  ```

]

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## A few words about this PostgreSQL setup

- In a real deployment, you would want to set a password

- This can be done by creating a `secret`:
  ```
  kubectl create secret generic postgres \
          --from-literal=password=$(base64 /dev/urandom | head -c16)
  ```

- And then passing that secret to the container:
  ```yaml
  env:
  - name: POSTGRES_PASSWORD
    valueFrom:
      secretKeyRef:
        name: postgres
        key: password
  ```

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Troubleshooting Portworx

- If we need to see what's going on with Portworx:
  ```
  PXPOD=$(kubectl -n kube-system get pod -l name=portworx -o json | 
  	      jq -r .items[0].metadata.name)
  kubectl -n kube-system exec $PXPOD -- /opt/pwx/bin/pxctl status
  ```

- We can also connect to Lighthouse (a web UI)

- check the port with `kubectl -n kube-system get svc px-lighthouse`

- connect to that port

- the default login/password is `admin/Password1`

- then specify `portworx-service` as the endpoint

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Removing Portworx

- Portworx provides a storage driver

- It needs to place itself "above" the Kubelet

(it installs itself straight on the nodes)

- To remove it, we need to do more than just deleting its Kubernetes resources

- It is done by applying a special label:
  ```
  kubectl label nodes --all px/enabled=remove --overwrite
  ```

- Then removing a bunch of local files:
  ```
  sudo chattr -i /etc/pwx/.private.json
  sudo rm -rf /etc/pwx /opt/pwx
  ```

(on each node where Portworx was running)

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Dynamic provisioning without a provider

- What if we want to use Stateful sets without a storage provider?

- We will have to create volumes manually

(by creating Persistent Volume objects)

- These volumes will be automatically bound with matching Persistent Volume Claims

- We can use local volumes (essentially bind mounts of host directories)

- Of course, these volumes won't be available in case of node failure

- Check [this blog post](https://kubernetes.io/blog/2018/04/13/local-persistent-volumes-beta/) for more information and gotchas

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

## Acknowledgements

The Portworx installation tutorial, and the PostgreSQL example,
were inspired by [Portworx examples on Katacoda](https://katacoda.com/portworx/scenarios/), in particular:

- [installing Portworx on Kubernetes](https://www.katacoda.com/portworx/scenarios/deploy-px-k8s)

(with adapatations to use a loop device and an embedded key/value store)

- [persistent volumes on Kubernetes using Portworx](https://www.katacoda.com/portworx/scenarios/px-k8s-vol-basic)

(with adapatations to specify a default Storage Class)

- [HA PostgreSQL on Kubernetes with Portworx](https://www.katacoda.com/portworx/scenarios/px-k8s-postgres-all-in-one)

(with adaptations to use a Stateful Set and simplify PostgreSQL's setup)

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/portworx.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

(Extra content: security)

.nav[
[Previous section](#toc-highly-available-persistent-volumes)
|
[Back to table of contents](#toc-chapter-12)
|
[Next section](#toc-network-policies)
]

---
# (Extra content: security)

.debug[[kube.yml](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/kube.yml)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Network policies

.nav[
[Previous section](#toc-extra-content-security)
|
[Back to table of contents](#toc-chapter-12)
|
[Next section](#toc-authentication-and-authorization)
]

---
# 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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/slides/k8s/netpol.md)]
---

## 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/2020-01-zr/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,
 
 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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/slides/k8s/netpol.md)]
---

## Running our test web server

- 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/2020-01-zr/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)

- 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/2020-01-zr/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/2020-01-zr/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 ...`

- 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/2020-01-zr/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/2020-01-zr/slides/k8s/netpol.md)]
---

## Testing the network policy

- Let's create pods with, and without, the required label

- 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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/slides/k8s/netpol.md)]
---

## Allowing traffic to `webui` pods

This policy selects all pods with label `app=webui`.

It allows traffic from any source.

(An empty `from` field means "all sources.")

```yaml
kind: NetworkPolicy
apiVersion: networking.k8s.io/v1
metadata:
  name: allow-webui
spec:
  podSelector:
    matchLabels:
      app: webui
  ingress:
  - from: []
```

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/netpol.md)]
---

## Applying both network policies

- Both network policies are declared in the file `k8s/netpol-dockercoins.yaml`

- 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
 
 (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/2020-01-zr/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!

- Remove all network policies:
  ```bash
  kubectl delete networkpolicies --all
  ```

]

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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
 
 (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/2020-01-zr/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/2020-01-zr/slides/k8s/netpol.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Authentication and authorization

.nav[
[Previous section](#toc-network-policies)
|
[Back to table of contents](#toc-chapter-12)
|
[Next section](#toc-securing-the-control-plane)
]

---
# Authentication and authorization

*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/2020-01-zr/slides/k8s/authn-authz.md)]
---

## Authentication in Kubernetes

- When the API server receives a request, it tries to authenticate it

(it examines headers, certificates... anything available)

- Many authentication methods are available and can be used simultaneously

(we will see them on the next slide)

- It's the job of the authentication method to produce:

- the user name
  - the user ID
  - a list of groups

- The API server doesn't interpret these; that'll be the job of *authorizers*

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/authn-authz.md)]
---

## Authentication methods

- TLS client certificates

(that's what we've been doing with `kubectl` so far)

- Bearer tokens

(a secret token in the HTTP headers of the request)

- [HTTP basic auth](https://en.wikipedia.org/wiki/Basic_access_authentication)

(carrying user and password in an HTTP header)

- Authentication proxy

(sitting in front of the API and setting trusted headers)

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/slides/k8s/authn-authz.md)]
---

## Authentication with TLS certificates

- This is enabled in most Kubernetes deployments

- The user name is derived from the `CN` in the client certificates

- The groups are derived from the `O` fields in the client certificate

- From the point of view of the Kubernetes API, users do not exist

(i.e. they are not stored in etcd or anywhere else)

- Users can be created (and added to groups) independently of the API

- The Kubernetes API can be set up to use your custom CA to validate client certs

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/authn-authz.md)]
---

## Viewing our admin certificate

- Let's inspect the certificate we've been using all this time!

- 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/2020-01-zr/slides/k8s/authn-authz.md)]
---

## 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/2020-01-zr/slides/k8s/authn-authz.md)]
---

## User certificates in practice

- The Kubernetes API server does not support certificate revocation

(see issue [#18982](https://github.com/kubernetes/kubernetes/issues/18982))

- As a result, we don't have an easy way to terminate someone's access

(if their key is compromised, or they leave the organization)

- Option 1: re-create a new CA and re-issue everyone's certificates 
 
 → Maybe OK if we only have a few users; no way otherwise

- Option 2: don't use groups; grant permissions to individual users
 
 → Inconvenient if we have many users and teams; error-prone

- Option 3: issue short-lived certificates (e.g. 24 hours) and renew them often
 
 → This can be facilitated by e.g. Vault or by the Kubernetes CSR API

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/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/2020-01-zr/slides/k8s/authn-authz.md)]
---

## 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/2020-01-zr/slides/k8s/authn-authz.md)]
---

## Listing service accounts

- The resource name is `serviceaccount` or `sa` for short:
  ```bash
  kubectl get sa
  ```

]

There should be just one service account in the default namespace: `default`.

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/authn-authz.md)]
---

## Finding the secret

- 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/2020-01-zr/slides/k8s/authn-authz.md)]
---

## Extracting the token

- The token is stored in the secret, wrapped with base64 encoding

- 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/2020-01-zr/slides/k8s/authn-authz.md)]
---

## Using the token

- Let's send a request to the API, without and with the token

- 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/2020-01-zr/slides/k8s/authn-authz.md)]
---

## Results

- In both cases, we will get a "Forbidden" error

- Without authentication, the user is `system:anonymous`

- With authentication, it is shown as `system:serviceaccount:default:default`

- The API "sees" us as a different user

- But neither user has any rights, so we can't do nothin'

- Let's change that!

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/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/2020-01-zr/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/2020-01-zr/slides/k8s/authn-authz.md)]
---

## Cluster-scope permissions

- API resources Role and RoleBinding are for objects within a namespace

- We can also define API resources ClusterRole and ClusterRoleBinding

- These are a superset, allowing us to:

- specify actions on cluster-wide objects (like nodes)

- operate across all namespaces

- We can create Role and RoleBinding resources within a namespace

- ClusterRole and ClusterRoleBinding resources are global

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/authn-authz.md)]
---

## Pods and service accounts

- A pod can be associated with a service account

- by default, it is associated with the `default` service account

- as we saw earlier, this service account has no permissions anyway

- The associated token is exposed to the pod's filesystem

(in `/var/run/secrets/kubernetes.io/serviceaccount/token`)

- Standard Kubernetes tooling (like `kubectl`) will look for it there

- So Kubernetes tools running in a pod will automatically use the service account

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/authn-authz.md)]
---

## In practice

- We are going to create a service account

- We will use a default cluster role (`view`)

- We will bind together this role and this service account

- Then we will run a pod using that service account

- In this pod, we will install `kubectl` and check our permissions

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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)

- 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/2020-01-zr/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`)

- 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/2020-01-zr/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
 (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/2020-01-zr/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/2020-01-zr/slides/k8s/authn-authz.md)]
---

## Testing

- We will run an `alpine` pod and install `kubectl` there

- 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/2020-01-zr/slides/k8s/authn-authz.md)]
---

## Running `kubectl` in the pod

- We'll try to use our `view` permissions, then to create an object

- 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`

]

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/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/2020-01-zr/slides/k8s/authn-authz.md)]
---

## Where does this `view` role come from?

- Kubernetes defines a number of ClusterRoles intended to be bound to users

- `cluster-admin` can do *everything* (think `root` on UNIX)

- `admin` can do *almost everything* (except e.g. changing resource quotas and limits)

- `edit` is similar to `admin`, but cannot view or edit permissions

- `view` has read-only access to most resources, except permissions and secrets

*In many situations, these roles will be all you need.*

*You can also customize them!*

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/authn-authz.md)]
---

## Customizing the default roles

- If you need to *add* permissions to these default roles (or others),
 
 you can do it through the [ClusterRole Aggregation](https://kubernetes.io/docs/reference/access-authn-authz/rbac/#aggregated-clusterroles) mechanism

- This happens by creating a ClusterRole with the following labels:
  ```yaml
    metadata:
      labels:
        rbac.authorization.k8s.io/aggregate-to-admin: "true"
        rbac.authorization.k8s.io/aggregate-to-edit: "true"
        rbac.authorization.k8s.io/aggregate-to-view: "true"
  ```

- This ClusterRole permissions will be added to `admin`/`edit`/`view` respectively

- This is particulary useful when using CustomResourceDefinitions

(since Kubernetes cannot guess which resources are sensitive and which ones aren't)

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/authn-authz.md)]
---

## 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/2020-01-zr/slides/k8s/authn-authz.md)]
---

## The `system:masters` group

- If we eyeball the output of `kubectl get clusterrolebindings -o yaml`, we'll find out!

- It is in the `cluster-admin` binding:
  ```bash
  kubectl describe clusterrolebinding cluster-admin
  ```

- This binding associates `system:masters` with the cluster role `cluster-admin`

- And the `cluster-admin` is, basically, `root`:
  ```bash
  kubectl describe clusterrole cluster-admin
  ```

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/authn-authz.md)]
---

## Figuring out who can do what

- For auditing purposes, sometimes we want to know who can perform an action

- There are a few tools to help us with that

- [kubectl-who-can](https://github.com/aquasecurity/kubectl-who-can) by Aqua Security

- [Review Access (aka Rakkess)](https://github.com/corneliusweig/rakkess)

- Both are available as standalone programs, or as plugins for `kubectl`

(`kubectl` plugins can be installed and managed with `krew`)

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/authn-authz.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Securing the control plane

.nav[
[Previous section](#toc-authentication-and-authorization)
|
[Back to table of contents](#toc-chapter-12)
|
[Next section](#toc-extra-content-templatization--packaging)
]

---
# Securing the control plane

- Many components accept connections (and requests) from others:

- API server

- etcd

- kubelet

- We must secure these connections:

- to deny unauthorized requests

- to prevent eavesdropping secrets, tokens, and other sensitive information

- Disabling authentication and/or authorization is **strongly discouraged**

(but it's possible to do it, e.g. for learning / troubleshooting purposes)

.debug[[k8s/control-plane-auth.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/control-plane-auth.md)]
---

## Authentication and authorization

- Authentication (checking "who you are") is done with mutual TLS

(both the client and the server need to hold a valid certificate)

- Authorization (checking "what you can do") is done in different ways

- the API server implements a sophisticated permission logic (with RBAC)
  
  - some services will defer authorization to the API server (through webhooks)

- some services require a certificate signed by a particular CA / sub-CA

.debug[[k8s/control-plane-auth.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/control-plane-auth.md)]
---

## In practice

- We will review the various communication channels in the control plane

- We will describe how they are secured

- When TLS certificates are used, we will indicate:

- which CA signs them

- what their subject (CN) should be, when applicable

- We will indicate how to configure security (client- and server-side)

.debug[[k8s/control-plane-auth.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/control-plane-auth.md)]
---

## etcd peers

- Replication and coordination of etcd happens on a dedicated port

(typically port 2380; the default port for normal client connections is 2379)

- Authentication uses TLS certificates with a separate sub-CA

(otherwise, anyone with a Kubernetes client certificate could access etcd!)

- The etcd command line flags involved are:

`--peer-client-cert-auth=true` to activate it

`--peer-cert-file`, `--peer-key-file`, `--peer-trusted-ca-file`

.debug[[k8s/control-plane-auth.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/control-plane-auth.md)]
---

## etcd clients

- The only¹ thing that connects to etcd is the API server

- Authentication uses TLS certificates with a separate sub-CA

(for the same reasons as for etcd inter-peer authentication)

- The etcd command line flags involved are:

`--client-cert-auth=true` to activate it

`--trusted-ca-file`, `--cert-file`, `--key-file`

- The API server command line flags involved are:

`--etcd-cafile`, `--etcd-certfile`, `--etcd-keyfile`

.debug[[k8s/control-plane-auth.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/control-plane-auth.md)]
---

## API server clients

- The API server has a sophisticated authentication and authorization system

- For connections coming from other components of the control plane:

- authentication uses certificates (trusting the certificates' subject or CN)

- authorization uses whatever mechanism is enabled (most oftentimes, RBAC)

- The relevant API server flags are:

`--client-ca-file`, `--tls-cert-file`, `--tls-private-key-file`

- Each component connecting to the API server takes a `--kubeconfig` flag

(to specify a kubeconfig file containing the CA cert, client key, and client cert)

- Yes, that kubeconfig file follows the same format as our `~/.kube/config` file!

.debug[[k8s/control-plane-auth.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/control-plane-auth.md)]
---

## Kubelet and API server

- Communication between kubelet and API server can be established both ways

- Kubelet → API server:

- kubelet registers itself ("hi, I'm node42, do you have work for me?")

- connection is kept open and re-established if it breaks

- that's how the kubelet knows which pods to start/stop

- API server → kubelet:

- used to retrieve logs, exec, attach to containers

.debug[[k8s/control-plane-auth.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/control-plane-auth.md)]
---

## Kubelet → API server

- Kubelet is started with `--kubeconfig` with API server information

- The client certificate of the kubelet will typically have:

`CN=system:node:<nodename>` and groups `O=system:nodes`

- Nothing special on the API server side

(it will authenticate like any other client)

.debug[[k8s/control-plane-auth.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/control-plane-auth.md)]
---

## API server → kubelet

- Kubelet is started with the flag `--client-ca-file`

(typically using the same CA as the API server)

- API server will use a dedicated key pair when contacting kubelet

(specified with `--kubelet-client-certificate` and `--kubelet-client-key`)

- Authorization uses webhooks

(enabled with `--authorization-mode=Webhook` on kubelet)

- The webhook server is the API server itself

(the kubelet sends back a request to the API server to ask, "can this person do that?")

.debug[[k8s/control-plane-auth.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/control-plane-auth.md)]
---

## Scheduler

- The scheduler connects to the API server like an ordinary client

- The certificate of the scheduler will have `CN=system:kube-scheduler`

.debug[[k8s/control-plane-auth.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/control-plane-auth.md)]
---

## Controller manager

- The controller manager is also a normal client to the API server

- Its certificate will have `CN=system:kube-controller-manager`

- If we use the CSR API, the controller manager needs the CA cert and key

(passed with flags `--cluster-signing-cert-file` and `--cluster-signing-key-file`)

- We usually want the controller manager to generate tokens for service accounts

- These tokens deserve some details (on the next slide!)

.debug[[k8s/control-plane-auth.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/control-plane-auth.md)]
---

## Service account tokens

- Each time we create a service account, the controller manager generates a token

- These tokens are JWT tokens, signed with a particular key

- These tokens are used for authentication with the API server

(and therefore, the API server needs to be able to verify their integrity)

- This uses another keypair:

- the private key (used for signature) is passed to the controller manager
 (using flags `--service-account-private-key-file` and `--root-ca-file`)

- the public key (used for verification) is passed to the API server
 (using flag `--service-account-key-file`)

.debug[[k8s/control-plane-auth.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/control-plane-auth.md)]
---

## kube-proxy

- kube-proxy is "yet another API server client"

- In many clusters, it runs as a Daemon Set

- In that case, it will have its own Service Account and associated permissions

- It will authenticate using the token of that Service Account

.debug[[k8s/control-plane-auth.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/control-plane-auth.md)]
---

## Webhooks

- We mentioned webhooks earlier; how does that really work?

- The Kubernetes API has special resource types to check permissions

- One of them is SubjectAccessReview

- To check if a particular user can do a particular action on a particular resource:

- we prepare a SubjectAccessReview object

- we send that object to the API server

- the API server responds with allow/deny (and optional explanations)

- Using webhooks for authorization = sending SAR to authorize each request

.debug[[k8s/control-plane-auth.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/control-plane-auth.md)]
---

## Subject Access Review

Here is an example showing how to check if `jean.doe` can `get` some `pods` in `kube-system`:

```bash
kubectl -v9 create -f- <<EOF
apiVersion: authorization.k8s.io/v1beta1
kind: SubjectAccessReview
spec:
 user: jean.doe
 group:
 - foo
 - bar
 resourceAttributes:
 #group: blah.k8s.io
 namespace: kube-system
 resource: pods
 verb: get
 #name: web-xyz1234567-pqr89
EOF
```

.debug[[k8s/control-plane-auth.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/control-plane-auth.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

(Extra content: templatization & packaging)

.nav[
[Previous section](#toc-securing-the-control-plane)
|
[Back to table of contents](#toc-chapter-13)
|
[Next section](#toc-kustomize)
]

---
# (Extra content: templatization & packaging)

.debug[[kube.yml](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/kube.yml)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Kustomize

.nav[
[Previous section](#toc-extra-content-templatization--packaging)
|
[Back to table of contents](#toc-chapter-13)
|
[Next section](#toc-managing-stacks-with-helm)
]

---
# Kustomize

- Kustomize lets us transform YAML files representing Kubernetes resources

- The original YAML files are valid resource files

(e.g. they can be loaded with `kubectl apply -f`)

- They are left untouched by Kustomize

- Kustomize lets us define *overlays* that extend or change the resource files

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kustomize.md)]
---

## Differences with Helm

- Helm charts use placeholders `{{ like.this }}`

- Kustomize "bases" are standard Kubernetes YAML

- It is possible to use an existing set of YAML as a Kustomize base

- As a result, writing a Helm chart is more work ...

- ... But Helm charts are also more powerful; e.g. they can:

- use flags to conditionally include resources or blocks

- check if a given Kubernetes API group is supported

- [and much more](https://helm.sh/docs/chart_template_guide/)

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kustomize.md)]
---

## Kustomize concepts

- Kustomize needs a `kustomization.yaml` file

- That file can be a *base* or a *variant*

- If it's a *base*:

- it lists YAML resource files to use

- If it's a *variant* (or *overlay*):

- it refers to (at least) one *base*

- and some *patches*

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kustomize.md)]
---

## An easy way to get started with Kustomize

- We are going to use [Replicated Ship](https://www.replicated.com/ship/) to experiment with Kustomize

- The [Replicated Ship CLI](https://github.com/replicatedhq/ship/releases) has been installed on our clusters

- Replicated Ship has multiple workflows; here is what we will do:

- initialize a Kustomize overlay from a remote GitHub repository

- customize some values using the web UI provided by Ship

- look at the resulting files and apply them to the cluster

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kustomize.md)]
---

## Getting started with Ship

- We need to run `ship init` in a new directory

- `ship init` requires a URL to a remote repository containing Kubernetes YAML

- It will clone that repository and start a web UI

- Later, it can watch that repository and/or update from it

- We will use the [jpetazzo/kubercoins](https://github.com/jpetazzo/kubercoins) repository

(it contains all the DockerCoins resources as YAML files)

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kustomize.md)]
---

## `ship init`

- Change to a new directory:
  ```bash
  mkdir ~/kustomcoins
  cd ~/kustomcoins
  ```

- Run `ship init` with the kustomcoins repository:
  ```bash
  ship init https://github.com/jpetazzo/kubercoins
  ```

]

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kustomize.md)]
---

## Access the web UI

- `ship init` tells us to connect on `localhost:8800`

- We need to replace `localhost` with the address of our node

(since we run on a remote machine)

- Follow the steps in the web UI, and change one parameter

(e.g. set the number of replicas in the worker Deployment)

- Complete the web workflow, and go back to the CLI

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kustomize.md)]
---

## Inspect the results

- Look at the content of our directory

- `base` contains the kubercoins repository + a `kustomization.yaml` file

- `overlays/ship` contains the Kustomize overlay referencing the base + our patch(es)

- `rendered.yaml` is a YAML bundle containing the patched application

- `.ship` contains a state file used by Ship

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kustomize.md)]
---

## Using the results

- We can `kubectl apply -f rendered.yaml`

(on any version of Kubernetes)

- Starting with Kubernetes 1.14, we can apply the overlay directly with:
  ```bash
  kubectl apply -k overlays/ship
  ```

- But let's not do that for now!

- We will create a new copy of DockerCoins in another namespace

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kustomize.md)]
---

## Deploy DockerCoins with Kustomize

- Create a new namespace:
  ```bash
  kubectl create namespace kustomcoins
  ```

- Deploy DockerCoins:
  ```bash
  kubectl apply -f rendered.yaml --namespace=kustomcoins
  ```

- Or, with Kubernetes 1.14, you can also do this:
  ```bash
  kubectl apply -k overlays/ship --namespace=kustomcoins
  ```

]

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kustomize.md)]
---

## Checking our new copy of DockerCoins

- We can check the worker logs, or the web UI

- Retrieve the NodePort number of the web UI:
  ```bash
  kubectl get service webui --namespace=kustomcoins
  ```

- Open it in a web browser

- Look at the worker logs:
  ```bash
  kubectl logs deploy/worker --tail=10 --follow --namespace=kustomcoins
  ```

]

Note: it might take a minute or two for the worker to start.

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/kustomize.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Managing stacks with Helm

.nav[
[Previous section](#toc-kustomize)
|
[Back to table of contents](#toc-chapter-13)
|
[Next section](#toc-creating-helm-charts)
]

---
# Managing stacks with Helm

- We created our first resources with `kubectl run`, `kubectl expose` ...

- We have also created resources by loading YAML files with `kubectl apply -f`

- For larger stacks, managing thousands of lines of YAML is unreasonable

- These YAML bundles need to be customized with variable parameters

(E.g.: number of replicas, image version to use ...)

- It would be nice to have an organized, versioned collection of bundles

- It would be nice to be able to upgrade/rollback these bundles carefully

- [Helm](https://helm.sh/) is an open source project offering all these things!

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/helm.md)]
---

## Helm concepts

- `helm` is a CLI tool

- It is used to find, install, upgrade *charts*

- A chart is an archive containing templatized YAML bundles

- Charts are versioned

- Charts can be stored on private or public repositories

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/helm.md)]
---

## Differences between charts and packages

- A package (deb, rpm...) contains binaries, libraries, etc.

- A chart contains YAML manifests

(the binaries, libraries, etc. are in the images referenced by the chart)

- On most distributions, a package can only be installed once

(installing another version replaces the installed one)

- A chart can be installed multiple times

- Each installation is called a *release*

- This allows to install e.g. 10 instances of MongoDB

(with potentially different versions and configurations)

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/helm.md)]
---

## Wait a minute ...

*But, on my Debian system, I have Python 2 **and** Python 3.
 
Also, I have multiple versions of the Postgres database engine!*

Yes!

But they have different package names:

- `python2.7`, `python3.8`

- `postgresql-10`, `postgresql-11`

Good to know: the Postgres package in Debian includes
provisions to deploy multiple Postgres servers on the
same system, but it's an exception (and it's a lot of
work done by the package maintainer, not by the `dpkg`
or `apt` tools).

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/helm.md)]
---

## Helm 2 vs Helm 3

- Helm 3 was released [November 13, 2019](https://helm.sh/blog/helm-3-released/)

- Charts remain compatible between Helm 2 and Helm 3

- The CLI is very similar (with minor changes to some commands)

- The main difference is that Helm 2 uses `tiller`, a server-side component

- Helm 3 doesn't use `tiller` at all, making it simpler (yay!)

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/helm.md)]
---

## With or without `tiller`

- With Helm 3:

- the `helm` CLI communicates directly with the Kubernetes API

- it creates resources (deployments, services...) with our credentials

- With Helm 2:

- the `helm` CLI communicates with `tiller`, telling `tiller` what to do

- `tiller` then communicates with the Kubernetes API, using its own credentials

- This indirect model caused significant permissions headaches

(`tiller` required very broad permissions to function)

- `tiller` was removed in Helm 3 to simplify the security aspects

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/helm.md)]
---

## Installing Helm

- If the `helm` CLI is not installed in your environment, install it

- Check if `helm` is installed:
  ```bash
  helm
  ```

- If it's not installed, run the following command:
  ```bash
  curl https://raw.githubusercontent.com/kubernetes/helm/master/scripts/get-helm-3 \
  | bash
  ```

]

(To install Helm 2, replace `get-helm-3` with `get`.)

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/helm.md)]
---

## Only if using Helm 2 ...

- We need to install Tiller and give it some permissions

- Tiller is composed of a *service* and a *deployment* in the `kube-system` namespace

- They can be managed (installed, upgraded...) with the `helm` CLI

- Deploy Tiller:
  ```bash
  helm init
  ```

]

At the end of the install process, you will see:

```
Happy Helming!
```

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/helm.md)]
---

## Only if using Helm 2 ...

- Tiller needs permissions to create Kubernetes resources

- In a more realistic deployment, you might create per-user or per-team
  service accounts, roles, and role bindings

- Grant `cluster-admin` role to `kube-system:default` service account:
  ```bash
  kubectl create clusterrolebinding add-on-cluster-admin \
      --clusterrole=cluster-admin --serviceaccount=kube-system:default
  ```

]

(Defining the exact roles and permissions on your cluster requires
a deeper knowledge of Kubernetes' RBAC model. The command above is
fine for personal and development clusters.)

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/helm.md)]
---

## Charts and repositories

- A *repository* (or repo in short) is a collection of charts

- It's just a bunch of files

(they can be hosted by a static HTTP server, or on a local directory)

- We can add "repos" to Helm, giving them a nickname

- The nickname is used when referring to charts on that repo

(for instance, if we try to install `hello/world`, that
  means the chart `world` on the repo `hello`; and that repo
  `hello` might be something like https://blahblah.hello.io/charts/)

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/helm.md)]
---

## Managing repositories

- Let's check what repositories we have, and add the `stable` repo

(the `stable` repo contains a set of official-ish charts)

- List our repos:
  ```bash
  helm repo list
  ```

- Add the `stable` repo:
  ```bash
  helm repo add stable https://kubernetes-charts.storage.googleapis.com/
  ```

]

Adding a repo can take a few seconds (it downloads the list of charts from the repo).

It's OK to add a repo that already exists (it will merely update it).

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/helm.md)]
---

## Search available charts

- We can search available charts with `helm search`

- We need to specify where to search (only our repos, or Helm Hub)

- Let's search for all charts mentioning tomcat!

- Search for tomcat in the repo that we added earlier:
  ```bash
  helm search repo tomcat
  ```

- Search for tomcat on the Helm Hub:
  ```bash
  helm search hub tomcat
  ```

]

[Helm Hub](https://hub.helm.sh/) indexes many repos, using the [Monocular](https://github.com/helm/monocular) server.

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/helm.md)]
---

## Charts and releases

- "Installing a chart" means creating a *release*

- We need to name that release

(or use the `--generate-name` to get Helm to generate one for us)

- Install the tomcat chart that we found earlier:
  ```bash
  helm install java4ever stable/tomcat
  ```

- List the releases:
  ```bash
  helm list
  ```

]

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/helm.md)]
---

## Searching and installing with Helm 2

- Helm 2 doesn't have support for the Helm Hub

- The `helm search` command only takes a search string argument

(e.g. `helm search tomcat`)

- With Helm 2, the name is optional:

`helm install stable/tomcat` will automatically generate a name

`helm install --name java4ever stable/tomcat` will specify a name

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/helm.md)]
---

## Viewing resources of a release

- This specific chart labels all its resources with a `release` label

- We can use a selector to see these resources

- List all the resources created by this release:
  ```bash
  kuectl get all --selector=release=java4ever
  ```

]

Note: this `release` label wasn't added automatically by Helm.
 
It is defined in that chart. In other words, not all charts will provide this label.

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/helm.md)]
---

## Configuring a release

- By default, `stable/tomcat` creates a service of type `LoadBalancer`

- We would like to change that to a `NodePort`

- We could use `kubectl edit service java4ever-tomcat`, but ...

... our changes would get overwritten next time we update that chart!

- Instead, we are going to *set a value*

- Values are parameters that the chart can use to change its behavior

- Values have default values

- Each chart is free to define its own values and their defaults

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/helm.md)]
---

## Checking possible values

- We can inspect a chart with `helm show` or `helm inspect`

- Look at the README for tomcat:
  ```bash
  helm show readme stable/tomcat
  ```

- Look at the values and their defaults:
  ```bash
  helm show values stable/tomcat
  ```

]

The `values` may or may not have useful comments.

The `readme` may or may not have (accurate) explanations for the values.

(If we're unlucky, there won't be any indication about how to use the values!)

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/helm.md)]
---

## Setting values

- Values can be set when installing a chart, or when upgrading it

- We are going to update `java4ever` to change the type of the service

- Update `java4ever`:
  ```bash
  helm upgrade java4ever stable/tomcat --set service.type=NodePort
  ```

]

Note that we have to specify the chart that we use (`stable/tomcat`),
even if we just want to update some values.

We can set multiple values. If we want to set many values, we can use `-f`/`--values` and pass a YAML file with all the values.

All unspecified values will take the default values defined in the chart.

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/helm.md)]
---

## Connecting to tomcat

- Let's check the tomcat server that we just installed

- Note: its readiness probe has a 60s delay

(so it will take 60s after the initial deployment before the service works)

- Check the node port allocated to the service:
  ```bash
  kubectl get service java4ever-tomcat
  PORT=$(kubectl get service java4ever-tomcat -o jsonpath={..nodePort})
  ```

- Connect to it, checking the demo app on `/sample/`:
  ```bash
  curl localhost:$PORT/sample/
  ```

]
.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/helm.md)]
---
## Creating a chart

- We are going to show a way to create a *very simplified* chart

- In a real chart, *lots of things* would be templatized

(Resource names, service types, number of replicas...)

- Create a sample chart:
  ```bash
  helm create dockercoins
  ```

- Move away the sample templates and create an empty template directory:
  ```bash
  mv dockercoins/templates dockercoins/default-templates
  mkdir dockercoins/templates
  ```

]

.debug[[k8s/create-chart.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-chart.md)]
---

## Exporting the YAML for our application

- The following section assumes that DockerCoins is currently running

- Create one YAML file for each resource that we need:
  .small[
  ```bash

while read kind name; do
	 kubectl get -o yaml $kind $name > dockercoins/templates/$name-$kind.yaml
	done <<EOF
	deployment worker
	deployment hasher
	daemonset rng
	deployment webui
	deployment redis
	service hasher
	service rng
	service webui
	service redis
	EOF
 ```
 ]

]

.debug[[k8s/create-chart.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-chart.md)]
---

## Testing our helm chart

- Let's install our helm chart! (`dockercoins` is the path to the chart)
  ```
  helm install dockercoins
  ```
]

- Since the application is already deployed, this will fail: 
`Error: release loitering-otter failed: services "hasher" already exists`

- To avoid naming conflicts, we will deploy the application in another *namespace*

.debug[[k8s/create-chart.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-chart.md)]
---

## Switching to another namespace

- We can create a new namespace and switch to it

(Helm will automatically use the namespace specified in our context)

- We can also tell Helm which namespace to use

- Tell Helm to use a specific namespace:
  ```bash
  helm install dockercoins --namespace=magenta
  ```

]

.debug[[k8s/create-chart.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-chart.md)]
---

## Checking our new copy of DockerCoins

- We can check the worker logs, or the web UI

- Retrieve the NodePort number of the web UI:
  ```bash
  kubectl get service webui --namespace=magenta
  ```

- Open it in a web browser

- Look at the worker logs:
  ```bash
  kubectl logs deploy/worker --tail=10 --follow --namespace=magenta
  ```

]

Note: it might take a minute or two for the worker to start.

.debug[[k8s/create-chart.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-chart.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Creating Helm charts

.nav[
[Previous section](#toc-managing-stacks-with-helm)
|
[Back to table of contents](#toc-chapter-13)
|
[Next section](#toc-extra-content-user-management)
]

---
# Creating Helm charts

- We are going to create a generic Helm chart

- We will use that Helm chart to deploy DockerCoins

- Each component of DockerCoins will have its own *release*

- In other words, we will "install" that Helm chart multiple times

(one time per component of DockerCoins)

.debug[[k8s/create-more-charts.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-more-charts.md)]
---

## Creating a generic chart

- Rather than starting from scratch, we will use `helm create`

- This will give us a basic chart that we will customize

- Create a basic chart:
  ```bash
  cd ~
  helm create helmcoins
  ```

]

This creates a basic chart in the directory `helmcoins`.

.debug[[k8s/create-more-charts.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-more-charts.md)]
---

## What's in the basic chart?

- The basic chart will create a Deployment and a Service

- Optionally, it will also include an Ingress

- If we don't pass any values, it will deploy the `nginx` image

- We can override many things in that chart

- Let's try to deploy DockerCoins components with that chart!

.debug[[k8s/create-more-charts.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-more-charts.md)]
---

## Writing `values.yaml` for our components

- We need to write one `values.yaml` file for each component

(hasher, redis, rng, webui, worker)

- We will start with the `values.yaml` of the chart, and remove what we don't need

- We will create 5 files:

hasher.yaml, redis.yaml, rng.yaml, webui.yaml, worker.yaml

.debug[[k8s/create-more-charts.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-more-charts.md)]
---

## Getting started

- For component X, we want to use the image dockercoins/X:v0.1

(for instance, for rng, we want to use the image dockercoins/rng:v0.1)

- Exception: for redis, we want to use the official image redis:latest

- Write minimal YAML files for the 5 components, specifying only the image

]

*Hint: our YAML files should look like this.*

```yaml
### rng.yaml
image:
  repository: dockercoins/`rng`
  tag: v0.1
```

.debug[[k8s/create-more-charts.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-more-charts.md)]
---

## Deploying DockerCoins components

- For convenience, let's work in a separate namespace

- Create a new namespace:
  ```bash
  kubectl create namespace helmcoins
  ```

- Switch to that namespace:
  ```bash
  kns helmcoins
  ```

]

.debug[[k8s/create-more-charts.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-more-charts.md)]
---

## Deploying the chart

- To install a chart, we can use the following command:
 ```bash
 helm install [--name `X`] <chart>
 ```

- We can also use the following command, which is idempotent:
  ```bash
  helm upgrade --install `X` chart
  ```

- Install the 5 components of DockerCoins:
  ```bash
    for COMPONENT in hasher redis rng webui worker; do
      helm upgrade --install $COMPONENT helmcoins/ --values=$COMPONENT.yaml
    done
  ```

]

.debug[[k8s/create-more-charts.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-more-charts.md)]
---

## Checking what we've done

- Let's see if DockerCoins is working!

- Check the logs of the worker:
  ```bash
  stern worker
  ```

- Look at the resources that were created:
  ```bash
  kubectl get all
  ```

]

There are *many* issues to fix!

.debug[[k8s/create-more-charts.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-more-charts.md)]
---

## Service names

- Our services should be named `rng`, `hasher`, etc., but they are named differently

- Look at the YAML template used for the services

- Does it look like we can override the name of the services?

- *Yes*, we can use `.Values.nameOverride`

- This means setting `nameOverride` in the values YAML file

.debug[[k8s/create-more-charts.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-more-charts.md)]
---

## Setting service names

- Let's add `nameOverride: X` in each values YAML file!

(where X is hasher, redis, rng, etc.)

- Edit the 5 YAML files to add `nameOverride: X`

- Deploy the updated Chart:
  ```bash
    for COMPONENT in hasher redis rng webui worker; do
      helm upgrade --install $COMPONENT helmcoins/ --values=$COMPONENT.yaml
    done
  ```
  (Yes, this is exactly the same command as before!)

]

.debug[[k8s/create-more-charts.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-more-charts.md)]
---

## Checking what we've done

- Check the service names:
  ```bash
  kubectl get services
  ```
  Great! (We have a useless service for `worker`, but let's ignore it for now.)

- Check the state of the pods:
  ```bash
  kubectl get pods
  ```
  Not so great... Some pods are *not ready.*

]

.debug[[k8s/create-more-charts.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-more-charts.md)]
---

## Troubleshooting pods

- The easiest way to troubleshoot pods is to look at *events*

- We can look at all the events on the cluster (with `kubectl get events`)

- Or we can use `kubectl describe` on the objects that have problems

(`kubectl describe` will retrieve the events related to the object)

- Check the events for the redis pods:
  ```bash
  kubectl describe pod -l app.kubernetes.io/name=redis
  ```

]

What's going on?

.debug[[k8s/create-more-charts.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-more-charts.md)]
---

## Healthchecks

- The default chart defines healthchecks doing HTTP requests on port 80

- That won't work for redis and worker

(redis is not HTTP, and not on port 80; worker doesn't even listen)

- We could comment out the healthchecks

- We could also make them conditional

- This sounds more interesting, let's do that!

.debug[[k8s/create-more-charts.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-more-charts.md)]
---

## Conditionals

- We need to enclose the healthcheck block with:

`{{ if CONDITION }}` at the beginning

`{{ end }}` at the end

- For the condition, we will use `.Values.healthcheck`

.debug[[k8s/create-more-charts.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-more-charts.md)]
---

## Updating the deployment template

- Edit `helmcoins/templates/deployment.yaml`

- Before the healthchecks section (it starts with `livenessProbe:`), add:

`{{ if .Values.healthcheck }}`

- After the healthchecks section (just before `resources:`), add:

`{{ end }}`

- Edit `hasher.yaml`, `rng.yaml`, `webui.yaml` to add:

`healthcheck: true`

]

.debug[[k8s/create-more-charts.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-more-charts.md)]
---

## Update the deployed charts

- We can now apply the new templates (and the new values)

- Use the same command as earlier to upgrade all five components

- Use `kubectl describe` to confirm that `redis` starts correctly

- Use `kubectl describe` to confirm that `hasher` still has healthchecks

]

.debug[[k8s/create-more-charts.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-more-charts.md)]
---

## Is it working now?

- If we look at the worker logs, it appears that the worker is still stuck

- What could be happening?

- The redis service is not on port 80!

- We need to update the port number in redis.yaml

- We also need to update the port number in deployment.yaml

(it is hard-coded to 80 there)

.debug[[k8s/create-more-charts.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-more-charts.md)]
---

## Setting the redis port

- Edit `redis.yaml` to add:
  ```yaml
    service:
      port: 6379
  ```

- Edit `helmcoins/templates/deployment.yaml`

- The line with `containerPort` should be:
  ```yaml
  containerPort: {{ .Values.service.port }}
  ```

]

.debug[[k8s/create-more-charts.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-more-charts.md)]
---

## Apply changes

- Re-run the for loop to execute `helm upgrade` one more time

- Check the worker logs

- This time, it should be working!

.debug[[k8s/create-more-charts.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-more-charts.md)]
---

## Extra steps

- We don't need to create a service for the worker

- We can put the whole service block in a conditional

(this will require additional changes in other files referencing the service)

- We can set the webui to be a NodePort service

- We can change the number of workers with `replicaCount`

- And much more!

.debug[[k8s/create-more-charts.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/create-more-charts.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

(Extra content: user management)

.nav[
[Previous section](#toc-creating-helm-charts)
|
[Back to table of contents](#toc-chapter-14)
|
[Next section](#toc-the-csr-api)
]

---
# (Extra content: user management)

.debug[[kube.yml](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/kube.yml)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

The CSR API

.nav[
[Previous section](#toc-extra-content-user-management)
|
[Back to table of contents](#toc-chapter-14)
|
[Next section](#toc-openid-connect)
]

---
# The CSR API

- The Kubernetes API exposes CSR resources

- We can use these resources to issue TLS certificates

- First, we will go through a quick reminder about TLS certificates

- Then, we will see how to obtain a certificate for a user

- We will use that certificate to authenticate with the cluster

- Finally, we will grant some privileges to that user

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

## Reminder about TLS

- TLS (Transport Layer Security) is a protocol providing:

- encryption (to prevent eavesdropping)

- authentication (using public key cryptography)

- When we access an https:// URL, the server authenticates itself

(it proves its identity to us; as if it were "showing its ID")

- But we can also have mutual TLS authentication (mTLS)

(client proves its identity to server; server proves its identity to client)

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

## Authentication with certificates

- To authenticate, someone (client or server) needs:

- a *private key* (that remains known only to them)

- a *public key* (that they can distribute)

- a *certificate* (associating the public key with an identity)

- A message encrypted with the private key can only be decrypted with the public key

(and vice versa)

- If I use someone's public key to encrypt/decrypt their messages,
 
 I can be certain that I am talking to them / they are talking to me

- The certificate proves that I have the correct public key for them

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

## Certificate generation workflow

This is what I do if I want to obtain a certificate.

1. Create public and private keys.

2. Create a Certificate Signing Request (CSR).

(The CSR contains the identity that I claim and a public key.)

3. Send that CSR to the Certificate Authority (CA).

4. The CA verifies that I can claim the identity in the CSR.

5. The CA generates my certificate and gives it to me.

The CA (or anyone else) never needs to know my private key.

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

## The CSR API

- The Kubernetes API has a CertificateSigningRequest resource type

(we can list them with e.g. `kubectl get csr`)

- We can create a CSR object

(= upload a CSR to the Kubernetes API)

- Then, using the Kubernetes API, we can approve/deny the request

- If we approve the request, the Kubernetes API generates a certificate

- The certificate gets attached to the CSR object and can be retrieved

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

## Using the CSR API

- We will show how to use the CSR API to obtain user certificates

- This will be a rather complex demo

- ... And yet, we will take a few shortcuts to simplify it

(but it will illustrate the general idea)

- The demo also won't be automated

(we would have to write extra code to make it fully functional)

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

## General idea

- We will create a Namespace named "users"

- Each user will get a ServiceAccount in that Namespace

- That ServiceAccount will give read/write access to *one* CSR object

- Users will use that ServiceAccount's token to submit a CSR

- We will approve the CSR (or not)

- Users can then retrieve their certificate from their CSR object

- ...And use that certificate for subsequent interactions

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

## Resource naming

For a user named `jean.doe`, we will have:

- ServiceAccount `jean.doe` in Namespace `users`

- CertificateSigningRequest `users:jean.doe`

- ClusterRole `users:jean.doe` giving read/write access to that CSR

- ClusterRoleBinding `users:jean.doe` binding ClusterRole and ServiceAccount

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

## Creating the user's resources

- Create the global namespace for all users:
  ```bash
  kubectl create namespace users
  ```

- Create the ServiceAccount, ClusterRole, ClusterRoleBinding for `jean.doe`:
  ```bash
  kubectl apply -f ~/container.training/k8s/users:jean.doe.yaml
  ```

]

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

## Extracting the user's token

- Let's obtain the user's token and give it to them

(the token will be their password)

- List the user's secrets:
  ```bash
  kubectl --namespace=users describe serviceaccount jean.doe
  ```

- Show the user's token:
  ```bash
  kubectl --namespace=users describe secret `jean.doe-token-xxxxx`
  ```

]

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

## Configure `kubectl` to use the token

- Let's create a new context that will use that token to access the API

- Add a new identity to our kubeconfig file:
  ```bash
  kubectl config set-credentials token:jean.doe --token=...
  ```

- Add a new context using that identity:
  ```bash
  kubectl config set-context jean.doe --user=token:jean.doe --cluster=kubernetes
  ```

]

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

## Access the API with the token

- Let's check that our access rights are set properly

- Try to access any resource:
  ```bash
  kubectl get pods
  ```
  (This should tell us "Forbidden")

- Try to access "our" CertificateSigningRequest:
  ```bash
  kubectl get csr users:jean.doe
  ```
  (This should tell us "NotFound")

]

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

## Create a key and a CSR

- There are many tools to generate TLS keys and CSRs

- Let's use OpenSSL; it's not the best one, but it's installed everywhere

(many people prefer cfssl, easyrsa, or other tools; that's fine too!)

- Generate the key and certificate signing request:
  ```bash
    openssl req -newkey rsa:2048 -nodes -keyout key.pem \
                -new -subj /CN=jean.doe/O=devs/ -out csr.pem
  ```

]

The command above generates:

- a 2048-bit RSA key, without encryption, stored in key.pem
- a CSR for the name `jean.doe` in group `devs`

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

## Inside the Kubernetes CSR object

- The Kubernetes CSR object is a thin wrapper around the CSR PEM file

- The PEM file needs to be encoded to base64 on a single line

(we will use `base64 -w0` for that purpose)

- The Kubernetes CSR object also needs to list the right "usages"

(these are flags indicating how the certificate can be used)

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

## Sending the CSR to Kubernetes

- Generate and create the CSR resource:
 ```bash
 kubectl apply -f - <<EOF
 apiVersion: certificates.k8s.io/v1beta1
 kind: CertificateSigningRequest
 metadata:
 name: users:jean.doe
 spec:
 request: $(base64 -w0 < csr.pem)
 usages:
 - digital signature
 - key encipherment
 - client auth
 EOF
 ```

]

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

## Adjusting certificate expiration

- By default, the CSR API generates certificates valid 1 year

- We want to generate short-lived certificates, so we will lower that to 1 hour

- Fow now, this is configured [through an experimental controller manager flag](https://github.com/kubernetes/kubernetes/issues/67324)

- Edit the static pod definition for the controller manager:
  ```bash
  sudo vim /etc/kubernetes/manifests/kube-controller-manager.yaml
  ```

- In the list of flags, add the following line:
  ```bash
  - --experimental-cluster-signing-duration=1h
  ```

]

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

## Verifying and approving the CSR

- Let's inspect the CSR, and if it is valid, approve it

- Switch back to `cluster-admin`:
  ```bash
  kctx -
  ```

- Inspect the CSR:
  ```bash
  kubectl describe csr users:jean.doe
  ```

- Approve it:
  ```bash
  kubectl certificate approve users:jean.doe
  ```

]

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

## Obtaining the certificate

- Switch back to the user's identity:
  ```bash
  kctx -
  ```

- Retrieve the updated CSR object and extract the certificate:
  ```bash
  kubectl get csr users:jean.doe \
          -o jsonpath={.status.certificate} \
          | base64 -d > cert.pem
  ```

- Inspect the certificate:
  ```bash
  openssl x509 -in cert.pem -text -noout
  ```

]

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

## Using the certificate

- Add the key and certificate to kubeconfig:
  ```bash
  kubectl config set-credentials cert:jean.doe --embed-certs \
          --client-certificate=cert.pem --client-key=key.pem
  ```

- Update the user's context to use the key and cert to authenticate:
  ```bash
  kubectl config set-context jean.doe --user cert:jean.doe
  ```

- Confirm that we are seen as `jean.doe` (but don't have permissions):
  ```bash
  kubectl get pods
  ```

]

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

## What's missing?

We have just shown, step by step, a method to issue short-lived certificates for users.

To be usable in real environments, we would need to add:

- a kubectl helper to automatically generate the CSR and obtain the cert

(and transparently renew the cert when needed)

- a Kubernetes controller to automatically validate and approve CSRs

(checking that the subject and groups are valid)

- a way for the users to know the groups to add to their CSR

(e.g.: annotations on their ServiceAccount + read access to the ServiceAccount)

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

## Is this realistic?

- Larger organizations typically integrate with their own directory

- The general principle, however, is the same:

- users have long-term credentials (password, token, ...)

- they use these credentials to obtain other, short-lived credentials

- This provides enhanced security:

- the long-term credentials can use long passphrases, 2FA, HSM...

- the short-term credentials are more convenient to use

- we get strong security *and* convenience

- Systems like Vault also have certificate issuance mechanisms

.debug[[k8s/csr-api.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/csr-api.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

OpenID Connect

.nav[
[Previous section](#toc-the-csr-api)
|
[Back to table of contents](#toc-chapter-14)
|
[Next section](#toc-pod-security-policies)
]

---
# OpenID Connect

- The Kubernetes API server can perform authentication with OpenID connect

- This requires an *OpenID provider*

(external authorization server using the OAuth 2.0 protocol)

- We can use a third-party provider (e.g. Google) or run our own (e.g. Dex)

- We are going to give an overview of the protocol

- We will show it in action (in a simplified scenario)

.debug[[k8s/openid-connect.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/openid-connect.md)]
---

## Workflow overview

- We want to access our resources (a Kubernetes cluster)

- We authenticate with the OpenID provider

- we can do this directly (e.g. by going to https://accounts.google.com)

- or maybe a kubectl plugin can open a browser page on our behalf

- After authenticating us, the OpenID provider gives us:

- an *id token* (a short-lived signed JSON Web Token, see next slide)

- a *refresh token* (to renew the *id token* when needed)

- We can now issue requests to the Kubernetes API with the *id token*

- The API server will verify that token's content to authenticate us

.debug[[k8s/openid-connect.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/openid-connect.md)]
---

## JSON Web Tokens

- A JSON Web Token (JWT) has three parts:

- a header specifying algorithms and token type

- a payload (indicating who issued the token, for whom, which purposes...)

- a signature generated by the issuer (the issuer = the OpenID provider)

- Anyone can verify a JWT without contacting the issuer

(except to obtain the issuer's public key)

- Pro tip: we can inspect a JWT with https://jwt.io/

.debug[[k8s/openid-connect.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/openid-connect.md)]
---

## How the Kubernetes API uses JWT

- Server side

- enable OIDC authentication

- indicate which issuer (provider) should be allowed

- indicate which audience (or "client id") should be allowed

- optionally, map or prefix user and group names

- Client side

- obtain JWT as described earlier

- pass JWT as authentication token

- renew JWT when needed (using the refresh token)

.debug[[k8s/openid-connect.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/openid-connect.md)]
---

## Demo time!

- We will use [Google Accounts](https://accounts.google.com) as our OpenID provider

- We will use the [Google OAuth Playground](https://developers.google.com/oauthplayground) as the "audience" or "client id"

- We will obtain a JWT through Google Accounts and the OAuth Playground

- We will enable OIDC in the Kubernetes API server

- We will use the JWT to authenticate

.footnote[If you can't or won't use a Google account, you can try to adapt this to another provider.]

.debug[[k8s/openid-connect.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/openid-connect.md)]
---

## Checking the API server logs

- The API server logs will be particularly useful in this section

(they will indicate e.g. why a specific token is rejected)

- Let's keep an eye on the API server output!

- Tail the logs of the API server:
  ```bash
  kubectl logs kube-apiserver-node1 --follow --namespace=kube-system
  ```

]

.debug[[k8s/openid-connect.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/openid-connect.md)]
---

## Authenticate with the OpenID provider

- We will use the Google OAuth Playground for convenience

- In a real scenario, we would need our own OAuth client instead of the playground

(even if we were still using Google as the OpenID provider)

- Open the Google OAuth Playground:
  ```
  https://developers.google.com/oauthplayground/
  ```

- Enter our own custom scope in the text field:
  ```
  https://www.googleapis.com/auth/userinfo.email
  ```

- Click on "Authorize APIs" and allow the playground to access our email address

]

.debug[[k8s/openid-connect.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/openid-connect.md)]
---

## Obtain our JSON Web Token

- The previous step gave us an "authorization code"

- We will use it to obtain tokens

- Click on "Exchange authorization code for tokens"

]

- The JWT is the very long `id_token` that shows up on the right hand side

(it is a base64-encoded JSON object, and should therefore start with `eyJ`)

.debug[[k8s/openid-connect.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/openid-connect.md)]
---

## Using our JSON Web Token

- We need to create a context (in kubeconfig) for our token

(if we just add the token or use `kubectl --token`, our certificate will still be used)

- Create a new authentication section in kubeconfig:
  ```bash
  kubectl config set-credentials myjwt --token=eyJ...
  ```

- Try to use it:
  ```bash
  kubectl --user=myjwt get nodes
  ```

]

We should get an `Unauthorized` response, since we haven't enabled OpenID Connect in the API server yet. We should also see `invalid bearer token` in the API server log output.

.debug[[k8s/openid-connect.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/openid-connect.md)]
---

## Enabling OpenID Connect

- We need to add a few flags to the API server configuration

- These two are mandatory:

`--oidc-issuer-url` → URL of the OpenID provider

`--oidc-client-id` → app requesting the authentication
 (in our case, that's the ID for the Google OAuth Playground)

- This one is optional:

`--oidc-username-claim` → which field should be used as user name
 (we will use the user's email address instead of an opaque ID)

- See the [API server documentation](https://kubernetes.io/docs/reference/access-authn-authz/authentication/#configuring-the-api-server
) for more details about all available flags

.debug[[k8s/openid-connect.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/openid-connect.md)]
---

## Updating the API server configuration

- The instructions below will work for clusters deployed with kubeadm

(or where the control plane is deployed in static pods)

- If your cluster is deployed differently, you will need to adapt them

- Edit `/etc/kubernetes/manifests/kube-apiserver.yaml`

- Add the following lines to the list of command-line flags:
  ```yaml
  - --oidc-issuer-url=https://accounts.google.com
  - --oidc-client-id=407408718192.apps.googleusercontent.com
  - --oidc-username-claim=email
  ```
]

.debug[[k8s/openid-connect.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/openid-connect.md)]
---

## Restarting the API server

- The kubelet monitors the files in `/etc/kubernetes/manifests`

- When we save the pod manifest, kubelet will restart the corresponding pod

(using the updated command line flags)

- After making the changes described on the previous slide, save the file

- Issue a simple command (like `kubectl version`) until the API server is back up

(it might take between a few seconds and one minute for the API server to restart)

- Restart the `kubectl logs` command to view the logs of the API server

]

.debug[[k8s/openid-connect.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/openid-connect.md)]
---

## Using our JSON Web Token

- Now that the API server is set up to recognize our token, try again!

- Try an API command with our token:
  ```bash
  kubectl --user=myjwt get nodes
  kubectl --user=myjwt get pods
  ```

]

We should see a message like:
```
Error from server (Forbidden): nodes is forbidden: User "jean.doe@gmail.com"
cannot list resource "nodes" in API group "" at the cluster scope
```

→ We were successfully *authenticated*, but not *authorized*.

.debug[[k8s/openid-connect.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/openid-connect.md)]
---

## Authorizing our user

- As an extra step, let's grant read access to our user

- We will use the pre-defined ClusterRole `view`

- Create a ClusterRoleBinding allowing us to view resources:
  ```bash
    kubectl create clusterrolebinding i-can-view \
            --user=`jean.doe@gmail.com` --clusterrole=view
  ```

(make sure to put *your* Google email address there)

- Confirm that we can now list pods with our token:
   ```bash
  kubectl --user=myjwt get pods
  ```

]

.debug[[k8s/openid-connect.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/openid-connect.md)]
---

## From demo to production

- We wouldn't use the Google OAuth Playground

- We *probably* wouldn't even use Google at all

(it doesn't seem to provide a way to include groups!)

- Some popular alternatives:

- [Dex](https://github.com/dexidp/dex),
    [Keycloak](https://www.keycloak.org/)
    (self-hosted)

- [Okta](https://developer.okta.com/docs/how-to/creating-token-with-groups-claim/#step-five-decode-the-jwt-to-verify)
    (SaaS)

- We would use a helper (like the [kubelogin](https://github.com/int128/kubelogin) plugin) to automatically obtain tokens

.debug[[k8s/openid-connect.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/openid-connect.md)]
---

## Service Account tokens

- The tokens used by Service Accounts are JWT tokens as well

- They are signed and verified using a special service account key pair

- Extract the token of a service account in the current namespace:
  ```bash
  kubectl get secrets -o jsonpath={..token} | base64 -d
  ```

- Copy-paste the token to a verification service like https://jwt.io

- Notice that it says "Invalid Signature"

]

.debug[[k8s/openid-connect.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/openid-connect.md)]
---

## Verifying Service Account tokens

- JSON Web Tokens embed the URL of the "issuer" (=OpenID provider)

- The issuer provides its public key through a well-known discovery endpoint

(similar to https://accounts.google.com/.well-known/openid-configuration)

- There is no such endpoint for the Service Account key pair

- But we can provide the public key ourselves for verification

.debug[[k8s/openid-connect.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/openid-connect.md)]
---

## Verifying a Service Account token

- On clusters provisioned with kubeadm, the Service Account key pair is:

`/etc/kubernetes/pki/sa.key` (used by the controller manager to generate tokens)

`/etc/kubernetes/pki/sa.pub` (used by the API server to validate the same tokens)

- Display the public key used to sign Service Account tokens:
  ```bash
  sudo cat /etc/kubernetes/pki/sa.pub
  ```

- Copy-paste the key in the "verify signature" area on https://jwt.io

- It should now say "Signature Verified"

]

.debug[[k8s/openid-connect.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/openid-connect.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

Pod Security Policies

.nav[
[Previous section](#toc-openid-connect)
|
[Back to table of contents](#toc-chapter-14)
|
[Next section](#toc-whats-next)
]

---
# Pod Security Policies

- By default, our pods and containers can do *everything*

(including taking over the entire cluster)

- We are going to show an example of a malicious pod

- Then we will explain how to avoid this with PodSecurityPolicies

- We will enable PodSecurityPolicies on our cluster

- We will create a couple of policies (restricted and permissive)

- Finally we will see how to use them to improve security on our cluster

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## Setting up a namespace

- For simplicity, let's work in a separate namespace

- Let's create a new namespace called "green"

- Create the "green" namespace:
  ```bash
  kubectl create namespace green
  ```

- Change to that namespace:
  ```bash
  kns green
  ```

]

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## Creating a basic Deployment

- Just to check that everything works correctly, deploy NGINX

- Create a Deployment using the official NGINX image:
  ```bash
  kubectl create deployment web --image=nginx
  ```

- Confirm that the Deployment, ReplicaSet, and Pod exist, and that the Pod is running:
  ```bash
  kubectl get all
  ```

]

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## One example of malicious pods

- We will now show an escalation technique in action

- We will deploy a DaemonSet that adds our SSH key to the root account

(on *each* node of the cluster)

- The Pods of the DaemonSet will do so by mounting `/root` from the host

- Check the file `k8s/hacktheplanet.yaml` with a text editor:
  ```bash
  vim ~/container.training/k8s/hacktheplanet.yaml
  ```

- If you would like, change the SSH key (by changing the GitHub user name)

]

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## Deploying the malicious pods

- Let's deploy our "exploit"!

- Create the DaemonSet:
  ```bash
  kubectl create -f ~/container.training/k8s/hacktheplanet.yaml
  ```

- Check that the pods are running:
  ```bash
  kubectl get pods
  ```

- Confirm that the SSH key was added to the node's root account:
  ```bash
  sudo cat /root/.ssh/authorized_keys
  ```

]

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## Cleaning up

- Before setting up our PodSecurityPolicies, clean up that namespace

- Remove the DaemonSet:
  ```bash
  kubectl delete daemonset hacktheplanet
  ```

- Remove the Deployment:
  ```bash
  kubectl delete deployment web
  ```

]

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## Pod Security Policies in theory

- To use PSPs, we need to activate their specific *admission controller*

- That admission controller will intercept each pod creation attempt

- It will look at:

- *who/what* is creating the pod

- which PodSecurityPolicies they can use

- which PodSecurityPolicies can be used by the Pod's ServiceAccount

- Then it will compare the Pod with each PodSecurityPolicy one by one

- If a PodSecurityPolicy accepts all the parameters of the Pod, it is created

- Otherwise, the Pod creation is denied and it won't even show up in `kubectl get pods`

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## Pod Security Policies fine print

- With RBAC, using a PSP corresponds to the verb `use` on the PSP

(that makes sense, right?)

- If no PSP is defined, no Pod can be created

(even by cluster admins)

- Pods that are already running are *not* affected

- If we create a Pod directly, it can use a PSP to which *we* have access

- If the Pod is created by e.g. a ReplicaSet or DaemonSet, it's different:

- the ReplicaSet / DaemonSet controllers don't have access to *our* policies

- therefore, we need to give access to the PSP to the Pod's ServiceAccount

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## Pod Security Policies in practice

- We are going to enable the PodSecurityPolicy admission controller

- At that point, we won't be able to create any more pods (!)

- Then we will create a couple of PodSecurityPolicies

- ...And associated ClusterRoles (giving `use` access to the policies)

- Then we will create RoleBindings to grant these roles to ServiceAccounts

- We will verify that we can't run our "exploit" anymore

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## Enabling Pod Security Policies

- To enable Pod Security Policies, we need to enable their *admission plugin*

- This is done by adding a flag to the API server

- On clusters deployed with `kubeadm`, the control plane runs in static pods

- These pods are defined in YAML files located in `/etc/kubernetes/manifests`

- Kubelet watches this directory

- Each time a file is added/removed there, kubelet creates/deletes the corresponding pod

- Updating a file causes the pod to be deleted and recreated

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## Updating the API server flags

- Let's edit the manifest for the API server pod

- Have a look at the static pods:
  ```bash
  ls -l /etc/kubernetes/manifests
  ```

- Edit the one corresponding to the API server:
  ```bash
  sudo vim /etc/kubernetes/manifests/kube-apiserver.yaml
  ```

]

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## Adding the PSP admission plugin

- There should already be a line with `--enable-admission-plugins=...`

- Let's add `PodSecurityPolicy` on that line

- Locate the line with `--enable-admission-plugins=`

- Add `PodSecurityPolicy`

It should read: `--enable-admission-plugins=NodeRestriction,PodSecurityPolicy`

- Save, quit

]

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## Waiting for the API server to restart

- The kubelet detects that the file was modified

- It kills the API server pod, and starts a new one

- During that time, the API server is unavailable

- Wait until the API server is available again

]

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## Check that the admission plugin is active

- Normally, we can't create any Pod at this point

- Try to create a Pod directly:
  ```bash
  kubectl run testpsp1 --image=nginx --restart=Never
  ```

- Try to create a Deployment:
  ```bash
  kubectl run testpsp2 --image=nginx
  ```

- Look at existing resources:
  ```bash
  kubectl get all
  ```

]

We can get hints at what's happening by looking at the ReplicaSet and Events.

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## Introducing our Pod Security Policies

- We will create two policies:

- privileged (allows everything)

- restricted (blocks some unsafe mechanisms)

- For each policy, we also need an associated ClusterRole granting *use*

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## Creating our Pod Security Policies

- We have a couple of files, each defining a PSP and associated ClusterRole:

- k8s/psp-privileged.yaml: policy `privileged`, role `psp:privileged`
  - k8s/psp-restricted.yaml: policy `restricted`, role `psp:restricted`

- Create both policies and their associated ClusterRoles:
  ```bash
  kubectl create -f ~/container.training/k8s/psp-restricted.yaml
  kubectl create -f ~/container.training/k8s/psp-privileged.yaml
  ```
]

- The privileged policy comes from [the Kubernetes documentation](https://kubernetes.io/docs/concepts/policy/pod-security-policy/#example-policies)

- The restricted policy is inspired by that same documentation page

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## Check that we can create Pods again

- We haven't bound the policy to any user yet

- But `cluster-admin` can implicitly `use` all policies

- Check that we can now create a Pod directly:
  ```bash
  kubectl run testpsp3 --image=nginx --restart=Never
  ```

- Create a Deployment as well:
  ```bash
  kubectl run testpsp4 --image=nginx
  ```

- Confirm that the Deployment is *not* creating any Pods:
  ```bash
  kubectl get all
  ```

]

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## What's going on?

- We can create Pods directly (thanks to our root-like permissions)

- The Pods corresponding to a Deployment are created by the ReplicaSet controller

- The ReplicaSet controller does *not* have root-like permissions

- We need to either:

- grant permissions to the ReplicaSet controller

*or*

- grant permissions to our Pods' ServiceAccount

- The first option would allow *anyone* to create pods

- The second option will allow us to scope the permissions better

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## Binding the restricted policy

- Let's bind the role `psp:restricted` to ServiceAccount `green:default`

(aka the default ServiceAccount in the green Namespace)

- This will allow Pod creation in the green Namespace

(because these Pods will be using that ServiceAccount automatically)

- Create the following RoleBinding:
  ```bash
    kubectl create rolebinding psp:restricted \
            --clusterrole=psp:restricted \
            --serviceaccount=green:default
  ```

]

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## Trying it out

- The Deployments that we created earlier will *eventually* recover

(the ReplicaSet controller will retry to create Pods once in a while)

- If we create a new Deployment now, it should work immediately

- Create a simple Deployment:
  ```bash
  kubectl create deployment testpsp5 --image=nginx
  ```

- Look at the Pods that have been created:
  ```bash
  kubectl get all
  ```

]

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## Trying to hack the cluster

- Let's create the same DaemonSet we used earlier

- Create a hostile DaemonSet:
  ```bash
  kubectl create -f ~/container.training/k8s/hacktheplanet.yaml
  ```

- Look at the state of the namespace:
  ```bash
  kubectl get all
  ```

]

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## What's in our restricted policy?

- The restricted PSP is similar to the one provided in the docs, but:

- it allows containers to run as root

- it doesn't drop capabilities

- Many containers run as root by default, and would require additional tweaks

- Many containers use e.g. `chown`, which requires a specific capability

(that's the case for the NGINX official image, for instance)

- We still block: hostPath, privileged containers, and much more!

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## The case of static pods

- If we list the pods in the `kube-system` namespace, `kube-apiserver` is missing

- However, the API server is obviously running

(otherwise, `kubectl get pods --namespace=kube-system` wouldn't work)

- The API server Pod is created directly by kubelet

(without going through the PSP admission plugin)

- Then, kubelet creates a "mirror pod" representing that Pod in etcd

- That "mirror pod" creation goes through the PSP admission plugin

- And it gets blocked!

- This can be fixed by binding `psp:privileged` to group `system:nodes`

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## .warning[Before moving on...]

- Our cluster is currently broken

(we can't create pods in namespaces kube-system, default, ...)

- We need to either:

- disable the PSP admission plugin

- allow use of PSP to relevant users and groups

- For instance, we could:

- bind `psp:restricted` to the group `system:authenticated`

- bind `psp:privileged` to the ServiceAccount `kube-system:default`

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

## Fixing the cluster

- Let's disable the PSP admission plugin

- Edit the Kubernetes API server static pod manifest

- Remove the PSP admission plugin

- This can be done with this one-liner:
  ```bash
  sudo sed -i s/,PodSecurityPolicy// /etc/kubernetes/manifests/kube-apiserver.yaml
  ```

]

.debug[[k8s/podsecuritypolicy.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/podsecuritypolicy.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

---

What's next?

.nav[
[Previous section](#toc-pod-security-policies)
|
[Back to table of contents](#toc-chapter-15)
|
[Next section](#toc-links-and-resources)
]

---
# What's next?

- Congratulations!

- We learned a lot about Kubernetes, its internals, its advanced concepts

- That was just the easy part

- The hard challenges will revolve around *culture* and *people*

- ... What does that mean?

.debug[[k8s/lastwords-admin.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/lastwords-admin.md)]
---

## Running an app involves many steps

- Write the app

- Tests, QA ...

- Ship *something* (more on that later)

- Provision resources (e.g. VMs, clusters)

- Deploy the *something* on the resources

- Manage, maintain, monitor the resources

- Manage, maintain, monitor the app

- And much more

.debug[[k8s/lastwords-admin.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/lastwords-admin.md)]
---

## Who does what?

- The old "devs vs ops" division has changed

- In some organizations, "ops" are now called "SRE" or "platform" teams

(and they have very different sets of skills)

- Do you know which team is responsible for each item on the list on the previous page?

- Acknowledge that a lot of tasks are outsourced

(e.g. if we add "buy/rack/provision machines" in that list)

.debug[[k8s/lastwords-admin.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/lastwords-admin.md)]
---

## What do we ship?

- Some organizations embrace "you build it, you run it"

- When "build" and "run" are owned by different teams, where's the line?

- What does the "build" team ship to the "run" team?

- Let's see a few options, and what they imply

.debug[[k8s/lastwords-admin.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/lastwords-admin.md)]
---

## Shipping code

- Team "build" ships code

(hopefully in a repository, identified by a commit hash)

- Team "run" containerizes that code

✔️ no extra work for developers

❌ very little advantage of using containers

.debug[[k8s/lastwords-admin.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/lastwords-admin.md)]
---

## Shipping container images

- Team "build" ships container images

(hopefully built automatically from a source repository)

- Team "run" uses theses images to create e.g. Kubernetes resources

✔️ universal artefact (support all languages uniformly)

✔️ easy to start a single component (good for monoliths)

❌ complex applications will require a lot of extra work

❌ adding/removing components in the stack also requires extra work

❌ complex applications will run very differently between dev and prod

.debug[[k8s/lastwords-admin.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/lastwords-admin.md)]
---

## Shipping Compose files

(Or another kind of dev-centric manifest)

- Team "build" ships a manifest that works on a single node

(as well as images, or ways to build them)

- Team "run" adapts that manifest to work on a cluster

✔️ all teams can start the stack in a reliable, deterministic manner

❌ adding/removing components still requires *some* work (but less than before)

❌ there will be *some* differences between dev and prod

.debug[[k8s/lastwords-admin.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/lastwords-admin.md)]
---

## Shipping Kubernetes manifests

- Team "build" ships ready-to-run manifests

(YAML, Helm charts, Kustomize ...)

- Team "run" adjusts some parameters and monitors the application

✔️ parity between dev and prod environments

✔️ "run" team can focus on SLAs, SLOs, and overall quality

❌ requires *a lot* of extra work (and new skills) from the "build" team

❌ Kubernetes is not a very convenient development platform (at least, not yet)

.debug[[k8s/lastwords-admin.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/lastwords-admin.md)]
---

## What's the right answer?

- It depends on our teams

- existing skills (do they know how to do it?)

- availability (do they have the time to do it?)

- potential skills (can they learn to do it?)

- It depends on our culture

- owning "run" often implies being on call

- do we reward on-call duty without encouraging hero syndrome?

- do we give people resources (time, money) to learn?

.debug[[k8s/lastwords-admin.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/lastwords-admin.md)]
---

## Tools to develop on Kubernetes

*If we decide to make Kubernetes the primary development platform, here
are a few tools that can help us.*

- Docker Desktop

- Draft

- Minikube

- Skaffold

- Tilt

- ...

.debug[[k8s/lastwords-admin.md](https://github.com/jpetazzo/container.training/tree/2020-01-zr/slides/k8s/lastwords-admin.md)]
---

## Where do we run?

- Managed vs. self-hosted

- Cloud vs. on-premises

- If cloud: public vs. private

- Which vendor/distribution to pick?

- Which versions/features to enable?

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## Some guidelines

- Start small

- Outsource what we don't know

- Start simple, and stay simple as long as possible

(try to stay away from complex features that we don't need)

- Automate

(regularly check that we can successfully redeploy by following scripts)

- Transfer knowledge

(make sure everyone is on the same page/level)

- Iterate!

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.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)]

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Links and resources

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# 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/)

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Thank you!

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That's all, folks! Questions?

![end](images/end.jpg)

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