Kubernetes 101

Kubernetes 101

```

These slides have been built from commit: 329e5d0

[common/title.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/title.md)]
---

Kubernetes 101

.footnote[
**Be kind to the WiFi!** 

*Don't use your hotspot.* 
*Don't stream videos or download big files during the workshop.* 
*Thank you!*

**Slides: http://container.training/**
]

.debug[[common/title.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/title.md)]
---
## Intros

- Hello! I am:

- .emoji[✨] Bridget ([@bridgetkromhout](https://twitter.com/bridgetkromhout))

- The workshop will run from 13:30-17:00

- There will be a break from 15:00-15:30

- Feel free to interrupt for questions at any time

- *Especially when you see full screen container pictures!*

.debug[[logistics-bridget.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/logistics-bridget.md)]
---

## Say hi!

- We encourage networking at [#velocityconf](https://twitter.com/hashtag/velocityconf?f=tweets&vertical=default&src=hash)

- Take a minute to introduce yourself to your neighbors

- Tell them where you're from (where you're based out of & what org you work at)

- Share what you're hoping to learn in this session!  .emoji[✨]

.debug[[logistics-bridget.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/logistics-bridget.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[[kube/intro.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/intro.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[common/about-slides.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/about-slides.md)]
---

## Extra details

- This slide has a little magnifying glass in the top left corner

- This magnifiying 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[[common/about-slides.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/about-slides.md)]
---

## Chapter 1

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

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

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

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

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

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

- [Setting up Kubernetes](#toc-setting-up-kubernetes)

## Chapter 2

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

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

- [Deploying a self-hosted registry](#toc-deploying-a-self-hosted-registry)

- [Exposing services internally](#toc-exposing-services-internally)

- [Exposing services for external access](#toc-exposing-services-for-external-access)

## Chapter 3

- [The Kubernetes dashboard](#toc-the-kubernetes-dashboard)

- [Security implications of `kubectl apply`](#toc-security-implications-of-kubectl-apply)

- [Scaling a deployment](#toc-scaling-a-deployment)

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

- [Updating a service through labels and selectors](#toc-updating-a-service-through-labels-and-selectors)

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

## Chapter 4

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

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

- [Namespaces](#toc-namespaces)

- [Next steps](#toc-next-steps)

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

.debug[[common/toc.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/toc.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/Container-Ship-Freighter-Navigation-Elbe-Romance-1782991.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[[common/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/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[[common/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/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://container.training/ to view these slides

- Join the chat room: In person!

]

.debug[[common/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/prereqs.md)]
---

## Where are we going to run our containers?

.debug[[common/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/prereqs.md)]
---

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

.debug[[common/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/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[[common/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/prereqs.md)]
---

## Why don't we run containers locally?

- Installing that stuff can be hard on some machines

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

- *"The whole team downloaded all these container images from the WiFi!
 ... 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[[common/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/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[[common/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/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[[common/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/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[[common/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/prereqs.md)]
---

## Connecting to our lab environment

- Log into the first VM (`node1`) with your SSH client

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

```bash
if which kubectl; then
  kubectl get all -o name | grep -v service/kubernetes | xargs -rn1 kubectl delete
fi
```
-->

- Check that you can SSH (without password) to `node2`:
  ```bash
  ssh node2
  ```
- Type `exit` or `^D` to come back to `node1`

]

If anything goes wrong — ask for help!

.debug[[common/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/prereqs.md)]
---

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

- Use something like
  [Play-With-Docker](http://play-with-docker.com/) or
  [Play-With-Kubernetes](https://medium.com/@marcosnils/introducing-pwk-play-with-k8s-159fcfeb787b)

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[[common/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/prereqs.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[[common/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/prereqs.md)]
---

## We will (mostly) interact with node1 only

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

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

- We will only checkout/copy the code on `node1`

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

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

- SSH (to access system logs, daemon status...)
  
  - Docker API (to check running containers and container engine status)

.debug[[common/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/prereqs.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[[common/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/prereqs.md)]
---

## Tmux cheatsheet

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

*You don't have to use it or even know about it to follow along.
 
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[[common/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/prereqs.md)]
---
## Versions installed

- Kubernetes 1.10.4
- Docker Engine 18.03.1-ce
- Docker Compose 1.21.1

- Check all installed versions:
  ```bash
  kubectl version
  docker version
  docker-compose -v
  ```

]

.debug[[kube/versions-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/versions-k8s.md)]
---

## Kubernetes and Docker compatibility

- Kubernetes 1.10.x only validates Docker Engine versions [1.11.2 to 1.13.1 and 17.03.x](https://github.com/kubernetes/kubernetes/blob/master/CHANGELOG-1.10.md#external-dependencies)

- Are we living dangerously?

- "Validates" = continuous integration builds

- The Docker API is versioned, and offers strong backward-compatibility

(If a client uses e.g. API v1.25, the Docker Engine will keep behaving the same way)

.debug[[kube/versions-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/versions-k8s.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 git://github.com/jpetazzo/container.training
  ```

]

(You can also fork the repository on GitHub and clone your fork if you prefer that.)

.debug[[common/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/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[[common/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/sampleapp.md)]
---

## More detail on our sample application

- Visit 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

- Let's look at the general layout of the source code:

there is a Compose file [docker-compose.yml](
  https://github.com/jpetazzo/container.training/blob/master/dockercoins/docker-compose.yml) ...

... and 4 other services, each in its own directory:

- `rng` = web service generating random bytes
  - `hasher` = web service computing hash of POSTed data
  - `worker` = background process using `rng` and `hasher`
  - `webui` = web interface to watch progress

.debug[[common/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/sampleapp.md)]
---

## Compose file format version

*Particularly relevant if you have used Compose before...*

- 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[[common/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/sampleapp.md)]
---

## Service discovery in container-land

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

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

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

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

.debug[[common/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/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[[common/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/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 did require "links" sections

- Network aliases are automatically namespaced

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

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

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

.debug[[common/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/sampleapp.md)]
---

## What's this application?

- It is a DockerCoin miner! .emoji[💰🐳📦🚢]

- No, you can't buy coffee with DockerCoins

- How DockerCoins works:

- `worker` asks to `rng` to generate a few random bytes

- `worker` feeds these bytes into `hasher`

- and repeat forever!

- every second, `worker` updates `redis` to indicate how many loops were done

- `webui` queries `redis`, and computes and exposes "hashing speed" in your browser

.debug[[common/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/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[[common/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/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[[common/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/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 Docker4Mac or Docker4Windows.

How to fix this?

Stop the app with `^C`, edit `dockercoins.yml`, comment out the `volumes` section, and try again.

.debug[[common/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/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[[common/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/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[[common/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/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[[common/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/sampleapp.md)]
---
## Clean up

- Before moving on, let's remove those containers

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

]

.debug[[common/composedown.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/composedown.md)]
---

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

---

Kubernetes concepts

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

---
# Kubernetes concepts

- Kubernetes is a container management system

- It runs and manages containerized applications on a cluster

- What does that really mean?

.debug[[kube/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/concepts-k8s.md)]
---

## Other things that Kubernetes can do for us

- Basic autoscaling

- Blue/green deployment, canary deployment

- Long running services, but also batch (one-off) jobs

- Overcommit our cluster and *evict* low-priority jobs

- Run services with *stateful* data (databases etc.)

- Fine-grained access control defining *what* can be done by *whom* on *which* resources

- Integrating third party services (*service catalog*)

- Automating complex tasks (*operators*)

.debug[[kube/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/concepts-k8s.md)]
---

## Kubernetes architecture

.debug[[kube/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/concepts-k8s.md)]
---

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

.debug[[kube/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/concepts-k8s.md)]
---

## Kubernetes architecture

- Ha ha ha ha

- OK, I was trying to scare you, it's much simpler than that ❤️

.debug[[kube/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/concepts-k8s.md)]
---

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

.debug[[kube/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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. GKE), 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[[kube/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/concepts-k8s.md)]
---

## Do we need to run Docker at all?

No!

- By default, Kubernetes uses the Docker Engine to run containers

- We could also use `rkt` ("Rocket") from CoreOS

- Or leverage other pluggable runtimes through the *Container Runtime Interface*

(like CRI-O, or containerd)

.debug[[kube/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/concepts-k8s.md)]
---

## Kubernetes resources

- The Kubernetes API defines a lot of objects called *resources*

- These resources are organized by type, or `Kind` (in the API)

- A few common resource types are:

- node (a machine — physical or virtual — in our cluster)
  - pod (group of containers running together on a node)
  - service (stable network endpoint to connect to one or multiple containers)
  - namespace (more-or-less isolated group of things)
  - secret (bundle of sensitive data to be passed to a container)
 
  And much more! (We can see the full list by running `kubectl get`)

.debug[[kube/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/concepts-k8s.md)]
---

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

.debug[[kube/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/concepts-k8s.md)]
---

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

.debug[[kube/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/concepts-k8s.md)]
---

## Credits

- The first diagram is courtesy of Weave Works

- a *pod* can have multiple containers working together

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

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

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

Both diagrams used with permission.

.debug[[kube/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/concepts-k8s.md)]
---

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

---

Declarative vs imperative

.nav[
[Previous section](#toc-kubernetes-concepts)
|
[Back to table of contents](#toc-chapter-1)
|
[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[[common/declarative.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/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[[common/declarative.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/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[[common/declarative.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/common/declarative.md)]
---
## Declarative vs imperative in Kubernetes

- Virtually everything we create in Kubernetes is created from a *spec*

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

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

- Kubernetes will *reconcile* the current state with the spec
 (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[[kube/declarative.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/declarative.md)]
---

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

---

Kubernetes network model

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

---
# Kubernetes network model

- TL,DR:

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

- In detail:

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

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

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

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

- Kubernetes doesn't mandate any particular implementation

.debug[[kube/kubenet.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/kubenet.md)]
---

## Kubernetes network model: the good

- Everything can reach everything

- No address translation

- No port translation

- No new protocol

- Pods cannot move from a node to another and keep their IP address

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

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

- The specification is simple enough to allow many various implementations

.debug[[kube/kubenet.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/kubenet.md)]
---

## Kubernetes network model: the less good

- Everything can reach everything

- if you want security, you need to add network policies

- the network implementation that you use needs to support them

- There are literally dozens of implementations out there

(15 are listed in the Kubernetes documentation)

- Pods have level 3 (IP) connectivity, but *services* are level 4

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

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

.debug[[kube/kubenet.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/kubenet.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/kubenet.md)]
---

## The Container Network Interface (CNI)

- The CNI has a well-defined [specification](https://github.com/containernetworking/cni/blob/master/SPEC.md#network-configuration) for network plugins

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

- Typically, a CNI plugin will:

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

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

- configure the interface as well as required routes etc.

- Using multiple plugins can be done with "meta-plugins" like CNI-Genie or Multus

- Not all CNI plugins are equal

(e.g. they don't all implement network policies, which are required to isolate pods)

.debug[[kube/kubenet.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/kubenet.md)]
---

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

---

First contact with `kubectl`

.nav[
[Previous section](#toc-kubernetes-network-model)
|
[Back to table of contents](#toc-chapter-1)
|
[Next section](#toc-setting-up-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[[kube/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/kubectlget.md)]
---

## What's available?

- `kubectl` has pretty good introspection facilities

- We can list all available resource types by running `kubectl get`

- We can view details about a resource with:
  ```bash
  kubectl describe type/name
  kubectl describe type name
  ```

- We can view the definition for a resource type with:
  ```bash
  kubectl explain type
  ```

Each time, `type` can be singular, plural, or abbreviated type name.

.debug[[kube/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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 error that we see is expected: the Kubernetes API requires authentication.

.debug[[kube/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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
  ```

]

*These are not the pods you're looking for.* But where are they?!?

.debug[[kube/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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.*

.debug[[kube/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/kubectlget.md)]
---

## Accessing namespaces

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

- We can switch to a different namespace with the `-n` option

- List the pods in the `kube-system` namespace:
  ```bash
  kubectl -n kube-system get pods
  ```

]

*Ding ding ding ding ding!*

The `kube-system` namespace is used for the control plane.

.debug[[kube/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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 master components

- `kube-dns` is an additional component (not mandatory but super useful, so it's there)

- `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

- the pods with a name ending with `-node1` are the master components
 
 (they have been specifically "pinned" to the master node)

.debug[[kube/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/kubectlget.md)]
---

## What about `kube-public`?

- List the pods in the `kube-public` namespace:
  ```bash
  kubectl -n kube-public get pods
  ```

]

- Maybe it doesn't have pods, but what secrets is `kube-public` keeping?

- List the secrets in the `kube-public` namespace:
  ```bash
  kubectl -n kube-public get secrets
  ```

]
--

- `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[[kube/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/kubectlget.md)]
---

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

---

Setting up Kubernetes

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

---
# Setting up Kubernetes

- How did we set up these Kubernetes clusters that we're using?

- We used `kubeadm` on freshly installed VM instances running Ubuntu 16.04 LTS

1. Install Docker

2. Install Kubernetes packages

3. Run `kubeadm init` on the master node

4. Set up Weave (the overlay network)
 
 (that step is just one `kubectl apply` command; discussed later)

5. Run `kubeadm join` on the other nodes (with the token produced by `kubeadm init`)

6. Copy the configuration file generated by `kubeadm init`

- Check the [prepare VMs README](https://github.com/jpetazzo/container.training/blob/master/prepare-vms/README.md) for more details

.debug[[kube/setup-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/setup-k8s.md)]
---

## `kubeadm` drawbacks

- Doesn't set up Docker or any other container engine

- Doesn't set up the overlay network

- Doesn't set up multi-master (no high availability)

(At least ... not yet!)

- "It's still twice as many steps as setting up a Swarm cluster 😕" -- Jérôme

.debug[[kube/setup-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/setup-k8s.md)]
---

## Other deployment options

- If you are on Azure:
  [AKS](https://azure.microsoft.com/services/container-service/)

- If you are on Google Cloud:
  [GKE](https://cloud.google.com/kubernetes-engine/)

- If you are on AWS:
  [EKS](https://aws.amazon.com/eks/)
  or
  [kops](https://github.com/kubernetes/kops)

- On a local machine:
  [minikube](https://kubernetes.io/docs/getting-started-guides/minikube/),
  [kubespawn](https://github.com/kinvolk/kube-spawn),
  [Docker4Mac](https://docs.docker.com/docker-for-mac/kubernetes/)

- If you want something customizable:
  [kubicorn](https://github.com/kubicorn/kubicorn)

Probably the closest to a multi-cloud/hybrid solution so far, but in development

.debug[[kube/setup-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/setup-k8s.md)]
---

## Even more deployment options

- If you like Ansible:
  [kubespray](https://github.com/kubernetes-incubator/kubespray)

- If you like Terraform:
  [typhoon](https://github.com/poseidon/typhoon/)

- You can also learn how to install every component manually, with
  the excellent tutorial [Kubernetes The Hard Way](https://github.com/kelseyhightower/kubernetes-the-hard-way)

*Kubernetes The Hard Way is optimized for learning, which means taking the long route to ensure you understand each task required to bootstrap a Kubernetes cluster.*

- There are also many commercial options available!

- For a longer list, check the Kubernetes documentation:
 
 it has a great guide to [pick the right solution](https://kubernetes.io/docs/setup/pick-right-solution/) to set up Kubernetes.

.debug[[kube/setup-k8s.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/setup-k8s.md)]
---

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

---

Running our first containers on Kubernetes

.nav[
[Previous section](#toc-setting-up-kubernetes)
|
[Back to table of contents](#toc-chapter-2)
|
[Next section](#toc-exposing-containers)
]

---
# 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[[kube/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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 `1.1.1.1`, Cloudflare's 
  [public DNS resolver](https://blog.cloudflare.com/announcing-1111/):
  ```bash
  kubectl run pingpong --image alpine ping 1.1.1.1
  ```

]

OK, what just happened?

.debug[[kube/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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
  ```

]

.debug[[kube/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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 8
  ```

]

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[[kube/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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, list pods, and keep watching them:
  ```bash
  kubectl get pods -w
  ```

- Destroy a pod:
  ```bash
  kubectl delete pod pingpong-xxxxxxxxxx-yyyyy
  ```
]

.debug[[kube/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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
  ```

]

Unfortunately, `--follow` cannot (yet) be used to stream the logs from multiple containers.

.debug[[kube/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/kubectlrun.md)]
---

## Aren't we flooding 1.1.1.1?

- If you're wondering this, good question!

- Don't worry, though:

*APNIC's research group held the IP addresses 1.1.1.1 and 1.0.0.1. While the addresses were valid, so many people had entered them into various random systems that they were continuously overwhelmed by a flood of garbage traffic. APNIC wanted to study this garbage traffic but any time they'd tried to announce the IPs, the flood would overwhelm any conventional network.*

(Source: https://blog.cloudflare.com/announcing-1111/)

- It's very unlikely that our concerted pings manage to produce
  even a modest blip at Cloudflare's NOC!

.debug[[kube/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/kubectlrun.md)]
---

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

---

Exposing containers

.nav[
[Previous section](#toc-running-our-first-containers-on-kubernetes)
|
[Back to table of contents](#toc-chapter-2)
|
[Next section](#toc-deploying-a-self-hosted-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, `kube-dns` will allow us to resolve it by name

(i.e. after creating service `hello`, the name `hello` will resolve to something)

- There are different types of services, detailed on the following slides:

`ClusterIP`, `NodePort`, `LoadBalancer`, `ExternalName`

.debug[[kube/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/kubectlexpose.md)]
---

## Basic service types

- `ClusterIP` (default type)

- a virtual IP address is allocated for the service (in an internal, private range)
  - this IP address is reachable only from within the cluster (nodes and pods)
  - our code can connect to the service using the original port number

- `NodePort`

- a port is allocated for the service (by default, in the 30000-32768 range)
  - that port is made available *on all our nodes* and anybody can connect to it
  - our code must be changed to connect to that new port number

These service types are always available.

Under the hood: `kube-proxy` is using a userland proxy and a bunch of `iptables` rules.

.debug[[kube/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/kubectlexpose.md)]
---

## More service types

- `LoadBalancer`

- an external load balancer is allocated for the service
 - the load balancer is configured accordingly
 (e.g.: a `NodePort` service is created, and the load balancer sends traffic to that port)

- `ExternalName`

- the DNS entry managed by `kube-dns` will just be a `CNAME` to a provided record
  - no port, no IP address, no nothing else is allocated

The `LoadBalancer` type is currently only available on AWS, Azure, and GCE.

.debug[[kube/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/kubectlexpose.md)]
---

## Running containers with open ports

- Since `ping` doesn't have anything to connect to, we'll have to run something else

- Start a bunch of ElasticSearch containers:
  ```bash
  kubectl run elastic --image=elasticsearch:2 --replicas=7
  ```

- Watch them being started:
  ```bash
  kubectl get pods -w
  ```

]

The `-w` option "watches" events happening on the specified resources.

Note: please DO NOT call the service `search`. It would collide with the TLD.

.debug[[kube/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/kubectlexpose.md)]
---

## Exposing our deployment

- We'll create a default `ClusterIP` service

- Expose the ElasticSearch HTTP API port:
  ```bash
  kubectl expose deploy/elastic --port 9200
  ```

- Look up which IP address was allocated:
  ```bash
  kubectl get svc
  ```

]

.debug[[kube/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/kubectlexpose.md)]
---

## Testing our service

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

- Let's obtain the IP address that was allocated for our service, *programatically:*
  ```bash
  IP=$(kubectl get svc elastic -o go-template --template '{{ .spec.clusterIP }}')
  ```

- Send a few requests:
  ```bash
  curl http://$IP:9200/
  ```

]

We may see `curl: (7) Failed to connect to _IP_ port 9200: Connection refused`.

This is normal while the service starts up.

Once it's running, our requests are load balanced across multiple pods.

.debug[[kube/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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

- `kube-dns` 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[[kube/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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 `elastic` service:
  ```bash
  kubectl describe service elastic
  ```

]

In the output, there will be a line starting with `Endpoints:`.

That line will list a bunch of addresses in `host:port` format.

.debug[[kube/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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 elastic
  kubectl get endpoints elastic -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 run=elastic -o wide
  ```

.debug[[kube/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/kubectlexpose.md)]
---
class: title

Our app on Kube

.debug[[kube/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/ourapponkube.md)]
---

## What's on the menu?

In this part, we will:

- **build** images for our app,

- **ship** these images with a registry,

- **run** deployments using these images,

- expose these deployments so they can communicate with each other,

- expose the web UI so we can access it from outside.

.debug[[kube/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/ourapponkube.md)]
---

## The plan

- Build on our control node (`node1`)

- Tag images so that they are named `$REGISTRY/servicename`

- Upload them to a registry

- Create deployments using the images

- Expose (with a ClusterIP) the services that need to communicate

- Expose (with a NodePort) the WebUI

.debug[[kube/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/ourapponkube.md)]
---

## Which registry do we want to use?

- We could use the Docker Hub

- Or a service offered by our cloud provider (ACR, GCR, ECR...)

- Or we could just self-host that registry

*We'll self-host the registry because it's the most generic solution for this workshop.*

.debug[[kube/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/ourapponkube.md)]
---

## Using the open source registry

- We need to run a `registry:2` container
 (make sure you specify tag `:2` to run the new version!)

- It will store images and layers to the local filesystem
 (but you can add a config file to use S3, Swift, etc.)

- Docker *requires* TLS when communicating with the registry

- unless for registries on `127.0.0.0/8` (i.e. `localhost`)

- or with the Engine flag `--insecure-registry`

- Our strategy: publish the registry container on a NodePort,
 so that it's available through `127.0.0.1:xxxxx` on each node

.debug[[kube/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/ourapponkube.md)]
---

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

---

Deploying a self-hosted registry

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

---

# Deploying a self-hosted registry

- We will deploy a registry container, and expose it with a NodePort

- Create the registry service:
  ```bash
  kubectl run registry --image=registry:2
  ```

- Expose it on a NodePort:
  ```bash
  kubectl expose deploy/registry --port=5000 --type=NodePort
  ```

]

.debug[[kube/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/ourapponkube.md)]
---

## Connecting to our registry

- We need to find out which port has been allocated

- View the service details:
  ```bash
  kubectl describe svc/registry
  ```

- Get the port number programmatically:
  ```bash
  NODEPORT=$(kubectl get svc/registry -o json | jq .spec.ports[0].nodePort)
  REGISTRY=127.0.0.1:$NODEPORT
  ```

]

.debug[[kube/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/ourapponkube.md)]
---

## Testing our registry

- A convenient Docker registry API route to remember is `/v2/_catalog`

- View the repositories currently held in our registry:
  ```bash
  curl $REGISTRY/v2/_catalog
  ```

]

We should see:
```json
{"repositories":[]}
```

.debug[[kube/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/ourapponkube.md)]
---

## Testing our local registry

- We can retag a small image, and push it to the registry

- Make sure we have the busybox image, and retag it:
  ```bash
  docker pull busybox
  docker tag busybox $REGISTRY/busybox
  ```

- Push it:
  ```bash
  docker push $REGISTRY/busybox
  ```

]

.debug[[kube/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/ourapponkube.md)]
---

## Checking again what's on our local registry

- Let's use the same endpoint as before

- Ensure that our busybox image is now in the local registry:
  ```bash
  curl $REGISTRY/v2/_catalog
  ```

]

The curl command should now output:
```json
{"repositories":["busybox"]}
```

.debug[[kube/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/ourapponkube.md)]
---

## Building and pushing our images

- We are going to use a convenient feature of Docker Compose

- Go to the `stacks` directory:
  ```bash
  cd ~/container.training/stacks
  ```

- Build and push the images:
  ```bash
  export REGISTRY
  export TAG=v0.1
  docker-compose -f dockercoins.yml build
  docker-compose -f dockercoins.yml push
  ```

]

Let's have a look at the `dockercoins.yml` file while this is building and pushing.

.debug[[kube/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/ourapponkube.md)]
---

```yaml
version: "3"

services:
  rng:
    build: dockercoins/rng
    image: ${REGISTRY-127.0.0.1:5000}/rng:${TAG-latest}
    deploy:
      mode: global
  ...
  redis:
    image: redis
  ...
  worker:
    build: dockercoins/worker
    image: ${REGISTRY-127.0.0.1:5000}/worker:${TAG-latest}
    ...
    deploy:
      replicas: 10
```

.debug[[kube/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/ourapponkube.md)]
---

## Avoiding the `latest` tag

- If you don't, the tag will default to `latest`

- The problem with `latest`: nobody knows what it points to!

- the latest commit in the repo?

- the latest commit in some branch? (Which one?)

- the latest tag?

- some random version pushed by a random team member?

- If you keep pushing the `latest` tag, how do you roll back?

- Image tags should be meaningful, i.e. correspond to code branches, tags, or hashes

.debug[[kube/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/ourapponkube.md)]
---

## Deploying all the things

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

- Deploy `redis`:
  ```bash
  kubectl run redis --image=redis
  ```

- Deploy everything else:
  ```bash
    for SERVICE in hasher rng webui worker; do
      kubectl run $SERVICE --image=$REGISTRY/$SERVICE:$TAG
    done
  ```

]

.debug[[kube/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/ourapponkube.md)]
---

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

---

Exposing services internally

.nav[
[Previous section](#toc-deploying-a-self-hosted-registry)
|
[Back to table of contents](#toc-chapter-2)
|
[Next section](#toc-exposing-services-for-external-access)
]

---

# Exposing services internally

- 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[[kube/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/ourapponkube.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/train-of-containers-1.jpg)]

---

Exposing services for external access

.nav[
[Previous section](#toc-exposing-services-internally)
|
[Back to table of contents](#toc-chapter-2)
|
[Next section](#toc-the-kubernetes-dashboard)
]

---

# 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[[kube/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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/)

]

*Alright, we're back to where we started, when we were running on a single node!*

.debug[[kube/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/ourapponkube.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/train-of-containers-2.jpg)]

---

The Kubernetes dashboard

.nav[
[Previous section](#toc-exposing-services-for-external-access)
|
[Back to table of contents](#toc-chapter-3)
|
[Next section](#toc-security-implications-of-kubectl-apply)
]

---
# The Kubernetes dashboard

- Kubernetes resources can also be viewed with a web dashboard

- We are going to deploy that dashboard with *three commands:*

1) actually *run* the dashboard

2) bypass SSL for the dashboard

3) bypass authentication for the dashboard

There is an additional step to make the dashboard available from outside (we'll get to that)

.footnote[.warning[Yes, this will open our cluster to all kinds of shenanigans. Don't do this at home.]]

.debug[[kube/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/dashboard.md)]
---

## 1) Running the dashboard

- We need to create a *deployment* and a *service* for the dashboard

- But also a *secret*, a *service account*, a *role* and a *role binding*

- All these things can be defined in a YAML file and created with `kubectl apply -f`

- Create all the dashboard resources, with the following command:
  ```bash
  kubectl apply -f https://goo.gl/Qamqab
  ```

]

The goo.gl URL expands to:
 
.small[https://raw.githubusercontent.com/kubernetes/dashboard/master/src/deploy/recommended/kubernetes-dashboard.yaml]

.debug[[kube/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/dashboard.md)]
---

## 2) Bypassing SSL for the dashboard

- The Kubernetes dashboard uses HTTPS, but we don't have a certificate

- Recent versions of Chrome (63 and later) and Edge will refuse to connect

(You won't even get the option to ignore a security warning!)

- We could (and should!) get a certificate, e.g. with [Let's Encrypt](https://letsencrypt.org/)

- ... But for convenience, for this workshop, we'll forward HTTP to HTTPS

.debug[[kube/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/dashboard.md)]
---

## Running the SSL unwrapper

- We are going to run [`socat`](http://www.dest-unreach.org/socat/doc/socat.html), telling it to accept TCP connections and relay them over SSL

- Then we will expose that `socat` instance with a `NodePort` service

- For convenience, these steps are neatly encapsulated into another YAML file

- Apply the convenient YAML file, and defeat SSL protection:
  ```bash
  kubectl apply -f https://goo.gl/tA7GLz
  ```

]

The goo.gl URL expands to:
 
.small[.small[https://gist.githubusercontent.com/jpetazzo/c53a28b5b7fdae88bc3c5f0945552c04/raw/da13ef1bdd38cc0e90b7a4074be8d6a0215e1a65/socat.yaml]]

.debug[[kube/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/dashboard.md)]
---

## Connecting to the dashboard

- Check which port the dashboard is on:
  ```bash
  kubectl -n kube-system get svc socat
  ```

]

You'll want the `3xxxx` port.

- Connect to http://oneofournodes:3xxxx/

]

The dashboard will then ask you which authentication you want to use.

.debug[[kube/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/dashboard.md)]
---

## Dashboard authentication

- We have three authentication options at this point:

- token (associated with a role that has appropriate permissions)

- kubeconfig (e.g. using the `~/.kube/config` file from `node1`)

- "skip" (use the dashboard "service account")

- Let's use "skip": we get a bunch of warnings and don't see much

.debug[[kube/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/dashboard.md)]
---

## 3) Bypass authentication for the dashboard

- The dashboard documentation [explains how to do this](https://github.com/kubernetes/dashboard/wiki/Access-control#admin-privileges)

- We just need to load another YAML file!

- Grant admin privileges to the dashboard so we can see our resources:
  ```bash
  kubectl apply -f https://goo.gl/CHsLTA
  ```

- Reload the dashboard and enjoy!

]

.debug[[kube/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/dashboard.md)]
---

## Exposing the dashboard over HTTPS

- We took a shortcut by forwarding HTTP to HTTPS inside the cluster

- Let's expose the dashboard over HTTPS!

- The dashboard is exposed through a `ClusterIP` service (internal traffic only)

- We will change that into a `NodePort` service (accepting outside traffic)

- Edit the service:
  ```bash
  kubectl edit service kubernetes-dashboard
  ```

]

`NotFound`?!? Y U NO WORK?!?

.debug[[kube/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/dashboard.md)]
---

## Editing the `kubernetes-dashboard` service

- If we look at the [YAML](https://goo.gl/Qamqab) that we loaded before, we'll get a hint

- The dashboard was created in the `kube-system` namespace

- Edit the service:
  ```bash
  kubectl -n kube-system edit service kubernetes-dashboard
  ```

- Change `ClusterIP` to `NodePort`, save, and exit

- Check the port that was assigned with `kubectl -n kube-system get services`

- Connect to https://oneofournodes:3xxxx/ (yes, https)

]

.debug[[kube/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/dashboard.md)]
---

## Running the Kubernetes dashboard securely

- The steps that we just showed you are *for educational purposes only!*

- If you do that on your production cluster, people [can and will abuse it](https://blog.redlock.io/cryptojacking-tesla)

- For an in-depth discussion about securing the dashboard,
 
 check [this excellent post on Heptio's blog](https://blog.heptio.com/on-securing-the-kubernetes-dashboard-16b09b1b7aca)

.debug[[kube/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/dashboard.md)]
---

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

---

Security implications of `kubectl apply`

.nav[
[Previous section](#toc-the-kubernetes-dashboard)
|
[Back to table of contents](#toc-chapter-3)
|
[Next section](#toc-scaling-a-deployment)
]

---

# Security implications of `kubectl apply`

- When we do `kubectl apply -f <URL>`, we create arbitrary resources

- Resources can be evil; imagine a `deployment` that ...

- starts bitcoin miners on the whole cluster

- hides in a non-default namespace

- bind-mounts our nodes' filesystem

- inserts SSH keys in the root account (on the node)

- encrypts our data and ransoms it

- ☠️☠️☠️

.debug[[kube/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/dashboard.md)]
---

## `kubectl apply` is the new `curl | sh`

- `curl | sh` is convenient

- It's safe if you use HTTPS URLs from trusted sources

- `kubectl apply -f` is convenient

- It's safe if you use HTTPS URLs from trusted sources

- Example: the official setup instructions for most pod networks

- It introduces new failure modes (like if you try to apply yaml from a link that's no longer valid)

.debug[[kube/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/dashboard.md)]
---

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

---

Scaling a deployment

.nav[
[Previous section](#toc-security-implications-of-kubectl-apply)
|
[Back to table of contents](#toc-chapter-3)
|
[Next section](#toc-daemon-sets)
]

---
# Scaling a deployment

- We will start with an easy one: the `worker` deployment

- Open two new terminals to check what's going on with pods and deployments:
  ```bash
  kubectl get pods -w
  kubectl get deployments -w
  ```

- Now, create more `worker` replicas:
  ```bash
  kubectl scale deploy/worker --replicas=10
  ```

]

After a few seconds, the graph in the web UI should show up.
 
(And peak at 10 hashes/second, just like when we were running on a single one.)

.debug[[kube/kubectlscale.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/kubectlscale.md)]
---

.interstitial[![Image separating from the next chapter](https://gallant-turing-d0d520.netlify.com/containers/Container-Ship-Freighter-Navigation-Elbe-Romance-1782991.jpg)]

---

Daemon sets

.nav[
[Previous section](#toc-scaling-a-deployment)
|
[Back to table of contents](#toc-chapter-3)
|
[Next section](#toc-updating-a-service-through-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

- What if we just scale up `deploy/rng` to the number of nodes?

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

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

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

- Instead of a `deployment`, we will use a `daemonset`

.debug[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/daemonset.md)]
---

## Creating a daemon set

- Unfortunately, as of Kubernetes 1.10, 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[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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 --export >rng.yml 
  ```

- Edit `rng.yml`

]

Note: `--export` will remove "cluster-specific" information, i.e.:
- namespace (so that the resource is not tied to a specific namespace)
- status and creation timestamp (useless when creating a new resource)
- resourceVersion and uid (these would cause... *interesting* problems)

.debug[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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 `status: {}` line at the end

- Or, we could also ...

.debug[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/daemonset.md)]
---

## What are all these pods doing?

- Let's check the logs of all these `rng` pods

- All these pods have a `run=rng` label:

- the first pod, because that's what `kubectl run` does
  - the other ones (in the daemon set), because we
    *copied the spec from the first one*

- Therefore, we can query everybody's logs using that `run=rng` selector

- Check the logs of all the pods having a label `run=rng`:
  ```bash
  kubectl logs -l run=rng --tail 1
  ```

]

It appears that *all the pods* are serving requests at the moment.

.debug[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/daemonset.md)]
---

## The magic of selectors

- The `rng` *service* is load balancing requests to a set of pods

- This set of pods is defined as "pods having the label `run=rng`"

- Check the *selector* in the `rng` service definition:
  ```bash
  kubectl describe service rng
  ```

]

When we created additional pods with this label, they were
automatically detected by `svc/rng` and added as *endpoints*
to the associated load balancer.

.debug[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/daemonset.md)]
---

## Removing the first pod from the load balancer

- What would happen if we removed that pod, with `kubectl delete pod ...`?

The `replicaset` would re-create it immediately.

- What would happen if we removed the `run=rng` label from that pod?

The `replicaset` would re-create it immediately.

... Because what matters to the `replicaset` is the number of pods *matching that selector.*

- But but but ... Don't we have more than one pod with `run=rng` now?

The answer lies in the exact selector used by the `replicaset` ...

.debug[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/daemonset.md)]
---

## Deep dive into selectors

- Let's look at the selectors for the `rng` *deployment* and the associated *replica set*

- Show detailed information about the `rng` deployment:
  ```bash
  kubectl describe deploy rng
  ```

- Show detailed information about the `rng` replica:
 (The second command doesn't require you to get the exact name of the replica set)
 ```bash
 kubectl describe rs rng-yyyy
 kubectl describe rs -l run=rng
 ```

]

The replica set selector also has a `pod-template-hash`, unlike the pods in our daemon set.

.debug[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/daemonset.md)]
---

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

---

Updating a service through labels and selectors

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

---

# Updating a service through labels and selectors

- What if we want to drop the `rng` deployment from the load balancer?

- Option 1:

- destroy it

- Option 2:

- add an extra *label* to the daemon set

- update the service *selector* to refer to that *label*

Of course, option 2 offers more learning opportunities. Right?

.debug[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/daemonset.md)]
---

## Add an extra label to the daemon set

- We will update the daemon set "spec"

- Option 1:

- edit the `rng.yml` file that we used earlier

- load the new definition with `kubectl apply`

- Option 2:

- use `kubectl edit`

*If you feel like you got this💕🌈, feel free to try directly.*

*We've included a few hints on the next slides for your convenience!*

.debug[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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)

- You 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[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/daemonset.md)]
---

## Adding our label

- Let's add a label `isactive: yes`

- In YAML, `yes` should be quoted; i.e. `isactive: "yes"`

- Update the daemon set to add `isactive: "yes"` to the selector and template label:
  ```bash
  kubectl edit daemonset rng
  ```

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

]

.debug[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/daemonset.md)]
---

## Checking what we've done

- Check the most recent log line of all `run=rng` pods to confirm that exactly one per node is now active:
  ```bash
  kubectl logs -l run=rng --tail 1
  ```

]

The timestamps should give us a hint about how many pods are currently receiving traffic.

- Look at the pods that we have right now:
  ```bash
  kubectl get pods
  ```

]

.debug[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/daemonset.md)]
---

## Cleaning up

- The pods of the deployment and the "old" daemon set are still running

- We are going to identify them programmatically

- List the pods with `run=rng` but without `isactive=yes`:
  ```bash
  kubectl get pods -l run=rng,isactive!=yes
  ```

- Remove these pods:
  ```bash
  kubectl delete pods -l run=rng,isactive!=yes
  ```

]

.debug[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/daemonset.md)]
---

## Cleaning up stale pods

```
$ kubectl get pods
NAME                        READY     STATUS        RESTARTS   AGE
rng-54f57d4d49-7pt82        1/1       Terminating   0          51m
rng-54f57d4d49-vgz9h        1/1       Running       0          22s
rng-b85tm                   1/1       Terminating   0          39m
rng-hfbrr                   1/1       Terminating   0          39m
rng-vplmj                   1/1       Running       0          7m
rng-xbpvg                   1/1       Running       0          7m
[...]
```

- The extra pods (noted `Terminating` above) are going away

- ... But a new one (`rng-54f57d4d49-vgz9h` above) was restarted immediately!

- Remember, the *deployment* still exists, and makes sure that one pod is up and running

- If we delete the pod associated to the deployment, it is recreated automatically

.debug[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/daemonset.md)]
---

## Deleting a deployment

- Remove the `rng` deployment:
  ```bash
  kubectl delete deployment rng
  ```
]

- The pod that was created by the deployment is now being terminated:

```
$ kubectl get pods
NAME                        READY     STATUS        RESTARTS   AGE
rng-54f57d4d49-vgz9h        1/1       Terminating   0          4m
rng-vplmj                   1/1       Running       0          11m
rng-xbpvg                   1/1       Running       0          11m
[...]
```

Ding, dong, the deployment is dead! And the daemon set lives on.

.debug[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/daemonset.md)]
---

## Avoiding extra pods

- When we changed the definition of the daemon set, it immediately created new pods. We had to remove the old ones manually.

- How could we have avoided this?

- By adding the `isactive: "yes"` label to the pods before changing the daemon set!

- This can be done programmatically with `kubectl patch`:

```bash
    PATCH='
    metadata:
      labels:
        isactive: "yes"
    '
    kubectl get pods -l run=rng -l controller-revision-hash -o name |
      xargs kubectl patch -p "$PATCH" 
  ```

.debug[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/daemonset.md)]
---

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

---

Rolling updates

.nav[
[Previous section](#toc-updating-a-service-through-labels-and-selectors)
|
[Back to table of contents](#toc-chapter-3)
|
[Next section](#toc-accessing-logs-from-the-cli)
]

---
# 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[[kube/rollout.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/rollout.md)]
---

## Rolling updates

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

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

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

- At any given time ...

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

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

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

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

.debug[[kube/rollout.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/rollout.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/rollout.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/rollout.md)]
---

## Building a new version of the `worker` service

- Go to the `stack` directory:
  ```bash
  cd ~/container.training/stacks
  ```

- Edit `dockercoins/worker/worker.py`, update the `sleep` line to sleep 1 second

- Build a new tag and push it to the registry:
  ```bash
  #export REGISTRY=localhost:3xxxx
  export TAG=v0.2
  docker-compose -f dockercoins.yml build
  docker-compose -f dockercoins.yml push
  ```

]

.debug[[kube/rollout.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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=$REGISTRY/worker:$TAG
  ```

]

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

.debug[[kube/rollout.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/rollout.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/rollout.md)]
---

## Rolling out something invalid

- What happens if we make a mistake?

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

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

]

Our rollout is stuck. However, the app is not dead (just 10% slower).

.debug[[kube/rollout.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/rollout.md)]
---

## What's going on with our rollout?

- Why is our app 10% slower?

- Because `MaxUnavailable=1`, so the rollout terminated 1 replica out of 10 available

- Okay, but why do we see 2 new replicas being rolled out?

- Because `MaxSurge=1`, so in addition to replacing the terminated one, the rollout is also starting one more

.debug[[kube/rollout.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/rollout.md)]
---

## The nitty-gritty details

- We start with 10 pods running for the `worker` deployment

- Current settings: MaxUnavailable=1 and MaxSurge=1

- When we start the rollout:

- one replica is taken down (as per MaxUnavailable=1)
  - another is created (with the new version) to replace it
  - another is created (with the new version) per MaxSurge=1

- Now we have 9 replicas up and running, and 2 being deployed

- Our rollout is stuck at this point!

.debug[[kube/rollout.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/rollout.md)]
---

## Checking the dashboard during the bad rollout

If you haven't deployed the Kubernetes dashboard earlier, just skip this slide.

- Check which port the dashboard is on:
  ```bash
  kubectl -n kube-system get svc socat
  ```

]

Note the `3xxxx` port.

- Connect to http://oneofournodes:3xxxx/

]

- We have failures in Deployments, Pods, and Replica Sets

.debug[[kube/rollout.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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 down:
  ```bash
  kubectl rollout undo deploy worker
  kubectl rollout status deploy worker
  ```

]

.debug[[kube/rollout.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/rollout.md)]
---

## Changing rollout parameters

- We want to:

- revert to `v0.1`
  - be conservative on availability (always have desired number of available workers)
  - be aggressive on rollout speed (update more than one pod at a time) 
  - give some time to our workers to "warm up" before starting more

The corresponding changes can be expressed in the following YAML snippet:

.small[
```yaml
spec:
  template:
    spec:
      containers:
      - name: worker
        image: $REGISTRY/worker:v0.1
  strategy:
    rollingUpdate:
      maxUnavailable: 0
      maxSurge: 3
  minReadySeconds: 10
```
]

.debug[[kube/rollout.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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: $REGISTRY/worker:v0.1
      strategy:
        rollingUpdate:
          maxUnavailable: 0
          maxSurge: 3
      minReadySeconds: 10
    "
  kubectl rollout status deployment worker
  kubectl get deploy -o json worker |
          jq "{name:.metadata.name} + .spec.strategy.rollingUpdate"
  ```
  ]

]

.debug[[kube/rollout.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/rollout.md)]
---

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

---

Accessing logs from the CLI

.nav[
[Previous section](#toc-rolling-updates)
|
[Back to table of contents](#toc-chapter-4)
|
[Next section](#toc-managing-stacks-with-helm)
]

---
# Accessing logs from the CLI

- The `kubectl logs` commands 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[[kube/logs-cli.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/logs-cli.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/logs-cli.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/logs-cli.md)]
---

## Installing Stern

- For simplicity, let's just grab a binary release

- Download a binary release from GitHub:
  ```bash
  sudo curl -L -o /usr/local/bin/stern \
       https://github.com/wercker/stern/releases/download/1.6.0/stern_linux_amd64
  sudo chmod +x /usr/local/bin/stern
  ```

]

These installation instructions will work on our clusters, since they are Linux amd64 VMs.

However, you will have to adapt them if you want to install Stern on your local machine.

.debug[[kube/logs-cli.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/logs-cli.md)]
---

## Using Stern

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

- `-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 rng containers:
  ```bash
  stern rng
  ```

]

.debug[[kube/logs-cli.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/logs-cli.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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`

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

]

.debug[[kube/logs-cli.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/logs-cli.md)]
---

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

---

Managing stacks with Helm

.nav[
[Previous section](#toc-accessing-logs-from-the-cli)
|
[Back to table of contents](#toc-chapter-4)
|
[Next section](#toc-namespaces)
]

---
# 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[[kube/helm.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/helm.md)]
---

## Helm concepts

- `helm` is a CLI tool

- `tiller` is its companion server-side component

- A "chart" is an archive containing templatized YAML bundles

- Charts are versioned

- Charts can be stored on private or public repositories

.debug[[kube/helm.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/helm.md)]
---

## Installing Helm

- We need to install the `helm` CLI; then use it to deploy `tiller`

- Install the `helm` CLI:
  ```bash
  curl https://raw.githubusercontent.com/kubernetes/helm/master/scripts/get | bash
  ```

- Deploy `tiller`:
  ```bash
  helm init
  ```

- Add the `helm` completion:
 ```bash
 . <(helm completion $(basename $SHELL))
 ```

]

.debug[[kube/helm.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/helm.md)]
---

## Fix account permissions

- Helm permission model requires us to tweak permissions

- 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[[kube/helm.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/helm.md)]
---

## View available charts

- A public repo is pre-configured when installing Helm

- We can view available charts with `helm search` (and an optional keyword)

- View all available charts:
  ```bash
  helm search
  ```

- View charts related to `prometheus`:
  ```bash
  helm search prometheus
  ```

]

.debug[[kube/helm.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/helm.md)]
---

## Install a chart

- Most charts use `LoadBalancer` service types by default

- Most charts require persistent volumes to store data

- We need to relax these requirements a bit

- Install the Prometheus metrics collector on our cluster:
  ```bash
  helm install stable/prometheus \
         --set server.service.type=NodePort \
         --set server.persistentVolume.enabled=false
  ```

]

Where do these `--set` options come from?

.debug[[kube/helm.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/helm.md)]
---

## Inspecting a chart

- `helm inspect` shows details about a chart (including available options)

- See the metadata and all available options for `stable/prometheus`:
  ```bash
  helm inspect stable/prometheus
  ```

]

The chart's metadata includes an URL to the project's home page.

(Sometimes it conveniently points to the documentation for the chart.)

.debug[[kube/helm.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/helm.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/helm.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 --export $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[[kube/helm.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/helm.md)]
---

## Testing our helm chart

- Let's install our helm chart! (`dockercoins` is the path to the chart)
  ```bash
  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[[kube/helm.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/helm.md)]
---

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

---

Namespaces

.nav[
[Previous section](#toc-managing-stacks-with-helm)
|
[Back to table of contents](#toc-chapter-4)
|
[Next section](#toc-next-steps)
]

---
# Namespaces

- We cannot have two resources with the same name

(Or can we...?)

- We cannot have two resources *of the same type* with the same name

(But it's OK to have a `rng` service, a `rng` deployment, and a `rng` daemon set!)

- We cannot have two resources of the same type with the same name *in the same namespace*

(But it's OK to have e.g. two `rng` services in different namespaces!)

- In other words: **the tuple *(type, name, namespace)* needs to be unique**

(In the resource YAML, the type is called `Kind`)

.debug[[kube/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/namespaces.md)]
---

## Pre-existing namespaces

- If we deploy a cluster with `kubeadm`, we have three namespaces:

- `default` (for our applications)

- `kube-system` (for the control plane)

- `kube-public` (contains one secret used for cluster discovery)

- If we deploy differently, we may have different namespaces

.debug[[kube/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/namespaces.md)]
---

## Creating namespaces

- Creating a namespace is done with the `kubectl create namespace` command:
  ```bash
  kubectl create namespace blue
  ```

- We can also get fancy and use a very minimal YAML snippet, e.g.:
 ```bash
	kubectl apply -f- <<EOF
	apiVersion: v1
	kind: Namespace
	metadata:
	 name: blue
	EOF
 ```

- The two methods above are identical

- If we are using a tool like Helm, it will create namespaces automatically

.debug[[kube/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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 use *contexts*

- A context is a *(user, cluster, namespace)* tuple

- We can manipulate contexts with the `kubectl config` command

.debug[[kube/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/namespaces.md)]
---

## Creating a context

- We are going to create a context for the `blue` namespace

- View existing contexts to see the cluster name and the current user:
  ```bash
  kubectl config get-contexts
  ```

- Create a new context:
  ```bash
  kubectl config set-context blue --namespace=blue \
      --cluster=kubernetes --user=kubernetes-admin
  ```

]

We have created a context; but this is just some configuration values.

The namespace doesn't exist yet.

.debug[[kube/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/namespaces.md)]
---

## Using a context

- Let's switch to our new context and deploy the DockerCoins chart

- Use the `blue` context:
  ```bash
  kubectl config use-context blue
  ```

- Deploy DockerCoins:
  ```bash
  helm install dockercoins
  ```

]

In the last command line, `dockercoins` is just the local path where
we created our Helm chart before.

.debug[[kube/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/namespaces.md)]
---

## Viewing the deployed app

- Let's see if our Helm chart 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

]

Note: it might take a minute or two for the app to be up and running.

.debug[[kube/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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

- `kube-dns` 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[[kube/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/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[[kube/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/namespaces.md)]
---

## Network policies overview

- We can create as many network policies as we want

- Each network policy has:

- a *pod selector*: "which pods are targeted by the policy?"

- lists of ingress and/or egress rules: "which peers and ports are allowed or blocked?"

- If a pod is not targeted by any policy, traffic is allowed by default

- If a pod is targeted by at least one policy, traffic must be allowed explicitly

.debug[[kube/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/namespaces.md)]
---

## More about network policies

- This remains a high level overview of network policies

- For more details, check:

- the [Kubernetes documentation about network policies](https://kubernetes.io/docs/concepts/services-networking/network-policies/)

- this [talk about network policies at KubeCon 2017 US](https://www.youtube.com/watch?v=3gGpMmYeEO8) by [@ahmetb](https://twitter.com/ahmetb)

.debug[[kube/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/namespaces.md)]
---

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

---

Next steps

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

---
# Next steps

*Alright, how do I get started and containerize my apps?*

Suggested containerization checklist:

.checklist[
- write a Dockerfile for one service in one app
- write Dockerfiles for the other (buildable) services
- write a Compose file for that whole app
- make sure that devs are empowered to run the app in containers
- set up automated builds of container images from the code repo
- set up a CI pipeline using these container images
- set up a CD pipeline (for staging/QA) using these images
]

And *then* it is time to look at orchestration!

.debug[[kube/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/whatsnext.md)]
---

## Namespaces

- Namespaces let you run multiple identical stacks side by side

- Two namespaces (e.g. `blue` and `green`) can each have their own `redis` service

- Each of the two `redis` services has its own `ClusterIP`

- `kube-dns` creates two entries, mapping to these two `ClusterIP` addresses:

`redis.blue.svc.cluster.local` and `redis.green.svc.cluster.local`

- Pods in the `blue` namespace get a *search suffix* of `blue.svc.cluster.local`

- As a result, resolving `redis` from a pod in the `blue` namespace yields the "local" `redis`

.debug[[kube/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/whatsnext.md)]
---

## Stateful services (databases etc.)

- As a first step, it is wiser to keep stateful services *outside* of the cluster

- Exposing them to pods can be done with multiple solutions:

- `ExternalName` services
 
 (`redis.blue.svc.cluster.local` will be a `CNAME` record)

- `ClusterIP` services with explicit `Endpoints`
 
 (instead of letting Kubernetes generate the endpoints from a selector)

- Ambassador services
 
 (application-level proxies that can provide credentials injection and more)

.debug[[kube/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/whatsnext.md)]
---

## Stateful services (second take)

- If you really want to host stateful services on Kubernetes, you can look into:

- volumes (to carry persistent data)

- storage plugins

- persistent volume claims (to ask for specific volume characteristics)

- stateful sets (pods that are *not* ephemeral)

.debug[[kube/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/whatsnext.md)]
---

## HTTP traffic handling

- *Services* are layer 4 constructs

- HTTP is a layer 7 protocol

- It is handled by *ingresses* (a different resource kind)

- *Ingresses* allow:

- virtual host routing
  - session stickiness
  - URI mapping
  - and much more!

- Check out e.g. [Træfik](https://docs.traefik.io/user-guide/kubernetes/)

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## Logging and metrics

- Logging is delegated to the container engine

- Metrics are typically handled with [Prometheus](https://prometheus.io/)

([Heapster](https://github.com/kubernetes/heapster) is a popular add-on)

.debug[[kube/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/whatsnext.md)]
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## Managing the configuration of our applications

- Two constructs are particularly useful: secrets and config maps

- They allow to expose arbitrary information to our containers

- **Avoid** storing configuration in container images

(There are some exceptions to that rule, but it's generally a Bad Idea)

- **Never** store sensitive information in container images

(It's the container equivalent of the password on a post-it note on your screen)

.debug[[kube/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/whatsnext.md)]
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## Managing stack deployments

- The best deployment tool will vary, depending on:

- the size and complexity of your stack(s)
  - how often you change it (i.e. add/remove components)
  - the size and skills of your team

- A few examples:

- shell scripts invoking `kubectl`
  - YAML resources descriptions committed to a repo
  - [Helm](https://github.com/kubernetes/helm) (~package manager)
  - [Spinnaker](https://www.spinnaker.io/) (Netflix' CD platform)
  - [Brigade](https://brigade.sh/) (event-driven scripting; no YAML)

.debug[[kube/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/whatsnext.md)]
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## Cluster federation

![Star Trek Federation](images/startrek-federation.jpg)

Sorry Star Trek fans, this is not the federation you're looking for!

(If I add "Your cluster is in another federation" I might get a 3rd fandom wincing!)

.debug[[kube/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/whatsnext.md)]
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## Cluster federation

- Kubernetes master operation relies on etcd

- etcd uses the [Raft](https://raft.github.io/) protocol

- Raft recommends low latency between nodes

- What if our cluster spreads to multiple regions?

- Break it down in local clusters

- Regroup them in a *cluster federation*

- Synchronize resources across clusters

- Discover resources across clusters

.debug[[kube/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocitysj2018/slides/kube/whatsnext.md)]
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## Developer experience

*I've put this last, but it's pretty important!*

- How do you on-board a new developer?

- What do they need to install to get a dev stack?

- How does a code change make it from dev to prod?

- How does someone add a component to a stack?

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

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Links and resources

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[Back to table of contents](#toc-chapter-4)
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# Links and resources

- [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)

- [Azure Container Service](https://docs.microsoft.com/azure/aks/)

- [Cloud Developer Advocates](https://developer.microsoft.com/advocates/)

- [Local meetups](https://www.meetup.com/)

- [devopsdays](https://www.devopsdays.org/)

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class: title, self-paced

Thank you!

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That's all, folks! Questions?

![end](images/end.jpg)

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