Deploying and Scaling applications with Kubernetes

```

These slides have been built from commit: 9064332

[shared/title.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/title.md)]
---

Deploying and Scaling applications with Kubernetes

*Don't stream videos or download big files during the workshop[.](https://www.youtube.com/watch?v=h16zyxiwDLY)* 
*Don't use your hotspot. Thank you!*

**Slides: http://velocity-2019-11.container.training/**
]

.debug[[shared/title.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/title.md)]
---
## Intros

- Hello! We are:

- AJ ([@s0ulshake](https://twitter.com/s0ulshake))
   - Jérôme ([@jpetazzo](https://twitter.com/jpetazzo))
   - Sean ([@someara](https://twitter.com/someara))

- The workshop will run from 9am to 5pm

- There will be a lunch break at 12:30pm

(And coffee breaks at 10:30am and 3pm)

- Feel free to interrupt for questions at any time

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

- Live feedback, questions, help: [Gitter](https://gitter.im/jpetazzo/workshop-20191104-berlin)

.debug[[logistics.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/logistics.md)]
---
## A brief introduction

- This was initially written by [Jérôme Petazzoni](https://twitter.com/jpetazzo) to support in-person,
  instructor-led workshops and tutorials
  
- Credit is also due to [multiple contributors](https://github.com/jpetazzo/container.training/graphs/contributors) — thank you!

- You can also follow along on your own, at your own pace

- We included as much information as possible in these slides

- We recommend having a mentor to help you ...

- ... Or be comfortable spending some time reading the Kubernetes [documentation](https://kubernetes.io/docs/) ...

- ... And looking for answers on [StackOverflow](http://stackoverflow.com/questions/tagged/kubernetes) and other outlets

.debug[[k8s/intro.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/intro.md)]
---

## Hands on, you shall practice

- Nobody ever became a Jedi by spending their lives reading Wookiepedia

- Likewise, it will take more than merely *reading* these slides
  to make you an expert

- These slides include *tons* of exercises and examples

- They assume that you have access to a Kubernetes cluster

- If you are attending a workshop or tutorial:
 you will be given specific instructions to access your cluster

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

.debug[[k8s/intro.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/intro.md)]
---
## About these slides

- All the content is available in a public GitHub repository:

https://github.com/jpetazzo/container.training

- You can get updated "builds" of the slides there:

http://container.training/

- Typos? Mistakes? Questions? Feel free to hover over the bottom of the slide ...

.footnote[.emoji[👇] Try it! The source file will be shown and you can view it on GitHub and fork and edit it.]

.debug[[shared/about-slides.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/about-slides.md)]
---

## Extra details

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

- This magnifying glass indicates slides that provide extra details

- Feel free to skip them if:

- you are in a hurry

- you are new to this and want to avoid cognitive overload

- you want only the most essential information

- You can review these slides another time if you want, they'll be waiting for you ☺

.debug[[shared/about-slides.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/about-slides.md)]
---

## Chapter 1

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

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

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

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

## Chapter 2

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

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

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

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

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

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

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

## Chapter 3

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

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

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

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

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

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

- [Authoring YAML](#toc-authoring-yaml)

## Chapter 4

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

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

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

- [Healthchecks](#toc-healthchecks)

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

- [End of day 1!](#toc-end-of-day-)

## Chapter 5

- [Namespaces](#toc-namespaces)

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

- [Kustomize](#toc-kustomize)

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

## Chapter 6

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

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

## Chapter 7

- [Volumes](#toc-volumes)

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

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

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

## Chapter 8

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

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

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

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

## Chapter 9

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

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

.debug[[shared/toc.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/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[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/prereqs.md)]
---

*Tell me and I forget.*
 
*Teach me and I remember.*
 
*Involve me and I learn.*

Misattributed to Benjamin Franklin

[(Probably inspired by Chinese Confucian philosopher Xunzi)](https://www.barrypopik.com/index.php/new_york_city/entry/tell_me_and_i_forget_teach_me_and_i_may_remember_involve_me_and_i_will_lear/)

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/prereqs.md)]
---

## Hands-on sections

- The whole workshop is hands-on

- We are going to build, ship, and run containers!

- You are invited to reproduce all the demos

- All hands-on sections are clearly identified, like the gray rectangle below

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

- Go to http://velocity-2019-11.container.training/ to view these slides

- Join the chat room: [Gitter](https://gitter.im/jpetazzo/workshop-20191104-berlin)

]

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/prereqs.md)]
---

## Where are we going to run our containers?

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/prereqs.md)]
---

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

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/prereqs.md)]
---

## You get a cluster of cloud VMs

- Each person gets a private cluster of cloud VMs (not shared with anybody else)

- They'll remain up for the duration of the workshop

- You should have a little card with login+password+IP addresses

- You can automatically SSH from one VM to another

- The nodes have aliases: `node1`, `node2`, etc.

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/prereqs.md)]
---

## Why don't we run containers locally?

- Installing this stuff can be hard on some machines

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

- *"The whole team downloaded all these container images from the WiFi!
 ... and it went great!"* (Literally no-one ever)

- All you need is a computer (or even a phone or tablet!), with:

- an internet connection

- a web browser

- an SSH client

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/prereqs.md)]
---

## SSH clients

- On Linux, OS X, FreeBSD... you are probably all set

- On Windows, get one of these:

- [putty](http://www.putty.org/)
  - Microsoft [Win32 OpenSSH](https://github.com/PowerShell/Win32-OpenSSH/wiki/Install-Win32-OpenSSH)
  - [Git BASH](https://git-for-windows.github.io/)
  - [MobaXterm](http://mobaxterm.mobatek.net/)

- On Android, [JuiceSSH](https://juicessh.com/)
  ([Play Store](https://play.google.com/store/apps/details?id=com.sonelli.juicessh))
  works pretty well

- Nice-to-have: [Mosh](https://mosh.org/) instead of SSH, if your internet connection tends to lose packets

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/prereqs.md)]
---

## What is this Mosh thing?

*You don't have to use Mosh or even know about it to follow along.
 
We're just telling you about it because some of us think it's cool!*

- Mosh is "the mobile shell"

- It is essentially SSH over UDP, with roaming features

- It retransmits packets quickly, so it works great even on lossy connections

(Like hotel or conference WiFi)

- It has intelligent local echo, so it works great even in high-latency connections

(Like hotel or conference WiFi)

- It supports transparent roaming when your client IP address changes

(Like when you hop from hotel to conference WiFi)

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/prereqs.md)]
---

## Using Mosh

- To install it: `(apt|yum|brew) install mosh`

- It has been pre-installed on the VMs that we are using

- To connect to a remote machine: `mosh user@host`

(It is going to establish an SSH connection, then hand off to UDP)

- It requires UDP ports to be open

(By default, it uses a UDP port between 60000 and 61000)

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/prereqs.md)]
---
## WebSSH

- The virtual machines are also accessible via WebSSH

- This can be useful if:

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

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

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

(each machine runs a WebSSH server)

- Then provide the login and password indicated on your card

.debug[[shared/webssh.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/webssh.md)]
---

## Good to know

- WebSSH uses WebSocket

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

(many HTTP proxies can't handle WebSocket properly)

- Most keyboard shortcuts should work, except Ctrl-W

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

.debug[[shared/webssh.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/webssh.md)]
---
class: in-person

## Connecting to our lab environment

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

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

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

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

]

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

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/connecting.md)]
---

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

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

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

- Create your own cluster (local or cloud VMs)

Small setup effort; small cost; flexible environments

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

Bigger setup effort; ideal for group training

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/connecting.md)]
---

## For a consistent Kubernetes experience ...

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

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

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

- These tools are used in many exercises in these slides

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

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/connecting.md)]
---

## Get your own Docker nodes

- If you already have some Docker nodes: great!

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

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

- Log in

- Create your first node

]

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

(Creating a Docker ID is free.)

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/connecting.md)]
---

## We will (mostly) interact with node1 only

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

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

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

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

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

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

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

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/connecting.md)]
---

## Terminals

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

There are multiple ways to do this:

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

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

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

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/connecting.md)]
---

## Tmux cheatsheet

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

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

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

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/connecting.md)]
---

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

---

Our sample application

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

---
# Our sample application

- We will clone the GitHub repository onto our `node1`

- The repository also contains scripts and tools that we will use through the workshop

- Clone the repository on `node1`:
  ```bash
  git clone https://github.com/jpetazzo/container.training
  ```

]

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/sampleapp.md)]
---

## Downloading and running the application

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

- Go to the `dockercoins` directory, in the cloned repo:
  ```bash
  cd ~/container.training/dockercoins
  ```

- Use Compose to build and run all containers:
  ```bash
  docker-compose up
  ```

]

Compose tells Docker to build all container images (pulling
the corresponding base images), then starts all containers,
and displays aggregated logs.

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/sampleapp.md)]
---

## What's this application?

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

- No, you can't buy coffee with DockerCoins

- How DockerCoins works:

- generate a few random bytes

- hash these bytes

- increment a counter (to keep track of speed)

- repeat forever!

- DockerCoins is *not* a cryptocurrency

(the only common points are "randomness," "hashing," and "coins" in the name)

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/sampleapp.md)]
---

## DockerCoins in the microservices era

- DockerCoins is made of 5 services:

- `rng` = web service generating random bytes

- `hasher` = web service computing hash of POSTed data

- `worker` = background process calling `rng` and `hasher`

- `webui` = web interface to watch progress

- `redis` = data store (holds a counter updated by `worker`)

- These 5 services are visible in the application's Compose file,
  [docker-compose.yml](
  https://github.com/jpetazzo/container.training/blob/master/dockercoins/docker-compose.yml)

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/sampleapp.md)]
---

## How DockerCoins works

- `worker` invokes web service `rng` to generate random bytes

- `worker` invokes web service `hasher` to hash these bytes

- `worker` does this in an infinite loop

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

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

*(See diagram on next slide!)*

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/sampleapp.md)]
---

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/sampleapp.md)]
---

## Service discovery in container-land

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

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

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

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

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/sampleapp.md)]
---

## Example in `worker/worker.py`

```python
redis = Redis("`redis`")

def get_random_bytes():
    r = requests.get("http://`rng`/32")
    return r.content

def hash_bytes(data):
    r = requests.post("http://`hasher`/",
                      data=data,
                      headers={"Content-Type": "application/octet-stream"})
```

(Full source code available [here](
https://github.com/jpetazzo/container.training/blob/8279a3bce9398f7c1a53bdd95187c53eda4e6435/dockercoins/worker/worker.py#L17
))

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/sampleapp.md)]
---

## Links, naming, and service discovery

- Containers can have network aliases (resolvable through DNS)

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

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

- Network aliases are automatically namespaced

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

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

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/sampleapp.md)]
---

## Show me the code!

- You can check the GitHub repository with all the materials of this workshop:
 https://github.com/jpetazzo/container.training

- The application is in the [dockercoins](
  https://github.com/jpetazzo/container.training/tree/master/dockercoins)
  subdirectory

- The Compose file ([docker-compose.yml](
  https://github.com/jpetazzo/container.training/blob/master/dockercoins/docker-compose.yml))
  lists all 5 services

- `redis` is using an official image from the Docker Hub

- `hasher`, `rng`, `worker`, `webui` are each built from a Dockerfile

- Each service's Dockerfile and source code is in its own directory

(`hasher` is in the [hasher](https://github.com/jpetazzo/container.training/blob/master/dockercoins/hasher/) directory,
  `rng` is in the [rng](https://github.com/jpetazzo/container.training/blob/master/dockercoins/rng/)
  directory, etc.)

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/sampleapp.md)]
---

## Compose file format version

*This is relevant only if you have used Compose before 2016...*

- Compose 1.6 introduced support for a new Compose file format (aka "v2")

- Services are no longer at the top level, but under a `services` section

- There has to be a `version` key at the top level, with value `"2"` (as a string, not an integer)

- Containers are placed on a dedicated network, making links unnecessary

- There are other minor differences, but upgrade is easy and straightforward

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/sampleapp.md)]
---

## Our application at work

- On the left-hand side, the "rainbow strip" shows the container names

- On the right-hand side, we see the output of our containers

- We can see the `worker` service making requests to `rng` and `hasher`

- For `rng` and `hasher`, we see HTTP access logs

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/sampleapp.md)]
---

## Connecting to the web UI

- "Logs are exciting and fun!" (No-one, ever)

- The `webui` container exposes a web dashboard; let's view it

- With a web browser, connect to `node1` on port 8000

- Remember: the `nodeX` aliases are valid only on the nodes themselves

- In your browser, you need to enter the IP address of your node

]

A drawing area should show up, and after a few seconds, a blue
graph will appear.

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/sampleapp.md)]
---

## If the graph doesn't load

If you just see a `Page not found` error, it might be because your
Docker Engine is running on a different machine. This can be the case if:

- you are using the Docker Toolbox

- you are using a VM (local or remote) created with Docker Machine

- you are controlling a remote Docker Engine

When you run DockerCoins in development mode, the web UI static files
are mapped to the container using a volume. Alas, volumes can only
work on a local environment, or when using Docker Desktop for Mac or Windows.

How to fix this?

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/sampleapp.md)]
---

## Why does the speed seem irregular?

- It *looks like* the speed is approximately 4 hashes/second

- Or more precisely: 4 hashes/second, with regular dips down to zero

- Why?

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

- Yes, and?

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/sampleapp.md)]
---

## The reason why this graph is *not awesome*

- The worker doesn't update the counter after every loop, but up to once per second

- The speed is computed by the browser, checking the counter about once per second

- Between two consecutive updates, the counter will increase either by 4, or by 0

- The perceived speed will therefore be 4 - 4 - 4 - 0 - 4 - 4 - 0 etc.

- What can we conclude from this?

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/sampleapp.md)]
---

## Stopping the application

- If we interrupt Compose (with `^C`), it will politely ask the Docker Engine to stop the app

- The Docker Engine will send a `TERM` signal to the containers

- If the containers do not exit in a timely manner, the Engine sends a `KILL` signal

- Stop the application by hitting `^C`

]

Some containers exit immediately, others take longer.

The containers that do not handle `SIGTERM` end up being killed after a 10s timeout. If we are very impatient, we can hit `^C` a second time!

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/sampleapp.md)]
---
## Clean up

- Before moving on, let's remove those containers

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

]

.debug[[shared/composedown.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/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-first-contact-with-kubectl)
]

---
# Kubernetes concepts

- Kubernetes is a container management system

- It runs and manages containerized applications on a cluster

- What does that really mean?

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/concepts-k8s.md)]
---

## Basic things we can ask Kubernetes to do

- Start 5 containers using image `atseashop/api:v1.3`

- Place an internal load balancer in front of these containers

- Start 10 containers using image `atseashop/webfront:v1.3`

- Place a public load balancer in front of these containers

- It's Black Friday (or Christmas), traffic spikes, grow our cluster and add containers

- New release! Replace my containers with the new image `atseashop/webfront:v1.4`

- Keep processing requests during the upgrade; update my containers one at a time

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/concepts-k8s.md)]
---

## Other things that Kubernetes can do for us

- Basic autoscaling

- Blue/green deployment, canary deployment

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

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

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

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

- Integrating third party services (*service catalog*)

- Automating complex tasks (*operators*)

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/concepts-k8s.md)]
---

## Kubernetes architecture

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/concepts-k8s.md)]
---

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/concepts-k8s.md)]
---

## Kubernetes architecture

- Ha ha ha ha

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/concepts-k8s.md)]
---

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/concepts-k8s.md)]
---

## Credits

- The first schema is a Kubernetes cluster with storage backed by multi-path iSCSI

(Courtesy of [Yongbok Kim](https://www.yongbok.net/blog/))

- The second one is a simplified representation of a Kubernetes cluster

(Courtesy of [Imesh Gunaratne](https://medium.com/containermind/a-reference-architecture-for-deploying-wso2-middleware-on-kubernetes-d4dee7601e8e))

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/concepts-k8s.md)]
---

## Kubernetes architecture: the nodes

- The nodes executing our containers run a collection of services:

- a container Engine (typically Docker)

- kubelet (the "node agent")

- kube-proxy (a necessary but not sufficient network component)

- Nodes were formerly called "minions"

(You might see that word in older articles or documentation)

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/concepts-k8s.md)]
---

## Kubernetes architecture: the control plane

- The Kubernetes logic (its "brains") is a collection of services:

- the API server (our point of entry to everything!)

- core services like the scheduler and controller manager

- `etcd` (a highly available key/value store; the "database" of Kubernetes)

- Together, these services form the control plane of our cluster

- The control plane is also called the "master"

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/concepts-k8s.md)]
---

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/concepts-k8s.md)]
---

## Running the control plane on special nodes

- It is common to reserve a dedicated node for the control plane

(Except for single-node development clusters, like when using minikube)

- This node is then called a "master"

(Yes, this is ambiguous: is the "master" a node, or the whole control plane?)

- Normal applications are restricted from running on this node

(By using a mechanism called ["taints"](https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/))

- When high availability is required, each service of the control plane must be resilient

- The control plane is then replicated on multiple nodes

(This is sometimes called a "multi-master" setup)

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/concepts-k8s.md)]
---

## Running the control plane outside containers

- The services of the control plane can run in or out of containers

- For instance: since `etcd` is a critical service, some people
  deploy it directly on a dedicated cluster (without containers)

(This is illustrated on the first "super complicated" schema)

- In some hosted Kubernetes offerings (e.g. AKS, GKE, EKS), the control plane is invisible

(We only "see" a Kubernetes API endpoint)

- In that case, there is no "master node"

*For this reason, it is more accurate to say "control plane" rather than "master."*

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/concepts-k8s.md)]
---

## Do we need to run Docker at all?

No!

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

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

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

(like CRI-O, or containerd)

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/concepts-k8s.md)]
---

## Do we need to run Docker at all?

Yes!

- In this workshop, we run our app on a single node first

- We will need to build images and ship them around

- We can do these things without Docker
 
 (and get diagnosed with NIH¹ syndrome)

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/concepts-k8s.md)]
---

## Do we need to run Docker at all?

- On our development environments, CI pipelines ... :

*Yes, almost certainly*

- On our production servers:

*Yes (today)*

*Probably not (in the future)*

.footnote[More information about CRI [on the Kubernetes blog](https://kubernetes.io/blog/2016/12/container-runtime-interface-cri-in-kubernetes)]

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/concepts-k8s.md)]
---

## Interacting with Kubernetes

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

- The Kubernetes API is (mostly) RESTful

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

- A few common resource types are:

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

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

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/concepts-k8s.md)]
---

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/concepts-k8s.md)]
---

## Credits

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

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

- The second diagram is courtesy of Weave Works

- a *pod* can have multiple containers working together

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

Both diagrams used with permission.

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/concepts-k8s.md)]
---

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

---

First contact with `kubectl`

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

---
# First contact with `kubectl`

- `kubectl` is (almost) the only tool we'll need to talk to Kubernetes

- It is a rich CLI tool around the Kubernetes API

(Everything you can do with `kubectl`, you can do directly with the API)

- On our machines, there is a `~/.kube/config` file with:

- the Kubernetes API address

- the path to our TLS certificates used to authenticate

- You can also use the `--kubeconfig` flag to pass a config file

- Or directly `--server`, `--user`, etc.

- `kubectl` can be pronounced "Cube C T L", "Cube cuttle", "Cube cuddle"...

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

## `kubectl get`

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

- Look at the composition of our cluster:
  ```bash
  kubectl get node
  ```

- These commands are equivalent:
  ```bash
  kubectl get no
  kubectl get node
  kubectl get nodes
  ```

]

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

## Obtaining machine-readable output

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

- Give us more info about the nodes:
  ```bash
  kubectl get nodes -o wide
  ```

- Let's have some YAML:
  ```bash
  kubectl get no -o yaml
  ```
  See that `kind: List` at the end? It's the type of our result!

]

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

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

- It's super easy to build custom reports

- Show the capacity of all our nodes as a stream of JSON objects:
  ```bash
    kubectl get nodes -o json | 
            jq ".items[] | {name:.metadata.name} + .status.capacity"
  ```

]

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

## Exploring types and definitions

- We can list all available resource types by running `kubectl api-resources`
 
 (In Kubernetes 1.10 and prior, this command used to be `kubectl get`)

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

- We can view the definition of a field in a resource, for instance:
  ```bash
  kubectl explain node.spec
  ```

- Or get the full definition of all fields and sub-fields:
  ```bash
  kubectl explain node --recursive
  ```

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

## Introspection vs. documentation

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

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

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

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

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

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

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

## Type names

- The most common resource names have three forms:

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

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

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

- Some resources do not have a short name

- `Endpoints` only have a plural form

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

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

## Viewing details

- We can use `kubectl get -o yaml` to see all available details

- However, YAML output is often simultaneously too much and not enough

- For instance, `kubectl get node node1 -o yaml` is:

- too much information (e.g.: list of images available on this node)

- not enough information (e.g.: doesn't show pods running on this node)

- difficult to read for a human operator

- For a comprehensive overview, we can use `kubectl describe` instead

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

## `kubectl describe`

- `kubectl describe` needs a resource type and (optionally) a resource name

- It is possible to provide a resource name *prefix*

(all matching objects will be displayed)

- `kubectl describe` will retrieve some extra information about the resource

- Look at the information available for `node1` with one of the following commands:
  ```bash
  kubectl describe node/node1
  kubectl describe node node1
  ```

]

(We should notice a bunch of control plane pods.)

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

## Services

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

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

- List the services on our cluster with one of these commands:
  ```bash
  kubectl get services
  kubectl get svc
  ```

]

There is already one service on our cluster: the Kubernetes API itself.

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

## ClusterIP services

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

- This is useful for introspection from within containers

- Try to connect to the API:
  ```bash
  curl -k https://`10.96.0.1`
  ```
  
  - `-k` is used to skip certificate verification

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

]

The error that we see is expected: the Kubernetes API requires authentication.

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

## Listing running containers

- Containers are manipulated through *pods*

- A pod is a group of containers:

- running together (on the same node)

- sharing resources (RAM, CPU; but also network, volumes)

- List pods on our cluster:
  ```bash
  kubectl get pods
  ```

]

*Where are the pods that we saw just a moment earlier?!?*

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

## Namespaces

- Namespaces allow us to segregate resources

- List the namespaces on our cluster with one of these commands:
  ```bash
  kubectl get namespaces
  kubectl get namespace
  kubectl get ns
  ```

]

*You know what ... This `kube-system` thing looks suspicious.*

*In fact, I'm pretty sure it showed up earlier, when we did:*

`kubectl describe node node1`

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

## Accessing namespaces

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

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

- List the pods in all namespaces:
  ```bash
  kubectl get pods --all-namespaces
  ```

- Since Kubernetes 1.14, we can also use `-A` as a shorter version:
  ```bash
  kubectl get pods -A
  ```

]

*Here are our system pods!*

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

## What are all these control plane pods?

- `etcd` is our etcd server

- `kube-apiserver` is the API server

- `kube-controller-manager` and `kube-scheduler` are other control plane components

- `coredns` provides DNS-based service discovery ([replacing kube-dns as of 1.11](https://kubernetes.io/blog/2018/07/10/coredns-ga-for-kubernetes-cluster-dns/))

- `kube-proxy` is the (per-node) component managing port mappings and such

- `weave` is the (per-node) component managing the network overlay

- the `READY` column indicates the number of containers in each pod

(1 for most pods, but `weave` has 2, for instance)

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

## Scoping another namespace

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

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

]

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

## Namespaces and other `kubectl` commands

- We can use `-n`/`--namespace` with almost every `kubectl` command

- Example:

- `kubectl create --namespace=X` to create something in namespace X

- We can use `-A`/`--all-namespaces` with most commands that manipulate multiple objects

- Examples:

- `kubectl delete` can delete resources across multiple namespaces

- `kubectl label` can add/remove/update labels across multiple namespaces

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

## What about `kube-public`?

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

]

Nothing!

`kube-public` is created by kubeadm & [used for security bootstrapping](https://kubernetes.io/blog/2017/01/stronger-foundation-for-creating-and-managing-kubernetes-clusters).

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

## Exploring `kube-public`

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

- List ConfigMap objects:
  ```bash
  kubectl -n kube-public get configmaps
  ```

- Inspect `cluster-info`:
  ```bash
  kubectl -n kube-public get configmap cluster-info -o yaml
  ```

]

Note the `selfLink` URI: `/api/v1/namespaces/kube-public/configmaps/cluster-info`

We can use that!

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

## Accessing `cluster-info`

- Earlier, when trying to access the API server, we got a `Forbidden` message

- But `cluster-info` is readable by everyone (even without authentication)

- Retrieve `cluster-info`:
  ```bash
  curl -k https://10.96.0.1/api/v1/namespaces/kube-public/configmaps/cluster-info
  ```

]

- We were able to access `cluster-info` (without auth)

- It contains a `kubeconfig` file

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

## Retrieving `kubeconfig`

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

- Display the content of `kubeconfig`:
  ```bash
    curl -sk https://10.96.0.1/api/v1/namespaces/kube-public/configmaps/cluster-info \
         | jq -r .data.kubeconfig
  ```

]

- This file holds the canonical address of the API server, and the public key of the CA

- This file *does not* hold client keys or tokens

- This is not sensitive information, but allows us to establish trust

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

## What about `kube-node-lease`?

- Starting with Kubernetes 1.14, there is a `kube-node-lease` namespace

(or in Kubernetes 1.13 if the NodeLease feature gate is enabled)

- That namespace contains one Lease object per node

- *Node leases* are a new way to implement node heartbeats

(i.e. node regularly pinging the control plane to say "I'm alive!")

- For more details, see [KEP-0009] or the [node controller documentation]

[KEP-0009]: https://github.com/kubernetes/enhancements/blob/master/keps/sig-node/0009-node-heartbeat.md
[node controller documentation]: https://kubernetes.io/docs/concepts/architecture/nodes/#node-controller

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlget.md)]
---

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

---

Running our first containers on Kubernetes

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

---
# Running our first containers on Kubernetes

- First things first: we cannot run a container

- We are going to run a pod, and in that pod there will be a single container

- In that container in the pod, we are going to run a simple `ping` command

- Then we are going to start additional copies of the pod

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## Starting a simple pod with `kubectl run`

- We need to specify at least a *name* and the image we want to use

- Let's ping `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
  ```

]

(Starting with Kubernetes 1.12, we get a message telling us that
`kubectl run` is deprecated. Let's ignore it for now.)

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## Behind the scenes of `kubectl run`

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

- List most resource types:
  ```bash
  kubectl get all
  ```

]

We should see the following things:
- `deployment.apps/pingpong` (the *deployment* that we just created)
- `replicaset.apps/pingpong-xxxxxxxxxx` (a *replica set* created by the deployment)
- `pod/pingpong-xxxxxxxxxx-yyyyy` (a *pod* created by the replica set)

Note: as of 1.10.1, resource types are displayed in more detail.

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## What are these different things?

- A *deployment* is a high-level construct

- allows scaling, rolling updates, rollbacks

- multiple deployments can be used together to implement a
    [canary deployment](https://kubernetes.io/docs/concepts/cluster-administration/manage-deployment/#canary-deployments)

- delegates pods management to *replica sets*

- A *replica set* is a low-level construct

- makes sure that a given number of identical pods are running

- allows scaling

- rarely used directly

- A *replication controller* is the (deprecated) predecessor of a replica set

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## Our `pingpong` deployment

- `kubectl run` created a *deployment*, `deployment.apps/pingpong`

```
NAME                       DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
deployment.apps/pingpong   1         1         1            1           10m
```

- That deployment created a *replica set*, `replicaset.apps/pingpong-xxxxxxxxxx`

```
NAME                                  DESIRED   CURRENT   READY     AGE
replicaset.apps/pingpong-7c8bbcd9bc   1         1         1         10m
```

- That replica set created a *pod*, `pod/pingpong-xxxxxxxxxx-yyyyy`

```
NAME                            READY     STATUS    RESTARTS   AGE
pod/pingpong-7c8bbcd9bc-6c9qz   1/1       Running   0          10m
```

- We'll see later how these folks play together for:

- scaling, high availability, rolling updates

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## Viewing container output

- Let's use the `kubectl logs` command

- We will pass either a *pod name*, or a *type/name*

(E.g. if we specify a deployment or replica set, it will get the first pod in it)

- Unless specified otherwise, it will only show logs of the first container in the pod

(Good thing there's only one in ours!)

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

]

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## Streaming logs in real time

- Just like `docker logs`, `kubectl logs` supports convenient options:

- `-f`/`--follow` to stream logs in real time (à la `tail -f`)

- `--tail` to indicate how many lines you want to see (from the end)

- `--since` to get logs only after a given timestamp

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

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

]

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## Scaling our application

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

- Scale our `pingpong` deployment:
  ```bash
  kubectl scale deploy/pingpong --replicas 3
  ```

- Note that this command does exactly the same thing:
  ```bash
  kubectl scale deployment pingpong --replicas 3
  ```

]

Note: what if we tried to scale `replicaset.apps/pingpong-xxxxxxxxxx`?

We could! But the *deployment* would notice it right away, and scale back to the initial level.

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## Log streaming

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

(the one we started before scaling up)

- `kubectl logs` shows us one line per second

- We could expect 3 lines per second

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

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## Streaming logs of multiple pods

- What happens if we restart `kubectl logs`?

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

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

]

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

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## Resilience

- The *deployment* `pingpong` watches its *replica set*

- The *replica set* ensures that the right number of *pods* are running

- What happens if pods disappear?

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

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## What happened?

- `kubectl delete pod` terminates the pod gracefully

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

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

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

- Until 30 seconds later, when the grace period expires

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## What if we wanted something different?

- What if we wanted to start a "one-shot" container that *doesn't* get restarted?

- We could use `kubectl run --restart=OnFailure` or `kubectl run --restart=Never`

- These commands would create *jobs* or *pods* instead of *deployments*

- Under the hood, `kubectl run` invokes "generators" to create resource descriptions

- We could also write these resource descriptions ourselves (typically in YAML),
 and create them on the cluster with `kubectl apply -f` (discussed later)

- With `kubectl run --schedule=...`, we can also create *cronjobs*

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## Scheduling periodic background work

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

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

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

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

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

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## Creating a Cron Job

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

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

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

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

]

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## Cron Jobs in action

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

- The Job will create a Pod

- The Job will make sure that the Pod completes

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

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

]

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## What about that deprecation warning?

- As we can see from the previous slide, `kubectl run` can do many things

- The exact type of resource created is not obvious

- To make things more explicit, it is better to use `kubectl create`:

- `kubectl create deployment` to create a deployment

- `kubectl create job` to create a job

- `kubectl create cronjob` to run a job periodically
 (since Kubernetes 1.14)

- Eventually, `kubectl run` will be used only to start one-shot pods

(see https://github.com/kubernetes/kubernetes/pull/68132)

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## Various ways of creating resources

- `kubectl run`

- easy way to get started
  - versatile

- `kubectl create <resource>`

- explicit, but lacks some features
  - can't create a CronJob before Kubernetes 1.14
  - can't pass command-line arguments to deployments

- `kubectl create -f foo.yaml` or `kubectl apply -f foo.yaml`

- all features are available
  - requires writing YAML

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## Viewing logs of multiple pods

- When we specify a deployment name, only one single pod's logs are shown

- We can view the logs of multiple pods by specifying a *selector*

- A selector is a logic expression using *labels*

- Conveniently, when you `kubectl run somename`, the associated objects have a `run=somename` label

- View the last line of log from all pods with the `run=pingpong` label:
  ```bash
  kubectl logs -l run=pingpong --tail 1
  ```

]

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

### Streaming logs of multiple pods

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

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

]

*Note: combining `-l` and `-f` is only possible since Kubernetes 1.14!*

*Let's try to understand why ...*

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

### Streaming logs of many pods

- Let's see what happens if we try to stream the logs for more than 5 pods

- Scale up our deployment:
  ```bash
  kubectl scale deployment pingpong --replicas=8
  ```

- Stream the logs:
  ```bash
  kubectl logs -l run=pingpong --tail 1 -f
  ```

]

We see a message like the following one:
```
error: you are attempting to follow 8 log streams,
but maximum allowed concurency is 5,
use --max-log-requests to increase the limit
```

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## Why can't we stream the logs of many pods?

- `kubectl` opens one connection to the API server per pod

- For each pod, the API server opens one extra connection to the corresponding kubelet

- If there are 1000 pods in our deployment, that's 1000 inbound + 1000 outbound connections on the API server

- This could easily put a lot of stress on the API server

- Prior Kubernetes 1.14, it was decided to *not* allow multiple connections

- From Kubernetes 1.14, it is allowed, but limited to 5 connections

(this can be changed with `--max-log-requests`)

- For more details about the rationale, see
  [PR #67573](https://github.com/kubernetes/kubernetes/pull/67573)

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## Shortcomings of `kubectl logs`

- We don't see which pod sent which log line

- If pods are restarted / replaced, the log stream stops

- If new pods are added, we don't see their logs

- To stream the logs of multiple pods, we need to write a selector

- There are external tools to address these shortcomings

(e.g.: [Stern](https://github.com/wercker/stern))

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## `kubectl logs -l ... --tail N`

- If we run this with Kubernetes 1.12, the last command shows multiple lines

- This is a regression when `--tail` is used together with `-l`/`--selector`

- It always shows the last 10 lines of output for each container

(instead of the number of lines specified on the command line)

- The problem was fixed in Kubernetes 1.13

*See [#70554](https://github.com/kubernetes/kubernetes/issues/70554) for details.*

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

## Aren't we flooding 1.1.1.1?

- If you're wondering this, good question!

- Don't worry, though:

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

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

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlrun.md)]
---

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

---

Accessing logs from the CLI

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

---
# Accessing logs from the CLI

- The `kubectl logs` command has limitations:

- it cannot stream logs from multiple pods at a time

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

- We are going to see how to do it better

.debug[[k8s/logs-cli.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/logs-cli.md)]
---

## Doing it manually

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

- take a selector as an argument

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

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

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

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

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

.debug[[k8s/logs-cli.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/logs-cli.md)]
---

## Stern

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

From the README:

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

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

Exactly what we need!

.debug[[k8s/logs-cli.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/logs-cli.md)]
---

## Installing Stern

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

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

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

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

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

- On OS X, just `brew install stern`

.debug[[k8s/logs-cli.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/logs-cli.md)]
---

## Using Stern

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

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

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

- These two ways can be combined if necessary

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

]

.debug[[k8s/logs-cli.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/logs-cli.md)]
---

## Stern convenient options

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

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

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

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

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

]

.debug[[k8s/logs-cli.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/logs-cli.md)]
---

## Using Stern with a selector

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

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

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

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

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

- View the logs for all the things started with `kubectl create deployment`:
  ```bash
  stern -l app
  ```

]

.debug[[k8s/logs-cli.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/logs-cli.md)]
---

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

---

Declarative vs imperative

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

---
# Declarative vs imperative

- Our container orchestrator puts a very strong emphasis on being *declarative*

- Declarative:

*I would like a cup of tea.*

- Imperative:

*Boil some water. Pour it in a teapot. Add tea leaves. Steep for a while. Serve in a cup.*

- Declarative seems simpler at first ...

- ... As long as you know how to brew tea

.debug[[shared/declarative.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/declarative.md)]
---

## Declarative vs imperative

- What declarative would really be:

*I want a cup of tea, obtained by pouring an infusion¹ of tea leaves in a cup.*

*¹An infusion is obtained by letting the object steep a few minutes in hot² water.*

*²Hot liquid is obtained by pouring it in an appropriate container³ and setting it on a stove.*

*³Ah, finally, containers! Something we know about. Let's get to work, shall we?*

.footnote[Did you know there was an [ISO standard](https://en.wikipedia.org/wiki/ISO_3103)
specifying how to brew tea?]

.debug[[shared/declarative.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/declarative.md)]
---

## Declarative vs imperative

- Imperative systems:

- simpler

- if a task is interrupted, we have to restart from scratch

- Declarative systems:

- if a task is interrupted (or if we show up to the party half-way through),
    we can figure out what's missing and do only what's necessary

- we need to be able to *observe* the system

- ... and compute a "diff" between *what we have* and *what we want*

.debug[[shared/declarative.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/declarative.md)]
---
## Declarative vs imperative in Kubernetes

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

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

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

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

- Then it will store it in etcd

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

.debug[[k8s/declarative.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/declarative.md)]
---

## Reconciling state

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

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

- Kubernetes will *reconcile* the current state with the spec
 (technically, this is done by a number of *controllers*)

- When we want to change some resource, we update the *spec*

- Kubernetes will then *converge* that resource

.debug[[k8s/declarative.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/declarative.md)]
---
## 19,000 words

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

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

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

.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/01.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/02.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/03.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/04.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/05.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/06.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/07.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/08.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/09.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/10.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/11.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/12.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/13.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/14.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/15.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/16.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/17.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/18.svg)
.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/deploymentslideshow.md)]
---
class: pic
![](images/kubectl-run-slideshow/19.svg)

.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/deploymentslideshow.md)]
---

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

---

Kubernetes network model

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

---
# Kubernetes network model

- TL,DR:

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

- In detail:

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

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

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

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

- pod IP addresses are assigned by the network implementation

- Kubernetes doesn't mandate any particular implementation

.debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubenet.md)]
---

## Kubernetes network model: the good

- Everything can reach everything

- No address translation

- No port translation

- No new protocol

- The network implementation can decide how to allocate addresses

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

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

- The specification is simple enough to allow many various implementations

.debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubenet.md)]
---

## Kubernetes network model: the less good

- Everything can reach everything

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

- the network implementation that you use needs to support them

- There are literally dozens of implementations out there

(15 are listed in the Kubernetes documentation)

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

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

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

.debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubenet.md)]
---

## Kubernetes network model: in practice

- The nodes that we are using have been set up to use [Weave](https://github.com/weaveworks/weave)

- We don't endorse Weave in a particular way, it just Works For Us

- Don't worry about the warning about `kube-proxy` performance

- Unless you:

- routinely saturate 10G network interfaces
 - count packet rates in millions per second
 - run high-traffic VOIP or gaming platforms
 - do weird things that involve millions of simultaneous connections
 (in which case you're already familiar with kernel tuning)

- If necessary, there are alternatives to `kube-proxy`; e.g.
  [`kube-router`](https://www.kube-router.io)

.debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubenet.md)]
---

## The Container Network Interface (CNI)

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

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

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

- Typically, CNI plugins will:

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

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

- configure the interface as well as required routes etc.

.debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubenet.md)]
---

## Multiple moving parts

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

- provides communication between pods and nodes

- is generally implemented with CNI plugins

- The "pod-to-service network":

- provides internal communication and load balancing

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

- Network policies:

- provide firewalling and isolation

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

.debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubenet.md)]
---

## Even more moving parts

- Inbound traffic can be handled by multiple components:

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

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

- It is possible to use multiple pod networks in parallel

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

- Some solutions can fill multiple roles

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

.debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubenet.md)]
---

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

---

Exposing containers

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

---
# Exposing containers

- `kubectl expose` creates a *service* for existing pods

- A *service* is a stable address for a pod (or a bunch of pods)

- If we want to connect to our pod(s), we need to create a *service*

- Once a service is created, CoreDNS will allow us to resolve it by name

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

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

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

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlexpose.md)]
---

## Basic service types

- `ClusterIP` (default type)

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

- `NodePort`

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

These service types are always available.

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

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/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)
 - available only when the underlying infrastructure provides some "load balancer as a service"
 (e.g. AWS, Azure, GCE, OpenStack...)

- `ExternalName`

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

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlexpose.md)]
---

## Running containers with open ports

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

- We could use the `nginx` official image, but ...

... we wouldn't be able to tell the backends from each other!

- We are going to use `jpetazzo/httpenv`, a tiny HTTP server written in Go

- `jpetazzo/httpenv` listens on port 8888

- It serves its environment variables in JSON format

- The environment variables will include `HOSTNAME`, which will be the pod name

(and therefore, will be different on each backend)

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlexpose.md)]
---

## Creating a deployment for our HTTP server

- We *could* do `kubectl run httpenv --image=jpetazzo/httpenv` ...

- But since `kubectl run` is being deprecated, let's see how to use `kubectl create` instead

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

- Create a deployment for this very lightweight HTTP server:
  ```bash
  kubectl create deployment httpenv --image=jpetazzo/httpenv
  ```

- Scale it to 10 replicas:
  ```bash
  kubectl scale deployment httpenv --replicas=10
  ```

]

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlexpose.md)]
---

## Exposing our deployment

- We'll create a default `ClusterIP` service

- Expose the HTTP port of our server:
  ```bash
  kubectl expose deployment httpenv --port 8888
  ```

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

]

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlexpose.md)]
---

## Services are layer 4 constructs

- You can assign IP addresses to services, but they are still *layer 4*

(i.e. a service is not an IP address; it's an IP address + protocol + port)

- This is caused by the current implementation of `kube-proxy`

(it relies on mechanisms that don't support layer 3)

- As a result: you *have to* indicate the port number for your service
    
- Running services with arbitrary port (or port ranges) requires hacks

(e.g. host networking mode)

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlexpose.md)]
---

## Testing our service

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

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

- Send a few requests:
  ```bash
  curl http://$IP:8888/
  ```

- Too much output? Filter it with `jq`:
  ```bash
  curl -s http://$IP:8888/ | jq .HOSTNAME
  ```

]

Try it a few times! Our requests are load balanced across multiple pods.

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlexpose.md)]
---

## If we don't need a load balancer

- Sometimes, we want to access our scaled services directly:

- if we want to save a tiny little bit of latency (typically less than 1ms)

- if we need to connect over arbitrary ports (instead of a few fixed ones)

- if we need to communicate over another protocol than UDP or TCP

- if we want to decide how to balance the requests client-side

- ...

- In that case, we can use a "headless service"

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlexpose.md)]
---

## Headless services

- A headless service is obtained by setting the `clusterIP` field to `None`

(Either with `--cluster-ip=None`, or by providing a custom YAML)

- As a result, the service doesn't have a virtual IP address

- Since there is no virtual IP address, there is no load balancer either

- CoreDNS will return the pods' IP addresses as multiple `A` records

- This gives us an easy way to discover all the replicas for a deployment

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlexpose.md)]
---

## Services and endpoints

- A service has a number of "endpoints"

- Each endpoint is a host + port where the service is available

- The endpoints are maintained and updated automatically by Kubernetes

- Check the endpoints that Kubernetes has associated with our `httpenv` service:
  ```bash
  kubectl describe service httpenv
  ```

]

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

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

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlexpose.md)]
---

## Viewing endpoint details

- When we have many endpoints, our display commands truncate the list
  ```bash
  kubectl get endpoints
  ```

- If we want to see the full list, we can use one of the following commands:
  ```bash
  kubectl describe endpoints httpenv
  kubectl get endpoints httpenv -o yaml
  ```

- These commands will show us a list of IP addresses

- These IP addresses should match the addresses of the corresponding pods:
  ```bash
  kubectl get pods -l app=httpenv -o wide
  ```

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlexpose.md)]
---

## `endpoints` not `endpoint`

- `endpoints` is the only resource that cannot be singular

```bash
$ kubectl get endpoint
error: the server doesn't have a resource type "endpoint"
```

- This is because the type itself is plural (unlike every other resource)

- There is no `endpoint` object: `type Endpoints struct`

- The type doesn't represent a single endpoint, but a list of endpoints

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlexpose.md)]
---

## Exposing services to the outside world

- The default type (ClusterIP) only works for internal traffic

- If we want to accept external traffic, we can use one of these:

- NodePort (expose a service on a TCP port between 30000-32768)

- LoadBalancer (provision a cloud load balancer for our service)

- ExternalIP (use one node's external IP address)

- Ingress (a special mechanism for HTTP services)

*We'll see NodePorts and Ingresses more in detail later.*

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kubectlexpose.md)]
---

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

---

Shipping images with a registry

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

---
# Shipping images with a registry

- Initially, our app was running on a single node

- We could *build* and *run* in the same place

- Therefore, we did not need to *ship* anything

- Now that we want to run on a cluster, things are different

- The easiest way to ship container images is to use a registry

.debug[[k8s/shippingimages.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/shippingimages.md)]
---

## How Docker registries work (a reminder)

- What happens when we execute `docker run alpine` ?

- If the Engine needs to pull the `alpine` image, it expands it into `library/alpine`

- `library/alpine` is expanded into `index.docker.io/library/alpine`

- The Engine communicates with `index.docker.io` to retrieve `library/alpine:latest`

- To use something else than `index.docker.io`, we specify it in the image name

- Examples:
  ```bash
  docker pull gcr.io/google-containers/alpine-with-bash:1.0

docker build -t registry.mycompany.io:5000/myimage:awesome .
  docker push registry.mycompany.io:5000/myimage:awesome
  ```

.debug[[k8s/shippingimages.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/shippingimages.md)]
---

## Running DockerCoins on Kubernetes

- Create one deployment for each component

(hasher, redis, rng, webui, worker)

- Expose deployments that need to accept connections

(hasher, redis, rng, webui)

- For redis, we can use the official redis image

- For the 4 others, we need to build images and push them to some registry

.debug[[k8s/shippingimages.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/shippingimages.md)]
---

## Building and shipping images

- There are *many* options!

- Manually:

- build locally (with `docker build` or otherwise)

- push to the registry

- Automatically:

- build and test locally

- when ready, commit and push a code repository

- the code repository notifies an automated build system

- that system gets the code, builds it, pushes the image to the registry

.debug[[k8s/shippingimages.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/shippingimages.md)]
---

## Which registry do we want to use?

- There are SAAS products like Docker Hub, Quay ...

- Each major cloud provider has an option as well

(ACR on Azure, ECR on AWS, GCR on Google Cloud...)

- There are also commercial products to run our own registry

(Docker EE, Quay...)

- And open source options, too!

- When picking a registry, pay attention to its build system

(when it has one)

.debug[[k8s/shippingimages.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/shippingimages.md)]
---
## Using images from the Docker Hub

- For everyone's convenience, we took care of building DockerCoins images

- We pushed these images to the DockerHub, under the [dockercoins](https://hub.docker.com/u/dockercoins) user

- These images are *tagged* with a version number, `v0.1`

- The full image names are therefore:

- `dockercoins/hasher:v0.1`

- `dockercoins/rng:v0.1`

- `dockercoins/webui:v0.1`

- `dockercoins/worker:v0.1`

.debug[[k8s/buildshiprun-dockerhub.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/buildshiprun-dockerhub.md)]
---

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

---

Running our application on Kubernetes

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

---
# Running our application on Kubernetes

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

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

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

]

.debug[[k8s/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ourapponkube.md)]
---

## Deploying other images

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

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

- ... We could use the following snippet:

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

.debug[[k8s/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ourapponkube.md)]
---

## Is this working?

- After waiting for the deployment to complete, let's look at the logs!

(Hint: use `kubectl get deploy -w` to watch deployment events)

- Look at some logs:
  ```bash
  kubectl logs deploy/rng
  kubectl logs deploy/worker
  ```

]

🤔 `rng` is fine ... But not `worker`.

💡 Oh right! We forgot to `expose`.

.debug[[k8s/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ourapponkube.md)]
---

## Connecting containers together

- Three deployments need to be reachable by others: `hasher`, `redis`, `rng`

- `worker` doesn't need to be exposed

- `webui` will be dealt with later

- Expose each deployment, specifying the right port:
  ```bash
  kubectl expose deployment redis --port 6379
  kubectl expose deployment rng --port 80
  kubectl expose deployment hasher --port 80
  ```

]

.debug[[k8s/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ourapponkube.md)]
---

## Is this working yet?

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

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

(Give it about 10 seconds to recover)

]

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

.debug[[k8s/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ourapponkube.md)]
---

## Exposing services for external access

- Now we would like to access the Web UI

- We will expose it with a `NodePort`

(just like we did for the registry)

- Create a `NodePort` service for the Web UI:
  ```bash
  kubectl expose deploy/webui --type=NodePort --port=80
  ```

- Check the port that was allocated:
  ```bash
  kubectl get svc
  ```

]

.debug[[k8s/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ourapponkube.md)]
---

## Accessing the web UI

- We can now connect to *any node*, on the allocated node port, to view the web UI

- Open the web UI in your browser (http://node-ip-address:3xxxx/)

]

Yes, this may take a little while to update. *(Narrator: it was DNS.)*

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

.debug[[k8s/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ourapponkube.md)]
---

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

---

Deploying with YAML

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

---
# Deploying with YAML

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

- `kubectl run`

- `kubectl create deployment`

- `kubectl expose`

- We can also create resources directly with YAML manifests

.debug[[k8s/yamldeploy.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/yamldeploy.md)]
---

## `kubectl apply` vs `create`

- `kubectl create -f whatever.yaml`

- creates resources if they don't exist

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

- `kubectl apply -f whatever.yaml`

- creates resources if they don't exist

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

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

.debug[[k8s/yamldeploy.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/yamldeploy.md)]
---

## Creating multiple resources

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

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

.debug[[k8s/yamldeploy.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/yamldeploy.md)]
---

## Creating multiple resources

- The manifest can also contain a list of resources

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

.debug[[k8s/yamldeploy.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/yamldeploy.md)]
---

## Deploying dockercoins with YAML

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

(Deployments and Services)

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

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

]

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

.debug[[k8s/yamldeploy.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/yamldeploy.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-deploying-with-yaml)
|
[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

- That dashboard is usually exposed over HTTPS

(this requires obtaining a proper TLS certificate)

- Dashboard users need to authenticate

- We are going to take a *dangerous* shortcut

.debug[[k8s/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/dashboard.md)]
---

## The insecure method

- We could (and should) use [Let's Encrypt](https://letsencrypt.org/) ...

- ... but we don't want to deal with TLS certificates

- We could (and should) learn how authentication and authorization work ...

- ... but we will use a guest account with admin access instead

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

.debug[[k8s/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/dashboard.md)]
---

## Running a very insecure dashboard

- We are going to deploy that dashboard with *one single command*

- This command will create all the necessary resources

(the dashboard itself, the HTTP wrapper, the admin/guest account)

- All these resources are defined in a YAML file

- All we have to do is load that YAML file with with `kubectl apply -f`

- Create all the dashboard resources, with the following command:
  ```bash
  kubectl apply -f ~/container.training/k8s/insecure-dashboard.yaml
  ```

]

.debug[[k8s/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/dashboard.md)]
---

## Connecting to the dashboard

- Check which port the dashboard is on:
  ```bash
  kubectl get svc dashboard
  ```

]

You'll want the `3xxxx` port.

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

]

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

.debug[[k8s/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/dashboard.md)]
---

## Dashboard authentication

- We have three authentication options at this point:

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

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

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

- Let's use "skip": we're logged in!

.debug[[k8s/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/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://redlock.io/blog/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[[k8s/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/dashboard.md)]
---

## Other dashboards

- [Kube Web View](https://codeberg.org/hjacobs/kube-web-view)

- read-only dashboard

- optimized for "troubleshooting and incident response"

- see [vision and goals](https://kube-web-view.readthedocs.io/en/latest/vision.html#vision) for details

- [Kube Ops View](https://github.com/hjacobs/kube-ops-view)

- "provides a common operational picture for multiple Kubernetes clusters"

.debug[[k8s/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/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-our-demo-app)
]

---

# Security implications of `kubectl apply`

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

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

- starts bitcoin miners on the whole cluster

- hides in a non-default namespace

- bind-mounts our nodes' filesystem

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

- encrypts our data and ransoms it

- ☠️☠️☠️

.debug[[k8s/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/dashboard.md)]
---

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

- `curl | sh` is convenient

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

- `kubectl apply -f` is convenient

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

- Example: the official setup instructions for most pod networks

- It introduces new failure modes

(for instance, if you try to apply YAML from a link that's no longer valid)

.debug[[k8s/dashboard.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/dashboard.md)]
---

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

---

Scaling our demo app

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

---
# Scaling our demo app

- Our ultimate goal is to get more DockerCoins

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

- Let's look at the architecture again:

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

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

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/scalingdockercoins.md)]
---

## Adding another worker

- All we have to do is scale 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 deployment worker --replicas=2
  ```

]

After a few seconds, the graph in the web UI should show up.

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/scalingdockercoins.md)]
---

## Adding more workers

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

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

]

The graph in the web UI should go up again.

(This is looking great! We're gonna be RICH!)

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/scalingdockercoins.md)]
---

## Adding even more workers

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

- Scale the `worker` Deployment to a bigger number:
  ```bash
  kubectl scale deployment worker --replicas=10
  ```

]

The graph will peak at 10 hashes/second.

(We can add as many workers as we want: we will never go past 10 hashes/second.)

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/scalingdockercoins.md)]
---

## Didn't we briefly exceed 10 hashes/second?

- It may *look like it*, because the web UI shows instant speed

- The instant speed can briefly exceed 10 hashes/second

- The average speed cannot

- The instant speed can be biased because of how it's computed

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/scalingdockercoins.md)]
---

## Why instant speed is misleading

- The instant speed is computed client-side by the web UI

- The web UI checks the hash counter once per second
 
 (and does a classic (h2-h1)/(t2-t1) speed computation)

- The counter is updated once per second by the workers

- These timings are not exact
 
 (e.g. the web UI check interval is client-side JavaScript)

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

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

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/scalingdockercoins.md)]
---

## Why are we stuck at 10 hashes per second?

- If this was high-quality, production code, we would have instrumentation

(Datadog, Honeycomb, New Relic, statsd, Sumologic, ...)

- It's not!

- Perhaps we could benchmark our web services?

(with tools like `ab`, or even simpler, `httping`)

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/scalingdockercoins.md)]
---

## Benchmarking our web services

- We want to check `hasher` and `rng`

- We are going to use `httping`

- It's just like `ping`, but using HTTP `GET` requests

(it measures how long it takes to perform one `GET` request)

- It's used like this:
  ```
  httping [-c count] http://host:port/path
  ```

- Or even simpler:
  ```
  httping ip.ad.dr.ess
  ```

- We will use `httping` on the ClusterIP addresses of our services

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/scalingdockercoins.md)]
---

## Obtaining ClusterIP addresses

- We can simply check the output of `kubectl get services`

- Or do it programmatically, as in the example below

- Retrieve the IP addresses:
  ```bash
  HASHER=$(kubectl get svc hasher -o go-template={{.spec.clusterIP}})
  RNG=$(kubectl get svc rng -o go-template={{.spec.clusterIP}})
  ```

]

Now we can access the IP addresses of our services through `$HASHER` and `$RNG`.

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/scalingdockercoins.md)]
---

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

- Check the response times for both services:
  ```bash
  httping -c 3 $HASHER
  httping -c 3 $RNG
  ```

]

- `hasher` is fine (it should take a few milliseconds to reply)

- `rng` is not (it should take about 700 milliseconds if there are 10 workers)

- Something is wrong with `rng`, but ... what?

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/scalingdockercoins.md)]
---
## Let's draw hasty conclusions

- The bottleneck seems to be `rng`

- *What if* we don't have enough entropy and can't generate enough random numbers?

- We need to scale out the `rng` service on multiple machines!

Note: this is a fiction! We have enough entropy. But we need a pretext to scale out.

(In fact, the code of `rng` uses `/dev/urandom`, which never runs out of entropy...
 
...and is [just as good as `/dev/random`](http://www.slideshare.net/PacSecJP/filippo-plain-simple-reality-of-entropy).)

.debug[[shared/hastyconclusions.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/hastyconclusions.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-our-demo-app)
|
[Back to table of contents](#toc-chapter-3)
|
[Next section](#toc-labels-and-selectors)
]

---
# Daemon sets

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

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

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

- We will do that with a *daemon set*

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Why not a deployment?

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

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

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

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

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

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

(as nodes are added or removed)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Daemon sets in practice

- Daemon sets are great for cluster-wide, per-node processes:

- `kube-proxy`

- `weave` (our overlay network)

- monitoring agents

- hardware management tools (e.g. SCSI/FC HBA agents)

- etc.

- They can also be restricted to run [only on some nodes](https://kubernetes.io/docs/concepts/workloads/controllers/daemonset/#running-pods-on-only-some-nodes)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Creating a daemon set

- Unfortunately, as of Kubernetes 1.15, the CLI cannot create daemon sets

- More precisely: it doesn't have a subcommand to create a daemon set

- But any kind of resource can always be created by providing a YAML description:
  ```bash
  kubectl apply -f foo.yaml
  ```

- How do we create the YAML file for our daemon set?

- option 1: [read the docs](https://kubernetes.io/docs/concepts/workloads/controllers/daemonset/#create-a-daemonset)

- option 2: `vi` our way out of it

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Creating the YAML file for our daemon set

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

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

- Edit `rng.yml`

]

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## "Casting" a resource to another

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

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

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

- Save, quit

- Try to create our new resource:
  ```
  kubectl apply -f rng.yml
  ```

]

We all knew this couldn't be that easy, right!

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Understanding the problem

- The core of the error is:
  ```
  error validating data:
  [ValidationError(DaemonSet.spec):
  unknown field "replicas" in io.k8s.api.extensions.v1beta1.DaemonSetSpec,
  ...
  ```

- *Obviously,* it doesn't make sense to specify a number of replicas for a daemon set

- Workaround: fix the YAML

- remove the `replicas` field
  - remove the `strategy` field (which defines the rollout mechanism for a deployment)
  - remove the `progressDeadlineSeconds` field (also used by the rollout mechanism)
  - remove the `status: {}` line at the end

- Or, we could also ...

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Use the `--force`, Luke

- We could also tell Kubernetes to ignore these errors and try anyway

- The `--force` flag's actual name is `--validate=false`

- Try to load our YAML file and ignore errors:
  ```bash
  kubectl apply -f rng.yml --validate=false
  ```

]

🎩✨🐇

Wait ... Now, can it be *that* easy?

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Checking what we've done

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

- Look at the resources that we have now:
  ```bash
  kubectl get all
  ```

]

We have two resources called `rng`:

- the *deployment* that was existing before

- the *daemon set* that we just created

We also have one too many pods.
 
(The pod corresponding to the *deployment* still exists.)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## `deploy/rng` and `ds/rng`

- You can have different resource types with the same name

(i.e. a *deployment* and a *daemon set* both named `rng`)

- We still have the old `rng` *deployment*

```
NAME                       DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
deployment.apps/rng        1         1         1            1           18m
  ```

- But now we have the new `rng` *daemon set* as well

```
NAME DESIRED CURRENT READY UP-TO-DATE AVAILABLE NODE SELECTOR AGE
daemonset.apps/rng 2 2 2 2 2 <none> 9s
 ```

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Too many pods

- If we check with `kubectl get pods`, we see:

- *one pod* for the deployment (named `rng-xxxxxxxxxx-yyyyy`)

- *one pod per node* for the daemon set (named `rng-zzzzz`)

```
  NAME                        READY     STATUS    RESTARTS   AGE
  rng-54f57d4d49-7pt82        1/1       Running   0          11m
  rng-b85tm                   1/1       Running   0          25s
  rng-hfbrr                   1/1       Running   0          25s
  [...]
  ```

The daemon set created one pod per node, except on the master node.

The master node has [taints](https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/) preventing pods from running there.

(To schedule a pod on this node anyway, the pod will require appropriate [tolerations](https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/).)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Is this working?

- Look at the web UI

- The graph should now go above 10 hashes per second!

- It looks like the newly created pods are serving traffic correctly

- How and why did this happen?

(We didn't do anything special to add them to the `rng` service load balancer!)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

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

---

Labels and selectors

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

---

# Labels and selectors

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

- That set of pods is defined by the *selector* of the `rng` service

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

]

- The selector is `app=rng`

- It means "all the pods having the label `app=rng`"

(They can have additional labels as well, that's OK!)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Selector evaluation

- We can use selectors with many `kubectl` commands

- For instance, with `kubectl get`, `kubectl logs`, `kubectl delete` ... and more

- Get the list of pods matching selector `app=rng`:
  ```bash
  kubectl get pods -l app=rng
  kubectl get pods --selector app=rng
  ```

]

But ... why do these pods (in particular, the *new* ones) have this `app=rng` label?

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Where do labels come from?

- When we create a deployment with `kubectl create deployment rng`,
 this deployment gets the label `app=rng`

- The replica sets created by this deployment also get the label `app=rng`

- The pods created by these replica sets also get the label `app=rng`

- When we created the daemon set from the deployment, we re-used the same spec

- Therefore, the pods created by the daemon set get the same labels

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Updating load balancer configuration

- We would like to remove a pod from the load balancer

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

It would be re-created immediately (by the replica set or the daemon set)

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

It would *also* be re-created immediately

Why?!?

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Selectors for replica sets and daemon sets

- The "mission" of a replica set is:

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

- The "mission" of a daemon set is:

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

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

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

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

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

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

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Isolation of replica sets and daemon sets

- Since both the `rng` daemon set and the `rng` replica set use `app=rng` ...

... Why don't they "find" each other's pods?

- *Replica sets* have a more specific selector, visible with `kubectl describe`

(It looks like `app=rng,pod-template-hash=abcd1234`)

- *Daemon sets* also have a more specific selector, but it's invisible

(It looks like `app=rng,controller-revision-hash=abcd1234`)

- As a result, each controller only "sees" the pods it manages

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Removing a pod from the load balancer

- Currently, the `rng` service is defined by the `app=rng` selector

- The only way to remove a pod is to remove or change the `app` label

- ... But that will cause another pod to be created instead!

- What's the solution?

- We need to change the selector of the `rng` service!

- Let's add another label to that selector (e.g. `enabled=yes`)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Complex selectors

- If a selector specifies multiple labels, they are understood as a logical *AND*

(In other words: the pods must match all the labels)

- Kubernetes has support for advanced, set-based selectors

(But these cannot be used with services, at least not yet!)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## The plan

1. Add the label `enabled=yes` to all our `rng` pods

2. Update the selector for the `rng` service to also include `enabled=yes`

3. Toggle traffic to a pod by manually adding/removing the `enabled` label

4. Profit!

*Note: if we swap steps 1 and 2, it will cause a short
service disruption, because there will be a period of time
during which the service selector won't match any pod.
During that time, requests to the service will time out.
By doing things in the order above, we guarantee that there won't
be any interruption.*

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Adding labels to pods

- We want to add the label `enabled=yes` to all pods that have `app=rng`

- We could edit each pod one by one with `kubectl edit` ...

- ... Or we could use `kubectl label` to label them all

- `kubectl label` can use selectors itself

- Add `enabled=yes` to all pods that have `app=rng`:
  ```bash
  kubectl label pods -l app=rng enabled=yes
  ```

]

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Updating the service selector

- We need to edit the service specification

- Reminder: in the service definition, we will see `app: rng` in two places

- the label of the service itself (we don't need to touch that one)

- the selector of the service (that's the one we want to change)

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

]

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

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## When the YAML parser is being too smart

- YAML parsers try to help us:

- `xyz` is the string `"xyz"`

- `42` is the integer `42`

- `yes` is the boolean value `true`

- If we want the string `"42"` or the string `"yes"`, we have to quote them

- So we have to use `enabled: "yes"`

.footnote[For a good laugh: if we had used "ja", "oui", "si" ... as the value, it would have worked!]

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Updating the service selector, take 2

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

]

This time it should work!

If we did everything correctly, the web UI shouldn't show any change.

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Updating labels

- We want to disable the pod that was created by the deployment

- All we have to do, is remove the `enabled` label from that pod

- To identify that pod, we can use its name

- ... Or rely on the fact that it's the only one with a `pod-template-hash` label

- Good to know:

- `kubectl label ... foo=` doesn't remove a label (it sets it to an empty string)

- to remove label `foo`, use `kubectl label ... foo-`

- to change an existing label, we would need to add `--overwrite`

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Removing a pod from the load balancer

- In one window, check the logs of that pod:
  ```bash
  POD=$(kubectl get pod -l app=rng,pod-template-hash -o name)
  kubectl logs --tail 1 --follow $POD

```
  (We should see a steady stream of HTTP logs)

- In another window, remove the label from the pod:
  ```bash
  kubectl label pod -l app=rng,pod-template-hash enabled-
  ```
  (The stream of HTTP logs should stop immediately)

]

There might be a slight change in the web UI (since we removed a bit
of capacity from the `rng` service). If we remove more pods,
the effect should be more visible.

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Updating the daemon set

- If we scale up our cluster by adding new nodes, the daemon set will create more pods

- These pods won't have the `enabled=yes` label

- If we want these pods to have that label, we need to edit the daemon set spec

- We can do that with e.g. `kubectl edit daemonset rng`

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## We've put resources in your resources

- Reminder: a daemon set is a resource that creates more resources!

- There is a difference between:

- the label(s) of a resource (in the `metadata` block in the beginning)

- the selector of a resource (in the `spec` block)

- the label(s) of the resource(s) created by the first resource (in the `template` block)

- We would need to update the selector and the template

(metadata labels are not mandatory)

- The template must match the selector

(i.e. the resource will refuse to create resources that it will not select)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Labels and debugging

- When a pod is misbehaving, we can delete it: another one will be recreated

- But we can also change its labels

- It will be removed from the load balancer (it won't receive traffic anymore)

- Another pod will be recreated immediately

- But the problematic pod is still here, and we can inspect and debug it

- We can even re-add it to the rotation if necessary

(Very useful to troubleshoot intermittent and elusive bugs)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

## Labels and advanced rollout control

- Conversely, we can add pods matching a service's selector

- These pods will then receive requests and serve traffic

- Examples:

- one-shot pod with all debug flags enabled, to collect logs

- pods created automatically, but added to rotation in a second step
 
 (by setting their label accordingly)

- This gives us building blocks for canary and blue/green deployments

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/daemonset.md)]
---

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

---

Authoring YAML

.nav[
[Previous section](#toc-labels-and-selectors)
|
[Back to table of contents](#toc-chapter-3)
|
[Next section](#toc-controlling-a-kubernetes-cluster-remotely)
]

---
# Authoring YAML

- There are various ways to generate YAML with Kubernetes, e.g.:

- `kubectl run`

- `kubectl create deployment` (and a few other `kubectl create` variants)

- `kubectl expose`

- When and why do we need to write our own YAML?

- How do we write YAML from scratch?

.debug[[k8s/dryrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/dryrun.md)]
---

## The limits of generated YAML

- Many advanced (and even not-so-advanced) features require to write YAML:

- pods with multiple containers

- resource limits

- healthchecks

- DaemonSets, StatefulSets

- and more!

- How do we access these features?

.debug[[k8s/dryrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/dryrun.md)]
---

## We don't have to start from scratch

- Create a resource (e.g. Deployment)

- Dump its YAML with `kubectl get -o yaml ...`

- Edit the YAML

- Use `kubectl apply -f ...` with the YAML file to:

- update the resource (if it's the same kind)

- create a new resource (if it's a different kind)

- Or: Use The Docs, Luke

(the documentation almost always has YAML examples)

.debug[[k8s/dryrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/dryrun.md)]
---

## Generating YAML without creating resources

- We can use the `--dry-run` option

- Generate the YAML for a Deployment without creating it:
  ```bash
  kubectl create deployment web --image nginx --dry-run
  ```

]

- We can clean up that YAML even more if we want

(for instance, we can remove the `creationTimestamp` and empty dicts)

.debug[[k8s/dryrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/dryrun.md)]
---

## Using `--dry-run` with `kubectl apply`

- The `--dry-run` option can also be used with `kubectl apply`

- However, it can be misleading (it doesn't do a "real" dry run)

- Let's see what happens in the following scenario:

- generate the YAML for a Deployment

- tweak the YAML to transform it into a DaemonSet

- apply that YAML to see what would actually be created

.debug[[k8s/dryrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/dryrun.md)]
---

## The limits of `kubectl apply --dry-run`

- Generate the YAML for a deployment:
  ```bash
  kubectl create deployment web --image=nginx -o yaml > web.yaml
  ```

- Change the `kind` in the YAML to make it a `DaemonSet`:
  ```bash
  sed -i s/Deployment/DaemonSet/ web.yaml
  ```

- Ask `kubectl` what would be applied:
  ```bash
  kubectl apply -f web.yaml --dry-run --validate=false -o yaml
  ```

]

The resulting YAML doesn't represent a valid DaemonSet.

.debug[[k8s/dryrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/dryrun.md)]
---

## Server-side dry run

- Since Kubernetes 1.13, we can use [server-side dry run and diffs](https://kubernetes.io/blog/2019/01/14/apiserver-dry-run-and-kubectl-diff/)

- Server-side dry run will do all the work, but *not* persist to etcd

(all validation and mutation hooks will be executed)

- Try the same YAML file as earlier, with server-side dry run:
  ```bash
  kubectl apply -f web.yaml --server-dry-run --validate=false -o yaml
  ```

]

The resulting YAML doesn't have the `replicas` field anymore.

Instead, it has the fields expected in a DaemonSet.

.debug[[k8s/dryrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/dryrun.md)]
---

## Advantages of server-side dry run

- The YAML is verified much more extensively

- The only step that is skipped is "write to etcd"

- YAML that passes server-side dry run *should* apply successfully

(unless the cluster state changes by the time the YAML is actually applied)

- Validating or mutating hooks that have side effects can also be an issue

.debug[[k8s/dryrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/dryrun.md)]
---

## `kubectl diff`

- Kubernetes 1.13 also introduced `kubectl diff`

- `kubectl diff` does a server-side dry run, *and* shows differences

- Try `kubectl diff` on the YAML that we tweaked earlier:
  ```bash
  kubectl diff -f web.yaml
  ```

]

Note: we don't need to specify `--validate=false` here.

.debug[[k8s/dryrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/dryrun.md)]
---

## Advantage of YAML

- Using YAML (instead of `kubectl run`/`create`/etc.) allows to be *declarative*

- The YAML describes the desired state of our cluster and applications

- YAML can be stored, versioned, archived (e.g. in git repositories)

- To change resources, change the YAML files

(instead of using `kubectl edit`/`scale`/`label`/etc.)

- Changes can be reviewed before being applied

(with code reviews, pull requests ...)

- This workflow is sometimes called "GitOps"

(there are tools like Weave Flux or GitKube to facilitate it)

.debug[[k8s/dryrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/dryrun.md)]
---

## YAML in practice

- Get started with `kubectl run`/`create`/`expose`/etc.

- Dump the YAML with `kubectl get -o yaml`

- Tweak that YAML and `kubectl apply` it back

- Store that YAML for reference (for further deployments)

- Feel free to clean up the YAML:

- remove fields you don't know

- check that it still works!

- That YAML will be useful later when using e.g. Kustomize or Helm

.debug[[k8s/dryrun.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/dryrun.md)]
---

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

---

Controlling a Kubernetes cluster remotely

.nav[
[Previous section](#toc-authoring-yaml)
|
[Back to table of contents](#toc-chapter-4)
|
[Next section](#toc-accessing-internal-services)
]

---
# Controlling a Kubernetes cluster remotely

- `kubectl` can be used either on cluster instances or outside the cluster

- Here, we are going to use `kubectl` from our local machine

.debug[[k8s/localkubeconfig.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/localkubeconfig.md)]
---

## Requirements

- `kubectl` is officially available on Linux, macOS, Windows

(and unofficially anywhere we can build and run Go binaries)

- You may skip these exercises if you are following along from:

- a tablet or phone

- a web-based terminal

- an environment where you can't install and run new binaries

.debug[[k8s/localkubeconfig.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/localkubeconfig.md)]
---

## Installing `kubectl`

- If you already have `kubectl` on your local machine, you can skip this

- Download the `kubectl` binary from one of these links:

[Linux](https://storage.googleapis.com/kubernetes-release/release/v1.15.3/bin/linux/amd64/kubectl)
  |
  [macOS](https://storage.googleapis.com/kubernetes-release/release/v1.15.3/bin/darwin/amd64/kubectl)
  |
  [Windows](https://storage.googleapis.com/kubernetes-release/release/v1.15.3/bin/windows/amd64/kubectl.exe)

- On Linux and macOS, make the binary executable with `chmod +x kubectl`

(And remember to run it with `./kubectl` or move it to your `$PATH`)

]

Note: if you are following along with a different platform (e.g. Linux on an architecture different from amd64, or with a phone or tablet), installing `kubectl` might be more complicated (or even impossible) so feel free to skip this section.

.debug[[k8s/localkubeconfig.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/localkubeconfig.md)]
---

## Testing `kubectl`

- Check that `kubectl` works correctly

(before even trying to connect to a remote cluster!)

- Ask `kubectl` to show its version number:
  ```bash
  kubectl version --client
  ```

]

The output should look like this:
```
Client Version: version.Info{Major:"1", Minor:"15", GitVersion:"v1.15.0",
GitCommit:"e8462b5b5dc2584fdcd18e6bcfe9f1e4d970a529", GitTreeState:"clean",
BuildDate:"2019-06-19T16:40:16Z", GoVersion:"go1.12.5", Compiler:"gc",
Platform:"darwin/amd64"}
```

.debug[[k8s/localkubeconfig.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/localkubeconfig.md)]
---

## Preserving the existing `~/.kube/config`

- If you already have a `~/.kube/config` file, rename it

(we are going to overwrite it in the following slides!)

- If you never used `kubectl` on your machine before: nothing to do!

- Make a copy of `~/.kube/config`; if you are using macOS or Linux, you can do:
  ```bash
  cp ~/.kube/config ~/.kube/config.before.training
  ```

- If you are using Windows, you will need to adapt this command

]

.debug[[k8s/localkubeconfig.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/localkubeconfig.md)]
---

## Copying the configuration file from `node1`

- The `~/.kube/config` file that is on `node1` contains all the credentials we need

- Let's copy it over!

- Copy the file from `node1`; if you are using macOS or Linux, you can do:
  ```
  scp `USER`@`X.X.X.X`:.kube/config ~/.kube/config
  # Make sure to replace X.X.X.X with the IP address of node1,
  # and USER with the user name used to log into node1!
  ```

- If you are using Windows, adapt these instructions to your SSH client

]

.debug[[k8s/localkubeconfig.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/localkubeconfig.md)]
---

## Updating the server address

- There is a good chance that we need to update the server address

- To know if it is necessary, run `kubectl config view`

- Look for the `server:` address:

- if it matches the public IP address of `node1`, you're good!

- if it is anything else (especially a private IP address), update it!

- To update the server address, run:
  ```bash
  kubectl config set-cluster kubernetes --server=https://`X.X.X.X`:6443
  # Make sure to replace X.X.X.X with the IP address of node1!
  ```

.debug[[k8s/localkubeconfig.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/localkubeconfig.md)]
---

## What if we get a certificate error?

- Generally, the Kubernetes API uses a certificate that is valid for:

- `kubernetes`
  - `kubernetes.default`
  - `kubernetes.default.svc`
  - `kubernetes.default.svc.cluster.local`
  - the ClusterIP address of the `kubernetes` service
  - the hostname of the node hosting the control plane (e.g. `node1`)
  - the IP address of the node hosting the control plane

- On most clouds, the IP address of the node is an internal IP address

- ... And we are going to connect over the external IP address

- ... And that external IP address was not used when creating the certificate!

.debug[[k8s/localkubeconfig.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/localkubeconfig.md)]
---

## Working around the certificate error

- We need to tell `kubectl` to skip TLS verification

(only do this with testing clusters, never in production!)

- The following command will do the trick:
  ```bash
  kubectl config set-cluster kubernetes --insecure-skip-tls-verify
  ```

.debug[[k8s/localkubeconfig.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/localkubeconfig.md)]
---

## Checking that we can connect to the cluster

- We can now run a couple of trivial commands to check that all is well

- Check the versions of the local client and remote server:
  ```bash
  kubectl version
  ```

- View the nodes of the cluster:
  ```bash
  kubectl get nodes
  ```

]

We can now utilize the cluster exactly as if we're logged into a node, except that it's remote.

.debug[[k8s/localkubeconfig.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/localkubeconfig.md)]
---

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

---

Accessing internal services

.nav[
[Previous section](#toc-controlling-a-kubernetes-cluster-remotely)
|
[Back to table of contents](#toc-chapter-4)
|
[Next section](#toc-rolling-updates)
]

---
# Accessing internal services

- When we are logged in on a cluster node, we can access internal services

(by virtue of the Kubernetes network model: all nodes can reach all pods and services)

- When we are accessing a remote cluster, things are different

(generally, our local machine won't have access to the cluster's internal subnet)

- How can we temporarily access a service without exposing it to everyone?

- `kubectl proxy`: gives us access to the API, which includes a proxy for HTTP resources

- `kubectl port-forward`: allows forwarding of TCP ports to arbitrary pods, services, ...

.debug[[k8s/accessinternal.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/accessinternal.md)]
---

## Suspension of disbelief

The exercises in this section assume that we have set up `kubectl` on our
local machine in order to access a remote cluster.

We will therefore show how to access services and pods of the remote cluster,
from our local machine.

You can also run these exercises directly on the cluster (if you haven't
installed and set up `kubectl` locally).

Running commands locally will be less useful
(since you could access services and pods directly),
but keep in mind that these commands will work anywhere as long as you have
installed and set up `kubectl` to communicate with your cluster.

.debug[[k8s/accessinternal.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/accessinternal.md)]
---

## `kubectl proxy` in theory

- Running `kubectl proxy` gives us access to the entire Kubernetes API

- The API includes routes to proxy HTTP traffic

- These routes look like the following:

`/api/v1/namespaces/<namespace>/services/<service>/proxy`

- We just add the URI to the end of the request, for instance:

`/api/v1/namespaces/<namespace>/services/<service>/proxy/index.html`

- We can access `services` and `pods` this way

.debug[[k8s/accessinternal.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/accessinternal.md)]
---

## `kubectl proxy` in practice

- Let's access the `webui` service through `kubectl proxy`

- Run an API proxy in the background:
  ```bash
  kubectl proxy &
  ```

- Access the `webui` service:
  ```bash
  curl localhost:8001/api/v1/namespaces/default/services/webui/proxy/index.html
  ```

- Terminate the proxy:
  ```bash
  kill %1
  ```

]

.debug[[k8s/accessinternal.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/accessinternal.md)]
---

## `kubectl port-forward` in theory

- What if we want to access a TCP service?

- We can use `kubectl port-forward` instead

- It will create a TCP relay to forward connections to a specific port

(of a pod, service, deployment...)

- The syntax is:

`kubectl port-forward service/name_of_service local_port:remote_port`

- If only one port number is specified, it is used for both local and remote ports

.debug[[k8s/accessinternal.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/accessinternal.md)]
---

## `kubectl port-forward` in practice

- Let's access our remote Redis server

- Forward connections from local port 10000 to remote port 6379:
  ```bash
  kubectl port-forward svc/redis 10000:6379 &
  ```

- Connect to the Redis server:
  ```bash
  telnet localhost 10000
  ```

- Issue a few commands, e.g. `INFO server` then `QUIT`

- Terminate the port forwarder:
  ```bash
  kill %1
  ```

]

.debug[[k8s/accessinternal.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/accessinternal.md)]
---

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

---

Rolling updates

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

---
# Rolling updates

- By default (without rolling updates), when a scaled resource is updated:

- new pods are created

- old pods are terminated

- ... all at the same time

- if something goes wrong, ¯\\\_(ツ)\_/¯

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/rollout.md)]
---

## Rolling updates

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

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

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

- At any given time ...

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

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

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

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

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/rollout.md)]
---

## Checking current rollout parameters

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

- Show the rollout plan for our deployments:
  ```bash
    kubectl get deploy -o json |
            jq ".items[] | {name:.metadata.name} + .spec.strategy.rollingUpdate"
  ```

]

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/rollout.md)]
---

## Rolling updates in practice

- As of Kubernetes 1.8, we can do rolling updates with:

`deployments`, `daemonsets`, `statefulsets`

- Editing one of these resources will automatically result in a rolling update

- Rolling updates can be monitored with the `kubectl rollout` subcommand

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/rollout.md)]
---

## Rolling out the new `worker` service

- Let's monitor what's going on by opening a few terminals, and run:
  ```bash
  kubectl get pods -w
  kubectl get replicasets -w
  kubectl get deployments -w
  ```

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

]

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

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/rollout.md)]
---

## Give it some time

- At first, it looks like nothing is happening (the graph remains at the same level)

- According to `kubectl get deploy -w`, the `deployment` was updated really quickly

- But `kubectl get pods -w` tells a different story

- The old `pods` are still here, and they stay in `Terminating` state for a while

- Eventually, they are terminated; and then the graph decreases significantly

- This delay is due to the fact that our worker doesn't handle signals

- Kubernetes sends a "polite" shutdown request to the worker, which ignores it

- After a grace period, Kubernetes gets impatient and kills the container

(The grace period is 30 seconds, but [can be changed](https://kubernetes.io/docs/concepts/workloads/pods/pod/#termination-of-pods) if needed)

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/rollout.md)]
---

## Rolling out something invalid

- What happens if we make a mistake?

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

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

]

Our rollout is stuck. However, the app is not dead.

(After a minute, it will stabilize to be 20-25% slower.)

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/rollout.md)]
---

## What's going on with our rollout?

- Why is our app a bit slower?

- Because `MaxUnavailable=25%`

... So the rollout terminated 2 replicas out of 10 available

- Okay, but why do we see 5 new replicas being rolled out?

- Because `MaxSurge=25%`

... So in addition to replacing 2 replicas, the rollout is also starting 3 more

- It rounded down the number of MaxUnavailable pods conservatively,
 
 but the total number of pods being rolled out is allowed to be 25+25=50%

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/rollout.md)]
---

## The nitty-gritty details

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

- Current settings: MaxUnavailable=25% and MaxSurge=25%

- When we start the rollout:

- two replicas are taken down (as per MaxUnavailable=25%)
  - two others are created (with the new version) to replace them
  - three others are created (with the new version) per MaxSurge=25%)

- Now we have 8 replicas up and running, and 5 being deployed

- Our rollout is stuck at this point!

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/rollout.md)]
---

## Checking the dashboard during the bad rollout

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

- Connect to the dashboard that we deployed earlier

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

- Can we see the reason for the failure?

]

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/rollout.md)]
---

## Recovering from a bad rollout

- We could push some `v0.3` image

(the pod retry logic will eventually catch it and the rollout will proceed)

- Or we could invoke a manual rollback

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

]

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/rollout.md)]
---

## Changing rollout parameters

- We want to:

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

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

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

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/rollout.md)]
---

## Applying changes through a YAML patch

- We could use `kubectl edit deployment worker`

- But we could also use `kubectl patch` with the exact YAML shown before

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

]

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/rollout.md)]
---

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

---

Healthchecks

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

---
# Healthchecks

- Kubernetes provides two kinds of healthchecks: liveness and readiness

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

- Each container can have two (optional) probes:

- liveness = is this container dead or alive?

- readiness = is this container ready to serve traffic?

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

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/healthchecks.md)]
---

## Liveness probe

- Indicates if the container is dead or alive

- A dead container cannot come back to life

- If the liveness probe fails, the container is killed

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

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

- `Never`: the container is not restarted

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/healthchecks.md)]
---

## When to use a liveness probe

- To indicate failures that can't be recovered

- deadlocks (causing all requests to time out)

- internal corruption (causing all requests to error)

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

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/healthchecks.md)]
---

## Readiness probe

- Indicates if the container is ready to serve traffic

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

- If the readiness probe fails:

- the container is *not* killed

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

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/healthchecks.md)]
---

## When to use a readiness probe

- To indicate failure due to an external cause

- database is down or unreachable

- mandatory auth or other backend service unavailable

- To indicate temporary failure or unavailability

- application can only service *N* parallel connections

- runtime is busy doing garbage collection or initial data load

- For processes that take a long time to start

(more on that later)

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/healthchecks.md)]
---

## Dependencies

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

- the web server's liveness probe should succeed

- the web server's readiness probe should fail

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/healthchecks.md)]
---

## Timing and thresholds

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

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

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

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

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

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

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/healthchecks.md)]
---

## Startup probe

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

(it is in `alpha` in Kubernetes 1.16)

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

- process is still starting

- loading external data, priming caches

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

(available for both liveness and readiness probes)

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

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/healthchecks.md)]
---

## Different types of probes

- HTTP request

- specify URL of the request (and optional headers)

- any status code between 200 and 399 indicates success

- TCP connection

- the probe succeeds if the TCP port is open

- arbitrary exec

- a command is executed in the container

- exit status of zero indicates success

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/healthchecks.md)]
---

## Benefits of using probes

- Rolling updates proceed when containers are *actually ready*

(as opposed to merely started)

- Containers in a broken state get killed and restarted

(instead of serving errors or timeouts)

- Unavailable backends get removed from load balancer rotation

(thus improving response times across the board)

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/healthchecks.md)]
---

## Example: HTTP probe

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

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

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/healthchecks.md)]
---

## Example: exec probe

Here is a pod template for a Redis server:

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

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/healthchecks.md)]
---

## Questions to ask before adding healthchecks

- Do we want liveness, readiness, both?

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

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

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

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

- Do they depend on additional services?

(this can be particularly tricky, see next slide)

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/healthchecks.md)]
---

## Healthchecks and dependencies

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

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

- Otherwise, they are considered to fail

- This might require to check the health of dependencies asynchronously

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/healthchecks.md)]
---

## Healthchecks for workers

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

- Readiness isn't useful

(because workers aren't backends for a service)

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

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

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

- touch a file during each iteration of the main loop

- check the timestamp of that file from an exec probe

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/healthchecks.md)]
---

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

---

Recording deployment actions

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

---
# Recording deployment actions

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

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

- That flag will store the command line in the Deployment

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

- It gets copied to the corresponding ReplicaSet

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

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

.debug[[k8s/record.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/record.md)]
---

## Using `--record`

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

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

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

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

]

.debug[[k8s/record.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/record.md)]
---

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

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

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

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

]

It recorded version 0.2 instead of 0.3! Why?

.debug[[k8s/record.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/record.md)]
---

## How `--record` really works

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

- The Deployment controller copies that annotation to the ReplicaSet

- `kubectl rollout history` shows the ReplicaSets' annotations

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

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

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

.debug[[k8s/record.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/record.md)]
---

## Pitfall #2: recording `scale` commands

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

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

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

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

]

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

.debug[[k8s/record.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/record.md)]
---

## Actions that don't create a new ReplicaSet

- The `scale` command updates the Deployment definition

- But it doesn't create a new ReplicaSet

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

- The annotation gets copied to the existing ReplicaSet

- This overwrites the previous annotation that was there

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

.debug[[k8s/record.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/record.md)]
---

## Updating the annotation directly

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

- Annotate the Deployment:
  ```bash
  kubectl annotate deployment worker kubernetes.io/change-cause="Just for fun"
  ```

- Check that our annotation shows up in the change history:
  ```bash
  kubectl rollout history deployment worker
  ```

]

Our annotation shows up (and overwrote whatever was there before).

.debug[[k8s/record.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/record.md)]
---

## Using change cause

- It sounds like a good idea to use `--record`, but:

*"Incorrect documentation is often worse than no documentation."*
 
 (Bertrand Meyer)

- If we use `--record` once, we need to either:

- use it every single time after that

- or clear the Deployment annotation after using `--record`
 
 (subsequent changes will show up with a `<none>` change cause)

- A safer way is to set it through our tooling

.debug[[k8s/record.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/record.md)]
---

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

---

End of day 1!

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

---
# End of day 1!

.debug[[electricity.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/electricity.md)]
---

![Electricity map](images/electricity-map.png)

.debug[[electricity.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/electricity.md)]
---

## Our VMs are low-carbon

- The closest EC2 region to Berlin is eu-central-1

(Frankfurt; Germany electricity mix: wind, coal, nuclear, gas)

- Instead, we deployed these VMs in eu-north-1

(Stockholm; Sweden electricity mix: hydro, nuclear, wind)

- According to [Electricity Map](https://electricitymap.org/), they produce ~5x less carbon

(at least Monday morning, when the VMs were deployed)

- The latency was a bit higher; let me know if you saw any difference!

.debug[[electricity.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/electricity.md)]
---

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

---

Namespaces

.nav[
[Previous section](#toc-end-of-day-)
|
[Back to table of contents](#toc-chapter-5)
|
[Next section](#toc-exposing-http-services-with-ingress-resources)
]

---
# Namespaces

- We would like to deploy another copy of DockerCoins on our cluster

- We could rename all our deployments and services:

hasher → hasher2, redis → redis2, rng → rng2, etc.

- That would require updating the code

- There has to be a better way!

- As hinted by the title of this section, we will use *namespaces*

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/namespaces.md)]
---

## Identifying a resource

- We cannot have two resources with the same name

(or can we...?)

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

(but it's OK to have an `rng` service, an `rng` deployment, and an `rng` daemon set)

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

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

- Except for resources that exist at the *cluster scope*

(these do not belong to a namespace)

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/namespaces.md)]
---

## Uniquely identifying a resource

- For *namespaced* resources:

the tuple *(kind, name, namespace)* needs to be unique

- For resources at the *cluster scope*:

the tuple *(kind, name)* needs to be unique

- List resource types again, and check the NAMESPACED column:
  ```bash
  kubectl api-resources
  ```

]

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/namespaces.md)]
---

## Pre-existing namespaces

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

- `default` (for our applications)

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

- `kube-public` (contains one ConfigMap for cluster discovery)

- `kube-node-lease` (in Kubernetes 1.14 and later; contains Lease objects)

- If we deploy differently, we may have different namespaces

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/namespaces.md)]
---

## Creating namespaces

- Let's see two identical methods to create a namespace

- We can use `kubectl create namespace`:
  ```bash
  kubectl create namespace blue
  ```

- Or we can construct a very minimal YAML snippet:
 ```bash
	kubectl apply -f- <<EOF
	apiVersion: v1
	kind: Namespace
	metadata:
	 name: blue
	EOF
 ```

]

- Some tools like Helm will create namespaces automatically when needed

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/namespaces.md)]
---

## Using namespaces

- We can pass a `-n` or `--namespace` flag to most `kubectl` commands:
  ```bash
  kubectl -n blue get svc
  ```

- We can also change our current *context*

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

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

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/namespaces.md)]
---

## Viewing existing contexts

- On our training environments, at this point, there should be only one context

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

]

- The current context (the only one!) is tagged with a `*`

- What are NAME, CLUSTER, AUTHINFO, and NAMESPACE?

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/namespaces.md)]
---

## What's in a context

- NAME is an arbitrary string to identify the context

- CLUSTER is a reference to a cluster

(i.e. API endpoint URL, and optional certificate)

- AUTHINFO is a reference to the authentication information to use

(i.e. a TLS client certificate, token, or otherwise)

- NAMESPACE is the namespace

(empty string = `default`)

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/namespaces.md)]
---

## Switching contexts

- We want to use a different namespace

- Solution 1: update the current context

*This is appropriate if we need to change just one thing (e.g. namespace or authentication).*

- Solution 2: create a new context and switch to it

*This is appropriate if we need to change multiple things and switch back and forth.*

- Let's go with solution 1!

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/namespaces.md)]
---

## Updating a context

- This is done through `kubectl config set-context`

- We can update a context by passing its name, or the current context with `--current`

- Update the current context to use the `blue` namespace:
  ```bash
  kubectl config set-context --current --namespace=blue
  ```

- Check the result:
  ```bash
  kubectl config get-contexts
  ```

]

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/namespaces.md)]
---

## Using our new namespace

- Let's check that we are in our new namespace, then deploy a new copy of Dockercoins

- Verify that the new context is empty:
  ```bash
  kubectl get all
  ```

]

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/namespaces.md)]
---

## Deploying DockerCoins with YAML files

- The GitHub repository `jpetazzo/kubercoins` contains everything we need!

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

- Create all the DockerCoins resources:
  ```bash
  kubectl create -f kubercoins
  ```

]

If the argument behind `-f` is a directory, all the files in that directory are processed.

The subdirectories are *not* processed, unless we also add the `-R` flag.

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/namespaces.md)]
---

## Viewing the deployed app

- Let's see if this worked correctly!

- Retrieve the port number allocated to the `webui` service:
  ```bash
  kubectl get svc webui
  ```

- Point our browser to http://X.X.X.X:3xxxx

]

If the graph shows up but stays at zero, give it a minute or two!

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/namespaces.md)]
---

## Namespaces and isolation

- Namespaces *do not* provide isolation

- A pod in the `green` namespace can communicate with a pod in the `blue` namespace

- A pod in the `default` namespace can communicate with a pod in the `kube-system` namespace

- CoreDNS uses a different subdomain for each namespace

- Example: from any pod in the cluster, you can connect to the Kubernetes API with:

`https://kubernetes.default.svc.cluster.local:443/`

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/namespaces.md)]
---

## Isolating pods

- Actual isolation is implemented with *network policies*

- Network policies are resources (like deployments, services, namespaces...)

- Network policies specify which flows are allowed:

- between pods

- from pods to the outside world

- and vice-versa

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/namespaces.md)]
---

## Switch back to the default namespace

- Let's make sure that we don't run future exercises in the `blue` namespace

- Switch back to the original context:
  ```bash
  kubectl config set-context --current --namespace=
  ```

]

Note: we could have used `--namespace=default` for the same result.

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/namespaces.md)]
---

## Switching namespaces more easily

- We can also use a little helper tool called `kubens`:

```bash
  # Switch to namespace foo
  kubens foo
  # Switch back to the previous namespace
  kubens -
  ```

- On our clusters, `kubens` is called `kns` instead

(so that it's even fewer keystrokes to switch namespaces)

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/namespaces.md)]
---

##  `kubens` and `kubectx`

- With `kubens`, we can switch quickly between namespaces

- With `kubectx`, we can switch quickly between contexts

- Both tools are simple shell scripts available from https://github.com/ahmetb/kubectx

- On our clusters, they are installed as `kns` and `kctx`

(for brevity and to avoid completion clashes between `kubectx` and `kubectl`)

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/namespaces.md)]
---

## `kube-ps1`

- It's easy to lose track of our current cluster / context / namespace

- `kube-ps1` makes it easy to track these, by showing them in our shell prompt

- It's a simple shell script available from https://github.com/jonmosco/kube-ps1

- On our clusters, `kube-ps1` is installed and included in `PS1`:
  ```
  [123.45.67.89] `(kubernetes-admin@kubernetes:default)` docker@node1 ~
  ```
  (The highlighted part is `context:namespace`, managed by `kube-ps1`)

- Highly recommended if you work across multiple contexts or namespaces!

.debug[[k8s/namespaces.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/namespaces.md)]
---

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

---

Exposing HTTP services with Ingress resources

.nav[
[Previous section](#toc-namespaces)
|
[Back to table of contents](#toc-chapter-5)
|
[Next section](#toc-kustomize)
]

---
# Exposing HTTP services with Ingress resources

- *Services* give us a way to access a pod or a set of pods

- Services can be exposed to the outside world:

- with type `NodePort` (on a port >30000)

- with type `LoadBalancer` (allocating an external load balancer)

- What about HTTP services?

- how can we expose `webui`, `rng`, `hasher`?

- the Kubernetes dashboard?

- a new version of `webui`?

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Exposing HTTP services

- If we use `NodePort` services, clients have to specify port numbers

(i.e. http://xxxxx:31234 instead of just http://xxxxx)

- `LoadBalancer` services are nice, but:

- they are not available in all environments

- they often carry an additional cost (e.g. they provision an ELB)

- they require one extra step for DNS integration
 
 (waiting for the `LoadBalancer` to be provisioned; then adding it to DNS)

- We could build our own reverse proxy

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Building a custom reverse proxy

- There are many options available:

Apache, HAProxy, Hipache, NGINX, Traefik, ...

(look at [jpetazzo/aiguillage](https://github.com/jpetazzo/aiguillage) for a minimal reverse proxy configuration using NGINX)

- Most of these options require us to update/edit configuration files after each change

- Some of them can pick up virtual hosts and backends from a configuration store

- Wouldn't it be nice if this configuration could be managed with the Kubernetes API?

- Enter.red[¹] *Ingress* resources!

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Ingress resources

- Kubernetes API resource (`kubectl get ingress`/`ingresses`/`ing`)

- Designed to expose HTTP services

- Basic features:

- load balancing
  - SSL termination
  - name-based virtual hosting

- Can also route to different services depending on:

- URI path (e.g. `/api`→`api-service`, `/static`→`assets-service`)
  - Client headers, including cookies (for A/B testing, canary deployment...)
  - and more!

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Principle of operation

- Step 1: deploy an *ingress controller*

- ingress controller = load balancer + control loop

- the control loop watches over ingress resources, and configures the LB accordingly

- Step 2: set up DNS

- associate DNS entries with the load balancer address

- Step 3: create *ingress resources*

- the ingress controller picks up these resources and configures the LB

- Step 4: profit!

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Ingress in action

- We will deploy the Traefik ingress controller

- this is an arbitrary choice

- maybe motivated by the fact that Traefik releases are named after cheeses

- For DNS, we will use [nip.io](http://nip.io/)

- `*.1.2.3.4.nip.io` resolves to `1.2.3.4`

- We will create ingress resources for various HTTP services

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Deploying pods listening on port 80

- We want our ingress load balancer to be available on port 80

- We could do that with a `LoadBalancer` service

... but it requires support from the underlying infrastructure

- We could use pods specifying `hostPort: 80`

... but with most CNI plugins, this [doesn't work or requires additional setup](https://github.com/kubernetes/kubernetes/issues/23920)

- We could use a `NodePort` service

... but that requires [changing the `--service-node-port-range` flag in the API server](https://kubernetes.io/docs/reference/command-line-tools-reference/kube-apiserver/)

- Last resort: the `hostNetwork` mode

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Without `hostNetwork`

- Normally, each pod gets its own *network namespace*

(sometimes called sandbox or network sandbox)

- An IP address is assigned to the pod

- This IP address is routed/connected to the cluster network

- All containers of that pod are sharing that network namespace

(and therefore using the same IP address)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## With `hostNetwork: true`

- No network namespace gets created

- The pod is using the network namespace of the host

- It "sees" (and can use) the interfaces (and IP addresses) of the host

- The pod can receive outside traffic directly, on any port

- Downside: with most network plugins, network policies won't work for that pod

- most network policies work at the IP address level

- filtering that pod = filtering traffic from the node

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Running Traefik

- The [Traefik documentation](https://docs.traefik.io/user-guide/kubernetes/#deploy-trfik-using-a-deployment-or-daemonset) tells us to pick between Deployment and Daemon Set

- We are going to use a Daemon Set so that each node can accept connections

- We will do two minor changes to the [YAML provided by Traefik](https://github.com/containous/traefik/blob/v1.7/examples/k8s/traefik-ds.yaml):

- enable `hostNetwork`

- add a *toleration* so that Traefik also runs on `node1`

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Taints and tolerations

- A *taint* is an attribute added to a node

- It prevents pods from running on the node

- ... Unless they have a matching *toleration*

- When deploying with `kubeadm`:

- a taint is placed on the node dedicated to the control plane

- the pods running the control plane have a matching toleration

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Checking taints on our nodes

- Check our nodes specs:
  ```bash
  kubectl get node node1 -o json | jq .spec
  kubectl get node node2 -o json | jq .spec
  ```

]

We should see a result only for `node1` (the one with the control plane):

```json
  "taints": [
    {
      "effect": "NoSchedule",
      "key": "node-role.kubernetes.io/master"
    }
  ]
```

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Understanding a taint

- The `key` can be interpreted as:

- a reservation for a special set of pods
 
 (here, this means "this node is reserved for the control plane")

- an error condition on the node
 
 (for instance: "disk full," do not start new pods here!)

- The `effect` can be:

- `NoSchedule` (don't run new pods here)

- `PreferNoSchedule` (try not to run new pods here)

- `NoExecute` (don't run new pods and evict running pods)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Checking tolerations on the control plane

- Check tolerations for CoreDNS:
  ```bash
  kubectl -n kube-system get deployments coredns -o json |
          jq .spec.template.spec.tolerations
  ```

]

The result should include:
```json
  {
    "effect": "NoSchedule",
    "key": "node-role.kubernetes.io/master"
  }
```

It means: "bypass the exact taint that we saw earlier on `node1`."

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Special tolerations

- Check tolerations on `kube-proxy`:
  ```bash
  kubectl -n kube-system get ds kube-proxy -o json | 
          jq .spec.template.spec.tolerations
  ```

]

The result should include:
```json
  {
    "operator": "Exists"
  }
```

This one is a special case that means "ignore all taints and run anyway."

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Running Traefik on our cluster

- We provide a YAML file (`k8s/traefik.yaml`) which is essentially the sum of:

- [Traefik's Daemon Set resources](https://github.com/containous/traefik/blob/v1.7/examples/k8s/traefik-ds.yaml) (patched with `hostNetwork` and tolerations)

- [Traefik's RBAC rules](https://github.com/containous/traefik/blob/v1.7/examples/k8s/traefik-rbac.yaml) allowing it to watch necessary API objects

- Apply the YAML:
  ```bash
  kubectl apply -f ~/container.training/k8s/traefik.yaml
  ```

]

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Checking that Traefik runs correctly

- If Traefik started correctly, we now have a web server listening on each node

- Check that Traefik is serving 80/tcp:
  ```bash
  curl localhost
  ```

]

We should get a `404 page not found` error.

This is normal: we haven't provided any ingress rule yet.

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Setting up DNS

- To make our lives easier, we will use [nip.io](http://nip.io)

- Check out `http://cheddar.A.B.C.D.nip.io`

(replacing A.B.C.D with the IP address of `node1`)

- We should get the same `404 page not found` error

(meaning that our DNS is "set up properly", so to speak!)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Traefik web UI

- Traefik provides a web dashboard

- With the current install method, it's listening on port 8080

- Go to `http://node1:8080` (replacing `node1` with its IP address)

]

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Setting up host-based routing ingress rules

- We are going to use `errm/cheese` images

(there are [3 tags available](https://hub.docker.com/r/errm/cheese/tags/): wensleydale, cheddar, stilton)

- These images contain a simple static HTTP server sending a picture of cheese

- We will run 3 deployments (one for each cheese)

- We will create 3 services (one for each deployment)

- Then we will create 3 ingress rules (one for each service)

- We will route `<name-of-cheese>.A.B.C.D.nip.io` to the corresponding deployment

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Running cheesy web servers

- Run all three deployments:
  ```bash
  kubectl create deployment cheddar --image=errm/cheese:cheddar
  kubectl create deployment stilton --image=errm/cheese:stilton
  kubectl create deployment wensleydale --image=errm/cheese:wensleydale
  ```

- Create a service for each of them:
  ```bash
  kubectl expose deployment cheddar --port=80
  kubectl expose deployment stilton --port=80
  kubectl expose deployment wensleydale --port=80
  ```

]

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## What does an ingress resource look like?

Here is a minimal host-based ingress resource:

```yaml
apiVersion: networking.k8s.io/v1beta1
kind: Ingress
metadata:
  name: cheddar
spec:
  rules:
  - host: cheddar.`A.B.C.D`.nip.io
    http:
      paths:
      - path: /
        backend:
          serviceName: cheddar
          servicePort: 80

```

(It is in `k8s/ingress.yaml`.)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Creating our first ingress resources

- Edit the file `~/container.training/k8s/ingress.yaml`

- Replace A.B.C.D with the IP address of `node1`

- Apply the file

- Open http://cheddar.A.B.C.D.nip.io

]

(An image of a piece of cheese should show up.)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Creating the other ingress resources

- Edit the file `~/container.training/k8s/ingress.yaml`

- Replace `cheddar` with `stilton` (in `name`, `host`, `serviceName`)

- Apply the file

- Check that `stilton.A.B.C.D.nip.io` works correctly

- Repeat for `wensleydale`

]

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Using multiple ingress controllers

- You can have multiple ingress controllers active simultaneously

(e.g. Traefik and NGINX)

- You can even have multiple instances of the same controller

(e.g. one for internal, another for external traffic)

- The `kubernetes.io/ingress.class` annotation can be used to tell which one to use

- It's OK if multiple ingress controllers configure the same resource

(it just means that the service will be accessible through multiple paths)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Ingress: the good

- The traffic flows directly from the ingress load balancer to the backends

- it doesn't need to go through the `ClusterIP`

- in fact, we don't even need a `ClusterIP` (we can use a headless service)

- The load balancer can be outside of Kubernetes

(as long as it has access to the cluster subnet)

- This allows the use of external (hardware, physical machines...) load balancers

- Annotations can encode special features

(rate-limiting, A/B testing, session stickiness, etc.)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

## Ingress: the bad

- Aforementioned "special features" are not standardized yet

- Some controllers will support them; some won't

- Even relatively common features (stripping a path prefix) can differ:

- [traefik.ingress.kubernetes.io/rule-type: PathPrefixStrip](https://docs.traefik.io/user-guide/kubernetes/#path-based-routing)

- [ingress.kubernetes.io/rewrite-target: /](https://github.com/kubernetes/contrib/tree/master/ingress/controllers/nginx/examples/rewrite)

- This should eventually stabilize

(remember that ingresses are currently `apiVersion: networking.k8s.io/v1beta1`)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/ingress.md)]
---

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

---

Kustomize

.nav[
[Previous section](#toc-exposing-http-services-with-ingress-resources)
|
[Back to table of contents](#toc-chapter-5)
|
[Next section](#toc-managing-stacks-with-helm)
]

---
# Kustomize

- Kustomize lets us transform YAML files representing Kubernetes resources

- The original YAML files are valid resource files

(e.g. they can be loaded with `kubectl apply -f`)

- They are left untouched by Kustomize

- Kustomize lets us define *overlays* that extend or change the resource files

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kustomize.md)]
---

## Differences with Helm

- Helm charts use placeholders `{{ like.this }}`

- Kustomize "bases" are standard Kubernetes YAML

- It is possible to use an existing set of YAML as a Kustomize base

- As a result, writing a Helm chart is more work ...

- ... But Helm charts are also more powerful; e.g. they can:

- use flags to conditionally include resources or blocks

- check if a given Kubernetes API group is supported

- [and much more](https://helm.sh/docs/chart_template_guide/)

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kustomize.md)]
---

## Kustomize concepts

- Kustomize needs a `kustomization.yaml` file

- That file can be a *base* or a *variant*

- If it's a *base*:

- it lists YAML resource files to use

- If it's a *variant* (or *overlay*):

- it refers to (at least) one *base*

- and some *patches*

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kustomize.md)]
---

## An easy way to get started with Kustomize

- We are going to use [Replicated Ship](https://www.replicated.com/ship/) to experiment with Kustomize

- The [Replicated Ship CLI](https://github.com/replicatedhq/ship/releases) has been installed on our clusters

- Replicated Ship has multiple workflows; here is what we will do:

- initialize a Kustomize overlay from a remote GitHub repository

- customize some values using the web UI provided by Ship

- look at the resulting files and apply them to the cluster

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kustomize.md)]
---

## Getting started with Ship

- We need to run `ship init` in a new directory

- `ship init` requires a URL to a remote repository containing Kubernetes YAML

- It will clone that repository and start a web UI

- Later, it can watch that repository and/or update from it

- We will use the [jpetazzo/kubercoins](https://github.com/jpetazzo/kubercoins) repository

(it contains all the DockerCoins resources as YAML files)

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kustomize.md)]
---

## `ship init`

- Change to a new directory:
  ```bash
  mkdir ~/kustomcoins
  cd ~/kustomcoins
  ```

- Run `ship init` with the kustomcoins repository:
  ```bash
  ship init https://github.com/jpetazzo/kubercoins
  ```

]

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kustomize.md)]
---

## Access the web UI

- `ship init` tells us to connect on `localhost:8800`

- We need to replace `localhost` with the address of our node

(since we run on a remote machine)

- Follow the steps in the web UI, and change one parameter

(e.g. set the number of replicas in the worker Deployment)

- Complete the web workflow, and go back to the CLI

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kustomize.md)]
---

## Inspect the results

- Look at the content of our directory

- `base` contains the kubercoins repository + a `kustomization.yaml` file

- `overlays/ship` contains the Kustomize overlay referencing the base + our patch(es)

- `rendered.yaml` is a YAML bundle containing the patched application

- `.ship` contains a state file used by Ship

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kustomize.md)]
---

## Using the results

- We can `kubectl apply -f rendered.yaml`

(on any version of Kubernetes)

- Starting with Kubernetes 1.14, we can apply the overlay directly with:
  ```bash
  kubectl apply -k overlays/ship
  ```

- But let's not do that for now!

- We will create a new copy of DockerCoins in another namespace

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kustomize.md)]
---

## Deploy DockerCoins with Kustomize

- Create a new namespace:
  ```bash
  kubectl create namespace kustomcoins
  ```

- Deploy DockerCoins:
  ```bash
  kubectl apply -f rendered.yaml --namespace=kustomcoins
  ```

- Or, with Kubernetes 1.14, you can also do this:
  ```bash
  kubectl apply -k overlays/ship --namespace=kustomcoins
  ```

]

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kustomize.md)]
---

## Checking our new copy of DockerCoins

- We can check the worker logs, or the web UI

- Retrieve the NodePort number of the web UI:
  ```bash
  kubectl get service webui --namespace=kustomcoins
  ```

- Open it in a web browser

- Look at the worker logs:
  ```bash
  kubectl logs deploy/worker --tail=10 --follow --namespace=kustomcoins
  ```

]

Note: it might take a minute or two for the worker to start.

.debug[[k8s/kustomize.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/kustomize.md)]
---

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

---

Managing stacks with Helm

.nav[
[Previous section](#toc-kustomize)
|
[Back to table of contents](#toc-chapter-5)
|
[Next section](#toc-network-policies)
]

---
# Managing stacks with Helm

- We created our first resources with `kubectl run`, `kubectl expose` ...

- We have also created resources by loading YAML files with `kubectl apply -f`

- For larger stacks, managing thousands of lines of YAML is unreasonable

- These YAML bundles need to be customized with variable parameters

(E.g.: number of replicas, image version to use ...)

- It would be nice to have an organized, versioned collection of bundles

- It would be nice to be able to upgrade/rollback these bundles carefully

- [Helm](https://helm.sh/) is an open source project offering all these things!

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/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[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/helm.md)]
---

## Installing Helm

- If the `helm` CLI is not installed in your environment, install it

- Check if `helm` is installed:
  ```bash
  helm
  ```

- If it's not installed, run the following command:
  ```bash
  curl https://raw.githubusercontent.com/kubernetes/helm/master/scripts/get | bash
  ```

]

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/helm.md)]
---

## Installing Tiller

- Tiller is composed of a *service* and a *deployment* in the `kube-system` namespace

- They can be managed (installed, upgraded...) with the `helm` CLI

- Deploy Tiller:
  ```bash
  helm init
  ```

]

If Tiller was already installed, don't worry: this won't break it.

At the end of the install process, you will see:

```
Happy Helming!
```

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/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[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/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[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/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[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/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 a URL to the project's home page.

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

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/helm.md)]
---

## Viewing installed charts

- Helm keeps track of what we've installed

- List installed Helm charts:
  ```bash
  helm list
  ```

]

.debug[[k8s/helm.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/helm.md)]
---
## Creating a chart

- We are going to show a way to create a *very simplified* chart

- In a real chart, *lots of things* would be templatized

(Resource names, service types, number of replicas...)

- Create a sample chart:
  ```bash
  helm create dockercoins
  ```

- Move away the sample templates and create an empty template directory:
  ```bash
  mv dockercoins/templates dockercoins/default-templates
  mkdir dockercoins/templates
  ```

]

.debug[[k8s/create-chart.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/create-chart.md)]
---

## Exporting the YAML for our application

- The following section assumes that DockerCoins is currently running

- Create one YAML file for each resource that we need:
  .small[
  ```bash

while read kind name; do
	 kubectl get -o yaml $kind $name > dockercoins/templates/$name-$kind.yaml
	done <<EOF
	deployment worker
	deployment hasher
	daemonset rng
	deployment webui
	deployment redis
	service hasher
	service rng
	service webui
	service redis
	EOF
 ```
 ]

]

.debug[[k8s/create-chart.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/create-chart.md)]
---

## Testing our helm chart

- Let's install our helm chart! (`dockercoins` is the path to the chart)
  ```
  helm install dockercoins
  ```
]

- Since the application is already deployed, this will fail: 
`Error: release loitering-otter failed: services "hasher" already exists`

- To avoid naming conflicts, we will deploy the application in another *namespace*

.debug[[k8s/create-chart.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/create-chart.md)]
---

## Switching to another namespace

- We can create a new namespace and switch to it

(Helm will automatically use the namespace specified in our context)

- We can also tell Helm which namespace to use

- Tell Helm to use a specific namespace:
  ```bash
  helm install dockercoins --namespace=magenta
  ```

]

.debug[[k8s/create-chart.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/create-chart.md)]
---

## Checking our new copy of DockerCoins

- We can check the worker logs, or the web UI

- Retrieve the NodePort number of the web UI:
  ```bash
  kubectl get service webui --namespace=magenta
  ```

- Open it in a web browser

- Look at the worker logs:
  ```bash
  kubectl logs deploy/worker --tail=10 --follow --namespace=magenta
  ```

]

Note: it might take a minute or two for the worker to start.

.debug[[k8s/create-chart.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/create-chart.md)]
---

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

---

Network policies

.nav[
[Previous section](#toc-managing-stacks-with-helm)
|
[Back to table of contents](#toc-chapter-6)
|
[Next section](#toc-authentication-and-authorization)
]

---
# Network policies

- Namespaces help us to *organize* resources

- Namespaces do not provide isolation

- By default, every pod can contact every other pod

- By default, every service accepts traffic from anyone

- If we want this to be different, we need *network policies*

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## What's a network policy?

A network policy is defined by the following things.

- A *pod selector* indicating which pods it applies to

e.g.: "all pods in namespace `blue` with the label `zone=internal`"

- A list of *ingress rules* indicating which inbound traffic is allowed

e.g.: "TCP connections to ports 8000 and 8080 coming from pods with label `zone=dmz`,
  and from the external subnet 4.42.6.0/24, except 4.42.6.5"

- A list of *egress rules* indicating which outbound traffic is allowed

A network policy can provide ingress rules, egress rules, or both.

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## How do network policies apply?

- A pod can be "selected" by any number of network policies

- If a pod isn't selected by any network policy, then its traffic is unrestricted

(In other words: in the absence of network policies, all traffic is allowed)

- If a pod is selected by at least one network policy, then all traffic is blocked ...

... unless it is explicitly allowed by one of these network policies

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## Traffic filtering is flow-oriented

- Network policies deal with *connections*, not individual packets

- Example: to allow HTTP (80/tcp) connections to pod A, you only need an ingress rule

(You do not need a matching egress rule to allow response traffic to go through)

- This also applies for UDP traffic

(Allowing DNS traffic can be done with a single rule)

- Network policy implementations use stateful connection tracking

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## Pod-to-pod traffic

- Connections from pod A to pod B have to be allowed by both pods:

- pod A has to be unrestricted, or allow the connection as an *egress* rule

- pod B has to be unrestricted, or allow the connection as an *ingress* rule

- As a consequence: if a network policy restricts traffic going from/to a pod,
 
 the restriction cannot be overridden by a network policy selecting another pod

- This prevents an entity managing network policies in namespace A
  (but without permission to do so in namespace B)
  from adding network policies giving them access to namespace B

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## The rationale for network policies

- In network security, it is generally considered better to "deny all, then allow selectively"

(The other approach, "allow all, then block selectively" makes it too easy to leave holes)

- As soon as one network policy selects a pod, the pod enters this "deny all" logic

- Further network policies can open additional access

- Good network policies should be scoped as precisely as possible

- In particular: make sure that the selector is not too broad

(Otherwise, you end up affecting pods that were otherwise well secured)

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## Our first network policy

This is our game plan:

- run a web server in a pod

- create a network policy to block all access to the web server

- create another network policy to allow access only from specific pods

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## Running our test web server

- Let's use the `nginx` image:
  ```bash
  kubectl create deployment testweb --image=nginx
  ```

- Find out the IP address of the pod with one of these two commands:
  ```bash
  kubectl get pods -o wide -l app=testweb
  IP=$(kubectl get pods -l app=testweb -o json | jq -r .items[0].status.podIP)
  ```

- Check that we can connect to the server:
  ```bash
  curl $IP
  ```
]

The `curl` command should show us the "Welcome to nginx!" page.

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## Adding a very restrictive network policy

- The policy will select pods with the label `app=testweb`

- It will specify an empty list of ingress rules (matching nothing)

- Apply the policy in this YAML file:
  ```bash
    kubectl apply -f ~/container.training/k8s/netpol-deny-all-for-testweb.yaml
  ```

- Check if we can still access the server:
  ```bash
  curl $IP
  ```

]

The `curl` command should now time out.

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## Looking at the network policy

This is the file that we applied:

```yaml
kind: NetworkPolicy
apiVersion: networking.k8s.io/v1
metadata:
  name: deny-all-for-testweb
spec:
  podSelector:
    matchLabels:
      app: testweb
  ingress: []
```

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## Allowing connections only from specific pods

- We want to allow traffic from pods with the label `run=testcurl`

- Reminder: this label is automatically applied when we do `kubectl run testcurl ...`

- Apply another policy:
  ```bash
  kubectl apply -f ~/container.training/k8s/netpol-allow-testcurl-for-testweb.yaml
  ```

]

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## Looking at the network policy

This is the second file that we applied:

```yaml
kind: NetworkPolicy
apiVersion: networking.k8s.io/v1
metadata:
  name: allow-testcurl-for-testweb
spec:
  podSelector:
    matchLabels:
      app: testweb
  ingress:
  - from:
    - podSelector:
        matchLabels:
          run: testcurl
```

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## Testing the network policy

- Let's create pods with, and without, the required label

- Try to connect to testweb from a pod with the `run=testcurl` label:
  ```bash
  kubectl run testcurl --rm -i --image=centos -- curl -m3 $IP
  ```

- Try to connect to testweb with a different label:
  ```bash
  kubectl run testkurl --rm -i --image=centos -- curl -m3 $IP
  ```

]

The first command will work (and show the "Welcome to nginx!" page).

The second command will fail and time out after 3 seconds.

(The timeout is obtained with the `-m3` option.)

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## An important warning

- Some network plugins only have partial support for network policies

- For instance, Weave added support for egress rules [in version 2.4](https://github.com/weaveworks/weave/pull/3313) (released in July 2018)

- But only recently added support for ipBlock [in version 2.5](https://github.com/weaveworks/weave/pull/3367) (released in Nov 2018)

- Unsupported features might be silently ignored

(Making you believe that you are secure, when you're not)

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## Network policies, pods, and services

- Network policies apply to *pods*

- A *service* can select multiple pods

(And load balance traffic across them)

- It is possible that we can connect to some pods, but not some others

(Because of how network policies have been defined for these pods)

- In that case, connections to the service will randomly pass or fail

(Depending on whether the connection was sent to a pod that we have access to or not)

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## Network policies and namespaces

- A good strategy is to isolate a namespace, so that:

- all the pods in the namespace can communicate together

- other namespaces cannot access the pods

- external access has to be enabled explicitly

- Let's see what this would look like for the DockerCoins app!

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## Network policies for DockerCoins

- We are going to apply two policies

- The first policy will prevent traffic from other namespaces

- The second policy will allow traffic to the `webui` pods

- That's all we need for that app!

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## Blocking traffic from other namespaces

This policy selects all pods in the current namespace.

It allows traffic only from pods in the current namespace.

(An empty `podSelector` means "all pods.")

```yaml
kind: NetworkPolicy
apiVersion: networking.k8s.io/v1
metadata:
  name: deny-from-other-namespaces
spec:
  podSelector: {}
  ingress:
  - from:
    - podSelector: {}
```

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## Allowing traffic to `webui` pods

This policy selects all pods with label `app=webui`.

It allows traffic from any source.

(An empty `from` field means "all sources.")

```yaml
kind: NetworkPolicy
apiVersion: networking.k8s.io/v1
metadata:
  name: allow-webui
spec:
  podSelector:
    matchLabels:
      app: webui
  ingress:
  - from: []
```

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## Applying both network policies

- Both network policies are declared in the file `k8s/netpol-dockercoins.yaml`

- Apply the network policies:
  ```bash
  kubectl apply -f ~/container.training/k8s/netpol-dockercoins.yaml
  ```

- Check that we can still access the web UI from outside
 
 (and that the app is still working correctly!)

- Check that we can't connect anymore to `rng` or `hasher` through their ClusterIP

]

Note: using `kubectl proxy` or `kubectl port-forward` allows us to connect
regardless of existing network policies. This allows us to debug and
troubleshoot easily, without having to poke holes in our firewall.

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## Cleaning up our network policies

- The network policies that we have installed block all traffic to the default namespace

- We should remove them, otherwise further exercises will fail!

- Remove all network policies:
  ```bash
  kubectl delete networkpolicies --all
  ```

]

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## Protecting the control plane

- Should we add network policies to block unauthorized access to the control plane?

(etcd, API server, etc.)

- At first, it seems like a good idea ...

- But it *shouldn't* be necessary:

- not all network plugins support network policies

- the control plane is secured by other methods (mutual TLS, mostly)

- the code running in our pods can reasonably expect to contact the API
 
 (and it can do so safely thanks to the API permission model)

- If we block access to the control plane, we might disrupt legitimate code

- ...Without necessarily improving security

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

## Further resources

- As always, the [Kubernetes documentation](https://kubernetes.io/docs/concepts/services-networking/network-policies/) is a good starting point

- The API documentation has a lot of detail about the format of various objects:

- [NetworkPolicy](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.12/#networkpolicy-v1-networking-k8s-io)

- [NetworkPolicySpec](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.12/#networkpolicyspec-v1-networking-k8s-io)

- [NetworkPolicyIngressRule](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.12/#networkpolicyingressrule-v1-networking-k8s-io)

- etc.

- And two resources by [Ahmet Alp Balkan](https://ahmet.im/):

- a [very good talk about network policies](https://www.youtube.com/watch?list=PLj6h78yzYM2P-3-xqvmWaZbbI1sW-ulZb&v=3gGpMmYeEO8) at KubeCon North America 2017

- a repository of [ready-to-use recipes](https://github.com/ahmetb/kubernetes-network-policy-recipes) for network policies

.debug[[k8s/netpol.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/netpol.md)]
---

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

---

Authentication and authorization

.nav[
[Previous section](#toc-network-policies)
|
[Back to table of contents](#toc-chapter-6)
|
[Next section](#toc-volumes)
]

---
# Authentication and authorization

*And first, a little refresher!*

- Authentication = verifying the identity of a person

On a UNIX system, we can authenticate with login+password, SSH keys ...

- Authorization = listing what they are allowed to do

On a UNIX system, this can include file permissions, sudoer entries ...

- Sometimes abbreviated as "authn" and "authz"

- In good modular systems, these things are decoupled

(so we can e.g. change a password or SSH key without having to reset access rights)

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Authentication in Kubernetes

- When the API server receives a request, it tries to authenticate it

(it examines headers, certificates... anything available)

- Many authentication methods are available and can be used simultaneously

(we will see them on the next slide)

- It's the job of the authentication method to produce:

- the user name
  - the user ID
  - a list of groups

- The API server doesn't interpret these; that'll be the job of *authorizers*

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Authentication methods

- TLS client certificates

(that's what we've been doing with `kubectl` so far)

- Bearer tokens

(a secret token in the HTTP headers of the request)

- [HTTP basic auth](https://en.wikipedia.org/wiki/Basic_access_authentication)

(carrying user and password in an HTTP header)

- Authentication proxy

(sitting in front of the API and setting trusted headers)

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Anonymous requests

- If any authentication method *rejects* a request, it's denied

(`401 Unauthorized` HTTP code)

- If a request is neither rejected nor accepted by anyone, it's anonymous

- the user name is `system:anonymous`

- the list of groups is `[system:unauthenticated]`

- By default, the anonymous user can't do anything

(that's what you get if you just `curl` the Kubernetes API)

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Authentication with TLS certificates

- This is enabled in most Kubernetes deployments

- The user name is derived from the `CN` in the client certificates

- The groups are derived from the `O` fields in the client certificate

- From the point of view of the Kubernetes API, users do not exist

(i.e. they are not stored in etcd or anywhere else)

- Users can be created (and added to groups) independently of the API

- The Kubernetes API can be set up to use your custom CA to validate client certs

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Viewing our admin certificate

- Let's inspect the certificate we've been using all this time!

- This command will show the `CN` and `O` fields for our certificate:
  ```bash
  kubectl config view \
          --raw \
          -o json \
          | jq -r .users[0].user[\"client-certificate-data\"] \
          | openssl base64 -d -A \
          | openssl x509 -text \
          | grep Subject:
  ```

]

Let's break down that command together! 😅

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Breaking down the command

- `kubectl config view` shows the Kubernetes user configuration
- `--raw` includes certificate information (which shows as REDACTED otherwise)
- `-o json` outputs the information in JSON format
- `| jq ...` extracts the field with the user certificate (in base64)
- `| openssl base64 -d -A` decodes the base64 format (now we have a PEM file)
- `| openssl x509 -text` parses the certificate and outputs it as plain text
- `| grep Subject:` shows us the line that interests us

→ We are user `kubernetes-admin`, in group `system:masters`.

(We will see later how and why this gives us the permissions that we have.)

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## User certificates in practice

- The Kubernetes API server does not support certificate revocation

(see issue [#18982](https://github.com/kubernetes/kubernetes/issues/18982))

- As a result, we don't have an easy way to terminate someone's access

(if their key is compromised, or they leave the organization)

- Option 1: re-create a new CA and re-issue everyone's certificates 
 
 → Maybe OK if we only have a few users; no way otherwise

- Option 2: don't use groups; grant permissions to individual users
 
 → Inconvenient if we have many users and teams; error-prone

- Option 3: issue short-lived certificates (e.g. 24 hours) and renew them often
 
 → This can be facilitated by e.g. Vault or by the Kubernetes CSR API

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Authentication with tokens

- Tokens are passed as HTTP headers:

`Authorization: Bearer and-then-here-comes-the-token`

- Tokens can be validated through a number of different methods:

- static tokens hard-coded in a file on the API server

- [bootstrap tokens](https://kubernetes.io/docs/reference/access-authn-authz/bootstrap-tokens/) (special case to create a cluster or join nodes)

- [OpenID Connect tokens](https://kubernetes.io/docs/reference/access-authn-authz/authentication/#openid-connect-tokens) (to delegate authentication to compatible OAuth2 providers)

- service accounts (these deserve more details, coming right up!)

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Service accounts

- A service account is a user that exists in the Kubernetes API

(it is visible with e.g. `kubectl get serviceaccounts`)

- Service accounts can therefore be created / updated dynamically

(they don't require hand-editing a file and restarting the API server)

- A service account is associated with a set of secrets

(the kind that you can view with `kubectl get secrets`)

- Service accounts are generally used to grant permissions to applications, services...

(as opposed to humans)

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Token authentication in practice

- We are going to list existing service accounts

- Then we will extract the token for a given service account

- And we will use that token to authenticate with the API

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Listing service accounts

- The resource name is `serviceaccount` or `sa` for short:
  ```bash
  kubectl get sa
  ```

]

There should be just one service account in the default namespace: `default`.

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Finding the secret

- List the secrets for the `default` service account:
  ```bash
  kubectl get sa default -o yaml
  SECRET=$(kubectl get sa default -o json | jq -r .secrets[0].name)
  ```

]

It should be named `default-token-XXXXX`.

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Extracting the token

- The token is stored in the secret, wrapped with base64 encoding

- View the secret:
  ```bash
  kubectl get secret $SECRET -o yaml
  ```

- Extract the token and decode it:
  ```bash
  TOKEN=$(kubectl get secret $SECRET -o json \
          | jq -r .data.token | openssl base64 -d -A)
  ```

]

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Using the token

- Let's send a request to the API, without and with the token

- Find the ClusterIP for the `kubernetes` service:
  ```bash
  kubectl get svc kubernetes
  API=$(kubectl get svc kubernetes -o json | jq -r .spec.clusterIP)
  ```

- Connect without the token:
  ```bash
  curl -k https://$API
  ```

- Connect with the token:
  ```bash
  curl -k -H "Authorization: Bearer $TOKEN" https://$API
  ```

]

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Results

- In both cases, we will get a "Forbidden" error

- Without authentication, the user is `system:anonymous`

- With authentication, it is shown as `system:serviceaccount:default:default`

- The API "sees" us as a different user

- But neither user has any rights, so we can't do nothin'

- Let's change that!

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Authorization in Kubernetes

- There are multiple ways to grant permissions in Kubernetes, called [authorizers](https://kubernetes.io/docs/reference/access-authn-authz/authorization/#authorization-modules):

- [Node Authorization](https://kubernetes.io/docs/reference/access-authn-authz/node/) (used internally by kubelet; we can ignore it)

- [Attribute-based access control](https://kubernetes.io/docs/reference/access-authn-authz/abac/) (powerful but complex and static; ignore it too)

- [Webhook](https://kubernetes.io/docs/reference/access-authn-authz/webhook/) (each API request is submitted to an external service for approval)

- [Role-based access control](https://kubernetes.io/docs/reference/access-authn-authz/rbac/) (associates permissions to users dynamically)

- The one we want is the last one, generally abbreviated as RBAC

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Role-based access control

- RBAC allows to specify fine-grained permissions

- Permissions are expressed as *rules*

- A rule is a combination of:

- [verbs](https://kubernetes.io/docs/reference/access-authn-authz/authorization/#determine-the-request-verb) like create, get, list, update, delete...

- resources (as in "API resource," like pods, nodes, services...)

- resource names (to specify e.g. one specific pod instead of all pods)

- in some case, [subresources](https://kubernetes.io/docs/reference/access-authn-authz/rbac/#referring-to-resources) (e.g. logs are subresources of pods)

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## From rules to roles to rolebindings

- A *role* is an API object containing a list of *rules*

Example: role "external-load-balancer-configurator" can:
  - [list, get] resources [endpoints, services, pods]
  - [update] resources [services]

- A *rolebinding* associates a role with a user

Example: rolebinding "external-load-balancer-configurator":
  - associates user "external-load-balancer-configurator"
  - with role "external-load-balancer-configurator"

- Yes, there can be users, roles, and rolebindings with the same name

- It's a good idea for 1-1-1 bindings; not so much for 1-N ones

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Cluster-scope permissions

- API resources Role and RoleBinding are for objects within a namespace

- We can also define API resources ClusterRole and ClusterRoleBinding

- These are a superset, allowing us to:

- specify actions on cluster-wide objects (like nodes)

- operate across all namespaces

- We can create Role and RoleBinding resources within a namespace

- ClusterRole and ClusterRoleBinding resources are global

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Pods and service accounts

- A pod can be associated with a service account

- by default, it is associated with the `default` service account

- as we saw earlier, this service account has no permissions anyway

- The associated token is exposed to the pod's filesystem

(in `/var/run/secrets/kubernetes.io/serviceaccount/token`)

- Standard Kubernetes tooling (like `kubectl`) will look for it there

- So Kubernetes tools running in a pod will automatically use the service account

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## In practice

- We are going to create a service account

- We will use a default cluster role (`view`)

- We will bind together this role and this service account

- Then we will run a pod using that service account

- In this pod, we will install `kubectl` and check our permissions

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Creating a service account

- We will call the new service account `viewer`

(note that nothing prevents us from calling it `view`, like the role)

- Create the new service account:
  ```bash
  kubectl create serviceaccount viewer
  ```

- List service accounts now:
  ```bash
  kubectl get serviceaccounts
  ```

]

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Binding a role to the service account

- Binding a role = creating a *rolebinding* object

- We will call that object `viewercanview`

(but again, we could call it `view`)

- Create the new role binding:
  ```bash
  kubectl create rolebinding viewercanview \
          --clusterrole=view \
          --serviceaccount=default:viewer
  ```

]

It's important to note a couple of details in these flags...

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Roles vs Cluster Roles

- We used `--clusterrole=view`

- What would have happened if we had used `--role=view`?

- we would have bound the role `view` from the local namespace
 (instead of the cluster role `view`)

- the command would have worked fine (no error)

- but later, our API requests would have been denied

- This is a deliberate design decision

(we can reference roles that don't exist, and create/update them later)

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Users vs Service Accounts

- We used `--serviceaccount=default:viewer`

- What would have happened if we had used `--user=default:viewer`?

- we would have bound the role to a user instead of a service account

- again, the command would have worked fine (no error)

- ...but our API requests would have been denied later

- What's about the `default:` prefix?

- that's the namespace of the service account

- yes, it could be inferred from context, but... `kubectl` requires it

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Testing

- We will run an `alpine` pod and install `kubectl` there

- Run a one-time pod:
  ```bash
  kubectl run eyepod --rm -ti --restart=Never \
          --serviceaccount=viewer \
          --image alpine
  ```

- Install `curl`, then use it to install `kubectl`:
  ```bash
  apk add --no-cache curl
  URLBASE=https://storage.googleapis.com/kubernetes-release/release
  KUBEVER=$(curl -s $URLBASE/stable.txt)
  curl -LO $URLBASE/$KUBEVER/bin/linux/amd64/kubectl
  chmod +x kubectl
  ```

]

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Running `kubectl` in the pod

- We'll try to use our `view` permissions, then to create an object

- Check that we can, indeed, view things:
  ```bash
  ./kubectl get all
  ```

- But that we can't create things:
  ```
  ./kubectl create deployment testrbac --image=nginx
  ```

- Exit the container with `exit` or `^D`

]

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Testing directly with `kubectl`

- We can also check for permission with `kubectl auth can-i`:
  ```bash
  kubectl auth can-i list nodes
  kubectl auth can-i create pods
  kubectl auth can-i get pod/name-of-pod
  kubectl auth can-i get /url-fragment-of-api-request/
  kubectl auth can-i '*' services
  ```

- And we can check permissions on behalf of other users:
 ```bash
 kubectl auth can-i list nodes \
 --as some-user
 kubectl auth can-i list nodes \
 --as system:serviceaccount:<namespace>:<name-of-service-account>
 ```

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Where does this `view` role come from?

- Kubernetes defines a number of ClusterRoles intended to be bound to users

- `cluster-admin` can do *everything* (think `root` on UNIX)

- `admin` can do *almost everything* (except e.g. changing resource quotas and limits)

- `edit` is similar to `admin`, but cannot view or edit permissions

- `view` has read-only access to most resources, except permissions and secrets

*In many situations, these roles will be all you need.*

*You can also customize them!*

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Customizing the default roles

- If you need to *add* permissions to these default roles (or others),
 
 you can do it through the [ClusterRole Aggregation](https://kubernetes.io/docs/reference/access-authn-authz/rbac/#aggregated-clusterroles) mechanism

- This happens by creating a ClusterRole with the following labels:
  ```yaml
    metadata:
      labels:
        rbac.authorization.k8s.io/aggregate-to-admin: "true"
        rbac.authorization.k8s.io/aggregate-to-edit: "true"
        rbac.authorization.k8s.io/aggregate-to-view: "true"
  ```

- This ClusterRole permissions will be added to `admin`/`edit`/`view` respectively

- This is particulary useful when using CustomResourceDefinitions

(since Kubernetes cannot guess which resources are sensitive and which ones aren't)

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Where do our permissions come from?

- When interacting with the Kubernetes API, we are using a client certificate

- We saw previously that this client certificate contained:

`CN=kubernetes-admin` and `O=system:masters`

- Let's look for these in existing ClusterRoleBindings:
  ```bash
  kubectl get clusterrolebindings -o yaml | 
    grep -e kubernetes-admin -e system:masters
  ```

(`system:masters` should show up, but not `kubernetes-admin`.)

- Where does this match come from?

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## The `system:masters` group

- If we eyeball the output of `kubectl get clusterrolebindings -o yaml`, we'll find out!

- It is in the `cluster-admin` binding:
  ```bash
  kubectl describe clusterrolebinding cluster-admin
  ```

- This binding associates `system:masters` with the cluster role `cluster-admin`

- And the `cluster-admin` is, basically, `root`:
  ```bash
  kubectl describe clusterrole cluster-admin
  ```

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

## Figuring out who can do what

- For auditing purposes, sometimes we want to know who can perform an action

- There are a few tools to help us with that

- [kubectl-who-can](https://github.com/aquasecurity/kubectl-who-can) by Aqua Security

- [Review Access (aka Rakkess)](https://github.com/corneliusweig/rakkess)

- Both are available as standalone programs, or as plugins for `kubectl`

(`kubectl` plugins can be installed and managed with `krew`)

.debug[[k8s/authn-authz.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/authn-authz.md)]
---

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

---

Volumes

.nav[
[Previous section](#toc-authentication-and-authorization)
|
[Back to table of contents](#toc-chapter-7)
|
[Next section](#toc-managing-configuration)
]

---
# Volumes

- Volumes are special directories that are mounted in containers

- Volumes can have many different purposes:

- share files and directories between containers running on the same machine

- share files and directories between containers and their host

- centralize configuration information in Kubernetes and expose it to containers

- manage credentials and secrets and expose them securely to containers

- store persistent data for stateful services

- access storage systems (like Ceph, EBS, NFS, Portworx, and many others)

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/volumes.md)]
---

## Kubernetes volumes vs. Docker volumes

- Kubernetes and Docker volumes are very similar

(the [Kubernetes documentation](https://kubernetes.io/docs/concepts/storage/volumes/) says otherwise ...
 
 but it refers to Docker 1.7, which was released in 2015!)

- Docker volumes allow us to share data between containers running on the same host

- Kubernetes volumes allow us to share data between containers in the same pod

- Both Docker and Kubernetes volumes enable access to storage systems

- Kubernetes volumes are also used to expose configuration and secrets

- Docker has specific concepts for configuration and secrets
 
 (but under the hood, the technical implementation is similar)

- If you're not familiar with Docker volumes, you can safely ignore this slide!

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/volumes.md)]
---

## Volumes ≠ Persistent Volumes

- Volumes and Persistent Volumes are related, but very different!

- *Volumes*:

- appear in Pod specifications (see next slide)

- do not exist as API resources (**cannot** do `kubectl get volumes`)

- *Persistent Volumes*:

- are API resources (**can** do `kubectl get persistentvolumes`)

- correspond to concrete volumes (e.g. on a SAN, EBS, etc.)

- cannot be associated with a Pod directly; but through a Persistent Volume Claim

- won't be discussed further in this section

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/volumes.md)]
---

## A simple volume example

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: nginx-with-volume
spec:
  volumes:
  - name: www
  containers:
  - name: nginx
    image: nginx
    volumeMounts:
    - name: www
      mountPath: /usr/share/nginx/html/
```

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/volumes.md)]
---

## A simple volume example, explained

- We define a standalone `Pod` named `nginx-with-volume`

- In that pod, there is a volume named `www`

- No type is specified, so it will default to `emptyDir`

(as the name implies, it will be initialized as an empty directory at pod creation)

- In that pod, there is also a container named `nginx`

- That container mounts the volume `www` to path `/usr/share/nginx/html/`

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/volumes.md)]
---

## A volume shared between two containers

.small[
```yaml
apiVersion: v1
kind: Pod
metadata:
  name: nginx-with-volume
spec:
  volumes:
  - name: www
  containers:
  - name: nginx
    image: nginx
    volumeMounts:
    - name: www
      mountPath: /usr/share/nginx/html/
  - name: git
    image: alpine
    command: [ "sh", "-c", "apk add --no-cache git && git clone https://github.com/octocat/Spoon-Knife /www" ]
    volumeMounts:
    - name: www
      mountPath: /www/
  restartPolicy: OnFailure
```
]

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/volumes.md)]
---

## Sharing a volume, explained

- We added another container to the pod

- That container mounts the `www` volume on a different path (`/www`)

- It uses the `alpine` image

- When started, it installs `git` and clones the `octocat/Spoon-Knife` repository

(that repository contains a tiny HTML website)

- As a result, NGINX now serves this website

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/volumes.md)]
---

## Sharing a volume, in action

- Let's try it!

- Create the pod by applying the YAML file:
  ```bash
  kubectl apply -f ~/container.training/k8s/nginx-with-volume.yaml
  ```

- Check the IP address that was allocated to our pod:
  ```bash
  kubectl get pod nginx-with-volume -o wide
  IP=$(kubectl get pod nginx-with-volume -o json | jq -r .status.podIP)
  ```

- Access the web server:
  ```bash
  curl $IP
  ```

]

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/volumes.md)]
---

## The devil is in the details

- The default `restartPolicy` is `Always`

- This would cause our `git` container to run again ... and again ... and again

(with an exponential back-off delay, as explained [in the documentation](https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle/#restart-policy))

- That's why we specified `restartPolicy: OnFailure`

- There is a short period of time during which the website is not available

(because the `git` container hasn't done its job yet)

- This could be avoided by using [Init Containers](https://kubernetes.io/docs/concepts/workloads/pods/init-containers/)

(we will see a live example in a few sections)

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/volumes.md)]
---

## Volume lifecycle

- The lifecycle of a volume is linked to the pod's lifecycle

- This means that a volume is created when the pod is created

- This is mostly relevant for `emptyDir` volumes

(other volumes, like remote storage, are not "created" but rather "attached" )

- A volume survives across container restarts

- A volume is destroyed (or, for remote storage, detached) when the pod is destroyed

.debug[[k8s/volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/volumes.md)]
---

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

---

Managing configuration

.nav[
[Previous section](#toc-volumes)
|
[Back to table of contents](#toc-chapter-7)
|
[Next section](#toc-centralized-logging)
]

---
# Managing configuration

- Some applications need to be configured (obviously!)

- There are many ways for our code to pick up configuration:

- command-line arguments

- environment variables

- configuration files

- configuration servers (getting configuration from a database, an API...)

- ... and more (because programmers can be very creative!)

- How can we do these things with containers and Kubernetes?

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Passing configuration to containers

- There are many ways to pass configuration to code running in a container:

- baking it into a custom image

- command-line arguments

- environment variables

- injecting configuration files

- exposing it over the Kubernetes API

- configuration servers

- Let's review these different strategies!

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Baking custom images

- Put the configuration in the image

(it can be in a configuration file, but also `ENV` or `CMD` actions)

- It's easy! It's simple!

- Unfortunately, it also has downsides:

- multiplication of images

- different images for dev, staging, prod ...

- minor reconfigurations require a whole build/push/pull cycle

- Avoid doing it unless you don't have the time to figure out other options

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Command-line arguments

- Pass options to `args` array in the container specification

- Example ([source](https://github.com/coreos/pods/blob/master/kubernetes.yaml#L29)): 
  ```yaml
      args: 
        - "--data-dir=/var/lib/etcd"
        - "--advertise-client-urls=http://127.0.0.1:2379"
        - "--listen-client-urls=http://127.0.0.1:2379"
        - "--listen-peer-urls=http://127.0.0.1:2380"
        - "--name=etcd"
  ```

- The options can be passed directly to the program that we run ...

... or to a wrapper script that will use them to e.g. generate a config file

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Command-line arguments, pros & cons

- Works great when options are passed directly to the running program

(otherwise, a wrapper script can work around the issue)

- Works great when there aren't too many parameters

(to avoid a 20-lines `args` array)

- Requires documentation and/or understanding of the underlying program

("which parameters and flags do I need, again?")

- Well-suited for mandatory parameters (without default values)

- Not ideal when we need to pass a real configuration file anyway

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Environment variables

- Pass options through the `env` map in the container specification

- Example:
  ```yaml
      env:
      - name: ADMIN_PORT
        value: "8080"
      - name: ADMIN_AUTH
        value: Basic
      - name: ADMIN_CRED
        value: "admin:0pensesame!"
  ```

🤔 Why this weird `{name: xxx, value: yyy}` scheme? It will be revealed soon!

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## The downward API

- In the previous example, environment variables have fixed values

- We can also use a mechanism called the *downward API*

- The downward API allows exposing pod or container information

- either through special files (we won't show that for now)

- or through environment variables

- The value of these environment variables is computed when the container is started

- Remember: environment variables won't (can't) change after container start

- Let's see a few concrete examples!

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Exposing the pod's namespace

```yaml
    - name: MY_POD_NAMESPACE
      valueFrom:
        fieldRef:
          fieldPath: metadata.namespace
```

- Useful to generate FQDN of services

(in some contexts, a short name is not enough)

- For instance, the two commands should be equivalent:
  ```
  curl api-backend
  curl api-backend.$MY_POD_NAMESPACE.svc.cluster.local
  ```

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Exposing the pod's IP address

```yaml
    - name: MY_POD_IP
      valueFrom:
        fieldRef:
          fieldPath: status.podIP
```

- Useful if we need to know our IP address

(we could also read it from `eth0`, but this is more solid)

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Exposing the container's resource limits

```yaml
    - name: MY_MEM_LIMIT
      valueFrom:
        resourceFieldRef:
          containerName: test-container
          resource: limits.memory
```

- Useful for runtimes where memory is garbage collected

- Example: the JVM

(the memory available to the JVM should be set with the `-Xmx ` flag)

- Best practice: set a memory limit, and pass it to the runtime

(see [this blog post](https://very-serio.us/2017/12/05/running-jvms-in-kubernetes/) for a detailed example)

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## More about the downward API

- [This documentation page](https://kubernetes.io/docs/tasks/inject-data-application/environment-variable-expose-pod-information/) tells more about these environment variables

- And [this one](https://kubernetes.io/docs/tasks/inject-data-application/downward-api-volume-expose-pod-information/) explains the other way to use the downward API

(through files that get created in the container filesystem)

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Environment variables, pros and cons

- Works great when the running program expects these variables

- Works great for optional parameters with reasonable defaults

(since the container image can provide these defaults)

- Sort of auto-documented

(we can see which environment variables are defined in the image, and their values)

- Can be (ab)used with longer values ...

- ... You *can* put an entire Tomcat configuration file in an environment ...

- ... But *should* you?

(Do it if you really need to, we're not judging! But we'll see better ways.)

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Injecting configuration files

- Sometimes, there is no way around it: we need to inject a full config file

- Kubernetes provides a mechanism for that purpose: `configmaps`

- A configmap is a Kubernetes resource that exists in a namespace

- Conceptually, it's a key/value map

(values are arbitrary strings)

- We can think about them in (at least) two different ways:

- as holding entire configuration file(s)

- as holding individual configuration parameters

*Note: to hold sensitive information, we can use "Secrets", which
are another type of resource behaving very much like configmaps.
We'll cover them just after!*

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Configmaps storing entire files

- In this case, each key/value pair corresponds to a configuration file

- Key = name of the file

- Value = content of the file

- There can be one key/value pair, or as many as necessary

(for complex apps with multiple configuration files)

- Examples:
  ```
  # Create a configmap with a single key, "app.conf"
  kubectl create configmap my-app-config --from-file=app.conf
  # Create a configmap with a single key, "app.conf" but another file
  kubectl create configmap my-app-config --from-file=app.conf=app-prod.conf
  # Create a configmap with multiple keys (one per file in the config.d directory)
  kubectl create configmap my-app-config --from-file=config.d/
  ```

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Configmaps storing individual parameters

- In this case, each key/value pair corresponds to a parameter

- Key = name of the parameter

- Value = value of the parameter

- Examples:
  ```
  # Create a configmap with two keys
  kubectl create cm my-app-config \
      --from-literal=foreground=red \
      --from-literal=background=blue
  
  # Create a configmap from a file containing key=val pairs
  kubectl create cm my-app-config \
      --from-env-file=app.conf
  ```

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Exposing configmaps to containers

- Configmaps can be exposed as plain files in the filesystem of a container

- this is achieved by declaring a volume and mounting it in the container

- this is particularly effective for configmaps containing whole files

- Configmaps can be exposed as environment variables in the container

- this is achieved with the downward API

- this is particularly effective for configmaps containing individual parameters

- Let's see how to do both!

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Passing a configuration file with a configmap

- We will start a load balancer powered by HAProxy

- We will use the [official `haproxy` image](https://hub.docker.com/_/haproxy/)

- It expects to find its configuration in `/usr/local/etc/haproxy/haproxy.cfg`

- We will provide a simple HAproxy configuration, `k8s/haproxy.cfg`

- It listens on port 80, and load balances connections between IBM and Google

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Creating the configmap

- Go to the `k8s` directory in the repository:
  ```bash
  cd ~/container.training/k8s
  ```

- Create a configmap named `haproxy` and holding the configuration file:
  ```bash
  kubectl create configmap haproxy --from-file=haproxy.cfg
  ```

- Check what our configmap looks like:
  ```bash
  kubectl get configmap haproxy -o yaml
  ```

]

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Using the configmap

We are going to use the following pod definition:

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: haproxy
spec:
  volumes:
  - name: config
    configMap:
      name: haproxy
  containers:
  - name: haproxy
    image: haproxy
    volumeMounts:
    - name: config
      mountPath: /usr/local/etc/haproxy/
```

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Using the configmap

- The resource definition from the previous slide is in `k8s/haproxy.yaml`

- Create the HAProxy pod:
  ```bash
  kubectl apply -f ~/container.training/k8s/haproxy.yaml
  ```

- Check the IP address allocated to the pod:
  ```bash
  kubectl get pod haproxy -o wide
  IP=$(kubectl get pod haproxy -o json | jq -r .status.podIP)
  ```

]

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Testing our load balancer

- The load balancer will send:

- half of the connections to Google

- the other half to IBM

- Access the load balancer a few times:
  ```bash
  curl $IP
  curl $IP
  curl $IP
  ```

]

We should see connections served by Google, and others served by IBM.
 
(Each server sends us a redirect page. Look at the URL that they send us to!)

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Exposing configmaps with the downward API

- We are going to run a Docker registry on a custom port

- By default, the registry listens on port 5000

- This can be changed by setting environment variable `REGISTRY_HTTP_ADDR`

- We are going to store the port number in a configmap

- Then we will expose that configmap as a container environment variable

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Creating the configmap

- Our configmap will have a single key, `http.addr`:
  ```bash
  kubectl create configmap registry --from-literal=http.addr=0.0.0.0:80
  ```

- Check our configmap:
  ```bash
  kubectl get configmap registry -o yaml
  ```

]

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Using the configmap

We are going to use the following pod definition:

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: registry
spec:
  containers:
  - name: registry
    image: registry
    env:
    - name: REGISTRY_HTTP_ADDR
      valueFrom:
        configMapKeyRef:
          name: registry
          key: http.addr
```

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Using the configmap

- The resource definition from the previous slide is in `k8s/registry.yaml`

- Create the registry pod:
  ```bash
  kubectl apply -f ~/container.training/k8s/registry.yaml
  ```

- Check the IP address allocated to the pod:
  ```bash
  kubectl get pod registry -o wide
  IP=$(kubectl get pod registry -o json | jq -r .status.podIP)
  ```

- Confirm that the registry is available on port 80:
  ```bash
  curl $IP/v2/_catalog
  ```

]

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Passwords, tokens, sensitive information

- For sensitive information, there is another special resource: *Secrets*

- Secrets and Configmaps work almost the same way

(we'll expose the differences on the next slide)

- The *intent* is different, though:

*"You should use secrets for things which are actually secret like API keys, 
  credentials, etc., and use config map for not-secret configuration data."*

*"In the future there will likely be some differentiators for secrets like rotation or support for backing the secret API w/ HSMs, etc."*

(Source: [the author of both features](https://stackoverflow.com/a/36925553/580281
))

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

## Differences between configmaps and secrets
 
- Secrets are base64-encoded when shown with `kubectl get secrets -o yaml`

- keep in mind that this is just *encoding*, not *encryption*

- it is very easy to [automatically extract and decode secrets](https://medium.com/@mveritym/decoding-kubernetes-secrets-60deed7a96a3)

- [Secrets can be encrypted at rest](https://kubernetes.io/docs/tasks/administer-cluster/encrypt-data/)

- With RBAC, we can authorize a user to access configmaps, but not secrets

(since they are two different kinds of resources)

.debug[[k8s/configuration.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/configuration.md)]
---

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

---

Centralized logging

.nav[
[Previous section](#toc-managing-configuration)
|
[Back to table of contents](#toc-chapter-7)
|
[Next section](#toc-collecting-metrics-with-prometheus)
]

---
# Centralized logging

- Using `kubectl` or `stern` is simple; but it has drawbacks:

- when a node goes down, its logs are not available anymore

- we can only dump or stream logs; we want to search/index/count...

- We want to send all our logs to a single place

- We want to parse them (e.g. for HTTP logs) and index them

- We want a nice web dashboard

- We are going to deploy an EFK stack

.debug[[k8s/logs-centralized.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/logs-centralized.md)]
---

## What is EFK?

- EFK is three components:

- ElasticSearch (to store and index log entries)

- Fluentd (to get container logs, process them, and put them in ElasticSearch)

- Kibana (to view/search log entries with a nice UI)

- The only component that we need to access from outside the cluster will be Kibana

.debug[[k8s/logs-centralized.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/logs-centralized.md)]
---

## Deploying EFK on our cluster

- We are going to use a YAML file describing all the required resources

- Load the YAML file into our cluster:
  ```bash
  kubectl apply -f ~/container.training/k8s/efk.yaml
  ```

]

If we [look at the YAML file](https://github.com/jpetazzo/container.training/blob/master/k8s/efk.yaml), we see that
it creates a daemon set, two deployments, two services,
and a few roles and role bindings (to give fluentd the required permissions).

.debug[[k8s/logs-centralized.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/logs-centralized.md)]
---

## The itinerary of a log line (before Fluentd)

- A container writes a line on stdout or stderr

- Both are typically piped to the container engine (Docker or otherwise)

- The container engine reads the line, and sends it to a logging driver

- The timestamp and stream (stdout or stderr) is added to the log line

- With the default configuration for Kubernetes, the line is written to a JSON file

(`/var/log/containers/pod-name_namespace_container-id.log`)

- That file is read when we invoke `kubectl logs`; we can access it directly too

.debug[[k8s/logs-centralized.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/logs-centralized.md)]
---

## The itinerary of a log line (with Fluentd)

- Fluentd runs on each node (thanks to a daemon set)

- It bind-mounts `/var/log/containers` from the host (to access these files)

- It continuously scans this directory for new files; reads them; parses them

- Each log line becomes a JSON object, fully annotated with extra information:
 container id, pod name, Kubernetes labels...

- These JSON objects are stored in ElasticSearch

- ElasticSearch indexes the JSON objects

- We can access the logs through Kibana (and perform searches, counts, etc.)

.debug[[k8s/logs-centralized.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/logs-centralized.md)]
---

## Accessing Kibana

- Kibana offers a web interface that is relatively straightforward

- Let's check it out!

- Check which `NodePort` was allocated to Kibana:
  ```bash
  kubectl get svc kibana
  ```

- With our web browser, connect to Kibana

]

.debug[[k8s/logs-centralized.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/logs-centralized.md)]
---

## Using Kibana

*Note: this is not a Kibana workshop! So this section is deliberately very terse.*

- The first time you connect to Kibana, you must "configure an index pattern"

- Just use the one that is suggested, `@timestamp`.red[*]

- Then click "Discover" (in the top-left corner)

- You should see container logs

- Advice: in the left column, select a few fields to display, e.g.:

`kubernetes.host`, `kubernetes.pod_name`, `stream`, `log`

.red[*]If you don't see `@timestamp`, it's probably because no logs exist yet.
 Wait a bit, and double-check the logging pipeline!

.debug[[k8s/logs-centralized.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/logs-centralized.md)]
---

## Caveat emptor

We are using EFK because it is relatively straightforward
to deploy on Kubernetes, without having to redeploy or reconfigure
our cluster. But it doesn't mean that it will always be the best
option for your use-case. If you are running Kubernetes in the
cloud, you might consider using the cloud provider's logging
infrastructure (if it can be integrated with Kubernetes).

The deployment method that we will use here has been simplified:
there is only one ElasticSearch node. In a real deployment, you
might use a cluster, both for performance and reliability reasons.
But this is outside of the scope of this chapter.

The YAML file that we used creates all the resources in the
`default` namespace, for simplicity. In a real scenario, you will
create the resources in the `kube-system` namespace or in a dedicated namespace.

.debug[[k8s/logs-centralized.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/logs-centralized.md)]
---

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

---

Collecting metrics with Prometheus

.nav[
[Previous section](#toc-centralized-logging)
|
[Back to table of contents](#toc-chapter-7)
|
[Next section](#toc-stateful-sets)
]

---
# Collecting metrics with Prometheus

- Prometheus is an open-source monitoring system including:

- multiple *service discovery* backends to figure out which metrics to collect

- a *scraper* to collect these metrics

- an efficient *time series database* to store these metrics

- a specific query language (PromQL) to query these time series

- an *alert manager* to notify us according to metrics values or trends

- We are going to use it to collect and query some metrics on our Kubernetes cluster

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Why Prometheus?

- We don't endorse Prometheus more or less than any other system

- It's relatively well integrated within the cloud-native ecosystem

- It can be self-hosted (this is useful for tutorials like this)

- It can be used for deployments of varying complexity:

- one binary and 10 lines of configuration to get started

- all the way to thousands of nodes and millions of metrics

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Exposing metrics to Prometheus

- Prometheus obtains metrics and their values by querying *exporters*

- An exporter serves metrics over HTTP, in plain text

- This is what the *node exporter* looks like:

http://demo.robustperception.io:9100/metrics

- Prometheus itself exposes its own internal metrics, too:

http://demo.robustperception.io:9090/metrics

- If you want to expose custom metrics to Prometheus:

- serve a text page like these, and you're good to go

- libraries are available in various languages to help with quantiles etc.

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## How Prometheus gets these metrics

- The *Prometheus server* will *scrape* URLs like these at regular intervals

(by default: every minute; can be more/less frequent)

- If you're worried about parsing overhead: exporters can also use protobuf

- The list of URLs to scrape (the *scrape targets*) is defined in configuration

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Defining scrape targets

This is maybe the simplest configuration file for Prometheus:
```yaml
scrape_configs:
  - job_name: 'prometheus'
    static_configs:
      - targets: ['localhost:9090']
```

- In this configuration, Prometheus collects its own internal metrics

- A typical configuration file will have multiple `scrape_configs`

- In this configuration, the list of targets is fixed

- A typical configuration file will use dynamic service discovery

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Service discovery

This configuration file will leverage existing DNS `A` records:
```yaml
scrape_configs:
  - ...
  - job_name: 'node'
    dns_sd_configs:
      - names: ['api-backends.dc-paris-2.enix.io']
        type: 'A'
        port: 9100
```

- In this configuration, Prometheus resolves the provided name(s)

(here, `api-backends.dc-paris-2.enix.io`)

- Each resulting IP address is added as a target on port 9100

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Dynamic service discovery

- In the DNS example, the names are re-resolved at regular intervals

- As DNS records are created/updated/removed, scrape targets change as well

- Existing data (previously collected metrics) is not deleted

- Other service discovery backends work in a similar fashion

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Other service discovery mechanisms

- Prometheus can connect to e.g. a cloud API to list instances

- Or to the Kubernetes API to list nodes, pods, services ...

- Or a service like Consul, Zookeeper, etcd, to list applications

- The resulting configurations files are *way more complex*

(but don't worry, we won't need to write them ourselves)

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Time series database

- We could wonder, "why do we need a specialized database?"

- One metrics data point = metrics ID + timestamp + value

- With a classic SQL or noSQL data store, that's at least 160 bits of data + indexes

- Prometheus is way more efficient, without sacrificing performance

(it will even be gentler on the I/O subsystem since it needs to write less)

- Would you like to know more? Check this video:

[Storage in Prometheus 2.0](https://www.youtube.com/watch?v=C4YV-9CrawA) by [Goutham V](https://twitter.com/putadent) at DC17EU

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Checking if Prometheus is installed

- Before trying to install Prometheus, let's check if it's already there

- Look for services with a label `app=prometheus` across all namespaces:
  ```bash
  kubectl get services --selector=app=prometheus --all-namespaces
  ```

]

If we see a `NodePort` service called `prometheus-server`, we're good!

(We can then skip to "Connecting to the Prometheus web UI".)

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Running Prometheus on our cluster

We need to:

- Run the Prometheus server in a pod

(using e.g. a Deployment to ensure that it keeps running)

- Expose the Prometheus server web UI (e.g. with a NodePort)

- Run the *node exporter* on each node (with a Daemon Set)

- Set up a Service Account so that Prometheus can query the Kubernetes API

- Configure the Prometheus server

(storing the configuration in a Config Map for easy updates)

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Helm charts to the rescue

- To make our lives easier, we are going to use a Helm chart

- The Helm chart will take care of all the steps explained above

(including some extra features that we don't need, but won't hurt)

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Step 1: install Helm

- If we already installed Helm earlier, these commands won't break anything

- Install Tiller (Helm's server-side component) on our cluster:
  ```bash
  helm init
  ```

- Give Tiller permission to deploy things on our cluster:
  ```bash
  kubectl create clusterrolebinding add-on-cluster-admin \
      --clusterrole=cluster-admin --serviceaccount=kube-system:default
  ```

]

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Step 2: install Prometheus

- Skip this if we already installed Prometheus earlier

(in doubt, check with `helm list`)

- Install Prometheus on our cluster:
  ```bash
    helm upgrade prometheus stable/prometheus \
        --install \
        --namespace kube-system \
        --set server.service.type=NodePort \
        --set server.service.nodePort=30090 \
        --set server.persistentVolume.enabled=false \
        --set alertmanager.enabled=false
  ```

]

Curious about all these flags? They're explained in the next slide.

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Explaining all the Helm flags

- `helm upgrade prometheus` → upgrade release "prometheus" to the latest version...

(a "release" is a unique name given to an app deployed with Helm)

- `stable/prometheus` → ... of the chart `prometheus` in repo `stable`

- `--install` → if the app doesn't exist, create it

- `--namespace kube-system` → put it in that specific namespace

- And set the following *values* when rendering the chart's templates:

- `server.service.type=NodePort` → expose the Prometheus server with a NodePort
  - `server.service.nodePort=30090` → set the specific NodePort number to use
  - `server.persistentVolume.enabled=false` → do not use a PersistentVolumeClaim
  - `alertmanager.enabled=false` → disable the alert manager entirely

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Connecting to the Prometheus web UI

- Let's connect to the web UI and see what we can do

- Figure out the NodePort that was allocated to the Prometheus server:
  ```bash
  kubectl get svc --all-namespaces | grep prometheus-server
  ```

- With your browser, connect to that port

]

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Querying some metrics

- This is easy... if you are familiar with PromQL

- Click on "Graph", and in "expression", paste the following:
  ```
    sum by (instance) (
      irate(
        container_cpu_usage_seconds_total{
          pod_name=~"worker.*"
          }[5m]
      )
    )
  ```

]

- Click on the blue "Execute" button and on the "Graph" tab just below

- We see the cumulated CPU usage of worker pods for each node
 
 (if we just deployed Prometheus, there won't be much data to see, though)

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Getting started with PromQL

- We can't learn PromQL in just 5 minutes

- But we can cover the basics to get an idea of what is possible

(and have some keywords and pointers)

- We are going to break down the query above

(building it one step at a time)

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Graphing one metric across all tags

This query will show us CPU usage across all containers:
```
container_cpu_usage_seconds_total
```

- The suffix of the metrics name tells us:

- the unit (seconds of CPU)

- that it's the total used since the container creation

- Since it's a "total," it is an increasing quantity

(we need to compute the derivative if we want e.g. CPU % over time)

- We see that the metrics retrieved have *tags* attached to them

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Selecting metrics with tags

This query will show us only metrics for worker containers:
```
container_cpu_usage_seconds_total{pod_name=~"worker.*"}
```

- The `=~` operator allows regex matching

- We select all the pods with a name starting with `worker`

(it would be better to use labels to select pods; more on that later)

- The result is a smaller set of containers

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Transforming counters in rates

This query will show us CPU usage % instead of total seconds used:
```
100*irate(container_cpu_usage_seconds_total{pod_name=~"worker.*"}[5m])
```

- The [`irate`](https://prometheus.io/docs/prometheus/latest/querying/functions/#irate) operator computes the "per-second instant rate of increase"

- `rate` is similar but allows decreasing counters and negative values

- with `irate`, if a counter goes back to zero, we don't get a negative spike

- The `[5m]` tells how far to look back if there is a gap in the data

- And we multiply with `100*` to get CPU % usage

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Aggregation operators

This query sums the CPU usage per node:
```
sum by (instance) (
  irate(container_cpu_usage_seconds_total{pod_name=~"worker.*"}[5m])
)
```

- `instance` corresponds to the node on which the container is running

- `sum by (instance) (...)` computes the sum for each instance

- Note: all the other tags are collapsed

(in other words, the resulting graph only shows the `instance` tag)

- PromQL supports many more [aggregation operators](https://prometheus.io/docs/prometheus/latest/querying/operators/#aggregation-operators)

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## What kind of metrics can we collect?

- Node metrics (related to physical or virtual machines)

- Container metrics (resource usage per container)

- Databases, message queues, load balancers, ...

(check out this [list of exporters](https://prometheus.io/docs/instrumenting/exporters/)!)

- Instrumentation (=deluxe `printf` for our code)

- Business metrics (customers served, revenue, ...)

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Node metrics

- CPU, RAM, disk usage on the whole node

- Total number of processes running, and their states

- Number of open files, sockets, and their states

- I/O activity (disk, network), per operation or volume

- Physical/hardware (when applicable): temperature, fan speed...

- ...and much more!

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Container metrics

- Similar to node metrics, but not totally identical

- RAM breakdown will be different

- active vs inactive memory
  - some memory is *shared* between containers, and specially accounted for

- I/O activity is also harder to track

- async writes can cause deferred "charges"
  - some page-ins are also shared between containers

For details about container metrics, see:
 
http://jpetazzo.github.io/2013/10/08/docker-containers-metrics/

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Application metrics

- Arbitrary metrics related to your application and business

- System performance: request latency, error rate...

- Volume information: number of rows in database, message queue size...

- Business data: inventory, items sold, revenue...

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Detecting scrape targets

- Prometheus can leverage Kubernetes service discovery

(with proper configuration)

- Services or pods can be annotated with:

- `prometheus.io/scrape: true` to enable scraping
  - `prometheus.io/port: 9090` to indicate the port number
  - `prometheus.io/path: /metrics` to indicate the URI (`/metrics` by default)

- Prometheus will detect and scrape these (without needing a restart or reload)

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Querying labels

- What if we want to get metrics for containers belonging to a pod tagged `worker`?

- The cAdvisor exporter does not give us Kubernetes labels

- Kubernetes labels are exposed through another exporter

- We can see Kubernetes labels through metrics `kube_pod_labels`

(each container appears as a time series with constant value of `1`)

- Prometheus *kind of* supports "joins" between time series

- But only if the names of the tags match exactly

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## Unfortunately ...

- The cAdvisor exporter uses tag `pod_name` for the name of a pod

- The Kubernetes service endpoints exporter uses tag `pod` instead

- See [this blog post](https://www.robustperception.io/exposing-the-software-version-to-prometheus) or [this other one](https://www.weave.works/blog/aggregating-pod-resource-cpu-memory-usage-arbitrary-labels-prometheus/) to see how to perform "joins"

- Alas, Prometheus cannot "join" time series with different labels

(see [Prometheus issue #2204](https://github.com/prometheus/prometheus/issues/2204) for the rationale)

- There is a workaround involving relabeling, but it's "not cheap"

- see [this comment](https://github.com/prometheus/prometheus/issues/2204#issuecomment-261515520) for an overview

- or [this blog post](https://5pi.de/2017/11/09/use-prometheus-vector-matching-to-get-kubernetes-utilization-across-any-pod-label/) for a complete description of the process

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

## In practice

- Grafana is a beautiful (and useful) frontend to display all kinds of graphs

- Not everyone needs to know Prometheus, PromQL, Grafana, etc.

- But in a team, it is valuable to have at least one person who know them

- That person can set up queries and dashboards for the rest of the team

- It's a little bit like knowing how to optimize SQL queries, Dockerfiles...

Don't panic if you don't know these tools!

...But make sure at least one person in your team is on it 💯

.debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/prometheus.md)]
---

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

---

Stateful sets

.nav[
[Previous section](#toc-collecting-metrics-with-prometheus)
|
[Back to table of contents](#toc-chapter-8)
|
[Next section](#toc-running-a-consul-cluster)
]

---
# Stateful sets

- Stateful sets are a type of resource in the Kubernetes API

(like pods, deployments, services...)

- They offer mechanisms to deploy scaled stateful applications

- At a first glance, they look like *deployments*:

- a stateful set defines a pod spec and a number of replicas *R*

- it will make sure that *R* copies of the pod are running

- that number can be changed while the stateful set is running

- updating the pod spec will cause a rolling update to happen

- But they also have some significant differences

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## Stateful sets unique features

- Pods in a stateful set are numbered (from 0 to *R-1*) and ordered

- They are started and updated in order (from 0 to *R-1*)

- A pod is started (or updated) only when the previous one is ready

- They are stopped in reverse order (from *R-1* to 0)

- Each pod know its identity (i.e. which number it is in the set)

- Each pod can discover the IP address of the others easily

- The pods can persist data on attached volumes

🤔 Wait a minute ... Can't we already attach volumes to pods and deployments?

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## Revisiting volumes

- [Volumes](https://kubernetes.io/docs/concepts/storage/volumes/) are used for many purposes:

- sharing data between containers in a pod

- exposing configuration information and secrets to containers

- accessing storage systems

- Let's see examples of the latter usage

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## Volumes types

- There are many [types of volumes](https://kubernetes.io/docs/concepts/storage/volumes/#types-of-volumes) available:

- public cloud storage (GCEPersistentDisk, AWSElasticBlockStore, AzureDisk...)

- private cloud storage (Cinder, VsphereVolume...)

- traditional storage systems (NFS, iSCSI, FC...)

- distributed storage (Ceph, Glusterfs, Portworx...)

- Using a persistent volume requires:

- creating the volume out-of-band (outside of the Kubernetes API)

- referencing the volume in the pod description, with all its parameters

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## Using a cloud volume

Here is a pod definition using an AWS EBS volume (that has to be created first):

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: pod-using-my-ebs-volume
spec:
  containers:
  - image: ...
    name: container-using-my-ebs-volume
    volumeMounts:
    - mountPath: /my-ebs
      name: my-ebs-volume
  volumes:
  - name: my-ebs-volume
    awsElasticBlockStore:
      volumeID: vol-049df61146c4d7901
      fsType: ext4
```

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## Using an NFS volume

Here is another example using a volume on an NFS server:

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: pod-using-my-nfs-volume
spec:
  containers:
  - image: ...
    name: container-using-my-nfs-volume
    volumeMounts:
    - mountPath: /my-nfs
      name: my-nfs-volume
  volumes:
  - name: my-nfs-volume
      nfs:
        server: 192.168.0.55
        path: "/exports/assets"
```

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## Shortcomings of volumes

- Their lifecycle (creation, deletion...) is managed outside of the Kubernetes API

(we can't just use `kubectl apply/create/delete/...` to manage them)

- If a Deployment uses a volume, all replicas end up using the same volume

- That volume must then support concurrent access

- some volumes do (e.g. NFS servers support multiple read/write access)

- some volumes support concurrent reads

- some volumes support concurrent access for colocated pods

- What we really need is a way for each replica to have its own volume

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## Persistent Volume Claims

- To abstract the different types of storage, a pod can use a special volume type

- This type is a *Persistent Volume Claim*

- A Persistent Volume Claim (PVC) is a resource type

(visible with `kubectl get persistentvolumeclaims` or `kubectl get pvc`)

- A PVC is not a volume; it is a *request for a volume*

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## Persistent Volume Claims in practice

- Using a Persistent Volume Claim is a two-step process:

- creating the claim

- using the claim in a pod (as if it were any other kind of volume)

- A PVC starts by being Unbound (without an associated volume)

- Once it is associated with a Persistent Volume, it becomes Bound

- A Pod referring an unbound PVC will not start

(but as soon as the PVC is bound, the Pod can start)

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## Binding PV and PVC

- A Kubernetes controller continuously watches PV and PVC objects

- When it notices an unbound PVC, it tries to find a satisfactory PV

("satisfactory" in terms of size and other characteristics; see next slide)

- If no PV fits the PVC, a PV can be created dynamically

(this requires to configure a *dynamic provisioner*, more on that later)

- Otherwise, the PVC remains unbound indefinitely

(until we manually create a PV or setup dynamic provisioning)

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## What's in a Persistent Volume Claim?

- At the very least, the claim should indicate:

- the size of the volume (e.g. "5 GiB")

- the access mode (e.g. "read-write by a single pod")

- Optionally, it can also specify a Storage Class

- The Storage Class indicates:

- which storage system to use (e.g. Portworx, EBS...)

- extra parameters for that storage system

e.g.: "replicate the data 3 times, and use SSD media"

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## What's a Storage Class?

- A Storage Class is yet another Kubernetes API resource

(visible with e.g. `kubectl get storageclass` or `kubectl get sc`)

- It indicates which *provisioner* to use

(which controller will create the actual volume)

- And arbitrary parameters for that provisioner

(replication levels, type of disk ... anything relevant!)

- Storage Classes are required if we want to use [dynamic provisioning](https://kubernetes.io/docs/concepts/storage/dynamic-provisioning/)

(but we can also create volumes manually, and ignore Storage Classes)

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## Defining a Persistent Volume Claim

Here is a minimal PVC:

```yaml
kind: PersistentVolumeClaim
apiVersion: v1
metadata:
   name: my-claim
spec:
   accessModes:
     - ReadWriteOnce
   resources:
     requests:
       storage: 1Gi
```

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## Using a Persistent Volume Claim

Here is a Pod definition like the ones shown earlier, but using a PVC:

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: pod-using-a-claim
spec:
  containers:
  - image: ...
    name: container-using-a-claim
    volumeMounts:
    - mountPath: /my-vol
      name: my-volume
  volumes:
  - name: my-volume
    persistentVolumeClaim:
      claimName: my-claim
```

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## Persistent Volume Claims and Stateful sets

- The pods in a stateful set can define a `volumeClaimTemplate`

- A `volumeClaimTemplate` will dynamically create one Persistent Volume Claim per pod

- Each pod will therefore have its own volume

- These volumes are numbered (like the pods)

- When updating the stateful set (e.g. image upgrade), each pod keeps its volume

- When pods get rescheduled (e.g. node failure), they keep their volume

(this requires a storage system that is not node-local)

- These volumes are not automatically deleted

(when the stateful set is scaled down or deleted)

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## Stateful set recap

- A Stateful sets manages a number of identical pods

(like a Deployment)

- These pods are numbered, and started/upgraded/stopped in a specific order

- These pods are aware of their number

(e.g., #0 can decide to be the primary, and #1 can be secondary)

- These pods can find the IP addresses of the other pods in the set

(through a *headless service*)

- These pods can each have their own persistent storage

(Deployments cannot do that)

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

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

---

Running a Consul cluster

.nav[
[Previous section](#toc-stateful-sets)
|
[Back to table of contents](#toc-chapter-8)
|
[Next section](#toc-local-persistent-volumes)
]

---

# Running a Consul cluster

- Here is a good use-case for Stateful sets!

- We are going to deploy a Consul cluster with 3 nodes

- Consul is a highly-available key/value store

(like etcd or Zookeeper)

- One easy way to bootstrap a cluster is to tell each node:

- the addresses of other nodes

- how many nodes are expected (to know when quorum is reached)

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## Bootstrapping a Consul cluster

*After reading the Consul documentation carefully (and/or asking around),
we figure out the minimal command-line to run our Consul cluster.*

```
consul agent -data-dir=/consul/data -client=0.0.0.0 -server -ui \
       -bootstrap-expect=3 \
       -retry-join=`X.X.X.X` \
       -retry-join=`Y.Y.Y.Y`
```

- Replace X.X.X.X and Y.Y.Y.Y with the addresses of other nodes

- The same command-line can be used on all nodes (convenient!)

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## Cloud Auto-join

- Since version 1.4.0, Consul can use the Kubernetes API to find its peers

- This is called [Cloud Auto-join]

- Instead of passing an IP address, we need to pass a parameter like this:

```
  consul agent -retry-join "provider=k8s label_selector=\"app=consul\""
  ```

- Consul needs to be able to talk to the Kubernetes API

- We can provide a `kubeconfig` file

- If Consul runs in a pod, it will use the *service account* of the pod

[Cloud Auto-join]: https://www.consul.io/docs/agent/cloud-auto-join.html#kubernetes-k8s-

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## Setting up Cloud auto-join

- We need to create a service account for Consul

- We need to create a role that can `list` and `get` pods

- We need to bind that role to the service account

- And of course, we need to make sure that Consul pods use that service account

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## Putting it all together

- The file `k8s/consul.yaml` defines the required resources

(service account, cluster role, cluster role binding, service, stateful set)

- It has a few extra touches:

- a `podAntiAffinity` prevents two pods from running on the same node

- a `preStop` hook makes the pod leave the cluster when shutdown gracefully

This was inspired by this [excellent tutorial](https://github.com/kelseyhightower/consul-on-kubernetes) by Kelsey Hightower.
Some features from the original tutorial (TLS authentication between
nodes and encryption of gossip traffic) were removed for simplicity.

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## Running our Consul cluster

- We'll use the provided YAML file

- Create the stateful set and associated service:
  ```bash
  kubectl apply -f ~/container.training/k8s/consul.yaml
  ```

- Check the logs as the pods come up one after another:
  ```bash
  stern consul
  ```

- Check the health of the cluster:
  ```bash
  kubectl exec consul-0 consul members
  ```

]

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## Caveats

- We haven't used a `volumeClaimTemplate` here

- That's because we don't have a storage provider yet

(except if you're running this on your own and your cluster has one)

- What happens if we lose a pod?

- a new pod gets rescheduled (with an empty state)

- the new pod tries to connect to the two others

- it will be accepted (after 1-2 minutes of instability)

- and it will retrieve the data from the other pods

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

## Failure modes

- What happens if we lose two pods?

- manual repair will be required

- we will need to instruct the remaining one to act solo

- then rejoin new pods

- What happens if we lose three pods? (aka all of them)

- we lose all the data (ouch)

- If we run Consul without persistent storage, backups are a good idea!

.debug[[k8s/statefulsets.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/statefulsets.md)]
---

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

---

Local Persistent Volumes

.nav[
[Previous section](#toc-running-a-consul-cluster)
|
[Back to table of contents](#toc-chapter-8)
|
[Next section](#toc-highly-available-persistent-volumes)
]

---
# Local Persistent Volumes

- We want to run that Consul cluster *and* actually persist data

- But we don't have a distributed storage system

- We are going to use local volumes instead

(similar conceptually to `hostPath` volumes)

- We can use local volumes without installing extra plugins

- However, they are tied to a node

- If that node goes down, the volume becomes unavailable

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/local-persistent-volumes.md)]
---

## With or without dynamic provisioning

- We will deploy a Consul cluster *with* persistence

- That cluster's StatefulSet will create PVCs

- These PVCs will remain unbound¹, until we will create local volumes manually

(we will basically do the job of the dynamic provisioner)

- Then, we will see how to automate that with a dynamic provisioner

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/local-persistent-volumes.md)]
---

## If we have a dynamic provisioner ...

- The labs in this section assume that we *do not* have a dynamic provisioner

- If we do have one, we need to disable it

- Check if we have a dynamic provisioner:
  ```bash
  kubectl get storageclass
  ```

- If the output contains a line with `(default)`, run this command:
  ```bash
  kubectl annotate sc storageclass.kubernetes.io/is-default-class- --all
  ```

- Check again that it is no longer marked as `(default)`

]

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/local-persistent-volumes.md)]
---

## Work in a separate namespace

- To avoid conflicts with existing resources, let's create and use a new namespace

- Create a new namespace:
  ```bash
  kubectl create namespace orange
  ```

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

]

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/local-persistent-volumes.md)]
---

## Deploying Consul

- We will use a slightly different YAML file

- The only differences between that file and the previous one are:

- `volumeClaimTemplate` defined in the Stateful Set spec

- the corresponding `volumeMounts` in the Pod spec

- the namespace `orange` used for discovery of Pods

- Apply the persistent Consul YAML file:
  ```bash
  kubectl apply -f ~/container.training/k8s/persistent-consul.yaml
  ```

]

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/local-persistent-volumes.md)]
---

## Observing the situation

- Let's look at Persistent Volume Claims and Pods

- Check that we now have an unbound Persistent Volume Claim:
  ```bash
  kubectl get pvc
  ```

- We don't have any Persistent Volume:
  ```bash
  kubectl get pv
  ```

- The Pod `consul-0` is not scheduled yet:
  ```bash
  kubectl get pods -o wide
  ```

]

*Hint: leave these commands running with `-w` in different windows.*

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/local-persistent-volumes.md)]
---

## Explanations

- In a Stateful Set, the Pods are started one by one

- `consul-1` won't be created until `consul-0` is running

- `consul-0` has a dependency on an unbound Persistent Volume Claim

- The scheduler won't schedule the Pod until the PVC is bound

(because the PVC might be bound to a volume that is only available on a subset of nodes; for instance EBS are tied to an availability zone)

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/local-persistent-volumes.md)]
---

## Creating Persistent Volumes

- Let's create 3 local directories (`/mnt/consul`) on node2, node3, node4

- Then create 3 Persistent Volumes corresponding to these directories

- Create the local directories:
  ```bash
    for NODE in node2 node3 node4; do
      ssh $NODE sudo mkdir -p /mnt/consul
    done
  ```

- Create the PV objects:
  ```bash
  kubectl apply -f ~/container.training/k8s/volumes-for-consul.yaml
  ```

]

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/local-persistent-volumes.md)]
---

## Check our Consul cluster

- The PVs that we created will be automatically matched with the PVCs

- Once a PVC is bound, its pod can start normally

- Once the pod `consul-0` has started, `consul-1` can be created, etc.

- Eventually, our Consul cluster is up, and backend by "persistent" volumes

- Check that our Consul clusters has 3 members indeed:
  ```bash
  kubectl exec consul-0 consul members
  ```

]

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/local-persistent-volumes.md)]
---

## Devil is in the details (1/2)

- The size of the Persistent Volumes is bogus

(it is used when matching PVs and PVCs together, but there is no actual quota or limit)

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/local-persistent-volumes.md)]
---

## Devil is in the details (2/2)

- This specific example worked because we had exactly 1 free PV per node:

- if we had created multiple PVs per node ...

- we could have ended with two PVCs bound to PVs on the same node ...

- which would have required two pods to be on the same node ...

- which is forbidden by the anti-affinity constraints in the StatefulSet

- To avoid that, we need to associated the PVs with a Storage Class that has:
  ```yaml
  volumeBindingMode: WaitForFirstConsumer
  ```
  (this means that a PVC will be bound to a PV only after being used by a Pod)

- See [this blog post](https://kubernetes.io/blog/2018/04/13/local-persistent-volumes-beta/) for more details

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/local-persistent-volumes.md)]
---

## Bulk provisioning

- It's not practical to manually create directories and PVs for each app

- We *could* pre-provision a number of PVs across our fleet

- We could even automate that with a Daemon Set:

- creating a number of directories on each node

- creating the corresponding PV objects

- We also need to recycle volumes

- ... This can quickly get out of hand

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/local-persistent-volumes.md)]
---

## Dynamic provisioning

- We could also write our own provisioner, which would:

- watch the PVCs across all namespaces

- when a PVC is created, create a corresponding PV on a node

- Or we could use one of the dynamic provisioners for local persistent volumes

(for instance the [Rancher local path provisioner](https://github.com/rancher/local-path-provisioner))

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/local-persistent-volumes.md)]
---

## Strategies for local persistent volumes

- Remember, when a node goes down, the volumes on that node become unavailable

- High availability will require another layer of replication

(like what we've just seen with Consul; or primary/secondary; etc)

- Pre-provisioning PVs makes sense for machines with local storage

(e.g. cloud instance storage; or storage directly attached to a physical machine)

- Dynamic provisioning makes sense for large number of applications

(when we can't or won't dedicate a whole disk to a volume)

- It's possible to mix both (using distinct Storage Classes)

.debug[[k8s/local-persistent-volumes.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/local-persistent-volumes.md)]
---

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

---

Highly available Persistent Volumes

.nav[
[Previous section](#toc-local-persistent-volumes)
|
[Back to table of contents](#toc-chapter-8)
|
[Next section](#toc-next-steps)
]

---
# Highly available Persistent Volumes

- How can we achieve true durability?

- How can we store data that would survive the loss of a node?

- We need to use Persistent Volumes backed by highly available storage systems

- There are many ways to achieve that:

- leveraging our cloud's storage APIs

- using NAS/SAN systems or file servers

- distributed storage systems

- We are going to see one distributed storage system in action

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Our test scenario

- We will set up a distributed storage system on our cluster

- We will use it to deploy a SQL database (PostgreSQL)

- We will insert some test data in the database

- We will disrupt the node running the database

- We will see how it recovers

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Portworx

- Portworx is a *commercial* persistent storage solution for containers

- It works with Kubernetes, but also Mesos, Swarm ...

- It provides [hyper-converged](https://en.wikipedia.org/wiki/Hyper-converged_infrastructure) storage

(=storage is provided by regular compute nodes)

- We're going to use it here because it can be deployed on any Kubernetes cluster

(it doesn't require any particular infrastructure)

- We don't endorse or support Portworx in any particular way

(but we appreciate that it's super easy to install!)

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## A useful reminder

- We're installing Portworx because we need a storage system

- If you are using AKS, EKS, GKE ... you already have a storage system

(but you might want another one, e.g. to leverage local storage)

- If you have setup Kubernetes yourself, there are other solutions available too

- on premises, you can use a good old SAN/NAS

- on a private cloud like OpenStack, you can use e.g. Cinder

- everywhere, you can use other systems, e.g. Gluster, StorageOS

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Portworx requirements

- Kubernetes cluster ✔️

- Optional key/value store (etcd or Consul) ❌

- At least one available block device ❌

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## The key-value store

- In the current version of Portworx (1.4) it is recommended to use etcd or Consul

- But Portworx also has beta support for an embedded key/value store

- For simplicity, we are going to use the latter option

(but if we have deployed Consul or etcd, we can use that, too)

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## One available block device

- Block device = disk or partition on a disk

- We can see block devices with `lsblk`

(or `cat /proc/partitions` if we're old school like that!)

- If we don't have a spare disk or partition, we can use a *loop device*

- A loop device is a block device actually backed by a file

- These are frequently used to mount ISO (CD/DVD) images or VM disk images

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Setting up a loop device

- We are going to create a 10 GB (empty) file on each node

- Then make a loop device from it, to be used by Portworx

- Create a 10 GB file on each node:
  ```bash
  for N in $(seq 1 4); do ssh node$N sudo truncate --size 10G /portworx.blk; done
  ```
  (If SSH asks to confirm host keys, enter `yes` each time.)

- Associate the file to a loop device on each node:
  ```bash
  for N in $(seq 1 4); do ssh node$N sudo losetup /dev/loop4 /portworx.blk; done
  ```

]

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Installing Portworx

- To install Portworx, we need to go to https://install.portworx.com/

- This website will ask us a bunch of questions about our cluster

- Then, it will generate a YAML file that we should apply to our cluster

- Or, we can just apply that YAML file directly (it's in `k8s/portworx.yaml`)

- Install Portworx:
  ```bash
  kubectl apply -f ~/container.training/k8s/portworx.yaml
  ```

]

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Generating a custom YAML file

If you want to generate a YAML file tailored to your own needs, the easiest
way is to use https://install.portworx.com/.

FYI, this is how we obtained the YAML file used earlier:
```
KBVER=$(kubectl version -o json | jq -r .serverVersion.gitVersion)
BLKDEV=/dev/loop4
curl https://install.portworx.com/1.4/?kbver=$KBVER&b=true&s=$BLKDEV&c=px-workshop&stork=true&lh=true
```
If you want to use an external key/value store, add one of the following:
```
&k=etcd://`XXX`:2379
&k=consul://`XXX`:8500
```
... where `XXX` is the name or address of your etcd or Consul server.

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Waiting for Portworx to be ready

- The installation process will take a few minutes

- Check out the logs:
  ```bash
  stern -n kube-system portworx
  ```

- Wait until it gets quiet

(you should see `portworx service is healthy`, too)

]

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Dynamic provisioning of persistent volumes

- We are going to run PostgreSQL in a Stateful set

- The Stateful set will specify a `volumeClaimTemplate`

- That `volumeClaimTemplate` will create Persistent Volume Claims

- Kubernetes' [dynamic provisioning](https://kubernetes.io/docs/concepts/storage/dynamic-provisioning/) will satisfy these Persistent Volume Claims

(by creating Persistent Volumes and binding them to the claims)

- The Persistent Volumes are then available for the PostgreSQL pods

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Storage Classes

- It's possible that multiple storage systems are available

- Or, that a storage system offers multiple tiers of storage

(SSD vs. magnetic; mirrored or not; etc.)

- We need to tell Kubernetes *which* system and tier to use

- This is achieved by creating a Storage Class

- A `volumeClaimTemplate` can indicate which Storage Class to use

- It is also possible to mark a Storage Class as "default"

(it will be used if a `volumeClaimTemplate` doesn't specify one)

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Our default Storage Class

This is our Storage Class (in `k8s/storage-class.yaml`):

```yaml
kind: StorageClass
apiVersion: storage.k8s.io/v1beta1
metadata:
  name: portworx-replicated
  annotations:
    storageclass.kubernetes.io/is-default-class: "true"
provisioner: kubernetes.io/portworx-volume
parameters:
 repl: "2"
 priority_io: "high"
```

- It says "use Portworx to create volumes"

- It tells Portworx to "keep 2 replicas of these volumes"

- It marks the Storage Class as being the default one

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Creating our Storage Class

- Let's apply that YAML file!

- Create the Storage Class:
  ```bash
  kubectl apply -f ~/container.training/k8s/storage-class.yaml
  ```

- Check that it is now available:
  ```bash
  kubectl get sc
  ```

]

It should show as `portworx-replicated (default)`.

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Our Postgres Stateful set

- The next slide shows `k8s/postgres.yaml`

- It defines a Stateful set

- With a `volumeClaimTemplate` requesting a 1 GB volume

- That volume will be mounted to `/var/lib/postgresql/data`

- There is another little detail: we enable the `stork` scheduler

- The `stork` scheduler is optional (it's specific to Portworx)

- It helps the Kubernetes scheduler to colocate the pod with its volume

(see [this blog post](https://portworx.com/stork-storage-orchestration-kubernetes/) for more details about that)

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

.small[
```yaml
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: postgres
spec:
  selector:
    matchLabels:
      app: postgres
  serviceName: postgres
  template:
    metadata:
      labels:
        app: postgres
    spec:
      schedulerName: stork
      containers:
      - name: postgres
        image: postgres:10.5
        volumeMounts:
        - mountPath: /var/lib/postgresql/data
          name: postgres
  volumeClaimTemplates:
  - metadata:
      name: postgres
    spec:
      accessModes: ["ReadWriteOnce"]
      resources:
        requests:
          storage: 1Gi
```
]

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Creating the Stateful set

- Before applying the YAML, watch what's going on with `kubectl get events -w`

- Apply that YAML:
  ```bash
  kubectl apply -f ~/container.training/k8s/postgres.yaml
  ```

]

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Testing our PostgreSQL pod

- We will use `kubectl exec` to get a shell in the pod

- Good to know: we need to use the `postgres` user in the pod

- Get a shell in the pod, as the `postgres` user:
  ```bash
  kubectl exec -ti postgres-0 su postgres
  ```

- Check that default databases have been created correctly:
  ```bash
  psql -l
  ```

]

(This should show us 3 lines: postgres, template0, and template1.)

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Inserting data in PostgreSQL

- We will create a database and populate it with `pgbench`

- Create a database named `demo`:
  ```bash
  createdb demo
  ```

- Populate it with `pgbench`:
  ```bash
  pgbench -i -s 10 demo
  ```

]

- The `-i` flag means "create tables"

- The `-s 10` flag means "create 10 x 100,000 rows"

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Checking how much data we have now

- The `pgbench` tool inserts rows in table `pgbench_accounts`

- Check that the `demo` base exists:
  ```bash
  psql -l
  ```

- Check how many rows we have in `pgbench_accounts`:
  ```bash
  psql demo -c "select count(*) from pgbench_accounts"
  ```

]

(We should see a count of 1,000,000 rows.)

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Find out which node is hosting the database

- We can find that information with `kubectl get pods -o wide`

- Check the node running the database:
  ```bash
  kubectl get pod postgres-0 -o wide
  ```

]

We are going to disrupt that node.

By "disrupt" we mean: "disconnect it from the network".

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Disconnect the node

- We will use `iptables` to block all traffic exiting the node

(except SSH traffic, so we can repair the node later if needed)

- SSH to the node to disrupt:
  ```bash
  ssh `nodeX`
  ```

- Allow SSH traffic leaving the node, but block all other traffic:
  ```bash
  sudo iptables -I OUTPUT -p tcp --sport 22 -j ACCEPT
  sudo iptables -I OUTPUT 2 -j DROP
  ```

]

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Check that the node is disconnected

- Check that the node can't communicate with other nodes:
  ```
  ping node1
  ```

- Logout to go back on `node1`

- Watch the events unfolding with `kubectl get events -w` and `kubectl get pods -w`

]

- It will take some time for Kubernetes to mark the node as unhealthy

- Then it will attempt to reschedule the pod to another node

- In about a minute, our pod should be up and running again

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Check that our data is still available

- We are going to reconnect to the (new) pod and check

- Get a shell on the pod:
  ```bash
  kubectl exec -ti postgres-0 su postgres
  ```

- Check the number of rows in the `pgbench_accounts` table:
  ```bash
  psql demo -c "select count(*) from pgbench_accounts"
  ```

]

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Double-check that the pod has really moved

- Just to make sure the system is not bluffing!

- Look at which node the pod is now running on
  ```bash
  kubectl get pod postgres-0 -o wide
  ```

]

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Re-enable the node

- Let's fix the node that we disconnected from the network

- SSH to the node:
  ```bash
  ssh `nodeX`
  ```

- Remove the iptables rule blocking traffic:
  ```bash
  sudo iptables -D OUTPUT 2
  ```

]

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## A few words about this PostgreSQL setup

- In a real deployment, you would want to set a password

- This can be done by creating a `secret`:
  ```
  kubectl create secret generic postgres \
          --from-literal=password=$(base64 /dev/urandom | head -c16)
  ```

- And then passing that secret to the container:
  ```yaml
  env:
  - name: POSTGRES_PASSWORD
    valueFrom:
      secretKeyRef:
        name: postgres
        key: password
  ```

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Troubleshooting Portworx

- If we need to see what's going on with Portworx:
  ```
  PXPOD=$(kubectl -n kube-system get pod -l name=portworx -o json | 
  	      jq -r .items[0].metadata.name)
  kubectl -n kube-system exec $PXPOD -- /opt/pwx/bin/pxctl status
  ```

- We can also connect to Lighthouse (a web UI)

- check the port with `kubectl -n kube-system get svc px-lighthouse`

- connect to that port

- the default login/password is `admin/Password1`

- then specify `portworx-service` as the endpoint

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Removing Portworx

- Portworx provides a storage driver

- It needs to place itself "above" the Kubelet

(it installs itself straight on the nodes)

- To remove it, we need to do more than just deleting its Kubernetes resources

- It is done by applying a special label:
  ```
  kubectl label nodes --all px/enabled=remove --overwrite
  ```

- Then removing a bunch of local files:
  ```
  sudo chattr -i /etc/pwx/.private.json
  sudo rm -rf /etc/pwx /opt/pwx
  ```

(on each node where Portworx was running)

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Dynamic provisioning without a provider

- What if we want to use Stateful sets without a storage provider?

- We will have to create volumes manually

(by creating Persistent Volume objects)

- These volumes will be automatically bound with matching Persistent Volume Claims

- We can use local volumes (essentially bind mounts of host directories)

- Of course, these volumes won't be available in case of node failure

- Check [this blog post](https://kubernetes.io/blog/2018/04/13/local-persistent-volumes-beta/) for more information and gotchas

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

## Acknowledgements

The Portworx installation tutorial, and the PostgreSQL example,
were inspired by [Portworx examples on Katacoda](https://katacoda.com/portworx/scenarios/), in particular:

- [installing Portworx on Kubernetes](https://www.katacoda.com/portworx/scenarios/deploy-px-k8s)

(with adapatations to use a loop device and an embedded key/value store)

- [persistent volumes on Kubernetes using Portworx](https://www.katacoda.com/portworx/scenarios/px-k8s-vol-basic)

(with adapatations to specify a default Storage Class)

- [HA PostgreSQL on Kubernetes with Portworx](https://www.katacoda.com/portworx/scenarios/px-k8s-postgres-all-in-one)

(with adaptations to use a Stateful Set and simplify PostgreSQL's setup)

.debug[[k8s/portworx.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/portworx.md)]
---

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

---

Next steps

.nav[
[Previous section](#toc-highly-available-persistent-volumes)
|
[Back to table of contents](#toc-chapter-9)
|
[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[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/whatsnext.md)]
---

## Options for our first production cluster

- Get a managed cluster from a major cloud provider (AKS, EKS, GKE...)

(price: $, difficulty: medium)

- Hire someone to deploy it for us

(price: $$, difficulty: easy)

- Do it ourselves

(price: $-$$$, difficulty: hard)

.debug[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/whatsnext.md)]
---

## One big cluster vs. multiple small ones

- Yes, it is possible to have prod+dev in a single cluster

(and implement good isolation and security with RBAC, network policies...)

- But it is not a good idea to do that for our first deployment

- Start with a production cluster + at least a test cluster

- Implement and check RBAC and isolation on the test cluster

(e.g. deploy multiple test versions side-by-side)

- Make sure that all our devs have usable dev clusters

(whether it's a local minikube or a full-blown multi-node cluster)

.debug[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/whatsnext.md)]
---

## Namespaces

- Namespaces let you run multiple identical stacks side by side

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

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

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

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

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

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

.debug[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/whatsnext.md)]
---

## Relevant sections

- [Namespaces](kube-selfpaced.yml.html#toc-namespaces)

- [Network Policies](kube-selfpaced.yml.html#toc-network-policies)

- [Role-Based Access Control](kube-selfpaced.yml.html#toc-authentication-and-authorization)

(covers permissions model, user and service accounts management ...)

.debug[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/whatsnext.md)]
---

## Stateful services (databases etc.)

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

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

- `ExternalName` services
 
 (`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[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/whatsnext.md)]
---

## Stateful services (second take)

- If we want to host stateful services on Kubernetes, we can use:

- a storage provider

- persistent volumes, persistent volume claims

- stateful sets

- Good questions to ask:

- what's the *operational cost* of running this service ourselves?

- what do we gain by deploying this stateful service on Kubernetes?

- Relevant sections:
  [Volumes](kube-selfpaced.yml.html#toc-volumes)
  |
  [Stateful Sets](kube-selfpaced.yml.html#toc-stateful-sets)
  |
  [Persistent Volumes](kube-selfpaced.yml.html#toc-highly-available-persistent-volumes)

- Excellent [blog post](http://www.databasesoup.com/2018/07/should-i-run-postgres-on-kubernetes.html) tackling the question: “Should I run Postgres on Kubernetes?”

.debug[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/whatsnext.md)]
---

## Congratulations!

- We learned a lot about Kubernetes, its internals, its advanced concepts

- That was just the easy part

- The hard challenges will revolve around *culture* and *people*

- ... What does that mean?

.debug[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/whatsnext.md)]
---

## Running an app involves many steps

- Write the app

- Tests, QA ...

- Ship *something* (more on that later)

- Provision resources (e.g. VMs, clusters)

- Deploy the *something* on the resources

- Manage, maintain, monitor the resources

- Manage, maintain, monitor the app

- And much more

.debug[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/whatsnext.md)]
---

## Who does what?

- The old "devs vs ops" division has changed

- In some organizations, "ops" are now called "SRE" or "platform" teams

(and they have very different sets of skills)

- Do you know which team is responsible for each item on the list on the previous page?

- Acknowledge that a lot of tasks are outsourced

(e.g. if we add "buy/rack/provision machines" in that list)

.debug[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/whatsnext.md)]
---

## What do we ship?

- Some organizations embrace "you build it, you run it"

- When "build" and "run" are owned by different teams, where's the line?

- What does the "build" team ship to the "run" team?

- Let's see a few options, and what they imply

.debug[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/whatsnext.md)]
---

## Shipping code

- Team "build" ships code

(hopefully in a repository, identified by a commit hash)

- Team "run" containerizes that code

✔️ no extra work for developers

❌ very little advantage of using containers

.debug[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/whatsnext.md)]
---

## Shipping container images

- Team "build" ships container images

(hopefully built automatically from a source repository)

- Team "run" uses theses images to create e.g. Kubernetes resources

✔️ universal artefact (support all languages uniformly)

✔️ easy to start a single component (good for monoliths)

❌ complex applications will require a lot of extra work

❌ adding/removing components in the stack also requires extra work

❌ complex applications will run very differently between dev and prod

.debug[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/whatsnext.md)]
---

## Shipping Compose files

(Or another kind of dev-centric manifest)

- Team "build" ships a manifest that works on a single node

(as well as images, or ways to build them)

- Team "run" adapts that manifest to work on a cluster

✔️ all teams can start the stack in a reliable, deterministic manner

❌ adding/removing components still requires *some* work (but less than before)

❌ there will be *some* differences between dev and prod

.debug[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/whatsnext.md)]
---

## Shipping Kubernetes manifests

- Team "build" ships ready-to-run manifests

(YAML, Helm charts, Kustomize ...)

- Team "run" adjusts some parameters and monitors the application

✔️ parity between dev and prod environments

✔️ "run" team can focus on SLAs, SLOs, and overall quality

❌ requires *a lot* of extra work (and new skills) from the "build" team

❌ Kubernetes is not a very convenient development platform (at least, not yet)

.debug[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/whatsnext.md)]
---

## What's the right answer?

- It depends on our teams

- existing skills (do they know how to do it?)

- availability (do they have the time to do it?)

- potential skills (can they learn to do it?)

- It depends on our culture

- owning "run" often implies being on call

- do we reward on-call duty without encouraging hero syndrome?

- do we give people resources (time, money) to learn?

.debug[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/whatsnext.md)]
---

## Tools to develop on Kubernetes

*If we decide to make Kubernetes the primary development platform, here
are a few tools that can help us.*

- Docker Desktop

- Draft

- Minikube

- Skaffold

- Tilt

- ...

.debug[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/whatsnext.md)]
---

## Where do we run?

- Managed vs. self-hosted

- Cloud vs. on-premises

- If cloud: public vs. private

- Which vendor/distribution to pick?

- Which versions/features to enable?

.debug[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/whatsnext.md)]
---

## Some guidelines

- Start small

- Outsource what we don't know

- Start simple, and stay simple as long as possible

(try to stay away from complex features that we don't need)

- Automate

(regularly check that we can successfully redeploy by following scripts)

- Transfer knowledge

(make sure everyone is on the same page/level)

- Iterate!

.debug[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/whatsnext.md)]
---

## Recommended sessions

Dev?

**The state of Kubernetes development tooling** 
by Ellen Korbes (Garden) 
13:25–14:05 Wednesday, Hall A1

Ops?

**Kubernetes the very hard way** 
by Laurent Bernaille (Datadog) 
11:35–12:15 Wednesday, Hall A1

.debug[[k8s/whatsnext.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/whatsnext.md)]
---

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

---

Links and resources

.nav[
[Previous section](#toc-next-steps)
|
[Back to table of contents](#toc-chapter-9)
|
[Next section](#toc-)
]

---
# Links and resources

All things Kubernetes:

- [Kubernetes Community](https://kubernetes.io/community/) - Slack, Google Groups, meetups
- [Kubernetes on StackOverflow](https://stackoverflow.com/questions/tagged/kubernetes)
- [Play With Kubernetes Hands-On Labs](https://medium.com/@marcosnils/introducing-pwk-play-with-k8s-159fcfeb787b)

All things Docker:

- [Docker documentation](http://docs.docker.com/)
- [Docker Hub](https://hub.docker.com)
- [Docker on StackOverflow](https://stackoverflow.com/questions/tagged/docker)
- [Play With Docker Hands-On Labs](http://training.play-with-docker.com/)

Everything else:

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

.debug[[k8s/links.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/k8s/links.md)]
---
class: title, self-paced

Thank you!

.debug[[shared/thankyou.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/thankyou.md)]
---

That's all, folks! Questions?

![end](images/end.jpg)

.debug[[shared/thankyou.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/thankyou.md)]
---

## Final words

Did you like that tutorial? Then:

1. Please [rate](https://conferences.oreilly.com/velocity/vl-eu/public/schedule/detail/79109) it on the O'Reilly website

(your feedback is important to the conference organizers!)

2. Feel free to use, re-use, and share these slides

(they will remain online for at least a year)

3. Hire me to train your team, anywhere in the world

(contact me: **jerome.petazzoni@gmail.com**)

*Keep the little cards with the VM IP addresses.
The VMs will be shut down shortly, but the URL
of the slides and my e-mail address are on the cards.*
.debug[[shared/thankyou.md](https://github.com/jpetazzo/container.training/tree/velocity-2019-11/slides/shared/thankyou.md)]