Jour 2 Fondamentaux Orchestration & Kubernetes

```

These slides have been built from commit: 86828ca

[shared/title.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/title.md)]
---

Jour 2 Fondamentaux Orchestration & Kubernetes

**Slides[:](https://www.youtube.com/watch?v=h16zyxiwDLY) http://2020-02-enix.container.training/**
]

.debug[[shared/title.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/title.md)]
---
## Intros

- Hello! We are:

- .emoji[🐳] Jérôme Petazzoni ([@jpetazzo](https://twitter.com/jpetazzo), Enix SAS)

- .emoji[☸️] Julien Girardin ([Zempashi](https://github.com/zempashi), Enix SAS)

- The training will run from 9am to 5:30pm (with lunch and coffee breaks)

- For lunch, we'll invite you at [Chameleon, 70 Rue René Boulanger](https://goo.gl/maps/h2XjmJN5weDSUios8)

(please let us know if you'll eat on your own)

- Feel free to interrupt for questions at any time

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

.debug[[logistics.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/logistics.md)]
---
## A brief introduction

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

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

- We included as much information as possible in these slides

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

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

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

.debug[[k8s/intro.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/intro.md)]
---

## Hands on, you shall practice

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

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

- These slides include *tons* of exercises and examples

- They assume that you have access to a Kubernetes cluster

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

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

.debug[[k8s/intro.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/intro.md)]
---
## Accessing these slides now

- We recommend that you open these slides in your browser:

http://2020-02-enix.container.training/

- Use arrows to move to next/previous slide

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

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

- The slide number is also visible in the URL bar

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

.debug[[shared/about-slides.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/about-slides.md)]
---

## Accessing these slides later

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

(let's say we'll keep them online at least 1 year, how about that?)

- You can download the slides using that URL:

http://2020-02-enix.container.training/slides.zip

(then open the file `2.yml.html`)

- You will to find new versions of these slides on:

https://container.training/

.debug[[shared/about-slides.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/about-slides.md)]
---

## These slides are open source

- You are welcome to use, re-use, share these slides

- These slides are written in markdown

- The sources of these slides are available in a public GitHub repository:

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

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

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

.debug[[shared/about-slides.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/about-slides.md)]
---

## Extra details

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

- This magnifying glass indicates slides that provide extra details

- Feel free to skip them if:

- you are in a hurry

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

- you want only the most essential information

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

.debug[[shared/about-slides.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/about-slides.md)]
---

## Chat room

- We've set up a chat room that we will monitor during the workshop

- Don't hesitate to use it to ask questions, or get help, or share feedback

- The chat room will also be available after the workshop

- Join the chat room: [Gitter](https://gitter.im/enix/formation-highfive-202002)

- Say hi in the chat room!

.debug[[shared/about-slides.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/about-slides.md)]
---

## Chapter 1

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

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

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

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

## Chapter 2

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

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

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

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

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

## Chapter 3

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

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

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

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

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

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

## Chapter 4

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

- [Healthchecks](#toc-healthchecks)

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

.debug[[shared/toc.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/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/2020-02-enix/slides/shared/prereqs.md)]
---

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

Misattributed to Benjamin Franklin

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

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/prereqs.md)]
---

## Hands-on sections

- The whole workshop is hands-on

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

- You are invited to reproduce all the demos

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

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

- Go to http://2020-02-enix.container.training/ to view these slides

- Join the chat room: [Gitter](https://gitter.im/enix/formation-highfive-202002)

]

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/prereqs.md)]
---

## Where are we going to run our containers?

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/prereqs.md)]
---

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

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/prereqs.md)]
---

## You get a cluster of cloud VMs

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

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

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

- You can automatically SSH from one VM to another

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

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/prereqs.md)]
---

## Why don't we run containers locally?

- Installing this stuff can be hard on some machines

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

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

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

- an internet connection

- a web browser

- an SSH client

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/prereqs.md)]
---

## SSH clients

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

- On Windows, get one of these:

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

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

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

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/prereqs.md)]
---

## What is this Mosh thing?

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

- Mosh is "the mobile shell"

- It is essentially SSH over UDP, with roaming features

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

(Like hotel or conference WiFi)

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

(Like hotel or conference WiFi)

- It supports transparent roaming when your client IP address changes

(Like when you hop from hotel to conference WiFi)

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/prereqs.md)]
---

## Using Mosh

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

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

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

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

- It requires UDP ports to be open

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

.debug[[shared/prereqs.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/prereqs.md)]
---
class: in-person

## Connecting to our lab environment

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

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

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

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

]

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

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/connecting.md)]
---

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

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

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

- Create your own cluster (local or cloud VMs)

Small setup effort; small cost; flexible environments

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

Bigger setup effort; ideal for group training

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/connecting.md)]
---

## For a consistent Kubernetes experience ...

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

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

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

- These tools are used in many exercises in these slides

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

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/connecting.md)]
---

## Get your own Docker nodes

- If you already have some Docker nodes: great!

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

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

- Log in

- Create your first node

]

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

(Creating a Docker ID is free.)

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/connecting.md)]
---

## We will (mostly) interact with node1 only

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

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

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

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

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

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

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

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/connecting.md)]
---

## Terminals

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

There are multiple ways to do this:

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

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

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

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/connecting.md)]
---

## Tmux cheatsheet

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

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

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

.debug[[shared/connecting.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/connecting.md)]
---
## Versions installed

- Kubernetes 1.17.2
- Docker Engine 19.03.5
- Docker Compose 1.24.1

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

]

.debug[[k8s/versions-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/versions-k8s.md)]
---

## Kubernetes and Docker compatibility

- Kubernetes 1.17 validates Docker Engine version [up to 19.03](https://github.com/kubernetes/kubernetes/pull/84476)

*however ...*

- Kubernetes 1.15 validates Docker Engine versions [up to 18.09](https://github.com/kubernetes/kubernetes/blob/master/CHANGELOG-1.15.md#dependencies)
 
 (the latest version when Kubernetes 1.14 was released)

- Kubernetes 1.13 only validates Docker Engine versions [up to 18.06](https://github.com/kubernetes/kubernetes/blob/master/CHANGELOG-1.13.md#external-dependencies)

- Is it a problem if I use Kubernetes with a "too recent" Docker Engine?

- No!

- "Validates" = continuous integration builds with very extensive (and expensive) testing

- The Docker API is versioned, and offers strong backward-compatibility
 
 (if a client uses e.g. API v1.25, the Docker Engine will keep behaving the same way)

.debug[[k8s/versions-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/versions-k8s.md)]
---

## Kubernetes versioning and cadence

- Kubernetes versions are expressed using *semantic versioning*

(a Kubernetes version is expressed as MAJOR.MINOR.PATCH)

- There is a new *patch* release whenever needed

(generally, there is about [2 to 4 weeks](https://github.com/kubernetes/sig-release/blob/master/release-engineering/role-handbooks/patch-release-team.md#release-timing) between patch releases,
  except when a critical bug or vulnerability is found:
  in that case, a patch release will follow as fast as possible)

- There is a new *minor* release approximately every 3 months

- At any given time, 3 *minor* releases are maintained

(in other words, a given *minor* release is maintained about 9 months)

.debug[[k8s/versions-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/versions-k8s.md)]
---

## Kubernetes version compatibility

*Should my version of `kubectl` match exactly my cluster version?*

- `kubectl` can be up to one minor version older or newer than the cluster

(if cluster version is 1.15.X, `kubectl` can be 1.14.Y, 1.15.Y, or 1.16.Y)

- Things *might* work with larger version differences

(but they will probably fail randomly, so be careful)

- This is an example of an error indicating version compability issues:
  ```
  error: SchemaError(io.k8s.api.autoscaling.v2beta1.ExternalMetricStatus):
  invalid object doesn't have additional properties
  ```

- Check [the documentation](https://kubernetes.io/docs/setup/release/version-skew-policy/#kubectl) for the whole story about compatibility

.debug[[k8s/versions-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/versions-k8s.md)]
---

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

---

Our sample application

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

---
# Our sample application

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

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

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

]

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/sampleapp.md)]
---

## Downloading and running the application

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

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

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

]

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/sampleapp.md)]
---

## What's this application?

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

- No, you can't buy coffee with DockerCoins

- How DockerCoins works:

- generate a few random bytes

- hash these bytes

- increment a counter (to keep track of speed)

- repeat forever!

- DockerCoins is *not* a cryptocurrency

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/sampleapp.md)]
---

## DockerCoins in the microservices era

- DockerCoins is made of 5 services:

- `rng` = web service generating random bytes

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

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

- `webui` = web interface to watch progress

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

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/sampleapp.md)]
---

## How DockerCoins works

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

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

- `worker` does this in an infinite loop

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

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

*(See diagram on next slide!)*

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/sampleapp.md)]
---

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/sampleapp.md)]
---

## Service discovery in container-land

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

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

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

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

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/sampleapp.md)]
---

## Example in `worker/worker.py`

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

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

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

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/sampleapp.md)]
---

## Links, naming, and service discovery

- Containers can have network aliases (resolvable through DNS)

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

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

- Network aliases are automatically namespaced

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

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

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/sampleapp.md)]
---

## Show me the code!

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

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

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

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

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

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

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/sampleapp.md)]
---

## Compose file format version

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

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

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

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

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

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/sampleapp.md)]
---

## Our application at work

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

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

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

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/sampleapp.md)]
---

## Connecting to the web UI

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

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

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

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

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

]

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/sampleapp.md)]
---

## If the graph doesn't load

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

- you are using the Docker Toolbox

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

- you are controlling a remote Docker Engine

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

How to fix this?

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/sampleapp.md)]
---

## Why does the speed seem irregular?

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

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

- Why?

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

- Yes, and?

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/sampleapp.md)]
---

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

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

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

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

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

- What can we conclude from this?

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/sampleapp.md)]
---

## Stopping the application

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

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

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

- Stop the application by hitting `^C`

]

Some containers exit immediately, others take longer.

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

.debug[[shared/sampleapp.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/sampleapp.md)]
---
## Clean up

- Before moving on, let's remove those containers

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

]

.debug[[shared/composedown.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/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/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

## What can we do with Kubernetes?

- Let's imagine that we have a 3-tier e-commerce app:

- web frontend

- API backend

- database (that we will keep out of Kubernetes for now)

- We have built images for our frontend and backend components

(e.g. with Dockerfiles and `docker build`)

- We are running them successfully with a local environment

(e.g. with Docker Compose)

- Let's see how we would deploy our app on Kubernetes!

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

## Basic things we can ask Kubernetes to do

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

- Place an internal load balancer in front of these containers

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

- Place a public load balancer in front of these containers

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

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

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

## Other things that Kubernetes can do for us

- Autoscaling

(straightforward on CPU; more complex on other metrics)

- Ressource management and scheduling

(reserve CPU/RAM for containers; placement constraints)

- Advanced rollout patterns

(blue/green deployment, canary deployment)

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

## More things that Kubernetes can do for us

- Batch jobs

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

- Fine-grained access control

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

- Stateful services

(databases, message queues, etc.)

- Automating complex tasks with *operators*

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

## Kubernetes architecture

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

## Kubernetes architecture

- Ha ha ha ha

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

## Credits

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

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

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

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

## Kubernetes architecture: the nodes

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

- a container Engine (typically Docker)

- kubelet (the "node agent")

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

- Nodes were formerly called "minions"

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

## Kubernetes architecture: the control plane

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

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

- core services like the scheduler and controller manager

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

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

- The control plane is also called the "master"

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

## Running the control plane on special nodes

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

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

- This node is then called a "master"

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

- Normal applications are restricted from running on this node

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

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

- The control plane is then replicated on multiple nodes

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

## Running the control plane outside containers

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

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

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

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

(We only "see" a Kubernetes API endpoint)

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

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

## How many nodes should a cluster have?

- There is no particular constraint

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

- A cluster can have zero node

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

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

- For production, make sure that you have extra capacity

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

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

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

## Do we need to run Docker at all?

No!

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

- We can leverage other pluggable runtimes through the *Container Runtime Interface*

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

## Some runtimes available through CRI

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

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

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

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

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

## Do we need to run Docker at all?

Yes!

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

- We will need to build images and ship them around

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

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

## Do we need to run Docker at all?

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

*Yes, almost certainly*

- On our production servers:

*Yes (today)*

*Probably not (in the future)*

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

## Interacting with Kubernetes

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

- The Kubernetes API is (mostly) RESTful

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

- A few common resource types are:

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

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

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

## Scaling

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

- **Do** create additional pods

- each pod can be on a different node

- each pod will have its own IP address

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

- all the NGINX containers would be on the same node

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

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

## Together or separate

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

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

- they have to be scaled together

- they can communicate very efficiently over `localhost`

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

- they can be scaled separately

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

- Both scenarios can make sense, depending on our goals

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/concepts-k8s.md)]
---

## Credits

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

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

- The second diagram is courtesy of Weave Works

- a *pod* can have multiple containers working together

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

Both diagrams used with permission.

.debug[[k8s/concepts-k8s.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/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/2020-02-enix/slides/k8s/kubectlget.md)]
---

## `kubectl` is the new SSH

- We often start managing servers with SSH

(installing packages, troubleshooting ...)

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

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

- In many cases, we still need SSH:

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

- to debug tricky scenarios

- to inspect and poke at things

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## The parallel with `kubectl`

- We often start managing Kubernetes clusters with `kubectl`

(deploying applications, troubleshooting ...)

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

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

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

- to debug tricky scenarios

- to inspect and poke at things

- The Kubernetes API is always the underlying access method

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## `kubectl get`

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

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

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

]

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## Obtaining machine-readable output

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

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

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

]

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

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

- It's super easy to build custom reports

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

]

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## Exploring types and definitions

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

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

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

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

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## Introspection vs. documentation

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

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

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

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

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

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

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## Type names

- The most common resource names have three forms:

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

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

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

- Some resources do not have a short name

- `Endpoints` only have a plural form

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

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## Viewing details

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

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

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

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

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

- difficult to read for a human operator

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

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## `kubectl describe`

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

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

(all matching objects will be displayed)

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

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

]

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

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## Listing running containers

- Containers are manipulated through *pods*

- A pod is a group of containers:

- running together (on the same node)

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

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

]

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

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## Namespaces

- Namespaces allow us to segregate resources

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

]

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

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

`kubectl describe node node1`

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## Accessing namespaces

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

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

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

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

]

*Here are our system pods!*

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## What are all these control plane pods?

- `etcd` is our etcd server

- `kube-apiserver` is the API server

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

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

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

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

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

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

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## Scoping another namespace

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

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

]

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## Namespaces and other `kubectl` commands

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

- Example:

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

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

- Examples:

- `kubectl delete` can delete resources across multiple namespaces

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

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## What about `kube-public`?

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

]

Nothing!

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

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## Exploring `kube-public`

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

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

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

]

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

We can use that!

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## Accessing `cluster-info`

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

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

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

]

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

- It contains a `kubeconfig` file

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## Retrieving `kubeconfig`

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

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

]

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

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

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

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

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

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

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

- That namespace contains one Lease object per node

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

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

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

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

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## Services

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

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

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

]

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

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## ClusterIP services

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

- This is useful for introspection from within containers

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

- `-k` is used to skip certificate verification

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

]

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

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## Time out

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

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

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

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

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

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## Authentication error

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

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

},
  "code": 403
}
```

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## Explanations

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

- These are typical of a Kubernetes API reply

- Because we *are* talking to the Kubernetes API

- The Kubernetes API tells us "Forbidden"

(because it requires authentication)

- The Kubernetes API is reachable from within the cluster

(many apps integrating with Kubernetes will use this)

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlget.md)]
---

## DNS integration

- Each service also gets a DNS record

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

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

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

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

.debug[[k8s/kubectlget.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/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/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## Starting a simple pod with `kubectl run`

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

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

]

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## Behind the scenes of `kubectl run`

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

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

]

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

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## What are these different things?

- A *deployment* is a high-level construct

- allows scaling, rolling updates, rollbacks

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

- delegates pods management to *replica sets*

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

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

- allows scaling

- rarely used directly

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## Our `pingpong` deployment

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

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

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

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

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

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

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

- scaling, high availability, rolling updates

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## Viewing container output

- Let's use the `kubectl logs` command

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

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

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

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

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

]

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## Streaming logs in real time

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

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

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

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

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

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

]

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## Scaling our application

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

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

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

]

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

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## Log streaming

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

(the one we started before scaling up)

- `kubectl logs` shows us one line per second

- We could expect 3 lines per second

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

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## Streaming logs of multiple pods

- What happens if we restart `kubectl logs`?

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

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

]

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

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## Resilience

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

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

- What happens if pods disappear?

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

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

]

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## What happened?

- `kubectl delete pod` terminates the pod gracefully

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

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

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

- Until 30 seconds later, when the grace period expires

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## What if we wanted something different?

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

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

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

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

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

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## Scheduling periodic background work

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

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

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

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

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

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## Creating a Cron Job

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

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

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

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

]

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## Cron Jobs in action

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

- The Job will create a Pod

- The Job will make sure that the Pod completes

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

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

]

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## What about that deprecation warning?

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

- The exact type of resource created is not obvious

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

- `kubectl create deployment` to create a deployment

- `kubectl create job` to create a job

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

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

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## Various ways of creating resources

- `kubectl run`

- easy way to get started
  - versatile

- `kubectl create <resource>`

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

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

- all features are available
  - requires writing YAML

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## Viewing logs of multiple pods

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

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

- A selector is a logic expression using *labels*

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

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

]

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

### Streaming logs of multiple pods

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

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

]

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

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

### Streaming logs of many pods

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

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

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

]

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

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

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

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

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

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

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

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

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

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## Shortcomings of `kubectl logs`

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

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

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

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

- There are external tools to address these shortcomings

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

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

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

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

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

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

- The problem was fixed in Kubernetes 1.13

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## Party tricks involving IP addresses

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

(example: `127.1`)

- Zeroes are then inserted in the middle

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

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

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlrun.md)]
---

## More party tricks with IP addresses

- We can also ping `1.1`

- `1.1` will expand to `1.0.0.1`

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

- This is a quick way to check connectivity

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

.debug[[k8s/kubectlrun.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/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/2020-02-enix/slides/k8s/logs-cli.md)]
---

## Doing it manually

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

- take a selector as an argument

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

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

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

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

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

.debug[[k8s/logs-cli.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/logs-cli.md)]
---

## Stern

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

From the README:

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

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

Exactly what we need!

.debug[[k8s/logs-cli.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/logs-cli.md)]
---

## Installing Stern

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

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

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

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

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

- On OS X, just `brew install stern`

.debug[[k8s/logs-cli.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/logs-cli.md)]
---

## Using Stern

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

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

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

- These two ways can be combined if necessary

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

]

.debug[[k8s/logs-cli.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/logs-cli.md)]
---

## Stern convenient options

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

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

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

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

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

]

.debug[[k8s/logs-cli.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/logs-cli.md)]
---

## Using Stern with a selector

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

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

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

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

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

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

]

.debug[[k8s/logs-cli.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/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/2020-02-enix/slides/shared/declarative.md)]
---

## Declarative vs imperative

- What declarative would really be:

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

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

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

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

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

.debug[[shared/declarative.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/declarative.md)]
---

## Declarative vs imperative

- Imperative systems:

- simpler

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

- Declarative systems:

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

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

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

.debug[[shared/declarative.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/declarative.md)]
---
## Declarative vs imperative in Kubernetes

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

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

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

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

- Then it will store it in etcd

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

.debug[[k8s/declarative.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/declarative.md)]
---

## Reconciling state

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

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

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

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

- Kubernetes will then *converge* that resource

.debug[[k8s/declarative.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/declarative.md)]
---
## 19,000 words

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

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

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

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

.debug[[k8s/deploymentslideshow.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/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/2020-02-enix/slides/k8s/kubenet.md)]
---

## Kubernetes network model: the good

- Everything can reach everything

- No address translation

- No port translation

- No new protocol

- The network implementation can decide how to allocate addresses

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

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

- The specification is simple enough to allow many various implementations

.debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubenet.md)]
---

## Kubernetes network model: the less good

- Everything can reach everything

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

- the network implementation that you use needs to support them

- There are literally dozens of implementations out there

(15 are listed in the Kubernetes documentation)

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

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

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

.debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubenet.md)]
---

## Kubernetes network model: in practice

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

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

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

- Unless you:

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

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

.debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubenet.md)]
---

## The Container Network Interface (CNI)

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

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

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

- Typically, CNI plugins will:

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

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

- configure the interface as well as required routes etc.

.debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubenet.md)]
---

## Multiple moving parts

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

- provides communication between pods and nodes

- is generally implemented with CNI plugins

- The "pod-to-service network":

- provides internal communication and load balancing

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

- Network policies:

- provide firewalling and isolation

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

.debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubenet.md)]
---

## Even more moving parts

- Inbound traffic can be handled by multiple components:

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

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

- It is possible to use multiple pod networks in parallel

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

- Some solutions can fill multiple roles

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

.debug[[k8s/kubenet.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/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

- We can connect to our pods using their IP address

- Then we need to figure out a lot of things:

- how do we look up the IP address of the pod(s)?

- how do we connect from outside the cluster?

- how do we load balance traffic?

- what if a pod fails?

- Kubernetes has a resource type named *Service*

- Services address all these questions!

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlexpose.md)]
---

## Services in a nutshell

- Services give us a *stable endpoint* to connect to a pod or a group of pods

- An easy way to create a service is to use `kubectl expose`

- If we have a deployment named `my-little-deploy`, we can run:

`kubectl expose deployment my-little-deploy --port=80`

... and this will create a service with the same name (`my-little-deploy`)

- Services are automatically added to an internal DNS zone

(in the example above, our code can now connect to http://my-little-deploy/)

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlexpose.md)]
---

## Advantages of services

- We don't need to look up the IP address of the pod(s)

(we resolve the IP address of the service using DNS)

- There are multiple service types; some of them allow external traffic

(e.g. `LoadBalancer` and `NodePort`)

- Services provide load balancing

(for both internal and external traffic)

- Service addresses are independent from pods' addresses

(when a pod fails, the service seamlessly sends traffic to its replacement)

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlexpose.md)]
---

## Many kinds and flavors of service

- There are different types of services:

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

- There are also *headless services*

- Services can also have optional *external IPs*

- There is also another resource type called *Ingress*

(specifically for HTTP services)

- Wow, that's a lot! Let's start with the basics ...

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlexpose.md)]
---

## `ClusterIP`

- It's the default service type

- A virtual IP address is allocated for the service

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

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

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

- Perfect for internal communication, within the cluster

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlexpose.md)]
---

## `LoadBalancer`

- An external load balancer is allocated for the service

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

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

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

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

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

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

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlexpose.md)]
---

## `NodePort`

- A port number is allocated for the service

(by default, in the 30000-32767 range)

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

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

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

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

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

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

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlexpose.md)]
---

## Running containers with open ports

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

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

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

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

- `jpetazzo/httpenv` listens on port 8888

- It serves its environment variables in JSON format

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

(and therefore, will be different on each backend)

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlexpose.md)]
---

## Creating a deployment for our HTTP server

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

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

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

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

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

]

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlexpose.md)]
---

## Exposing our deployment

- We'll create a default `ClusterIP` service

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

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

]

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlexpose.md)]
---

## Services are layer 4 constructs

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

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

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

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

- As a result: you *have to* indicate the port number for your service
    
  (with some exceptions, like `ExternalName` or headless services, covered later)

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlexpose.md)]
---

## Testing our service

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

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

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

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

]

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

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlexpose.md)]
---

## `ExternalName`

- Services of type `ExternalName` are quite different

- No load balancer (internal or external) is created

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

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

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

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlexpose.md)]
---

## External IPs

- We can add an External IP to a service, e.g.:
  ```bash
  kubectl expose deploy my-little-deploy --port=80 --external-ip=1.2.3.4
  ```

- `1.2.3.4` should be the address of one of our nodes

(it could also be a virtual address, service address, or VIP, shared by multiple nodes)

- Connections to `1.2.3.4:80` will be sent to our service

- External IPs will also show up on services of type `LoadBalancer`

(they will be added automatically by the process provisioning the load balancer)

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlexpose.md)]
---

## Headless services

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

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

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

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

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

- ...

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

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlexpose.md)]
---

## Creating a headless services

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

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

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

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

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

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

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlexpose.md)]
---

## Services and endpoints

- A service has a number of "endpoints"

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

- The endpoints are maintained and updated automatically by Kubernetes

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

]

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

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

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlexpose.md)]
---

## Viewing endpoint details

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

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

- These commands will show us a list of IP addresses

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

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlexpose.md)]
---

## `endpoints` not `endpoint`

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

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

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

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

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

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlexpose.md)]
---

## The DNS zone

- In the `kube-system` namespace, there should be a service named `kube-dns`

- This is the internal DNS server that can resolve service names

- The default domain name for the service we created is `default.svc.cluster.local`

- Get the IP address of the internal DNS server:
  ```bash
  IP=$(kubectl -n kube-system get svc kube-dns -o jsonpath={.spec.clusterIP})
  ```

- Resolve the cluster IP for the `httpenv` service:
  ```bash
  host httpenv.default.svc.cluster.local $IP
  ```

]

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/kubectlexpose.md)]
---

## `Ingress`

- Ingresses are another type (kind) of resource

- They are specifically for HTTP services

(not TCP or UDP)

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

- They require an *Ingress Controller* to function

.debug[[k8s/kubectlexpose.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/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-3)
|
[Next section](#toc-running-our-application-on-kubernetes)
]

---
# Shipping images with a registry

- Initially, our app was running on a single node

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

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

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

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

.debug[[k8s/shippingimages.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/shippingimages.md)]
---

## How Docker registries work (a reminder)

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

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

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

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

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

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

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

.debug[[k8s/shippingimages.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/shippingimages.md)]
---

## Running DockerCoins on Kubernetes

- Create one deployment for each component

(hasher, redis, rng, webui, worker)

- Expose deployments that need to accept connections

(hasher, redis, rng, webui)

- For redis, we can use the official redis image

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

.debug[[k8s/shippingimages.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/shippingimages.md)]
---

## Building and shipping images

- There are *many* options!

- Manually:

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

- push to the registry

- Automatically:

- build and test locally

- when ready, commit and push a code repository

- the code repository notifies an automated build system

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

.debug[[k8s/shippingimages.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/shippingimages.md)]
---

## Which registry do we want to use?

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

- Each major cloud provider has an option as well

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

- There are also commercial products to run our own registry

(Docker EE, Quay...)

- And open source options, too!

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

(when it has one)

.debug[[k8s/shippingimages.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/shippingimages.md)]
---

## Building on the fly

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

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

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

]

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

It is possible to view the build logs by setting up an account on [ctr.run](https://ctr.run/).
.debug[[k8s/shippingimages.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/shippingimages.md)]
---
## Using images from the Docker Hub

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

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

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

- The full image names are therefore:

- `dockercoins/hasher:v0.1`

- `dockercoins/rng:v0.1`

- `dockercoins/webui:v0.1`

- `dockercoins/worker:v0.1`

.debug[[k8s/buildshiprun-dockerhub.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/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-3)
|
[Next section](#toc-deploying-with-yaml)
]

---
# Running our application on Kubernetes

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

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

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

]

.debug[[k8s/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ourapponkube.md)]
---

## Deploying other images

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

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

- ... We could use the following snippet:

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

.debug[[k8s/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ourapponkube.md)]
---

## Is this working?

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

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

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

]

🤔 `rng` is fine ... But not `worker`.

💡 Oh right! We forgot to `expose`.

.debug[[k8s/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ourapponkube.md)]
---

## Connecting containers together

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

- `worker` doesn't need to be exposed

- `webui` will be dealt with later

- Expose each deployment, specifying the right port:
  ```bash
  kubectl expose deployment redis --port 6379
  kubectl expose deployment rng --port 80
  kubectl expose deployment hasher --port 80
  ```

]

.debug[[k8s/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ourapponkube.md)]
---

## Is this working yet?

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

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

(Give it about 10 seconds to recover)

]

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

.debug[[k8s/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ourapponkube.md)]
---

## Exposing services for external access

- Now we would like to access the Web UI

- We will expose it with a `NodePort`

(just like we did for the registry)

- Create a `NodePort` service for the Web UI:
  ```bash
  kubectl expose deploy/webui --type=NodePort --port=80
  ```

- Check the port that was allocated:
  ```bash
  kubectl get svc
  ```

]

.debug[[k8s/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ourapponkube.md)]
---

## Accessing the web UI

- We can now connect to *any node*, on the allocated node port, to view the web UI

- Open the web UI in your browser (http://node-ip-address:3xxxx/)

]

Yes, this may take a little while to update. *(Narrator: it was DNS.)*

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

.debug[[k8s/ourapponkube.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/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-scaling-our-demo-app)
]

---
# Deploying with YAML

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

- `kubectl run`

- `kubectl create deployment`

- `kubectl expose`

- We can also create resources directly with YAML manifests

.debug[[k8s/yamldeploy.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/yamldeploy.md)]
---

## `kubectl apply` vs `create`

- `kubectl create -f whatever.yaml`

- creates resources if they don't exist

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

- `kubectl apply -f whatever.yaml`

- creates resources if they don't exist

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

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

.debug[[k8s/yamldeploy.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/yamldeploy.md)]
---

## Creating multiple resources

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

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

.debug[[k8s/yamldeploy.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/yamldeploy.md)]
---

## Creating multiple resources

- The manifest can also contain a list of resources

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

.debug[[k8s/yamldeploy.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/yamldeploy.md)]
---

## Deploying dockercoins with YAML

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

(Deployments and Services)

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

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

]

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

.debug[[k8s/yamldeploy.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/yamldeploy.md)]
---

## Deleting resources

- We can also use a YAML file to *delete* resources

- `kubectl delete -f ...` will delete all the resources mentioned in a YAML file

(useful to clean up everything that was created by `kubectl apply -f ...`)

- The definitions of the resources don't matter

(just their `kind`, `apiVersion`, and `name`)

.debug[[k8s/yamldeploy.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/yamldeploy.md)]
---

## Pruning¹ resources

- We can also tell `kubectl` to remove old resources

- This is done with `kubectl apply -f ... --prune`

- It will remove resources that don't exist in the YAML file(s)

- But only if they were created with `kubectl apply` in the first place

(technically, if they have an annotation `kubectl.kubernetes.io/last-applied-configuration`)

.footnote[¹If English is not your first language: *to prune* means to remove dead or overgrown branches in a tree, to help it to grow.]

.debug[[k8s/yamldeploy.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/yamldeploy.md)]
---

## YAML as source of truth

- Imagine the following workflow:

- do not use `kubectl run`, `kubectl create deployment`, `kubectl expose` ...

- define everything with YAML

- `kubectl apply -f ... --prune --all` that YAML

- keep that YAML under version control

- enforce all changes to go through that YAML (e.g. with pull requests)

- Our version control system now has a full history of what we deploy

- Compares to "Infrastructure-as-Code", but for app deployments

.debug[[k8s/yamldeploy.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/yamldeploy.md)]
---

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

---

Scaling our demo app

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

---
# Scaling our demo app

- Our ultimate goal is to get more DockerCoins

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

- Let's look at the architecture again:

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

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

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/scalingdockercoins.md)]
---

## Adding another worker

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

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

- Now, create more `worker` replicas:
  ```bash
  kubectl scale deployment worker --replicas=2
  ```

]

After a few seconds, the graph in the web UI should show up.

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/scalingdockercoins.md)]
---

## Adding more workers

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

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

]

The graph in the web UI should go up again.

(This is looking great! We're gonna be RICH!)

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/scalingdockercoins.md)]
---

## Adding even more workers

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

- Scale the `worker` Deployment to a bigger number:
  ```bash
  kubectl scale deployment worker --replicas=10
  ```

]

The graph will peak at 10 hashes/second.

(We can add as many workers as we want: we will never go past 10 hashes/second.)

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/scalingdockercoins.md)]
---

## Didn't we briefly exceed 10 hashes/second?

- It may *look like it*, because the web UI shows instant speed

- The instant speed can briefly exceed 10 hashes/second

- The average speed cannot

- The instant speed can be biased because of how it's computed

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/scalingdockercoins.md)]
---

## Why instant speed is misleading

- The instant speed is computed client-side by the web UI

- The web UI checks the hash counter once per second
 
 (and does a classic (h2-h1)/(t2-t1) speed computation)

- The counter is updated once per second by the workers

- These timings are not exact
 
 (e.g. the web UI check interval is client-side JavaScript)

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

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

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/scalingdockercoins.md)]
---

## Why are we stuck at 10 hashes per second?

- If this was high-quality, production code, we would have instrumentation

(Datadog, Honeycomb, New Relic, statsd, Sumologic, ...)

- It's not!

- Perhaps we could benchmark our web services?

(with tools like `ab`, or even simpler, `httping`)

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/scalingdockercoins.md)]
---

## Benchmarking our web services

- We want to check `hasher` and `rng`

- We are going to use `httping`

- It's just like `ping`, but using HTTP `GET` requests

(it measures how long it takes to perform one `GET` request)

- It's used like this:
  ```
  httping [-c count] http://host:port/path
  ```

- Or even simpler:
  ```
  httping ip.ad.dr.ess
  ```

- We will use `httping` on the ClusterIP addresses of our services

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/scalingdockercoins.md)]
---

## Obtaining ClusterIP addresses

- We can simply check the output of `kubectl get services`

- Or do it programmatically, as in the example below

- Retrieve the IP addresses:
  ```bash
  HASHER=$(kubectl get svc hasher -o go-template={{.spec.clusterIP}})
  RNG=$(kubectl get svc rng -o go-template={{.spec.clusterIP}})
  ```

]

Now we can access the IP addresses of our services through `$HASHER` and `$RNG`.

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/scalingdockercoins.md)]
---

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

- Check the response times for both services:
  ```bash
  httping -c 3 $HASHER
  httping -c 3 $RNG
  ```

]

- `hasher` is fine (it should take a few milliseconds to reply)

- `rng` is not (it should take about 700 milliseconds if there are 10 workers)

- Something is wrong with `rng`, but ... what?

.debug[[k8s/scalingdockercoins.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/scalingdockercoins.md)]
---
## Let's draw hasty conclusions

- The bottleneck seems to be `rng`

- *What if* we don't have enough entropy and can't generate enough random numbers?

- We need to scale out the `rng` service on multiple machines!

Note: this is a fiction! We have enough entropy. But we need a pretext to scale out.

(In fact, the code of `rng` uses `/dev/urandom`, which never runs out of entropy...
 
...and is [just as good as `/dev/random`](http://www.slideshare.net/PacSecJP/filippo-plain-simple-reality-of-entropy).)

.debug[[shared/hastyconclusions.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/hastyconclusions.md)]
---

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

---

Daemon sets

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

---
# Daemon sets

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

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

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

- We will do that with a *daemon set*

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Why not a deployment?

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

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

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

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

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

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

(as nodes are added or removed)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Daemon sets in practice

- Daemon sets are great for cluster-wide, per-node processes:

- `kube-proxy`

- `weave` (our overlay network)

- monitoring agents

- hardware management tools (e.g. SCSI/FC HBA agents)

- etc.

- They can also be restricted to run [only on some nodes](https://kubernetes.io/docs/concepts/workloads/controllers/daemonset/#running-pods-on-only-some-nodes)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Creating a daemon set

- Unfortunately, as of Kubernetes 1.17, the CLI cannot create daemon sets

- More precisely: it doesn't have a subcommand to create a daemon set

- But any kind of resource can always be created by providing a YAML description:
  ```bash
  kubectl apply -f foo.yaml
  ```

- How do we create the YAML file for our daemon set?

- option 1: [read the docs](https://kubernetes.io/docs/concepts/workloads/controllers/daemonset/#create-a-daemonset)

- option 2: `vi` our way out of it

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Creating the YAML file for our daemon set

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

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

- Edit `rng.yml`

]

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## "Casting" a resource to another

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

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

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

- Save, quit

- Try to create our new resource:
  ```bash
  kubectl apply -f rng.yml
  ```

]

We all knew this couldn't be that easy, right!

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Understanding the problem

- The core of the error is:
  ```
  error validating data:
  [ValidationError(DaemonSet.spec):
  unknown field "replicas" in io.k8s.api.extensions.v1beta1.DaemonSetSpec,
  ...
  ```

- *Obviously,* it doesn't make sense to specify a number of replicas for a daemon set

- Workaround: fix the YAML

- remove the `replicas` field
  - remove the `strategy` field (which defines the rollout mechanism for a deployment)
  - remove the `progressDeadlineSeconds` field (also used by the rollout mechanism)
  - remove the `status: {}` line at the end

- Or, we could also ...

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Use the `--force`, Luke

- We could also tell Kubernetes to ignore these errors and try anyway

- The `--force` flag's actual name is `--validate=false`

- Try to load our YAML file and ignore errors:
  ```bash
  kubectl apply -f rng.yml --validate=false
  ```

]

🎩✨🐇

Wait ... Now, can it be *that* easy?

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Checking what we've done

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

- Look at the resources that we have now:
  ```bash
  kubectl get all
  ```

]

We have two resources called `rng`:

- the *deployment* that was existing before

- the *daemon set* that we just created

We also have one too many pods.
 
(The pod corresponding to the *deployment* still exists.)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## `deploy/rng` and `ds/rng`

- You can have different resource types with the same name

(i.e. a *deployment* and a *daemon set* both named `rng`)

- We still have the old `rng` *deployment*

```
NAME                       DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
deployment.apps/rng        1         1         1            1           18m
  ```

- But now we have the new `rng` *daemon set* as well

```
NAME DESIRED CURRENT READY UP-TO-DATE AVAILABLE NODE SELECTOR AGE
daemonset.apps/rng 2 2 2 2 2 <none> 9s
 ```

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Too many pods

- If we check with `kubectl get pods`, we see:

- *one pod* for the deployment (named `rng-xxxxxxxxxx-yyyyy`)

- *one pod per node* for the daemon set (named `rng-zzzzz`)

```
  NAME                        READY     STATUS    RESTARTS   AGE
  rng-54f57d4d49-7pt82        1/1       Running   0          11m
  rng-b85tm                   1/1       Running   0          25s
  rng-hfbrr                   1/1       Running   0          25s
  [...]
  ```

The daemon set created one pod per node, except on the master node.

The master node has [taints](https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/) preventing pods from running there.

(To schedule a pod on this node anyway, the pod will require appropriate [tolerations](https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/).)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Is this working?

- Look at the web UI

- The graph should now go above 10 hashes per second!

- It looks like the newly created pods are serving traffic correctly

- How and why did this happen?

(We didn't do anything special to add them to the `rng` service load balancer!)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

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

---

Labels and selectors

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

---

# Labels and selectors

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

- That set of pods is defined by the *selector* of the `rng` service

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

]

- The selector is `app=rng`

- It means "all the pods having the label `app=rng`"

(They can have additional labels as well, that's OK!)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Selector evaluation

- We can use selectors with many `kubectl` commands

- For instance, with `kubectl get`, `kubectl logs`, `kubectl delete` ... and more

- Get the list of pods matching selector `app=rng`:
  ```bash
  kubectl get pods -l app=rng
  kubectl get pods --selector app=rng
  ```

]

But ... why do these pods (in particular, the *new* ones) have this `app=rng` label?

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Where do labels come from?

- When we create a deployment with `kubectl create deployment rng`,
 this deployment gets the label `app=rng`

- The replica sets created by this deployment also get the label `app=rng`

- The pods created by these replica sets also get the label `app=rng`

- When we created the daemon set from the deployment, we re-used the same spec

- Therefore, the pods created by the daemon set get the same labels

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Updating load balancer configuration

- We would like to remove a pod from the load balancer

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

It would be re-created immediately (by the replica set or the daemon set)

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

It would *also* be re-created immediately

Why?!?

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Selectors for replica sets and daemon sets

- The "mission" of a replica set is:

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

- The "mission" of a daemon set is:

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

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

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

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

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

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

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Isolation of replica sets and daemon sets

- Since both the `rng` daemon set and the `rng` replica set use `app=rng` ...

... Why don't they "find" each other's pods?

- *Replica sets* have a more specific selector, visible with `kubectl describe`

(It looks like `app=rng,pod-template-hash=abcd1234`)

- *Daemon sets* also have a more specific selector, but it's invisible

(It looks like `app=rng,controller-revision-hash=abcd1234`)

- As a result, each controller only "sees" the pods it manages

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Removing a pod from the load balancer

- Currently, the `rng` service is defined by the `app=rng` selector

- The only way to remove a pod is to remove or change the `app` label

- ... But that will cause another pod to be created instead!

- What's the solution?

- We need to change the selector of the `rng` service!

- Let's add another label to that selector (e.g. `active=yes`)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Complex selectors

- If a selector specifies multiple labels, they are understood as a logical *AND*

(In other words: the pods must match all the labels)

- Kubernetes has support for advanced, set-based selectors

(But these cannot be used with services, at least not yet!)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## The plan

1. Add the label `active=yes` to all our `rng` pods

2. Update the selector for the `rng` service to also include `active=yes`

3. Toggle traffic to a pod by manually adding/removing the `active` label

4. Profit!

*Note: if we swap steps 1 and 2, it will cause a short
service disruption, because there will be a period of time
during which the service selector won't match any pod.
During that time, requests to the service will time out.
By doing things in the order above, we guarantee that there won't
be any interruption.*

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Adding labels to pods

- We want to add the label `active=yes` to all pods that have `app=rng`

- We could edit each pod one by one with `kubectl edit` ...

- ... Or we could use `kubectl label` to label them all

- `kubectl label` can use selectors itself

- Add `active=yes` to all pods that have `app=rng`:
  ```bash
  kubectl label pods -l app=rng active=yes
  ```

]

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/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 `active: yes` to its selector:
  ```bash
  kubectl edit service rng
  ```

]

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

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## When the YAML parser is being too smart

- YAML parsers try to help us:

- `xyz` is the string `"xyz"`

- `42` is the integer `42`

- `yes` is the boolean value `true`

- If we want the string `"42"` or the string `"yes"`, we have to quote them

- So we have to use `active: "yes"`

.footnote[For a good laugh: if we had used "ja", "oui", "si" ... as the value, it would have worked!]

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Updating the service selector, take 2

- Update the YAML manifest of the service

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

]

This time it should work!

If we did everything correctly, the web UI shouldn't show any change.

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Updating labels

- We want to disable the pod that was created by the deployment

- All we have to do, is remove the `active` label from that pod

- To identify that pod, we can use its name

- ... Or rely on the fact that it's the only one with a `pod-template-hash` label

- Good to know:

- `kubectl label ... foo=` doesn't remove a label (it sets it to an empty string)

- to remove label `foo`, use `kubectl label ... foo-`

- to change an existing label, we would need to add `--overwrite`

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/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 active-
  ```
  (The stream of HTTP logs should stop immediately)

]

There might be a slight change in the web UI (since we removed a bit
of capacity from the `rng` service). If we remove more pods,
the effect should be more visible.

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/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 `active=yes` label

- If we want these pods to have that label, we need to edit the daemon set spec

- We can do that with e.g. `kubectl edit daemonset rng`

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## We've put resources in your resources

- Reminder: a daemon set is a resource that creates more resources!

- There is a difference between:

- the label(s) of a resource (in the `metadata` block in the beginning)

- the selector of a resource (in the `spec` block)

- the label(s) of the resource(s) created by the first resource (in the `template` block)

- We would need to update the selector and the template

(metadata labels are not mandatory)

- The template must match the selector

(i.e. the resource will refuse to create resources that it will not select)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Labels and debugging

- When a pod is misbehaving, we can delete it: another one will be recreated

- But we can also change its labels

- It will be removed from the load balancer (it won't receive traffic anymore)

- Another pod will be recreated immediately

- But the problematic pod is still here, and we can inspect and debug it

- We can even re-add it to the rotation if necessary

(Very useful to troubleshoot intermittent and elusive bugs)

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

## Labels and advanced rollout control

- Conversely, we can add pods matching a service's selector

- These pods will then receive requests and serve traffic

- Examples:

- one-shot pod with all debug flags enabled, to collect logs

- pods created automatically, but added to rotation in a second step
 
 (by setting their label accordingly)

- This gives us building blocks for canary and blue/green deployments

.debug[[k8s/daemonset.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/daemonset.md)]
---

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

---

Rolling updates

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

---
# Rolling updates

- By default (without rolling updates), when a scaled resource is updated:

- new pods are created

- old pods are terminated

- ... all at the same time

- if something goes wrong, ¯\\\_(ツ)\_/¯

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---

## Rolling updates

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

- The Deployment controls multiple Replica Sets

- Each Replica Set is a group of identical Pods

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

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

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

- at least one "old" set

- We can have multiple "old" sets

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

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---

## Update strategy

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

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

- At any given time ...

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

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

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

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

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---

## Checking current rollout parameters

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

- Show the rollout plan for our deployments:
  ```bash
    kubectl get deploy -o json |
            jq ".items[] | {name:.metadata.name} + .spec.strategy.rollingUpdate"
  ```

]

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---

## Rolling updates in practice

- As of Kubernetes 1.8, we can do rolling updates with:

`deployments`, `daemonsets`, `statefulsets`

- Editing one of these resources will automatically result in a rolling update

- Rolling updates can be monitored with the `kubectl rollout` subcommand

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---
class: hide-exercise

## Rolling out the new `worker` service

- Let's monitor what's going on by opening a few terminals, and run:
  ```bash
  kubectl get pods -w
  kubectl get replicasets -w
  kubectl get deployments -w
  ```

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

]

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

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---
class: hide-exercise

## Give it some time

- At first, it looks like nothing is happening (the graph remains at the same level)

- According to `kubectl get deploy -w`, the `deployment` was updated really quickly

- But `kubectl get pods -w` tells a different story

- The old `pods` are still here, and they stay in `Terminating` state for a while

- Eventually, they are terminated; and then the graph decreases significantly

- This delay is due to the fact that our worker doesn't handle signals

- Kubernetes sends a "polite" shutdown request to the worker, which ignores it

- After a grace period, Kubernetes gets impatient and kills the container

(The grace period is 30 seconds, but [can be changed](https://kubernetes.io/docs/concepts/workloads/pods/pod/#termination-of-pods) if needed)

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---
class: hide-exercise

## Rolling out something invalid

- What happens if we make a mistake?

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

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

/

]

Our rollout is stuck. However, the app is not dead.

(After a minute, it will stabilize to be 20-25% slower.)

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---
class: hide-exercise

## What's going on with our rollout?

- Why is our app a bit slower?

- Because `MaxUnavailable=25%`

... So the rollout terminated 2 replicas out of 10 available

- Okay, but why do we see 5 new replicas being rolled out?

- Because `MaxSurge=25%`

... So in addition to replacing 2 replicas, the rollout is also starting 3 more

- It rounded down the number of MaxUnavailable pods conservatively,
 
 but the total number of pods being rolled out is allowed to be 25+25=50%

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---

## The nitty-gritty details

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

- Current settings: MaxUnavailable=25% and MaxSurge=25%

- When we start the rollout:

- two replicas are taken down (as per MaxUnavailable=25%)
  - two others are created (with the new version) to replace them
  - three others are created (with the new version) per MaxSurge=25%)

- Now we have 8 replicas up and running, and 5 being deployed

- Our rollout is stuck at this point!

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---
class: hide-exercise

## Checking the dashboard during the bad rollout

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

- Connect to the dashboard that we deployed earlier

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

- Can we see the reason for the failure?

]

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---
class: hide-exercise

## Recovering from a bad rollout

- We could push some `v0.3` image

(the pod retry logic will eventually catch it and the rollout will proceed)

- Or we could invoke a manual rollback

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

]

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---
class: hide-exercise

## Rolling back to an older version

- We reverted to `v0.2`

- But this version still has a performance problem

- How can we get back to the previous version?

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---
class: hide-exercise

## Multiple "undos"

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

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

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

]

🤔 That didn't work.

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---
class: hide-exercise

## Multiple "undos" don't work

- If we see successive versions as a stack:

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

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

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

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

]

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---

## In this specific scenario

- Our version numbers are easy to guess

- What if we had used git hashes?

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

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---

## Listing versions

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

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

]

We don't see *all* revisions.

We might see something like 1, 4, 5.

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

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---
class: hide-exercise

## Explaining deployment revisions

- These revisions correspond to our Replica Sets

- This information is stored in the Replica Set annotations

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

]

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---

## What about the missing revisions?

- The missing revisions are stored in another annotation:

`deployment.kubernetes.io/revision-history`

- These are not shown in `kubectl rollout history`

- We could easily reconstruct the full list with a script

(if we wanted to!)

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---
class: hide-exercise

## Rolling back to an older version

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

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

- Check the web UI or the list of pods

]

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---

## Changing rollout parameters

- We want to:

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

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

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

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---

## Applying changes through a YAML patch

- We could use `kubectl edit deployment worker`

- But we could also use `kubectl patch` with the exact YAML shown before

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

]

.debug[[k8s/rollout.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/rollout.md)]
---

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

---

Healthchecks

.nav[
[Previous section](#toc-rolling-updates)
|
[Back to table of contents](#toc-chapter-4)
|
[Next section](#toc-exposing-http-services-with-ingress-resources)
]

---
# Healthchecks

- Kubernetes provides two kinds of healthchecks: liveness and readiness

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

- Each container can have two (optional) probes:

- liveness = is this container dead or alive?

- readiness = is this container ready to serve traffic?

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

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/healthchecks.md)]
---

## Liveness probe

- Indicates if the container is dead or alive

- A dead container cannot come back to life

- If the liveness probe fails, the container is killed

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

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

- `Never`: the container is not restarted

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/healthchecks.md)]
---

## When to use a liveness probe

- To indicate failures that can't be recovered

- deadlocks (causing all requests to time out)

- internal corruption (causing all requests to error)

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

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/healthchecks.md)]
---

## Readiness probe

- Indicates if the container is ready to serve traffic

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

- If the readiness probe fails:

- the container is *not* killed

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

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/healthchecks.md)]
---

## When to use a readiness probe

- To indicate failure due to an external cause

- database is down or unreachable

- mandatory auth or other backend service unavailable

- To indicate temporary failure or unavailability

- application can only service *N* parallel connections

- runtime is busy doing garbage collection or initial data load

- For processes that take a long time to start

(more on that later)

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/healthchecks.md)]
---

## Dependencies

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

- the web server's liveness probe should succeed

- the web server's readiness probe should fail

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/healthchecks.md)]
---

## Timing and thresholds

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

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

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

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

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

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

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/healthchecks.md)]
---

## Startup probe

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

(it is in `alpha` in Kubernetes 1.16)

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

- process is still starting

- loading external data, priming caches

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

(available for both liveness and readiness probes)

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

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/healthchecks.md)]
---

## Different types of probes

- HTTP request

- specify URL of the request (and optional headers)

- any status code between 200 and 399 indicates success

- TCP connection

- the probe succeeds if the TCP port is open

- arbitrary exec

- a command is executed in the container

- exit status of zero indicates success

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/healthchecks.md)]
---

## Benefits of using probes

- Rolling updates proceed when containers are *actually ready*

(as opposed to merely started)

- Containers in a broken state get killed and restarted

(instead of serving errors or timeouts)

- Unavailable backends get removed from load balancer rotation

(thus improving response times across the board)

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/healthchecks.md)]
---

## Example: HTTP probe

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

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

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/healthchecks.md)]
---

## Example: exec probe

Here is a pod template for a Redis server:

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

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/healthchecks.md)]
---

## Questions to ask before adding healthchecks

- Do we want liveness, readiness, both?

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

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

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

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

- Do they depend on additional services?

(this can be particularly tricky, see next slide)

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/healthchecks.md)]
---

## Healthchecks and dependencies

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

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

- Otherwise, they are considered to fail

- This might require to check the health of dependencies asynchronously

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/healthchecks.md)]
---

## Healthchecks for workers

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

- Readiness isn't useful

(because workers aren't backends for a service)

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

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

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

- touch a file during each iteration of the main loop

- check the timestamp of that file from an exec probe

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

.debug[[k8s/healthchecks.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/healthchecks.md)]
---

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

---

Exposing HTTP services with Ingress resources

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

---
# Exposing HTTP services with Ingress resources

- *Services* give us a way to access a pod or a set of pods

- Services can be exposed to the outside world:

- with type `NodePort` (on a port >30000)

- with type `LoadBalancer` (allocating an external load balancer)

- What about HTTP services?

- how can we expose `webui`, `rng`, `hasher`?

- the Kubernetes dashboard?

- a new version of `webui`?

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Exposing HTTP services

- If we use `NodePort` services, clients have to specify port numbers

(i.e. http://xxxxx:31234 instead of just http://xxxxx)

- `LoadBalancer` services are nice, but:

- they are not available in all environments

- they often carry an additional cost (e.g. they provision an ELB)

- they require one extra step for DNS integration
 
 (waiting for the `LoadBalancer` to be provisioned; then adding it to DNS)

- We could build our own reverse proxy

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Building a custom reverse proxy

- There are many options available:

Apache, HAProxy, Hipache, NGINX, Traefik, ...

(look at [jpetazzo/aiguillage](https://github.com/jpetazzo/aiguillage) for a minimal reverse proxy configuration using NGINX)

- Most of these options require us to update/edit configuration files after each change

- Some of them can pick up virtual hosts and backends from a configuration store

- Wouldn't it be nice if this configuration could be managed with the Kubernetes API?

- Enter.red[¹] *Ingress* resources!

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Ingress resources

- Kubernetes API resource (`kubectl get ingress`/`ingresses`/`ing`)

- Designed to expose HTTP services

- Basic features:

- load balancing
  - SSL termination
  - name-based virtual hosting

- Can also route to different services depending on:

- URI path (e.g. `/api`→`api-service`, `/static`→`assets-service`)
  - Client headers, including cookies (for A/B testing, canary deployment...)
  - and more!

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Principle of operation

- Step 1: deploy an *ingress controller*

- ingress controller = load balancer + control loop

- the control loop watches over ingress resources, and configures the LB accordingly

- Step 2: set up DNS

- associate DNS entries with the load balancer address

- Step 3: create *ingress resources*

- the ingress controller picks up these resources and configures the LB

- Step 4: profit!

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Ingress in action

- We will deploy the Traefik ingress controller

- this is an arbitrary choice

- maybe motivated by the fact that Traefik releases are named after cheeses

- For DNS, we will use [nip.io](http://nip.io/)

- `*.1.2.3.4.nip.io` resolves to `1.2.3.4`

- We will create ingress resources for various HTTP services

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Deploying pods listening on port 80

- We want our ingress load balancer to be available on port 80

- The best way to do that would be with a `LoadBalancer` service

... but it requires support from the underlying infrastructure

- Instead, we are going to use the `hostNetwork` mode on the Traefik pods

- Let's see what this `hostNetwork` mode is about ...

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Without `hostNetwork`

- Normally, each pod gets its own *network namespace*

(sometimes called sandbox or network sandbox)

- An IP address is assigned to the pod

- This IP address is routed/connected to the cluster network

- All containers of that pod are sharing that network namespace

(and therefore using the same IP address)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## With `hostNetwork: true`

- No network namespace gets created

- The pod is using the network namespace of the host

- It "sees" (and can use) the interfaces (and IP addresses) of the host

- The pod can receive outside traffic directly, on any port

- Downside: with most network plugins, network policies won't work for that pod

- most network policies work at the IP address level

- filtering that pod = filtering traffic from the node

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Other techniques to expose port 80

- We could use pods specifying `hostPort: 80`

... but with most CNI plugins, this [doesn't work or requires additional setup](https://github.com/kubernetes/kubernetes/issues/23920)

- We could use a `NodePort` service

... but that requires [changing the `--service-node-port-range` flag in the API server](https://kubernetes.io/docs/reference/command-line-tools-reference/kube-apiserver/)

- We could create a service with an external IP

... this would work, but would require a few extra steps

(figuring out the IP address and adding it to the service)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Running Traefik

- The [Traefik documentation](https://docs.traefik.io/user-guide/kubernetes/#deploy-trfik-using-a-deployment-or-daemonset) tells us to pick between Deployment and Daemon Set

- We are going to use a Daemon Set so that each node can accept connections

- We will do two minor changes to the [YAML provided by Traefik](https://github.com/containous/traefik/blob/v1.7/examples/k8s/traefik-ds.yaml):

- enable `hostNetwork`

- add a *toleration* so that Traefik also runs on `node1`

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Taints and tolerations

- A *taint* is an attribute added to a node

- It prevents pods from running on the node

- ... Unless they have a matching *toleration*

- When deploying with `kubeadm`:

- a taint is placed on the node dedicated to the control plane

- the pods running the control plane have a matching toleration

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Checking taints on our nodes

- Check our nodes specs:
  ```bash
  kubectl get node node1 -o json | jq .spec
  kubectl get node node2 -o json | jq .spec
  ```

]

We should see a result only for `node1` (the one with the control plane):

```json
  "taints": [
    {
      "effect": "NoSchedule",
      "key": "node-role.kubernetes.io/master"
    }
  ]
```

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Understanding a taint

- The `key` can be interpreted as:

- a reservation for a special set of pods
 
 (here, this means "this node is reserved for the control plane")

- an error condition on the node
 
 (for instance: "disk full," do not start new pods here!)

- The `effect` can be:

- `NoSchedule` (don't run new pods here)

- `PreferNoSchedule` (try not to run new pods here)

- `NoExecute` (don't run new pods and evict running pods)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Checking tolerations on the control plane

- Check tolerations for CoreDNS:
  ```bash
  kubectl -n kube-system get deployments coredns -o json |
          jq .spec.template.spec.tolerations
  ```

]

The result should include:
```json
  {
    "effect": "NoSchedule",
    "key": "node-role.kubernetes.io/master"
  }
```

It means: "bypass the exact taint that we saw earlier on `node1`."

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Special tolerations

- Check tolerations on `kube-proxy`:
  ```bash
  kubectl -n kube-system get ds kube-proxy -o json | 
          jq .spec.template.spec.tolerations
  ```

]

The result should include:
```json
  {
    "operator": "Exists"
  }
```

This one is a special case that means "ignore all taints and run anyway."

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Running Traefik on our cluster

- We provide a YAML file (`k8s/traefik.yaml`) which is essentially the sum of:

- [Traefik's Daemon Set resources](https://github.com/containous/traefik/blob/v1.7/examples/k8s/traefik-ds.yaml) (patched with `hostNetwork` and tolerations)

- [Traefik's RBAC rules](https://github.com/containous/traefik/blob/v1.7/examples/k8s/traefik-rbac.yaml) allowing it to watch necessary API objects

- Apply the YAML:
  ```bash
  kubectl apply -f ~/container.training/k8s/traefik.yaml
  ```

]

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Checking that Traefik runs correctly

- If Traefik started correctly, we now have a web server listening on each node

- Check that Traefik is serving 80/tcp:
  ```bash
  curl localhost
  ```

]

We should get a `404 page not found` error.

This is normal: we haven't provided any ingress rule yet.

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Setting up DNS

- To make our lives easier, we will use [nip.io](http://nip.io)

- Check out `http://cheddar.A.B.C.D.nip.io`

(replacing A.B.C.D with the IP address of `node1`)

- We should get the same `404 page not found` error

(meaning that our DNS is "set up properly", so to speak!)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Traefik web UI

- Traefik provides a web dashboard

- With the current install method, it's listening on port 8080

- Go to `http://node1:8080` (replacing `node1` with its IP address)

]

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Setting up host-based routing ingress rules

- We are going to use `errm/cheese` images

(there are [3 tags available](https://hub.docker.com/r/errm/cheese/tags/): wensleydale, cheddar, stilton)

- These images contain a simple static HTTP server sending a picture of cheese

- We will run 3 deployments (one for each cheese)

- We will create 3 services (one for each deployment)

- Then we will create 3 ingress rules (one for each service)

- We will route `<name-of-cheese>.A.B.C.D.nip.io` to the corresponding deployment

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Running cheesy web servers

- Run all three deployments:
  ```bash
  kubectl create deployment cheddar --image=errm/cheese:cheddar
  kubectl create deployment stilton --image=errm/cheese:stilton
  kubectl create deployment wensleydale --image=errm/cheese:wensleydale
  ```

- Create a service for each of them:
  ```bash
  kubectl expose deployment cheddar --port=80
  kubectl expose deployment stilton --port=80
  kubectl expose deployment wensleydale --port=80
  ```

]

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## What does an ingress resource look like?

Here is a minimal host-based ingress resource:

```yaml
apiVersion: networking.k8s.io/v1beta1
kind: Ingress
metadata:
  name: cheddar
spec:
  rules:
  - host: cheddar.`A.B.C.D`.nip.io
    http:
      paths:
      - path: /
        backend:
          serviceName: cheddar
          servicePort: 80

```

(It is in `k8s/ingress.yaml`.)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Creating our first ingress resources

- Edit the file `~/container.training/k8s/ingress.yaml`

- Replace A.B.C.D with the IP address of `node1`

- Apply the file

- Open http://cheddar.A.B.C.D.nip.io

]

(An image of a piece of cheese should show up.)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Creating the other ingress resources

- Edit the file `~/container.training/k8s/ingress.yaml`

- Replace `cheddar` with `stilton` (in `name`, `host`, `serviceName`)

- Apply the file

- Check that `stilton.A.B.C.D.nip.io` works correctly

- Repeat for `wensleydale`

]

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Using multiple ingress controllers

- You can have multiple ingress controllers active simultaneously

(e.g. Traefik and NGINX)

- You can even have multiple instances of the same controller

(e.g. one for internal, another for external traffic)

- The `kubernetes.io/ingress.class` annotation can be used to tell which one to use

- It's OK if multiple ingress controllers configure the same resource

(it just means that the service will be accessible through multiple paths)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Ingress: the good

- The traffic flows directly from the ingress load balancer to the backends

- it doesn't need to go through the `ClusterIP`

- in fact, we don't even need a `ClusterIP` (we can use a headless service)

- The load balancer can be outside of Kubernetes

(as long as it has access to the cluster subnet)

- This allows the use of external (hardware, physical machines...) load balancers

- Annotations can encode special features

(rate-limiting, A/B testing, session stickiness, etc.)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Ingress: the bad

- Aforementioned "special features" are not standardized yet

- Some controllers will support them; some won't

- Even relatively common features (stripping a path prefix) can differ:

- [traefik.ingress.kubernetes.io/rule-type: PathPrefixStrip](https://docs.traefik.io/user-guide/kubernetes/#path-based-routing)

- [ingress.kubernetes.io/rewrite-target: /](https://github.com/kubernetes/contrib/tree/master/ingress/controllers/nginx/examples/rewrite)

- This should eventually stabilize

(remember that ingresses are currently `apiVersion: networking.k8s.io/v1beta1`)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## A special feature in action

- We're going to see how to implement *canary releases* with Traefik

- This feature is available on multiple ingress controllers

- ... But it is configured very differently on each of them

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Canary releases

- A *canary release* (or canary launch or canary deployment) is a release that will process only a small fraction of the workload

- After deploying the canary, we compare its metrics to the normal release

- If the metrics look good, the canary will progressively receive more traffic

(until it gets 100% and becomes the new normal release)

- If the metrics aren't good, the canary is automatically removed

- When we deploy a bad release, only a tiny fraction of traffic is affected

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Various ways to implement canary

- Example 1: canary for a microservice

- 1% of all requests (sampled randomly) are sent to the canary
  - the remaining 99% are sent to the normal release

- Example 2: canary for a web app

- 1% of users are sent to the canary web site
  - the remaining 99% are sent to the normal release

- Example 3: canary for shipping physical goods

- 1% of orders are shipped with the canary process
  - the reamining 99% are shipped with the normal process

- We're going to implement example 1 (per-request routing)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Canary releases with Traefik

- We need to deploy the canary and expose it with a separate service

- Then, in the Ingress resource, we need:

- multiple `paths` entries (one for each service, canary and normal)

- an extra annotation indicating the weight of each service

- If we want, we can send requests to more than 2 services

- Let's send requests to our 3 cheesy services!

- Create the resource shown on the next slide

]

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## The Ingress resource

.small[
```yaml
apiVersion: networking.k8s.io/v1beta1
kind: Ingress
metadata:
  name: cheeseplate
  annotations:
    traefik.ingress.kubernetes.io/service-weights: |
      cheddar: 50%
      wensleydale: 25%
      stilton: 25%
spec:
  rules:
  - host: cheeseplate.`A.B.C.D`.nip.io
    http:
      paths:
      - path: /
        backend:
          serviceName: cheddar
          servicePort: 80
      - path: /
        backend:
          serviceName: wensledale
          servicePort: 80
      - path: /
        backend:
          serviceName: stilton
          servicePort: 80
```
]

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Testing the canary

- Let's check the percentage of requests going to each service

- Continuously send HTTP requests to the new ingress:
  ```bash
    while sleep 0.1; do
      curl -s http://cheeseplate.A.B.C.D.nip.io/
    done
  ```

]

We should see a 50/25/25 request mix.

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Load balancing fairness

Note: if we use odd request ratios, the load balancing algorithm might appear to be broken on a small scale (when sending a small number of requests), but on a large scale (with many requests) it will be fair.

For instance, with a 11%/89% ratio, we can see 79 requests going to the 89%-weighted service, and then requests alternating between the two services; then 79 requests again, etc.

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## Other ingress controllers

*Just to illustrate how different things are ...*

- With the NGINX ingress controller:

- define two ingress ressources
 
 (specifying rules with the same host+path)

- add `nginx.ingress.kubernetes.io/canary` annotations on each

- With Linkerd2:

- define two services

- define an extra service for the weighted aggregate of the two

- define a TrafficSplit (this is a CRD introduced by the SMI spec)

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---

## We need more than that

What we saw is just one of the multiple building blocks that we need to achieve a canary release.

We also need:

- metrics (latency, performance ...) for our releases

- automation to alter canary weights

(increase canary weight if metrics look good; decrease otherwise)

- a mechanism to manage the lifecycle of the canary releases

(create them, promote them, delete them ...)

For inspiration, check [flagger by Weave](https://github.com/weaveworks/flagger).

.debug[[k8s/ingress.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/k8s/ingress.md)]
---
class: title, self-paced

Thank you!

.debug[[shared/thankyou.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/thankyou.md)]
---

That's all, folks! Questions?

![end](images/end.jpg)

.debug[[shared/thankyou.md](https://github.com/jpetazzo/container.training/tree/2020-02-enix/slides/shared/thankyou.md)]