LISA17 M7: Getting Started with Docker and Containers

LISA17 M7

Getting Started with Docker and Containers

**Be kind to the WiFi!**

*Use the 5G network*
 
*Don't use your hotspot*
 
*Don't stream videos from YouTube, Netflix, etc.
 (if you're bored, watch local content instead)*

Thank you!

]
]
]

## Logistics

- Hello! We are
  Jérôme ([@jpetazzo](https://twitter.com/jpetazzo), Docker Inc.)
  &
  AJ ([@s0ulshake](https://twitter.com/s0ulshake), Travis CI)

- The tutorial will run from 1:30pm to 5:00pm

- This will be fast-paced, but DON'T PANIC!

- There will be a coffee break at 3:00pm
 
 (please remind me if I forget about it!)

- All the content is publicly available

One URL to remember: http://container.training

- Feel free to interrupt for questions at any time

- Live feedback, questions, help on [Slack](https://usenix-lisa.slack.com/messages/C0E6N1NJW)

## Chapter 1

- [Docker 30,000ft overview](#toc-docker-ft-overview)

- [Our training environment](#toc-our-training-environment)

- [Install Docker](#toc-install-docker)

- [Our First Containers](#toc-our-first-containers)

- [Background Containers](#toc-background-containers)

- [Restarting and attaching to containers](#toc-restarting-and-attaching-to-containers)

## Chapter 2

- [Understanding Docker Images](#toc-understanding-docker-images)

- [Building Images Interactively](#toc-building-images-interactively)

- [Building Docker images with a Dockerfile](#toc-building-docker-images-with-a-dockerfile)

- [CMD and ENTRYPOINT](#toc-cmd-and-entrypoint)

- [Copying files during the build](#toc-copying-files-during-the-build)

- [Multi-stage builds](#toc-multi-stage-builds)

- [Tips for efficient Dockerfiles](#toc-tips-for-efficient-dockerfiles)

- [Publishing images to the Docker Hub](#toc-publishing-images-to-the-docker-hub)

## Chapter 3

- [Naming and inspecting containers](#toc-naming-and-inspecting-containers)

- [Container Networking Basics](#toc-container-networking-basics)

- [The Container Network Model](#toc-the-container-network-model)

- [Ambassadors](#toc-ambassadors)

## Chapter 4

- [Local Development Workflow with Docker](#toc-local-development-workflow-with-docker)

- [Working with Volumes](#toc-working-with-volumes)

- [Compose For Development Stacks](#toc-compose-for-development-stacks)

- [Course Conclusion](#toc-course-conclusion)

Docker 30,000ft overview

---
# Docker 30,000ft overview

In this lesson, we will learn about:

* Why containers (non-technical elevator pitch)

* Why containers (technical elevator pitch)

* How Docker helps us to build, ship, and run

* The history of containers

We won't actually run Docker or containers in this chapter (yet!).

Don't worry, we will get to that fast enough!

.debug[[intro/Docker_Overview.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Docker_Overview.md)]
---

## Elevator pitch

### (for your manager, your boss...)

.debug[[intro/Docker_Overview.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Docker_Overview.md)]
---

## OK... Why the buzz around containers?

* The software industry has changed

* Before:
  * monolithic applications
  * long development cycles
  * single environment
  * slowly scaling up

* Now:
  * decoupled services
  * fast, iterative improvements
  * multiple environments
  * quickly scaling out

.debug[[intro/Docker_Overview.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Docker_Overview.md)]
---

## Deployment becomes very complex

* Many different stacks:
  * languages
  * frameworks
  * databases

* Many different targets:
  * individual development environments
  * pre-production, QA, staging...
  * production: on prem, cloud, hybrid

.debug[[intro/Docker_Overview.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Docker_Overview.md)]
---

## The deployment problem

![problem](images/problem.png)

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---

## The matrix from hell

![matrix](images/matrix.png)

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---

## The parallel with the shipping indsutry

![history](images/shippingindustry.png)

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---

## Intermodal shipping containers

![shipping](images/shipping.png)

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---

## A new shipping ecosystem

![shipeco](images/shipeco.png)

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---

## A shipping container system for applications

![shipapp](images/appcont.png)

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---

## Eliminate the matrix from hell

![elimatrix](images/elimatrix.png)

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---

## Results

* Dev-to-prod reduced from 9 months to 15 minutes (ING)

* Continuous integration job time reduced by more than 60% (BBC)

* Dev-to-prod reduced from weeks to minutes (GILT)

* etc.

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---

## Elevator pitch

### (for your fellow devs and ops)

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---

## Escape dependency hell

1. Write installation instructions into an `INSTALL.txt` file

2. Using this file, write an `install.sh` script that works *for you*

3. Turn this file into a `Dockerfile`, test it on your machine

4. If the Dockerfile builds on your machine, it will build *anywhere*

5. Rejoice as you escape dependency hell and "works on my machine"

Never again "worked in dev - ops problem now!"

.debug[[intro/Docker_Overview.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Docker_Overview.md)]
---

## On-board developers and contributors rapidly

1. Write Dockerfiles for your application components

2. Use pre-made images from the Docker Hub (mysql, redis...)

3. Describe your stack with a Compose file

4. On-board somebody with two commands:

```bash
git clone ...
docker-compose up
```

With this, you can create development, integration, QA environments in minutes!

.debug[[intro/Docker_Overview.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Docker_Overview.md)]
---

## Implement reliable CI easily

1. Build test environment with a Dockerfile or Compose file

2. For each test run, stage up a new container or stack

3. Each run is now in a clean environment

4. No pollution from previous tests

Way faster and cheaper than creating VMs each time!

.debug[[intro/Docker_Overview.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Docker_Overview.md)]
---

## Use container images as build artefacts

1. Build your app from Dockerfiles

2. Store the resulting images in a registry

3. Keep them forever (or as long as necessary)

4. Test those images in QA, CI, integration...

5. Run the same images in production

6. Something goes wrong? Rollback to previous image

7. Investigating old regression? Old image has your back!

Images contain all the libraries, dependencies, etc. needed to run the app.

.debug[[intro/Docker_Overview.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Docker_Overview.md)]
---

## Decouple "plumbing" from application logic

1. Write your code to connect to named services ("db", "api"...)

2. Use Compose to start your stack

3. Docker will setup per-container DNS resolver for those names

4. You can now scale, add load balancers, replication ... without changing your code

Note: this is not covered in this intro level workshop!

.debug[[intro/Docker_Overview.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Docker_Overview.md)]
---

## What did Docker bring to the table?

### Docker before/after

.debug[[intro/Docker_Overview.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Docker_Overview.md)]
---

## Formats and APIs, before Docker

* No standardized exchange format.
 (No, a rootfs tarball is *not* a format!)

* Containers are hard to use for developers.
 (Where's the equivalent of `docker run debian`?)

* As a result, they are *hidden* from the end users.

* No re-usable components, APIs, tools.
 (At best: VM abstractions, e.g. libvirt.)

Analogy:

* Shipping containers are not just steel boxes.
* They are steel boxes that are a standard size, with the same hooks and holes.

.debug[[intro/Docker_Overview.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Docker_Overview.md)]
---

## Formats and APIs, after Docker

* Standardize the container format, because containers were not portable.

* Make containers easy to use for developers.

* Emphasis on re-usable components, APIs, ecosystem of standard tools.

* Improvement over ad-hoc, in-house, specific tools.

.debug[[intro/Docker_Overview.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Docker_Overview.md)]
---

## Shipping, before Docker

* Ship packages: deb, rpm, gem, jar, homebrew...

* Dependency hell.

* "Works on my machine."

* Base deployment often done from scratch (debootstrap...) and unreliable.

.debug[[intro/Docker_Overview.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Docker_Overview.md)]
---

## Shipping, after Docker

* Ship container images with all their dependencies.

* Images are bigger, but they are broken down into layers.

* Only ship layers that have changed.

* Save disk, network, memory usage.

.debug[[intro/Docker_Overview.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Docker_Overview.md)]
---

## Example

Layers:

* CentOS
* JRE
* Tomcat
* Dependencies
* Application JAR
* Configuration

.debug[[intro/Docker_Overview.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Docker_Overview.md)]
---

## Devs vs Ops, before Docker

* Drop a tarball (or a commit hash) with instructions.

* Dev environment very different from production.

* Ops don't always have a dev environment themselves ...

* ... and when they do, it can differ from the devs'.

* Ops have to sort out differences and make it work ...

* ... or bounce it back to devs.

* Shipping code causes frictions and delays.

.debug[[intro/Docker_Overview.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Docker_Overview.md)]
---

## Devs vs Ops, after Docker

* Drop a container image or a Compose file.

* Ops can always run that container image.

* Ops can always run that Compose file.

* Ops still have to adapt to prod environment,
  but at least they have a reference point.

* Ops have tools allowing to use the same image
  in dev and prod.

* Devs can be empowered to make releases themselves
  more easily.

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---

Our training environment

---
class: title

# Our training environment

![SSH terminal](images/ssh.jpg)

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---

## Our training environment

- If you are attending a tutorial or workshop:

- a VM has been provisioned for each student

- If you are doing or re-doing this course on your own, you can:

- install Docker locally (as explained in the chapter "Installing Docker")

- install Docker on e.g. a cloud VM

- use http://www.play-with-docker.com/ to instantly get a training environment

.debug[[intro/Training_Environment.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Training_Environment.md)]
---

## Our Docker VM

*This section assumes that you are following this course as part of
a tutorial, training or workshop, where each student is given an
individual Docker VM.*

- The VM is created just before the training.

- It will stay up during the whole training.

- It will be destroyed shortly after the training.

- It comes pre-loaded with Docker and some other useful tools.

.debug[[intro/Training_Environment.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Training_Environment.md)]
---

## Connecting to your Virtual Machine

You need an SSH client.

* On OS X, Linux, and other UNIX systems, just use `ssh`:

```bash
$ ssh <login>@<ip-address>
```

* On Windows, if you don't have an SSH client, you can download:

* Putty (www.putty.org)

* Git BASH (https://git-for-windows.github.io/)

* MobaXterm (http://moabaxterm.mobatek.net)

.debug[[intro/Training_Environment.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Training_Environment.md)]
---

## Checking your Virtual Machine

Once logged in, make sure that you can run a basic Docker command:

.small[
```bash
$ docker version
Client:
 Version:      17.09.0-ce
 API version:  1.32
 Go version:   go1.8.3
 Git commit:   afdb6d4
 Built:        Tue Sep 26 22:40:09 2017
 OS/Arch:      darwin/amd64

Server:
 Version:      17.09.0-ce
 API version:  1.32 (minimum version 1.12)
 Go version:   go1.8.3
 Git commit:   afdb6d4
 Built:        Tue Sep 26 22:45:38 2017
 OS/Arch:      linux/amd64
 Experimental: true
```
]

If this doesn't work, raise your hand so that an instructor can assist you!

.debug[[intro/Training_Environment.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Training_Environment.md)]
---

Install Docker

---

# Install Docker

![install](images/install.jpg)

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---

## Objectives

At the end of this lesson, you will know:

* How to install Docker.

* When to use `sudo` when running Docker commands.

*Note:* if you were provided with a training VM for a hands-on
tutorial, you can skip this chapter, since that VM already
has Docker installed, and Docker has already been setup to run
without `sudo`.

.debug[[intro/Install_Docker.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Install_Docker.md)]
---

## Installing Docker

There are many ways to install Docker.

We can arbitrarily distinguish:

* Installing Docker on an existing Linux machine (physical or VM)

* Installing Docker on MacOS or Windows

* Installing Docker on a fleet of cloud VMs

.debug[[intro/Install_Docker.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Install_Docker.md)]
---

## Installing Docker on Linux

* The recommended method is to install the packages supplied by Docker Inc.

* The general method is:

- add Docker Inc.'s package repositories to your system configuration

- install the Docker Engine

* Detailed installation instructions (distro by distro) are available on:

https://docs.docker.com/engine/installation/

* You can also install from binaries (if your distro is not supported):

https://docs.docker.com/engine/installation/linux/docker-ce/binaries/

.debug[[intro/Install_Docker.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Install_Docker.md)]
---

## Installing Docker on MacOS and Windows

* On MacOS, the recommended method is to use Docker4Mac:

https://docs.docker.com/docker-for-mac/install/

* On Windows 10 Pro, Enterprise, and Eduction, you can use Docker4Windows:

https://docs.docker.com/docker-for-windows/install/

* On older versions of Windows, you can use the Docker Toolbox:

https://docs.docker.com/toolbox/toolbox_install_windows/

.debug[[intro/Install_Docker.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Install_Docker.md)]
---

## Running Docker on MacOS and Windows

When you execute `docker version` from the terminal:

* the CLI connects to the Docker Engine over a standard socket,
* the Docker Engine is, in fact, running in a VM,
* ... but the CLI doesn't know or care about that,
* the CLI sends a request using the REST API,
* the Docker Engine in the VM processes the request,
* the CLI gets the response and displays it to you.

All communication with the Docker Engine happens over the API.

This will also allow to use remote Engines exactly as if they were local.

.debug[[intro/Install_Docker.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Install_Docker.md)]
---

## Docker4Mac and Docker4Windows

* They let you run Docker without VirtualBox

* They are installed like normal applications (think QEMU, but faster)

* They access network resources like normal applications
 (and therefore, play well with enterprise VPNs and firewalls)

* They support filesystem sharing through volumes (we'll talk about this later)

* They only support running one Docker VM at a time ...

... so if you want to run a full cluster locally, install e.g. the Docker Toolbox

* They can co-exist with the Docker Toolbox

.debug[[intro/Install_Docker.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Install_Docker.md)]
---

## Important PSA about security

* If you have access to the Docker control socket, you can take over the machine

(Because you can run containers that will access the machine's resources)

* Therefore, on Linux machines, the `docker` user is equivalent to `root`

* You should restrict access to it like you would protect `root`

* By default, the Docker control socket belongs to the `docker` group

* You can add trusted users to the `docker` group

* Otherwise, you will have to prefix every `docker` command with `sudo`, e.g.:

```bash
  sudo docker version
  ```

.debug[[intro/Install_Docker.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Install_Docker.md)]
---

Our First Containers

---

# Our First Containers

![Plastic Containers](images/firstcontainers.jpg)

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---

## Objectives

At the end of this lesson, you will have:

* Seen Docker in action.

* Started your first containers.

.debug[[intro/First_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/First_Containers.md)]
---

## Hello World

In your Docker environment, just run the following command:

```bash
$ docker run busybox echo hello world
hello world
```

(If your Docker install is brand new, you will also see a few extra lines,
corresponding to the download of the `busybox` image.)

.debug[[intro/First_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/First_Containers.md)]
---

## That was our first container!

* We used one of the smallest, simplest images available: `busybox`.

* `busybox` is typically used in embedded systems (phones, routers...)

* We ran a single process and echo'ed `hello world`.

.debug[[intro/First_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/First_Containers.md)]
---

## A more useful container

Let's run a more exciting container:

```bash
$ docker run -it ubuntu
root@04c0bb0a6c07:/#
```

* This is a brand new container.
* It runs a bare-bones, no-frills `ubuntu` system.
* `-it` is shorthand for `-i -t`.

* `-i` tells Docker to connect us to the container's stdin.
  * `-t` tells Docker that we want a pseudo-terminal.

.debug[[intro/First_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/First_Containers.md)]
---

## Do something in our container

Try to run `figlet` in our container.

```bash
root@04c0bb0a6c07:/# figlet hello
bash: figlet: command not found
```

Alright, we need to install it.

.debug[[intro/First_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/First_Containers.md)]
---

## An observation

Let's check how many packages are installed here.

```bash
root@04c0bb0a6c07:/# dpkg -l | wc -l
189
```

* `dpkg -l` lists the packages installed in our container
* `wc -l` counts them
* If you have a Debian or Ubuntu machine, you can run the same command 
  and compare the results.

.debug[[intro/First_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/First_Containers.md)]
---

## Install a package in our container

We want `figlet`, so let's install it:

```bash
root@04c0bb0a6c07:/# apt-get update
...
Fetched 1514 kB in 14s (103 kB/s)
Reading package lists... Done
root@04c0bb0a6c07:/# apt-get install figlet
Reading package lists... Done
...
```

One minute later, `figlet` is installed!

```bash
root@04c0bb0a6c07:/# figlet hello
 _          _ _       
| |__   ___| | | ___  
| '_ \ / _ \ | |/ _ \ 
| | | |  __/ | | (_) |
|_| |_|\___|_|_|\___/ 
```

.debug[[intro/First_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/First_Containers.md)]
---

## Exiting our container

Just exit the shell, like you would usually do.

(E.g. with `^D` or `exit`)

```bash
root@04c0bb0a6c07:/# exit
```

* Our container is now in a *stopped* state.

* It still exists on disk, but all compute resources have been freed up.

.debug[[intro/First_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/First_Containers.md)]
---

## Starting another container

What if we start a new container, and try to run `figlet` again?
 
```bash
$ docker run -it ubuntu
root@b13c164401fb:/# figlet
bash: figlet: command not found
```

* We started a *brand new container*.

* The basic Ubuntu image was used, and `figlet` is not here.

* We will see in the next chapters how to bake a custom image with `figlet`.

.debug[[intro/First_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/First_Containers.md)]
---

Background Containers

---

# Background Containers

![Background Containers](images/background-containers.jpg)

.debug[[intro/Background_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Background_Containers.md)]
---

## Objectives

Our first containers were *interactive*.

We will now see how to:

* Run a non-interactive container.
* Run a container in the background.
* List running containers.
* Check the logs of a container.
* Stop a container.
* List stopped containers.

.debug[[intro/Background_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Background_Containers.md)]
---

## A non-interactive container

We will run a small custom container.

This container just displays the time every second.

```bash
$ docker run jpetazzo/clock
Fri Feb 20 00:28:53 UTC 2015
Fri Feb 20 00:28:54 UTC 2015
Fri Feb 20 00:28:55 UTC 2015
...
```

* This container will run forever.
* To stop it, press `^C`.
* Docker has automatically downloaded the image `jpetazzo/clock`.
* This image is a user image, created by `jpetazzo`.
* We will hear more about user images (and other types of images) later.

.debug[[intro/Background_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Background_Containers.md)]
---

## Run a container in the background

Containers can be started in the background, with the `-d` flag (daemon mode):

```bash
$ docker run -d jpetazzo/clock
47d677dcfba4277c6cc68fcaa51f932b544cab1a187c853b7d0caf4e8debe5ad
```

* We don't see the output of the container.
* But don't worry: Docker collects that output and logs it!
* Docker gives us the ID of the container.

.debug[[intro/Background_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Background_Containers.md)]
---

## List running containers

How can we check that our container is still running?

With `docker ps`, just like the UNIX `ps` command, lists running processes.

```bash
$ docker ps
CONTAINER ID  IMAGE           ...  CREATED        STATUS        ...
47d677dcfba4  jpetazzo/clock  ...  2 minutes ago  Up 2 minutes  ...
```

Docker tells us:

* The (truncated) ID of our container.
* The image used to start the container.
* That our container has been running (`Up`) for a couple of minutes.
* Other information (COMMAND, PORTS, NAMES) that we will explain later.

.debug[[intro/Background_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Background_Containers.md)]
---

## Starting more containers

Let's start two more containers.

```bash
$ docker run -d jpetazzo/clock
57ad9bdfc06bb4407c47220cf59ce21585dce9a1298d7a67488359aeaea8ae2a
```

```bash
$ docker run -d jpetazzo/clock
068cc994ffd0190bbe025ba74e4c0771a5d8f14734af772ddee8dc1aaf20567d
```

Check that `docker ps` correctly reports all 3 containers.

.debug[[intro/Background_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Background_Containers.md)]
---

## Viewing only the last container started

When many containers are already running, it can be useful to
see only the last container that was started.

This can be achieved with the `-l` ("Last") flag:

```bash
$ docker ps -l
CONTAINER ID  IMAGE           ...  CREATED        STATUS        ...
068cc994ffd0  jpetazzo/clock  ...  2 minutes ago  Up 2 minutes  ...
```

.debug[[intro/Background_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Background_Containers.md)]
---

## View only the IDs of the containers

Many Docker commands will work on container IDs: `docker stop`, `docker rm`...

If we want to list only the IDs of our containers (without the other colums
or the header line),
we can use the `-q` ("Quiet", "Quick") flag:

```bash
$ docker ps -q
068cc994ffd0
57ad9bdfc06b
47d677dcfba4
```

.debug[[intro/Background_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Background_Containers.md)]
---

## Combining flags

We can combine `-l` and `-q` to see only the ID of the last container started:

```bash
$ docker ps -lq
068cc994ffd0
```

At a first glance, it looks like this would be particularly useful in scripts.

However, if we want to start a container and get its ID in a reliable way,
it is better to use `docker run -d`, which we will cover in a bit.

.debug[[intro/Background_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Background_Containers.md)]
---

## View the logs of a container

We told you that Docker was logging the container output.

Let's see that now.

```bash
$ docker logs 068
Fri Feb 20 00:39:52 UTC 2015
Fri Feb 20 00:39:53 UTC 2015
...
```

* We specified a *prefix* of the full container ID.
* You can, of course, specify the full ID.
* The `logs` command will output the *entire* logs of the container.
 (Sometimes, that will be too much. Let's see how to address that.)

.debug[[intro/Background_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Background_Containers.md)]
---

## View only the tail of the logs

To avoid being spammed with eleventy pages of output,
we can use the `--tail` option:

```bash
$ docker logs --tail 3 068
Fri Feb 20 00:55:35 UTC 2015
Fri Feb 20 00:55:36 UTC 2015
Fri Feb 20 00:55:37 UTC 2015
```

* The parameter is the number of lines that we want to see.

.debug[[intro/Background_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Background_Containers.md)]
---

## Follow the logs in real time

Just like with the standard UNIX command `tail -f`, we can
follow the logs of our container:

```bash
$ docker logs --tail 1 --follow 068
Fri Feb 20 00:57:12 UTC 2015
Fri Feb 20 00:57:13 UTC 2015
^C
```

* This will display the last line in the log file.
* Then, it will continue to display the logs in real time.
* Use `^C` to exit.

.debug[[intro/Background_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Background_Containers.md)]
---

## Stop our container

There are two ways we can terminate our detached container.

* Killing it using the `docker kill` command.
* Stopping it using the `docker stop` command.

The first one stops the container immediately, by using the
`KILL` signal.

The second one is more graceful. It sends a `TERM` signal,
and after 10 seconds, if the container has not stopped, it
sends `KILL.`

Reminder: the `KILL` signal cannot be intercepted, and will
forcibly terminate the container.

.debug[[intro/Background_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Background_Containers.md)]
---

## Stopping our containers

Let's stop one of those containers:

```bash
$ docker stop 47d6
47d6
```

This will take 10 seconds:

* Docker sends the TERM signal;
* the container doesn't react to this signal
  (it's a simple Shell script with no special
  signal handling);
* 10 seconds later, since the container is still
  running, Docker sends the KILL signal;
* this terminates the container.

.debug[[intro/Background_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Background_Containers.md)]
---

## Killing the remaining containers

Let's be less patient with the two other containers:

```bash
$ docker kill 068 57ad
068
57ad
```

The `stop` and `kill` commands can take multiple container IDs.

Those containers will be terminated immediately (without
the 10 seconds delay).

Let's check that our containers don't show up anymore:

```bash
$ docker ps
```

.debug[[intro/Background_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Background_Containers.md)]
---

## List stopped containers

We can also see stopped containers, with the `-a` (`--all`) option.

```bash
$ docker ps -a
CONTAINER ID  IMAGE           ...  CREATED      STATUS
068cc994ffd0  jpetazzo/clock  ...  21 min. ago  Exited (137) 3 min. ago
57ad9bdfc06b  jpetazzo/clock  ...  21 min. ago  Exited (137) 3 min. ago
47d677dcfba4  jpetazzo/clock  ...  23 min. ago  Exited (137) 3 min. ago
5c1dfd4d81f1  jpetazzo/clock  ...  40 min. ago  Exited (0) 40 min. ago
b13c164401fb  ubuntu          ...  55 min. ago  Exited (130) 53 min. ago
```

.debug[[intro/Background_Containers.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Background_Containers.md)]
---

Restarting and attaching to containers

---
# Restarting and attaching to containers

We have started containers in the foreground, and in the background.

In this chapter, we will see how to:

* Put a container in the background.
* Attach to a background container to bring it to the foreground.
* Restart a stopped container.

.debug[[intro/Start_And_Attach.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Start_And_Attach.md)]
---

## Background and foreground

The distinction between foreground and background containers is arbitrary.

From Docker's point of view, all containers are the same.

All containers run the same way, whether there is a client attached to them or not.

It is always possible to detach from a container, and to reattach to a container.

Analogy: attaching to a container is like plugging a keyboard and screen to a physical server.

.debug[[intro/Start_And_Attach.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Start_And_Attach.md)]
---

## Detaching from a container

* If you have started an *interactive* container (with option `-it`), you can detach from it.

* The "detach" sequence is `^P^Q`.

* Otherwise you can detach by killing the Docker client.
  
  (But not by hitting `^C`, as this would deliver `SIGINT` to the container.)

What does `-it` stand for?

* `-t` means "allocate a terminal."
* `-i` means "connect stdin to the terminal."

.debug[[intro/Start_And_Attach.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Start_And_Attach.md)]
---

## Specifying a custom detach sequence

* You don't like `^P^Q`? No problem!
* You can change the sequence with `docker run --detach-keys`.
* This can also be passed as a global option to the engine.

Start a container with a custom detach command:

```bash
$ docker run -ti --detach-keys ctrl-x,x jpetazzo/clock
```

Detach by hitting `^X x`. (This is ctrl-x then x, not ctrl-x twice!)

Check that our container is still running:

```bash
$ docker ps -l
```

.debug[[intro/Start_And_Attach.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Start_And_Attach.md)]
---

## Attaching to a container

You can attach to a container:

```bash
$ docker attach <containerID>
```

* The container must be running.
* There *can* be multiple clients attached to the same container.
* If you don't specify `--detach-keys` when attaching, it defaults back to `^P^Q`.

Try it on our previous container:

```bash
$ docker attach $(docker ps -lq)
```

Check that `^X x` doesn't work, but `^P ^Q` does.

.debug[[intro/Start_And_Attach.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Start_And_Attach.md)]
---

## Detaching from non-interactive containers

* **Warning:** if the container was started without `-it`...

* You won't be able to detach with `^P^Q`.
  * If you hit `^C`, the signal will be proxied to the container.

* Remember: you can always detach by killing the Docker client.

.debug[[intro/Start_And_Attach.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Start_And_Attach.md)]
---

## Checking container output

* Use `docker attach` if you intend to send input to the container.

* If you just want to see the output of a container, use `docker logs`.

```bash
$ docker logs --tail 1 --follow <containerID>
```

.debug[[intro/Start_And_Attach.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Start_And_Attach.md)]
---

## Restarting a container

When a container has exited, it is in stopped state.

It can then be restarted with the `start` command.

```bash
$ docker start <yourContainerID>
```

The container will be restarted using the same options you launched it
with.

You can re-attach to it if you want to interact with it:

```bash
$ docker attach <yourContainerID>
```

Use `docker ps -a` to identify the container ID of a previous `jpetazzo/clock` container,
and try those commands.

.debug[[intro/Start_And_Attach.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Start_And_Attach.md)]
---

## Attaching to a REPL

* REPL = Read Eval Print Loop

* Shells, interpreters, TUI ...

* Symptom: you `docker attach`, and see nothing

* The REPL doesn't know that you just attached, and doesn't print anything

* Try hitting `^L` or `Enter`

.debug[[intro/Start_And_Attach.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Start_And_Attach.md)]
---

## SIGWINCH

* When you `docker attach`, the Docker Engine sends SIGWINCH signals to the container.

* SIGWINCH = WINdow CHange; indicates a change in window size.

* This will cause some CLI and TUI programs to redraw the screen.

* But not all of them.

.debug[[intro/Start_And_Attach.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Start_And_Attach.md)]
---

Understanding Docker Images

---

# Understanding Docker Images

![image](images/image.png)

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## Objectives

In this section, we will explain:

* What is an image.

* What is a layer.

* The various image namespaces.

* How to search and download images.

* Image tags and when to use them.

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## What is an image?

* Image = files + metadata

* These files form the root filesystem of our container.

* The metadata can indicate a number of things, e.g.:

* the author of the image
  * the command to execute in the container when starting it
  * environment variables to be set
  * etc.

* Images are made of *layers*, conceptually stacked on top of each other.

* Each layer can add, change, and remove files and/or metadata.

* Images can share layers to optimize disk usage, transfer times, and memory use.

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## Example for a Java webapp

* CentOS base layer
* Packages and configuration files added by our local IT
* JRE
* Tomcat
* Our application's dependencies
* Our application code and assets
* Our application configuration

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## Differences between containers and images

* An image is a read-only filesystem.

* A container is an encapsulated set of processes running in a
  read-write copy of that filesystem.

* To optimize container boot time, *copy-on-write* is used 
  instead of regular copy.

* `docker run` starts a container from a given image.

Let's give a couple of metaphors to illustrate those concepts.

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## Image as stencils

Images are like templates or stencils that you can create containers from.

![stencil](images/stenciling-wall.jpg)

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## Object-oriented programming

* Images are conceptually similar to *classes*.

* Layers are conceptually similar to *inheritance*.

* Containers are conceptually similar to *instances*.

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## Wait a minute...

If an image is read-only, how do we change it?

* We don't.

* We create a new container from that image.

* Then we make changes to that container.
 
* When we are satisfied with those changes, we transform them into a new layer.

* A new image is created by stacking the new layer on top of the old image.

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## A chicken-and-egg problem

* The only way to create an image is by "freezing" a container.

* The only way to create a container is by instanciating an image.

* Help!

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## Creating the first images

There is a special empty image called `scratch`.

* It allows to *build from scratch*.

The `docker import` command loads a tarball into Docker.

* The imported tarball becomes a standalone image.
* That new image has a single layer.

Note: you will probably never have to do this yourself.

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## Creating other images

`docker commit`

* Saves all the changes made to a container into a new layer.
* Creates a new image (effectively a copy of the container).

`docker build`

* Performs a repeatable build sequence.
* This is the preferred method!

We will explain both methods in a moment.

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## Images namespaces

There are three namespaces:

* Official images

e.g. `ubuntu`, `busybox` ...

* User (and organizations) images

e.g. `jpetazzo/clock`

* Self-hosted images

e.g. `registry.example.com:5000/my-private/image`

Let's explain each of them.

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## Root namespace

The root namespace is for official images. They are put there by Docker Inc.,
but they are generally authored and maintained by third parties.

Those images include:

* Small, "swiss-army-knife" images like busybox.

* Distro images to be used as bases for your builds, like ubuntu, fedora...

* Ready-to-use components and services, like redis, postgresql...

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## User namespace

The user namespace holds images for Docker Hub users and organizations.

For example:

```bash
jpetazzo/clock
```

The Docker Hub user is:

```bash
jpetazzo
```

The image name is:

```bash
clock
```

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## Self-Hosted namespace

This namespace holds images which are not hosted on Docker Hub, but on third
party registries.

They contain the hostname (or IP address), and optionally the port, of the
registry server.

For example:

```bash
localhost:5000/wordpress
```

* `localhost:5000` is the host and port of the registry
* `wordpress` is the name of the image

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## How do you store and manage images?

Images can be stored:

* On your Docker host.
* In a Docker registry.

You can use the Docker client to download (pull) or upload (push) images.

To be more accurate: you can use the Docker client to tell a Docker Engine
to push and pull images to and from a registry.

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## Showing current images

Let's look at what images are on our host now.

```bash
$ docker images
REPOSITORY       TAG       IMAGE ID       CREATED         SIZE
fedora           latest    ddd5c9c1d0f2   3 days ago      204.7 MB
centos           latest    d0e7f81ca65c   3 days ago      196.6 MB
ubuntu           latest    07c86167cdc4   4 days ago      188 MB
redis            latest    4f5f397d4b7c   5 days ago      177.6 MB
postgres         latest    afe2b5e1859b   5 days ago      264.5 MB
alpine           latest    70c557e50ed6   5 days ago      4.798 MB
debian           latest    f50f9524513f   6 days ago      125.1 MB
busybox          latest    3240943c9ea3   2 weeks ago     1.114 MB
training/namer   latest    902673acc741   9 months ago    289.3 MB
jpetazzo/clock   latest    12068b93616f   12 months ago   2.433 MB
```

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## Searching for images

We cannot list *all* images on a remote registry, but
we can search for a specific keyword:

```bash
$ docker search marathon
NAME                     DESCRIPTION                     STARS  OFFICIAL  AUTOMATED
mesosphere/marathon      A cluster-wide init and co...   105              [OK]
mesoscloud/marathon      Marathon                        31               [OK]
mesosphere/marathon-lb   Script to update haproxy b...   22               [OK]
tobilg/mongodb-marathon  A Docker image to start a ...   4                [OK]
```

* "Stars" indicate the popularity of the image.

* "Official" images are those in the root namespace.

* "Automated" images are built automatically by the Docker Hub.
 (This means that their build recipe is always available.)

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## Downloading images

There are two ways to download images.

* Explicitly, with `docker pull`.

* Implicitly, when executing `docker run` and the image is not found locally.

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## Pulling an image

```bash
$ docker pull debian:jessie
Pulling repository debian
b164861940b8: Download complete 
b164861940b8: Pulling image (jessie) from debian 
d1881793a057: Download complete 
```

* As seen previously, images are made up of layers.

* Docker has downloaded all the necessary layers.

* In this example, `:jessie` indicates which exact version of Debian
  we would like.

It is a *version tag*.

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## Image and tags

* Images can have tags.

* Tags define image versions or variants.

* `docker pull ubuntu` will refer to `ubuntu:latest`.

* The `:latest` tag is generally updated often.

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## When to (not) use tags

Don't specify tags:

* When doing rapid testing and prototyping.
* When experimenting.
* When you want the latest version.

Do specify tags:

* When recording a procedure into a script.
* When going to production.
* To ensure that the same version will be used everywhere.
* To ensure repeatability later.

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

## Section summary

We've learned how to:

* Understand images and layers.
* Understand Docker image namespacing.
* Search and download images.

.debug[[intro/Initial_Images.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Initial_Images.md)]
---

Building Images Interactively

---
# Building Images Interactively

In this section, we will create our first container image.

It will be a basic distribution image, but we will pre-install
the package `figlet`.

We will:

* Create a container from a base image.

* Install software manually in the container, and turn it
  into a new image.

* Learn about new commands: `docker commit`, `docker tag`, and `docker diff`.

.debug[[intro/Building_Images_Interactively.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_Interactively.md)]
---

## Building Images Interactively

As we have seen, the images on the Docker Hub are sometimes very basic.

How do we want to construct our own images?

As an example, we will build an image that has `figlet`.

First, we will do it manually with `docker commit`.

Then, in an upcoming chapter, we will use a `Dockerfile` and `docker build`.

.debug[[intro/Building_Images_Interactively.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_Interactively.md)]
---

## Building from a base

Our base will be the `ubuntu` image.

.debug[[intro/Building_Images_Interactively.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_Interactively.md)]
---

## Create a new container and make some changes

Start an Ubuntu container:

```bash
$ docker run -it ubuntu
root@<yourContainerId>:#/
```

Run the command `apt-get update` to refresh the list of packages available to install.

Then run the command `apt-get install figlet` to install the program we are interested in.

```bash
root@<yourContainerId>:#/ apt-get update && apt-get install figlet
.... OUTPUT OF APT-GET COMMANDS ....
```

.debug[[intro/Building_Images_Interactively.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_Interactively.md)]
---

## Inspect the changes

Type `exit` at the container prompt to leave the interactive session.

Now let's run `docker diff` to see the difference between the base image
and our container.

```bash
$ docker diff <yourContainerId>
C /root
A /root/.bash_history
C /tmp
C /usr
C /usr/bin
A /usr/bin/figlet
...
```

.debug[[intro/Building_Images_Interactively.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_Interactively.md)]
---

## Docker tracks filesystem changes

As explained before:

* An image is read-only.

* When we make changes, they happen in a copy of the image.

* Docker can show the difference between the image, and its copy.

* For performance, Docker uses copy-on-write systems.
 (i.e. starting a container based on a big image
 doesn't incur a huge copy.)

.debug[[intro/Building_Images_Interactively.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_Interactively.md)]
---

## Copy-on-write security benefits

* `docker diff` gives us an easy way to audit changes

(à la Tripwire)

* Containers can also be started in read-only mode

(their root filesystem will be read-only, but they can still have read-write data volumes)

.debug[[intro/Building_Images_Interactively.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_Interactively.md)]
---

## Commit and run your image

The `docker commit` command will create a new layer with those changes,
and a new image using this new layer.

```bash
$ docker commit <yourContainerId>
<newImageId>
```

The output of the `docker commit` command will be the ID for your newly created image.

We can run this image:

```bash
$ docker run -it <newImageId>
root@fcfb62f0bfde:/# figlet hello
 _ _ _ 
| |__ ___| | | ___ 
| '_ \ / _ \ | |/ _ \ 
| | | | __/ | | (_) |
|_| |_|\___|_|_|\___/ 
```

.debug[[intro/Building_Images_Interactively.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_Interactively.md)]
---

## Tagging images

Referring to an image by its ID is not convenient. Let's tag it instead.

We can use the `tag` command:

```bash
$ docker tag <newImageId> figlet
```

But we can also specify the tag as an extra argument to `commit`:

```bash
$ docker commit <containerId> figlet
```

And then run it using its tag:

```bash
$ docker run -it figlet
```

.debug[[intro/Building_Images_Interactively.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_Interactively.md)]
---

## What's next?

Manual process = bad.

Automated process = good.

In the next chapter, we will learn how to automate the build
process by writing a `Dockerfile`.

.debug[[intro/Building_Images_Interactively.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_Interactively.md)]
---

Building Docker images with a Dockerfile

---

# Building Docker images with a Dockerfile

![construction](images/construction.jpg)

.debug[[intro/Building_Images_With_Dockerfiles.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_With_Dockerfiles.md)]
---

## Objectives

We will build a container image automatically, with a `Dockerfile`.

At the end of this lesson, you will be able to:

* Write a `Dockerfile`.

* Build an image from a `Dockerfile`.

.debug[[intro/Building_Images_With_Dockerfiles.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_With_Dockerfiles.md)]
---

## `Dockerfile` overview

* A `Dockerfile` is a build recipe for a Docker image.

* It contains a series of instructions telling Docker how an image is constructed.

* The `docker build` command builds an image from a `Dockerfile`.

.debug[[intro/Building_Images_With_Dockerfiles.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_With_Dockerfiles.md)]
---

## Writing our first `Dockerfile`

Our Dockerfile must be in a **new, empty directory**.

1. Create a directory to hold our `Dockerfile`.

```bash
$ mkdir myimage
```

2. Create a `Dockerfile` inside this directory.

```bash
$ cd myimage
$ vim Dockerfile
```

Of course, you can use any other editor of your choice.

.debug[[intro/Building_Images_With_Dockerfiles.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_With_Dockerfiles.md)]
---

## Type this into our Dockerfile...

```dockerfile
FROM ubuntu
RUN apt-get update
RUN apt-get install figlet
```

* `FROM` indicates the base image for our build.

* Each `RUN` line will be executed by Docker during the build.

* Our `RUN` commands **must be non-interactive.**
 (No input can be provided to Docker during the build.)

* In many cases, we will add the `-y` flag to `apt-get`.

.debug[[intro/Building_Images_With_Dockerfiles.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_With_Dockerfiles.md)]
---

## Build it!

Save our file, then execute:

```bash
$ docker build -t figlet .
```

* `-t` indicates the tag to apply to the image.

* `.` indicates the location of the *build context*.

We will talk more about the build context later.

To keep things simple for now: this is the directory where our Dockerfile is located.

.debug[[intro/Building_Images_With_Dockerfiles.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_With_Dockerfiles.md)]
---

## What happens when we build the image?

The output of `docker build` looks like this:

.small[
```bash
$ docker build -t figlet .
Sending build context to Docker daemon 2.048 kB
Sending build context to Docker daemon 
Step 0 : FROM ubuntu
 ---> e54ca5efa2e9
Step 1 : RUN apt-get update
 ---> Running in 840cb3533193
 ---> 7257c37726a1
Removing intermediate container 840cb3533193
Step 2 : RUN apt-get install figlet
 ---> Running in 2b44df762a2f
 ---> f9e8f1642759
Removing intermediate container 2b44df762a2f
Successfully built f9e8f1642759
```
]

* The output of the `RUN` commands has been omitted.
* Let's explain what this output means.

.debug[[intro/Building_Images_With_Dockerfiles.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_With_Dockerfiles.md)]
---

## Sending the build context to Docker

```bash
Sending build context to Docker daemon 2.048 kB
```

* The build context is the `.` directory given to `docker build`.

* It is sent (as an archive) by the Docker client to the Docker daemon.

* This allows to use a remote machine to build using local files.

* Be careful (or patient) if that directory is big and your link is slow.

.debug[[intro/Building_Images_With_Dockerfiles.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_With_Dockerfiles.md)]
---

## Executing each step

```bash
Step 1 : RUN apt-get update
 ---> Running in 840cb3533193
(...output of the RUN command...)
 ---> 7257c37726a1
Removing intermediate container 840cb3533193
```

* A container (`840cb3533193`) is created from the base image.

* The `RUN` command is executed in this container.

* The container is committed into an image (`7257c37726a1`).

* The build container (`840cb3533193`) is removed.

* The output of this step will be the base image for the next one.

.debug[[intro/Building_Images_With_Dockerfiles.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_With_Dockerfiles.md)]
---

## The caching system

If you run the same build again, it will be instantaneous. Why?

* After each build step, Docker takes a snapshot of the resulting image.

* Before executing a step, Docker checks if it has already built the same sequence.

* Docker uses the exact strings defined in your Dockerfile, so:

* `RUN apt-get install figlet cowsay ` 
 is different from
 `RUN apt-get install cowsay figlet`
 
 * `RUN apt-get update` is not re-executed when the mirrors are updated

You can force a rebuild with `docker build --no-cache ...`.

.debug[[intro/Building_Images_With_Dockerfiles.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_With_Dockerfiles.md)]
---

## Running the image

The resulting image is not different from the one produced manually.

```bash
$ docker run -ti figlet
root@91f3c974c9a1:/# figlet hello
 _          _ _       
| |__   ___| | | ___  
| '_ \ / _ \ | |/ _ \ 
| | | |  __/ | | (_) |
|_| |_|\___|_|_|\___/ 
```

Yay! 🎉

.debug[[intro/Building_Images_With_Dockerfiles.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_With_Dockerfiles.md)]
---

## Using image and viewing history

The `history` command lists all the layers composing an image.

For each layer, it shows its creation time, size, and creation command.

When an image was built with a Dockerfile, each layer corresponds to
a line of the Dockerfile.

```bash
$ docker history figlet
IMAGE CREATED CREATED BY SIZE
f9e8f1642759 About an hour ago /bin/sh -c apt-get install fi 1.627 MB
7257c37726a1 About an hour ago /bin/sh -c apt-get update 21.58 MB
07c86167cdc4 4 days ago /bin/sh -c #(nop) CMD ["/bin 0 B
<missing> 4 days ago /bin/sh -c sed -i 's/^#\s*\( 1.895 kB
<missing> 4 days ago /bin/sh -c echo '#!/bin/sh' 194.5 kB
<missing> 4 days ago /bin/sh -c #(nop) ADD file:b 187.8 MB
```

.debug[[intro/Building_Images_With_Dockerfiles.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_With_Dockerfiles.md)]
---

## Introducing JSON syntax

Most Dockerfile arguments can be passed in two forms:

* plain string:
 `RUN apt-get install figlet`

* JSON list:
 `RUN ["apt-get", "install", "figlet"]`

We are going to change our Dockerfile to see how it affects the resulting image.

.debug[[intro/Building_Images_With_Dockerfiles.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_With_Dockerfiles.md)]
---

## Using JSON syntax in our Dockerfile

Let's change our Dockerfile as follows!

```dockerfile
FROM ubuntu
RUN apt-get update
RUN ["apt-get", "install", "figlet"]
```

Then build the new Dockerfile.

```bash
$ docker build -t figlet .
```

.debug[[intro/Building_Images_With_Dockerfiles.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_With_Dockerfiles.md)]
---

## JSON syntax vs string syntax

Compare the new history:

```bash
$ docker history figlet
IMAGE CREATED CREATED BY SIZE
27954bb5faaf 10 seconds ago apt-get install figlet 1.627 MB
7257c37726a1 About an hour ago /bin/sh -c apt-get update 21.58 MB
07c86167cdc4 4 days ago /bin/sh -c #(nop) CMD ["/bin 0 B
<missing> 4 days ago /bin/sh -c sed -i 's/^#\s*\( 1.895 kB
<missing> 4 days ago /bin/sh -c echo '#!/bin/sh' 194.5 kB
<missing> 4 days ago /bin/sh -c #(nop) ADD file:b 187.8 MB
```

* JSON syntax specifies an *exact* command to execute.

* String syntax specifies a command to be wrapped within `/bin/sh -c "..."`.

.debug[[intro/Building_Images_With_Dockerfiles.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_With_Dockerfiles.md)]
---

## When to use JSON syntax and string syntax

* String syntax:

* is easier to write
  * interpolates environment variables and other shell expressions
  * creates an extra process (`/bin/sh -c ...`) to parse the string
  * requires `/bin/sh` to exist in the container

* JSON syntax:

* is harder to write (and read!)
  * passes all arguments without extra processing
  * doesn't create an extra process
  * doesn't require `/bin/sh` to exist in the container

.debug[[intro/Building_Images_With_Dockerfiles.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Building_Images_With_Dockerfiles.md)]
---

CMD and ENTRYPOINT

---

# CMD and ENTRYPOINT

![Container entry doors](images/entrypoint.jpg)

.debug[[intro/Cmd_And_Entrypoint.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Cmd_And_Entrypoint.md)]
---

## Objectives

In this lesson, we will learn about two important
Dockerfile commands:

`CMD` and `ENTRYPOINT`.

These commands allow us to set the default command
to run in a container.

.debug[[intro/Cmd_And_Entrypoint.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Cmd_And_Entrypoint.md)]
---

## Defining a default command

When people run our container, we want to greet them with a nice hello message, and using a custom font.

For that, we will execute:

```bash
figlet -f script hello
```

* `-f script` tells figlet to use a fancy font.

* `hello` is the message that we want it to display.

.debug[[intro/Cmd_And_Entrypoint.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Cmd_And_Entrypoint.md)]
---

## Adding `CMD` to our Dockerfile

Our new Dockerfile will look like this:

```dockerfile
FROM ubuntu
RUN apt-get update
RUN ["apt-get", "install", "figlet"]
CMD figlet -f script hello
```

* `CMD` defines a default command to run when none is given.

* It can appear at any point in the file.

* Each `CMD` will replace and override the previous one.

* As a result, while you can have multiple `CMD` lines, it is useless.

.debug[[intro/Cmd_And_Entrypoint.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Cmd_And_Entrypoint.md)]
---

## Build and test our image

Let's build it:

```bash
$ docker build -t figlet .
...
Successfully built 042dff3b4a8d
```

And run it:

```bash
$ docker run figlet
 _          _   _       
| |        | | | |      
| |     _  | | | |  __  
|/ \   |/  |/  |/  /  \_
|   |_/|__/|__/|__/\__/ 
```

.debug[[intro/Cmd_And_Entrypoint.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Cmd_And_Entrypoint.md)]
---

## Overriding `CMD`

If we want to get a shell into our container (instead of running
`figlet`), we just have to specify a different program to run:

```bash
$ docker run -it figlet bash
root@7ac86a641116:/# 
```

* We specified `bash`.

* It replaced the value of `CMD`.

.debug[[intro/Cmd_And_Entrypoint.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Cmd_And_Entrypoint.md)]
---

## Using `ENTRYPOINT`

We want to be able to specify a different message on the command line,
while retaining `figlet` and some default parameters.

In other words, we would like to be able to do this:

```bash
$ docker run figlet salut
           _            
          | |           
 ,   __,  | |       _|_ 
/ \_/  |  |/  |   |  |  
 \/ \_/|_/|__/ \_/|_/|_/
```

We will use the `ENTRYPOINT` verb in Dockerfile.

.debug[[intro/Cmd_And_Entrypoint.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Cmd_And_Entrypoint.md)]
---

## Adding `ENTRYPOINT` to our Dockerfile

Our new Dockerfile will look like this:

```dockerfile
FROM ubuntu
RUN apt-get update
RUN ["apt-get", "install", "figlet"]
ENTRYPOINT ["figlet", "-f", "script"]
```

* `ENTRYPOINT` defines a base command (and its parameters) for the container.

* The command line arguments are appended to those parameters.

* Like `CMD`, `ENTRYPOINT` can appear anywhere, and replaces the previous value.

Why did we use JSON syntax for our `ENTRYPOINT`?

.debug[[intro/Cmd_And_Entrypoint.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Cmd_And_Entrypoint.md)]
---

## Implications of JSON vs string syntax

* When CMD or ENTRYPOINT use string syntax, they get wrapped in `sh -c`.

* To avoid this wrapping, you must use JSON syntax.

What if we used `ENTRYPOINT` with string syntax?

```bash
$ docker run figlet salut
```

This would run the following command in the `figlet` image:

```bash
sh -c "figlet -f script" salut
```

.debug[[intro/Cmd_And_Entrypoint.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Cmd_And_Entrypoint.md)]
---

## Build and test our image

Let's build it:

```bash
$ docker build -t figlet .
...
Successfully built 36f588918d73
```

And run it:

```bash
$ docker run figlet salut
           _            
          | |           
 ,   __,  | |       _|_ 
/ \_/  |  |/  |   |  |  
 \/ \_/|_/|__/ \_/|_/|_/
```

Great success!

.debug[[intro/Cmd_And_Entrypoint.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Cmd_And_Entrypoint.md)]
---

## Using `CMD` and `ENTRYPOINT` together

What if we want to define a default message for our container?

Then we will use `ENTRYPOINT` and `CMD` together.

* `ENTRYPOINT` will define the base command for our container.

* `CMD` will define the default parameter(s) for this command.

* They *both* have to use JSON syntax.

.debug[[intro/Cmd_And_Entrypoint.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Cmd_And_Entrypoint.md)]
---

## `CMD` and `ENTRYPOINT` together

Our new Dockerfile will look like this:

```dockerfile
FROM ubuntu
RUN apt-get update
RUN ["apt-get", "install", "figlet"]
ENTRYPOINT ["figlet", "-f", "script"]
CMD ["hello world"]
```

* `ENTRYPOINT` defines a base command (and its parameters) for the container.

* If we don't specify extra command-line arguments when starting the container,
  the value of `CMD` is appended.

* Otherwise, our extra command-line arguments are used instead of `CMD`.

.debug[[intro/Cmd_And_Entrypoint.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Cmd_And_Entrypoint.md)]
---

## Build and test our image

Let's build it:

```bash
$ docker build -t figlet .
...
Successfully built 6e0b6a048a07
```

And run it:

.small[
```bash
$ docker run figlet
 _          _   _                             _        
| |        | | | |                           | |    |  
| |     _  | | | |  __             __   ,_   | |  __|  
|/ \   |/  |/  |/  /  \_  |  |  |_/  \_/  |  |/  /  |  
|   |_/|__/|__/|__/\__/    \/ \/  \__/    |_/|__/\_/|_/

$ docker run figlet hola mundo
 _           _                                               
| |         | |                                      |       
| |     __  | |  __,     _  _  _           _  _    __|   __  
|/ \   /  \_|/  /  |    / |/ |/ |  |   |  / |/ |  /  |  /  \_
|   |_/\__/ |__/\_/|_/    |  |  |_/ \_/|_/  |  |_/\_/|_/\__/ 
```
]

.debug[[intro/Cmd_And_Entrypoint.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Cmd_And_Entrypoint.md)]
---

## Overriding `ENTRYPOINT`

What if we want to run a shell in our container?

We cannot just do `docker run figlet bash` because
that would just tell figlet to display the word "bash."

We use the `--entrypoint` parameter:

```bash
$ docker run -it --entrypoint bash figlet
root@6027e44e2955:/# 
```

.debug[[intro/Cmd_And_Entrypoint.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Cmd_And_Entrypoint.md)]
---

Copying files during the build

---

# Copying files during the build

![Monks copying books](images/copy.jpg)

.debug[[intro/Copying_Files_During_Build.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Copying_Files_During_Build.md)]
---

## Objectives

So far, we have installed things in our container images
by downloading packages.

We can also copy files from the *build context* to the
container that we are building.

Remember: the *build context* is the directory containing
the Dockerfile.

In this chapter, we will learn a new Dockerfile keyword: `COPY`.

.debug[[intro/Copying_Files_During_Build.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Copying_Files_During_Build.md)]
---

## Build some C code

We want to build a container that compiles a basic "Hello world" program in C.

Here is the program, `hello.c`:

```bash
int main () {
  puts("Hello, world!");
  return 0;
}
```

Let's create a new directory, and put this file in there.

Then we will write the Dockerfile.

.debug[[intro/Copying_Files_During_Build.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Copying_Files_During_Build.md)]
---

## The Dockerfile

On Debian and Ubuntu, the package `build-essential` will get us a compiler.

When installing it, don't forget to specify the `-y` flag, otherwise the build will fail (since the build cannot be interactive).

Then we will use `COPY` to place the source file into the container.

```bash
FROM ubuntu
RUN apt-get update
RUN apt-get install -y build-essential
COPY hello.c /
RUN make hello
CMD /hello
```

Create this Dockerfile.

.debug[[intro/Copying_Files_During_Build.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Copying_Files_During_Build.md)]
---

## Testing our C program

* Create `hello.c` and `Dockerfile` in the same direcotry.

* Run `docker build -t hello .` in this directory.

* Run `docker run hello`, you should see `Hello, world!`.

Success!

.debug[[intro/Copying_Files_During_Build.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Copying_Files_During_Build.md)]
---

## `COPY` and the build cache

* Run the build again.

* Now, modify `hello.c` and run the build again.

* Docker can cache steps involving `COPY`.

* Those steps will not be executed again if the files haven't been changed.

.debug[[intro/Copying_Files_During_Build.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Copying_Files_During_Build.md)]
---

## Details

* You can `COPY` whole directories recursively.

* Older Dockerfiles also have the `ADD` instruction.
 It is similar but can automatically extract archives.

* If we really wanted to compile C code in a compiler, we would:

* Place it in a different directory, with the `WORKDIR` instruction.

* Even better, use the `gcc` official image.

.debug[[intro/Copying_Files_During_Build.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Copying_Files_During_Build.md)]
---

Multi-stage builds

---
# Multi-stage builds

* In the previous example, our final image contain:

* our `hello` program

* its source code

* the compiler

* Only the first one is strictly necessary.

* We are going to see how to obtain an image without the superfluous components.

.debug[[intro/Multi_Stage_Builds.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Multi_Stage_Builds.md)]
---

## Multi-stage builds principles

* At any point in our `Dockerfile`, we can add a new `FROM` line.

* This line starts a new stage of our build.

* Each stage can access the files of the previous stages with `COPY --from=...`.

* When a build is tagged (with `docker build -t ...`), the last stage is tagged.

* Previous stages are not discarded: they will be used for caching, and can be referenced.

.debug[[intro/Multi_Stage_Builds.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Multi_Stage_Builds.md)]
---

## Multi-stage builds in practice

* Each stage is numbered, starting at `0`

* We can copy a file from a previous stage by indicating its number, e.g.:

```dockerfile
  COPY --from=0 /file/from/first/stage /location/in/current/stage
  ```

* We can also name stages, and reference these names:

```dockerfile
  FROM golang AS builder
  RUN ...
  FROM alpine
  COPY --from=builder /go/bin/mylittlebinary /usr/local/bin/
  ```

.debug[[intro/Multi_Stage_Builds.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Multi_Stage_Builds.md)]
---

## Implementing multi-stage builds for our C program

We will change our Dockerfile to:

* give a nickname to the first stage: `compiler`

* add a second stage using the same `ubuntu` base image

* add the `hello` binary to the second stage

* make sure that `CMD` is in the second stage

The resulting Dockerfile is on the next slide.

.debug[[intro/Multi_Stage_Builds.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Multi_Stage_Builds.md)]
---

## Revised Dockerfile implementing multi-stage build

Here is the final Dockerfile:

```dockerfile
FROM ubuntu AS compiler
RUN apt-get update
RUN apt-get install -y build-essential
COPY hello.c /
RUN make hello
FROM ubuntu
COPY --from=compiler /hello /hello
CMD /hello
```

Let's build it, and check that it works correctly:

```bash
docker build -t hellomultistage .
docker run hellomultistage
```

.debug[[intro/Multi_Stage_Builds.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Multi_Stage_Builds.md)]
---

## Comparing single-stage and multi-stage image sizes

List our images with `docker images`, and check the size of:

- the `ubuntu` base image,

- the single-stage `hello` image,

- the multi-stage `hellomultistage` image.

We can achieve even smaller images if we use smaller base images.

However, if we use common base images (e.g. if we standardize on `ubuntu`),
these common images will be pulled only once per node, so they are
virtually "free."

.debug[[intro/Multi_Stage_Builds.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Multi_Stage_Builds.md)]
---

Tips for efficient Dockerfiles

---
# Tips for efficient Dockerfiles

We will see how to:

* Reduce the number of layers.

* Leverage the build cache so that builds can be faster.

* Embed unit testing in the build process.

.debug[[intro/Dockerfile_Tips.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Dockerfile_Tips.md)]
---

## Reducing the number of layers

* Each line in a `Dockerfile` creates a new layer.

* Build your `Dockerfile` to take advantage of Docker's caching system.

* Combine commands by using `&&` to continue commands and `\` to wrap lines.

Note: it is frequent to build a Dockerfile line by line:

```dockerfile
RUN apt-get install thisthing
RUN apt-get install andthatthing andthatotherone
RUN apt-get install somemorestuff
```

And then refactor it trivially before shipping:

```dockerfile
RUN apt-get install thisthing andthatthing andthatotherone somemorestuff
```

.debug[[intro/Dockerfile_Tips.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Dockerfile_Tips.md)]
---

## Avoid re-installing dependencies at each build

* Classic Dockerfile problem:

"each time I change a line of code, all my dependencies are re-installed!"

* Solution: `COPY` dependency lists (`package.json`, `requirements.txt`, etc.)
  by themselves to avoid reinstalling unchanged dependencies every time.

.debug[[intro/Dockerfile_Tips.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Dockerfile_Tips.md)]
---

## Example "bad" `Dockerfile`

The dependencies are reinstalled every time, because the build system does not know if `requirements.txt` has been updated.

```bash
FROM python
MAINTAINER Docker Education Team <education@docker.com>
COPY . /src/
WORKDIR /src
RUN pip install -qr requirements.txt
EXPOSE 5000
CMD ["python", "app.py"]
```

.debug[[intro/Dockerfile_Tips.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Dockerfile_Tips.md)]
---

## Fixed `Dockerfile`

Adding the dependencies as a separate step means that Docker can cache more efficiently and only install them when `requirements.txt` changes.

```bash
FROM python
MAINTAINER Docker Education Team <education@docker.com>
COPY ./requirements.txt /tmp/requirements.txt
RUN pip install -qr /tmp/requirements.txt
COPY . /src/
WORKDIR /src
EXPOSE 5000
CMD ["python", "app.py"]
```

.debug[[intro/Dockerfile_Tips.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Dockerfile_Tips.md)]
---

## Embedding unit tests in the build process

```dockerfile
FROM <baseimage>
RUN <install dependencies>
COPY <code>
RUN <build code>
RUN <install test dependencies>
COPY <test data sets and fixtures>
RUN <unit tests>
FROM <baseimage>
RUN <install dependencies>
COPY <vcode>
RUN <build code>
CMD, EXPOSE ...
```

* The build fails as soon as an instructions fails
* If `RUN <unit tests>` fails, the build doesn't produce an image
* If it succeeds, it produces a clean image (without test libraries and data)

.debug[[intro/Dockerfile_Tips.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Dockerfile_Tips.md)]
---

Publishing images to the Docker Hub

---
# Publishing images to the Docker Hub

We have built our first images.

If we were so inclined, we could share those images through the Docker Hub.

We won't do it since we don't want to force everyone to create a Docker Hub account (although it's free, yay!) but the steps would be:

* have an account on the Docker Hub

* tag our image accordingly (i.e. `username/imagename`)

* `docker push username/imagename`

Anybody can now `docker run username/imagename` from any Docker host.

Images can be set to be private as well.

.debug[[intro/Docker_Hub_Tease.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Docker_Hub_Tease.md)]
---

## The goodness of automated builds

* You can link a Docker Hub repository with a GitHub or BitBucket repository

* Each push to GitHub or BitBucket will trigger a build on Docker Hub

* If the build succeeds, the new image is available on Docker Hub

* You can map tags and branches between source and container images

* If you work with public repositories, this is free

* Corollary: this gives you a very simple way to get free, basic CI

(With the technique presented earlier)

.debug[[intro/Docker_Hub_Tease.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Docker_Hub_Tease.md)]
---

Naming and inspecting containers

---

# Naming and inspecting containers

![Markings on container door](images/containermarkings.jpg)

.debug[[intro/Naming_And_Inspecting.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Naming_And_Inspecting.md)]
---

## Objectives

In this lesson, we will learn about an important
Docker concept: container *naming*.

Naming allows us to:

* Reference easily a container.

* Ensure unicity of a specific container.

We will also see the `inspect` command, which gives a lot of details about a container.

.debug[[intro/Naming_And_Inspecting.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Naming_And_Inspecting.md)]
---

## Naming our containers

So far, we have referenced containers with their ID.

We have copy-pasted the ID, or used a shortened prefix.

But each container can also be referenced by its name.

If a container is named `thumbnail-worker`, I can do:

```bash
$ docker logs thumbnail-worker
$ docker stop thumbnail-worker
etc.
```

.debug[[intro/Naming_And_Inspecting.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Naming_And_Inspecting.md)]
---

## Default names

When we create a container, if we don't give a specific
name, Docker will pick one for us.

It will be the concatenation of:

* A mood (furious, goofy, suspicious, boring...)

* The name of a famous inventor (tesla, darwin, wozniak...)

Examples: `happy_curie`, `clever_hopper`, `jovial_lovelace` ...

.debug[[intro/Naming_And_Inspecting.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Naming_And_Inspecting.md)]
---

## Specifying a name

You can set the name of the container when you create it.

```bash
$ docker run --name ticktock jpetazzo/clock
```

If you specify a name that already exists, Docker will refuse
to create the container.

This lets us enforce unicity of a given resource.

.debug[[intro/Naming_And_Inspecting.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Naming_And_Inspecting.md)]
---

## Renaming containers

* You can rename containers with `docker rename`.

* This allows you to "free up" a name without destroying the associated container.

.debug[[intro/Naming_And_Inspecting.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Naming_And_Inspecting.md)]
---

## Inspecting a container

The `docker inspect` command will output a very detailed JSON map.

```bash
$ docker inspect <containerID>
[{
	"AppArmorProfile": "",
	"Args": [],
	"Config": {
	 "AttachStderr": true,
	 "AttachStdin": false,
	 "AttachStdout": true,
	 "Cmd": [
		"bash"
	 ],
	 "CpuShares": 0,
...
```

There are multiple ways to consume that information.

.debug[[intro/Naming_And_Inspecting.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Naming_And_Inspecting.md)]
---

## Parsing JSON with the Shell

* You *could* grep and cut or awk the output of `docker inspect`.

* Please, don't.

* It's painful.

* If you really must parse JSON from the Shell, use JQ! (It's great.)

```bash
$ docker inspect <containerID> | jq .
```

* We will see a better solution which doesn't require extra tools.

.debug[[intro/Naming_And_Inspecting.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Naming_And_Inspecting.md)]
---

## Using `--format`

You can specify a format string, which will be parsed by 
Go's text/template package.

```bash
$ docker inspect --format '{{ json .Created }}' <containerID>
"2015-02-24T07:21:11.712240394Z"
```

* The generic syntax is to wrap the expression with double curly braces.

* The expression starts with a dot representing the JSON object.

* Then each field or member can be accessed in dotted notation syntax.

* The optional `json` keyword asks for valid JSON output.
 (e.g. here it adds the surrounding double-quotes.)

.debug[[intro/Naming_And_Inspecting.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Naming_And_Inspecting.md)]
---

Container Networking Basics

---

# Container Networking Basics

![A dense graph network](images/network.jpg)

.debug[[intro/Container_Networking_Basics.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Networking_Basics.md)]
---

## Objectives

We will now run network services (accepting requests) in containers.

At the end of this section, you will be able to:

* Run a network service in a container.

* Manipulate container networking basics.

* Find a container's IP address.

We will also explain the different network models used by Docker.

.debug[[intro/Container_Networking_Basics.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Networking_Basics.md)]
---

## A simple, static web server

Run the Docker Hub image `nginx`, which contains a basic web server:

```bash
$ docker run -d -P nginx
66b1ce719198711292c8f34f84a7b68c3876cf9f67015e752b94e189d35a204e
```

* Docker will download the image from the Docker Hub.

* `-d` tells Docker to run the image in the background.

* `-P` tells Docker to make this service reachable from other computers.
 (`-P` is the short version of `--publish-all`.)

But, how do we connect to our web server now?

.debug[[intro/Container_Networking_Basics.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Networking_Basics.md)]
---

## Finding our web server port

We will use `docker ps`:

```bash
$ docker ps
CONTAINER ID  IMAGE  ...  PORTS                                          ...
e40ffb406c9e  nginx  ...  0.0.0.0:32769->80/tcp, 0.0.0.0:32768->443/tcp  ...
```

* The web server is running on ports 80 and 443 inside the container.

* Those ports are mapped to ports 32769 and 32768 on our Docker host.

We will explain the whys and hows of this port mapping.

But first, let's make sure that everything works properly.

.debug[[intro/Container_Networking_Basics.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Networking_Basics.md)]
---

## Connecting to our web server (GUI)

Point your browser to the IP address of your Docker host, on the port
shown by `docker ps` for container port 80.

![Screenshot](images/web.png)

.debug[[intro/Container_Networking_Basics.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Networking_Basics.md)]
---

## Connecting to our web server (CLI)

You can also use `curl` directly from the Docker host.

Make sure to use the right port number if it is different
from the example below:

```bash
$ curl localhost:32769
<!DOCTYPE html>
<html>
<head>
<title>Welcome to nginx!</title>
...
```

.debug[[intro/Container_Networking_Basics.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Networking_Basics.md)]
---

## Why are we mapping ports?

* We are out of IPv4 addresses.

* Containers cannot have public IPv4 addresses.

* They have private addresses.

* Services have to be exposed port by port.

* Ports have to be mapped to avoid conflicts.

.debug[[intro/Container_Networking_Basics.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Networking_Basics.md)]
---

## Finding the web server port in a script

Parsing the output of `docker ps` would be painful.

There is a command to help us:

```bash
$ docker port <containerID> 80
32769
```

.debug[[intro/Container_Networking_Basics.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Networking_Basics.md)]
---

## Manual allocation of port numbers

If you want to set port numbers yourself, no problem:

```bash
$ docker run -d -p 80:80 nginx
$ docker run -d -p 8000:80 nginx
$ docker run -d -p 8080:80 -p 8888:80 nginx
```

* We are running two NGINX web servers.
* The first one is exposed on port 80.
* The second one is exposed on port 8000.
* The third one is exposed on ports 8080 and 8888.

Note: the convention is `port-on-host:port-on-container`.

.debug[[intro/Container_Networking_Basics.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Networking_Basics.md)]
---

## Plumbing containers into your infrastructure

There are many ways to integrate containers in your network.

* Start the container, letting Docker allocate a public port for it.
 Then retrieve that port number and feed it to your configuration.

* Pick a fixed port number in advance, when you generate your configuration.
 Then start your container by setting the port numbers manually.

* Use a network plugin, connecting your containers with e.g. VLANs, tunnels...

* Enable *Swarm Mode* to deploy across a cluster.
 The container will then be reachable through any node of the cluster.

When using Docker through an extra management layer like Mesos or Kubernetes,
these will usually provide their own mechanism to expose containers.

.debug[[intro/Container_Networking_Basics.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Networking_Basics.md)]
---

## Finding the container's IP address

We can use the `docker inspect` command to find the IP address of the
container.

```bash
$ docker inspect --format '{{ .NetworkSettings.IPAddress }}' <yourContainerID>
172.17.0.3
```

* `docker inspect` is an advanced command, that can retrieve a ton
  of information about our containers.

* Here, we provide it with a format string to extract exactly the
  private IP address of the container.

.debug[[intro/Container_Networking_Basics.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Networking_Basics.md)]
---

## Pinging our container

We can test connectivity to the container using the IP address we've
just discovered. Let's see this now by using the `ping` tool.

```bash
$ ping <ipAddress>
64 bytes from <ipAddress>: icmp_req=1 ttl=64 time=0.085 ms
64 bytes from <ipAddress>: icmp_req=2 ttl=64 time=0.085 ms
64 bytes from <ipAddress>: icmp_req=3 ttl=64 time=0.085 ms
```

.debug[[intro/Container_Networking_Basics.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Networking_Basics.md)]
---

## The different network drivers

A container can use one of the following drivers:

* `bridge` (default)
* `none`
* `host`
* `container`

The driver is selected with `docker run --net ...`.

The different drivers are explained with more details on the following slides.

.debug[[intro/Container_Networking_Basics.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Networking_Basics.md)]
---

## The default bridge

* By default, the container gets a virtual `eth0` interface.
 (In addition to its own private `lo` loopback interface.)

* That interface is provided by a `veth` pair.

* It is connected to the Docker bridge.
 (Named `docker0` by default; configurable with `--bridge`.)

* Addresses are allocated on a private, internal subnet.
 (Docker uses 172.17.0.0/16 by default; configurable with `--bip`.)

* Outbound traffic goes through an iptables MASQUERADE rule.

* Inbound traffic goes through an iptables DNAT rule.

* The container can have its own routes, iptables rules, etc.

.debug[[intro/Container_Networking_Basics.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Networking_Basics.md)]
---

## The null driver

* Container is started with `docker run --net none ...`

* It only gets the `lo` loopback interface. No `eth0`.

* It can't send or receive network traffic.

* Useful for isolated/untrusted workloads.

.debug[[intro/Container_Networking_Basics.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Networking_Basics.md)]
---

## The host driver

* Container is started with `docker run --net host ...`

* It sees (and can access) the network interfaces of the host.

* It can bind any address, any port (for ill and for good).

* Network traffic doesn't have to go through NAT, bridge, or veth.

* Performance = native!

Use cases:

* Performance sensitive applications (VOIP, gaming, streaming...)

* Peer discovery (e.g. Erlang port mapper, Raft, Serf...)

.debug[[intro/Container_Networking_Basics.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Networking_Basics.md)]
---

## The container driver

* Container is started with `docker run --net container:id ...`

* It re-uses the network stack of another container.

* It shares with this other container the same interfaces, IP address(es), routes, iptables rules, etc.

* Those containers can communicate over their `lo` interface.
 (i.e. one can bind to 127.0.0.1 and the others can connect to it.)

.debug[[intro/Container_Networking_Basics.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Networking_Basics.md)]
---

## Section summary

We've learned how to:

* Expose a network port.

* Manipulate container networking basics.

* Find a container's IP address.

In the next chapter, we will see how to connect
containers together without exposing their ports.

.debug[[intro/Container_Networking_Basics.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Networking_Basics.md)]
---

The Container Network Model

---

# The Container Network Model

![A denser graph network](images/complex-network.jpg)

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Objectives

We will learn about the CNM (Container Network Model).

At the end of this lesson, you will be able to:

* Create a private network for a group of containers.

* Use container naming to connect services together.

* Dynamically connect and disconnect containers to networks.

* Set the IP address of a container.

We will also explain the principle of overlay networks and network plugins.

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## The Container Network Model

The CNM was introduced in Engine 1.9.0 (November 2015).

The CNM adds the notion of a *network*, and a new top-level command to manipulate and see those networks: `docker network`.

```bash
$ docker network ls
NETWORK ID          NAME                DRIVER
6bde79dfcf70        bridge              bridge
8d9c78725538        none                null
eb0eeab782f4        host                host
4c1ff84d6d3f        blog-dev            overlay
228a4355d548        blog-prod           overlay
```

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## What's in a network?

* Conceptually, a network is a virtual switch.

* It can be local (to a single Engine) or global (spanning multiple hosts).

* A network has an IP subnet associated to it.

* Docker will allocate IP addresses to the containers connected to a network.

* Containers can be connected to multiple networks.

* Containers can be given per-network names and aliases.

* The names and aliases can be resolved via an embedded DNS server.

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Network implementation details

* A network is managed by a *driver*.

* All the drivers that we have seen before are available.

* A new multi-host driver, *overlay*, is available out of the box.

* More drivers can be provided by plugins (OVS, VLAN...)

* A network can have a custom IPAM (IP allocator).

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Differences with the CNI

* CNI = Container Network Interface

* CNI is used notably by Kubernetes

* With CNI, all the nodes and containers are on a single IP network

* Both CNI and CNM offer the same functionality, but with very different methods

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Creating a network

Let's create a network called `dev`.

```bash
$ docker network create dev
4c1ff84d6d3f1733d3e233ee039cac276f425a9d5228a4355d54878293a889ba
```

The network is now visible with the `network ls` command:

```bash
$ docker network ls
NETWORK ID          NAME                DRIVER
6bde79dfcf70        bridge              bridge
8d9c78725538        none                null
eb0eeab782f4        host                host
4c1ff84d6d3f        dev                 bridge
```

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Placing containers on a network

We will create a *named* container on this network.

It will be reachable with its name, `es`.

```bash
$ docker run -d --name es --net dev elasticsearch:2
8abb80e229ce8926c7223beb69699f5f34d6f1d438bfc5682db893e798046863
```

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Communication between containers

Now, create another container on this network.

```bash
$ docker run -ti --net dev alpine sh
root@0ecccdfa45ef:/#
```

From this new container, we can resolve and ping the other one, using its assigned name:

```bash
/ # ping es
PING es (172.18.0.2) 56(84) bytes of data.
64 bytes from es.dev (172.18.0.2): icmp_seq=1 ttl=64 time=0.221 ms
64 bytes from es.dev (172.18.0.2): icmp_seq=2 ttl=64 time=0.114 ms
64 bytes from es.dev (172.18.0.2): icmp_seq=3 ttl=64 time=0.114 ms
^C
--- es ping statistics ---
3 packets transmitted, 3 received, 0% packet loss, time 2000ms
rtt min/avg/max/mdev = 0.114/0.149/0.221/0.052 ms
root@0ecccdfa45ef:/#
```

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Resolving container addresses

In Docker Engine 1.9, name resolution is implemented with `/etc/hosts`, and
updating it each time containers are added/removed.

```bash
[root@0ecccdfa45ef /]# cat /etc/hosts
172.18.0.3  0ecccdfa45ef
127.0.0.1       localhost
::1     localhost ip6-localhost ip6-loopback
fe00::0 ip6-localnet
ff00::0 ip6-mcastprefix
ff02::1 ip6-allnodes
ff02::2 ip6-allrouters
172.18.0.2      es
172.18.0.2      es.dev
```

In Docker Engine 1.10, this has been replaced by a dynamic resolver.

(This avoids race conditions when updating `/etc/hosts`.)

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Connecting multiple containers together

* Let's try to run an application that requires two containers.

* The first container is a web server.

* The other one is a redis data store.

* We will place them both on the `dev` network created before.

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Running the web server

* The application is provided by the container image `jpetazzo/trainingwheels`.

* We don't know much about it so we will try to run it and see what happens!

Start the container, exposing all its ports:

```bash
$ docker run --net dev -d -P jpetazzo/trainingwheels
```

Check the port that has been allocated to it:

```bash
$ docker ps -l
```

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Test the web server

* If we connect to the application now, we will see an error page:

* This is because the Redis service is not running.
* This container tries to resolve the name `redis`.

Note: we're not using a FQDN or an IP address here; just `redis`.

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Start the data store

* We need to start a Redis container.

* That container must be on the same network as the web server.

* It must have the right name (`redis`) so the application can find it.

Start the container:

```bash
$ docker run --net dev --name redis -d redis
```

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Test the web server again

* If we connect to the application now, we should see that the app is working correctly:

* When the app tries to resolve `redis`, instead of getting a DNS error, it gets the IP address of our Redis container.

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## A few words on *scope*

* What if we want to run multiple copies of our application?

* Since names are unique, there can be only one container named `redis` at a time.

* However, we can specify the network name of our container with `--net-alias`.

* `--net-alias` is scoped per network, and independent from the container name.

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Using a network alias instead of a name

Let's remove the `redis` container:

```bash
$ docker rm -f redis
```

And create one that doesn't block the `redis` name:

```bash
$ docker run --net dev --net-alias redis -d redis
```

Check that the app still works (but the counter is back to 1,
since we wiped out the old Redis container).

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Names are *local* to each network

Let's try to ping our `es` container from another container, when that other container is *not* on the `dev` network.

```bash
$ docker run --rm alpine ping es
ping: bad address 'es'
```

Names can be resolved only when containers are on the same network.

Containers can contact each other only when they are on the same network (you can try to ping using the IP address to verify).

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Network aliases

We would like to have another network, `prod`, with its own `es` container. But there can be only one container named `es`!

We will use *network aliases*.

A container can have multiple network aliases.

Network aliases are *local* to a given network (only exist in this network).

Multiple containers can have the same network alias (even on the same network). In Docker Engine 1.11, resolving a network alias yields the IP addresses of all containers holding this alias.

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Creating containers on another network

Create the `prod` network.

```bash
$ docker create network prod
5a41562fecf2d8f115bedc16865f7336232a04268bdf2bd816aecca01b68d50c
```

We can now create multiple containers with the `es` alias on the new `prod` network.

```bash
$ docker run -d --name prod-es-1 --net-alias es --net prod elasticsearch:2
38079d21caf0c5533a391700d9e9e920724e89200083df73211081c8a356d771
$ docker run -d --name prod-es-2 --net-alias es --net prod elasticsearch:2
1820087a9c600f43159688050dcc164c298183e1d2e62d5694fd46b10ac3bc3d
```

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Resolving network aliases

Let's try DNS resolution first, using the `nslookup` tool that ships with the `alpine` image.

```bash
$ docker run --net prod --rm alpine nslookup es
Name:      es
Address 1: 172.23.0.3 prod-es-2.prod
Address 2: 172.23.0.2 prod-es-1.prod
```

(You can ignore the `can't resolve '(null)'` errors.)

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Connecting to aliased containers

Each ElasticSearch instance has a name (generated when it is started). This name can be seen when we issue a simple HTTP request on the ElasticSearch API endpoint.

Try the following command a few times:

```bash
$ docker run --rm --net dev centos curl -s es:9200
{
  "name" : "Tarot",
...
}
```

Then try it a few times by replacing `--net dev` with `--net prod`:

```bash
$ docker run --rm --net prod centos curl -s es:9200
{
  "name" : "The Symbiote",
...
}
```

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Good to know ...

* Docker will not create network names and aliases on the default `bridge` network.

* Therefore, if you want to use those features, you have to create a custom network first.

* Network aliases are *not* unique on a given network.

* i.e., multiple containers can have the same alias on the same network.

* In that scenario, the Docker DNS server will return multiple records.
 
 (i.e. you will get DNS round robin out of the box.)

* Enabling *Swarm Mode* gives access to clustering and load balancing with IPVS.

* Creation of networks and network aliases is generally automated with tools like Compose.

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## A few words about round robin DNS

Don't rely exclusively on round robin DNS to achieve load balancing.

Many factors can affect DNS resolution, and you might see:

- all traffic going to a single instance;
- traffic being split (unevenly) between some instances;
- different behavior depending on your application language;
- different behavior depending on your base distro;
- different behavior depending on other factors (sic).

It's OK to use DNS to discover available endpoints, but remember that you have to re-resolve every now and then to discover new endpoints.

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Custom networks

When creating a network, extra options can be provided.

* `--internal` disables outbound traffic (the network won't have a default gateway).

* `--gateway` indicates which address to use for the gateway (when utbound traffic is allowed).

* `--subnet` (in CIDR notation) indicates the subnet to use.

* `--ip-range` (in CIDR notation) indicates the subnet to allocate from.

* `--aux-address` allows to specify a list of reserved addresses (which won't be allocated to containers).

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Setting containers' IP address

* It is possible to set a container's address with `--ip`.
* The IP address has to be within the subnet used for the container.

A full example would look like this.

```bash
$ docker network create --subnet 10.66.0.0/16 pubnet
42fb16ec412383db6289a3e39c3c0224f395d7f85bcb1859b279e7a564d4e135
$ docker run --net pubnet --ip 10.66.66.66 -d nginx
b2887adeb5578a01fd9c55c435cad56bbbe802350711d2743691f95743680b09
```

*Note: don't hard code container IP addresses in your code!*

*I repeat: don't hard code container IP addresses in your code!*

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Overlay networks

* The features we've seen so far only work when all containers are on a single host.

* If containers span multiple hosts, we need an *overlay* network to connect them together.

* Docker ships with a default network plugin, `overlay`, implementing an overlay network leveraging VXLAN.

* Other plugins (Weave, Calico...) can provide overlay networks as well.

* Once you have an overlay network, *all the features that we've used in this chapter work identically.*

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Multi-host networking (overlay)

Out of the scope for this intro-level workshop!

Very short instructions:

- enable Swarm Mode (`docker swarm init` then `docker swarm join` on other nodes)
- `docker network create mynet --driver overlay`
- `docker service create --network mynet myimage`

See http://jpetazzo.github.io/orchestration-workshop for all the deets about clustering!

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Multi-host networking (plugins)

Out of the scope for this intro-level workshop!

General idea:

- install the plugin (they often ship within containers)

- run the plugin (if it's in a container, it will often require extra parameters; don't just `docker run` it blindly!)

- some plugins require configuration or activation (creating a special file that tells Docker "use the plugin whose control socket is at the following location")

- you can then `docker network create --driver pluginname`

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

## Section summary

We've learned how to:

* Create private networks for groups of containers.

* Assign IP addresses to containers.

* Use container naming to implement service discovery.

.debug[[intro/Container_Network_Model.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Container_Network_Model.md)]
---

Ambassadors

---

# Ambassadors

![](images/ambassador.jpg)

.debug[[intro/Ambassadors.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Ambassadors.md)]
---

## The ambassador pattern

Ambassadors are containers that "masquerade" or "proxy" for another service.

They abstract the connection details for this services, and can help with:

* discovery (where is my service actually running?)

* migration (what if my service has to be moved while I use it?)

* fail over (how do I know to which instance of a replicated service I should connect?)

* load balancing (how to I spread my requests across multiple instances of a service?)

* authentication (what if my service requires credentials, certificates, or otherwise?)

.debug[[intro/Ambassadors.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Ambassadors.md)]
---

## Introduction to Ambassadors

The ambassador pattern:

* Takes advantage of Docker's per-container naming system and abstracts
  connections between services.

* Allows you to manage services without hard-coding connection
  information inside applications.

To do this, instead of directly connecting containers you insert
ambassador containers.

.debug[[intro/Ambassadors.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Ambassadors.md)]
---

![ambassador](images/diagram.png)

.debug[[intro/Ambassadors.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Ambassadors.md)]
---

## Interacting with ambassadors

* The web container uses normal Docker networking to connect
  to the ambassador.

* The database container also talks with an ambassador.

* For both containers, the ambassador is totally transparent.
 
 (There is no difference between normal
 operation and operation with an ambassador.)

* If the database container is moved (or a failover happens), its new location will
  be tracked by the ambassador containers, and the web application
  container will still be able to connect, without reconfiguration.

.debug[[intro/Ambassadors.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Ambassadors.md)]
---

## Ambassadors for simple service discovery

Use case:

* my application code connects to `redis` on the default port (6379),
* my Redis service runs on another machine, on a non-default port (e.g. 12345),
* I want to use an ambassador to let my application connect without modification.

The ambassador will be:

* a container running right next to my application,
* using the name `redis` (or linked as `redis`),
* listening on port 6379,
* forwarding connections to the actual Redis service.

.debug[[intro/Ambassadors.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Ambassadors.md)]
---

## Ambassadors for service migration

Use case:

* my application code still connects to `redis`,
* my Redis service runs somewhere else,
* my Redis service is moved to a different host+port,
* the location of the Redis service is given to me via e.g. DNS SRV records,
* I want to use an ambassador to automatically connect to the new location, with as little disruption as possible.

The ambassador will be:

* the same kind of container as before,
* running an additional routine to monitor DNS SRV records,
* updating the forwarding destination when the DNS SRV records are updated.

.debug[[intro/Ambassadors.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Ambassadors.md)]
---

## Ambassadors for credentials injection

Use case:

* my application code still connects to `redis`,
* my application code doesn't provide Redis credentials,
* my production Redis service requires credentials,
* my staging Redis service requires different credentials,
* I want to use an ambassador to abstract those credentials.

The ambassador will be:

* a container using the name `redis` (or a link),
* passed the credentials to use,
* running a custom proxy that accepts connections on Redis default port,
* performing authentication with the target Redis service before forwarding traffic.

.debug[[intro/Ambassadors.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Ambassadors.md)]
---

## Ambassadors for load balancing

Use case:

* my application code connects to a web service called `api`,
* I want to run multiple instances of the `api` backend,
* those instances will be on different machines and ports,
* I want to use an ambassador to abstract those details.

The ambassador will be:

* a container using the name `api` (or a link),
* passed the list of backends to use (statically or dynamically),
* running a load balancer (e.g. HAProxy or NGINX),
* dispatching requests across all backends transparently.

.debug[[intro/Ambassadors.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Ambassadors.md)]
---

## "Ambassador" is a *pattern*

There are many ways to implement the pattern.

Different deployments will use different underlying technologies.

* On-premise deployments with a trusted network can track
  container locations in e.g. Zookeeper, and generate HAproxy
  configurations each time a location key changes.
* Public cloud deployments or deployments across unsafe
  networks can add TLS encryption.
* Ad-hoc deployments can use a master-less discovery protocol
  like avahi to register and discover services.
* It is also possible to do one-shot reconfiguration of the
  ambassadors. It is slightly less dynamic but has much less
  requirements.
* Ambassadors can be used in addition to, or instead of, overlay networks.

.debug[[intro/Ambassadors.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Ambassadors.md)]
---

## Section summary

We've learned how to:

* Understand the ambassador pattern and what it is used for (service portability).

For more information about the ambassador pattern, including demos on Swarm and ECS:

* AWS re:invent 2015 [DVO317](https://www.youtube.com/watch?v=7CZFpHUPqXw)

* [SwarmWeek video about Swarm+Compose](https://youtube.com/watch?v=qbIvUvwa6As)

.debug[[intro/Ambassadors.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Ambassadors.md)]
---

Local Development Workflow with Docker

---

# Local Development Workflow with Docker

![construction](images/construction.jpg)

.debug[[intro/Local_Development_Workflow.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Local_Development_Workflow.md)]
---

## Objectives

At the end of this section, you will be able to:

* Share code between container and host.

* Use a simple local development workflow.

.debug[[intro/Local_Development_Workflow.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Local_Development_Workflow.md)]
---

## Using a Docker container for local development

We want to solve the following issues:

- "Works on my machine"

- "Not the same version"

- "Missing dependency"

By using Docker containers, we will get a consistent development environment.

.debug[[intro/Local_Development_Workflow.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Local_Development_Workflow.md)]
---

## Our "namer" application

* The code is available on https://github.com/jpetazzo/namer.

* The image jpetazzo/namer is automatically built by the Docker Hub.

Let's run it with:

```bash
$ docker run -dP jpetazzo/namer
```

Check the port number with `docker ps` and open the application.

.debug[[intro/Local_Development_Workflow.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Local_Development_Workflow.md)]
---

## Let's look at the code

Let's download our application's source code.

```bash
$ git clone https://github.com/jpetazzo/namer
$ cd namer
$ ls -1
company_name_generator.rb
config.ru
docker-compose.yml
Dockerfile
Gemfile
```

.debug[[intro/Local_Development_Workflow.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Local_Development_Workflow.md)]
---

## Where's my code?

According to the Dockerfile, the code is copied into `/src` :

```dockerfile
FROM ruby
MAINTAINER Education Team at Docker <education@docker.com>

COPY . /src
WORKDIR /src
RUN bundler install

CMD ["rackup", "--host", "0.0.0.0"]
EXPOSE 9292
```

We want to make changes *inside the container* without rebuilding it each time.

For that, we will use a *volume*.

.debug[[intro/Local_Development_Workflow.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Local_Development_Workflow.md)]
---

## Our first volume

We will tell Docker to map the current directory to `/src` in the container.

```bash
$ docker run -d -v $(pwd):/src -p 80:9292 jpetazzo/namer
```

* `-d`: the container should run in detached mode (in the background).

* `-v`: the following host directory should be mounted inside the container.

* `-p`: connections to port 80 on the host should be routed to port 9292 in the container.

* `jpetazzo/namer` is the name of the image we will run.

* We don't specify a command to run because is is already set in the Dockerfile.

.debug[[intro/Local_Development_Workflow.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Local_Development_Workflow.md)]
---

## Mounting volumes inside containers

The `-v` flag mounts a directory from your host into your Docker
container. The flag structure is:

```bash
[host-path]:[container-path]:[rw|ro]
```

* If [host-path] or [container-path] doesn't exist it is created.

* You can control the write status of the volume with the `ro` and
  `rw` options.

* If you don't specify `rw` or `ro`, it will be `rw` by default.

There will be a full chapter about volumes!

.debug[[intro/Local_Development_Workflow.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Local_Development_Workflow.md)]
---

## Testing the development container

Now let us see if our new container is running.

```bash
$ docker ps
CONTAINER ID  IMAGE   COMMAND CREATED       STATUS PORTS                NAMES
045885b68bc5  trai... rackup  3 seconds ago Up ... 0.0.0.0:80->9292/tcp ...
```

.debug[[intro/Local_Development_Workflow.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Local_Development_Workflow.md)]
---

## Viewing our application

Now let's browse to our web application on:

```bash 
http://<yourHostIP>:80
```

We can see our company naming application.

![web application 1](images/webapp1.png)

.debug[[intro/Local_Development_Workflow.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Local_Development_Workflow.md)]
---

## Making a change to our application

Our customer really doesn't like the color of our text. Let's change it.

```bash
$ vi company_name_generator.rb
```

And change

```css
color: royalblue;
```

To:

```css
color: red;
```

.debug[[intro/Local_Development_Workflow.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Local_Development_Workflow.md)]
---

## Refreshing our application

Now let's refresh our browser:

```bash
http://<yourHostIP>:80
```

We can see the updated color of our company naming application.

![web application 2](images/webapp2.png)

.debug[[intro/Local_Development_Workflow.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Local_Development_Workflow.md)]
---

## Improving the workflow with Compose

* You can also start the container with the following command:

```bash
$ docker-compose up -d
```

* This works thanks to the Compose file, `docker-compose.yml`:

```yaml
www:
  build: .
  volumes:
    - .:/src
  ports:
    - 80:9292
```

.debug[[intro/Local_Development_Workflow.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Local_Development_Workflow.md)]
---

## Why Compose?

* Specifying all those "docker run" parameters is tedious.

* And error-prone.

* We can "encode" those parameters in a "Compose file."

* When you see a `docker-compose.yml` file, you know that you can use `docker-compose up`.

* Compose can also deal with complex, multi-container apps.
 (More on this later.)

.debug[[intro/Local_Development_Workflow.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Local_Development_Workflow.md)]
---

## Recap of the development workflow

1. Write a Dockerfile to build an image containing our development environment.
 
 (Rails, Django, ... and all the dependencies for our app)

2. Start a container from that image.
 
 Use the `-v` flag to mount our source code inside the container.

3. Edit the source code outside the containers, using regular tools.
 
 (vim, emacs, textmate...)

4. Test the application.
 
 (Some frameworks pick up changes automatically.
 Others require you to Ctrl-C + restart after each modification.)

5. Iterate and repeat steps 3 and 4 until satisfied.

6. When done, commit+push source code changes.

.debug[[intro/Local_Development_Workflow.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Local_Development_Workflow.md)]
---

## Debugging inside the container

Docker has a command called `docker exec`.

It allows users to run a new process in a container which is already running.

If sometimes you find yourself wishing you could SSH into a container: you can use `docker exec` instead.

You can get a shell prompt inside an existing container this way, or run an arbitrary process for automation.

.debug[[intro/Local_Development_Workflow.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Local_Development_Workflow.md)]
---

## `docker exec` example

```bash
$ # You can run ruby commands in the area the app is running and more!
$ docker exec -it <yourContainerId> bash
root@5ca27cf74c2e:/opt/namer# irb
irb(main):001:0> [0, 1, 2, 3, 4].map {|x| x ** 2}.compact
=> [0, 1, 4, 9, 16]
irb(main):002:0> exit
```

.debug[[intro/Local_Development_Workflow.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Local_Development_Workflow.md)]
---

## Stopping the container

Now that we're done let's stop our container.

```bash
$ docker stop <yourContainerID>
```

And remove it.

```bash
$ docker rm <yourContainerID>
```

.debug[[intro/Local_Development_Workflow.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Local_Development_Workflow.md)]
---

## Section summary

We've learned how to:

* Share code between container and host.

* Set our working directory.

* Use a simple local development workflow.

.debug[[intro/Local_Development_Workflow.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Local_Development_Workflow.md)]
---

Working with Volumes

---

# Working with Volumes

![volume](images/volume.jpg)

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## Objectives

At the end of this section, you will be able to:

* Create containers holding volumes.

* Share volumes across containers.

* Share a host directory with one or many containers.

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## Working with Volumes

Docker volumes can be used to achieve many things, including:

* Bypassing the copy-on-write system to obtain native disk I/O performance.

* Bypassing copy-on-write to leave some files out of `docker commit`.

* Sharing a directory between multiple containers.

* Sharing a directory between the host and a container.

* Sharing a *single file* between the host and a container.

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## Volumes are special directories in a container

Volumes can be declared in two different ways.

* Within a `Dockerfile`, with a `VOLUME` instruction.

```dockerfile
VOLUME /uploads
```

* On the command-line, with the `-v` flag for `docker run`.

```bash
$ docker run -d -v /uploads myapp
```

In both cases, `/uploads` (inside the container) will be a volume.

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## Volumes bypass the copy-on-write system

Volumes act as passthroughs to the host filesystem.

* The I/O performance on a volume is exactly the same as I/O performance
  on the Docker host.

* When you `docker commit`, the content of volumes is not brought into
  the resulting image.

* If a `RUN` instruction in a `Dockerfile` changes the content of a
  volume, those changes are not recorded neither.

* If a container is started with the `--read-only` flag, the volume
  will still be writable (unless the volume is a read-only volume).

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## Volumes can be shared across containers

You can start a container with *exactly the same volumes* as another one.

The new container will have the same volumes, in the same directories.

They will contain exactly the same thing, and remain in sync.

Under the hood, they are actually the same directories on the host anyway.

This is done using the `--volumes-from` flag for `docker run`.

We will see an example in the following slides.

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## Sharing web application logs with another container

Let's start a Tomcat container:

```bash
$ docker run --name webapp -d -p 8080:8080 -v /usr/local/tomcat/logs
```

Now, start an `alpine` container accessing the same volume:

```bash
$ docker run --volumes-from webapp alpine sh -c "tail -f /usr/local/tomcat/logs/*"
```

Then, from another window, send requests to our Tomcat container:
```bash
$ curl localhost:8080
```

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## Volumes exist independently of containers

If a container is stopped, its volumes still exist and are available.

Volumes can be listed and manipulated with `docker volume` subcommands:

```bash
$ docker volume ls
DRIVER              VOLUME NAME
local               5b0b65e4316da67c2d471086640e6005ca2264f3...
local               pgdata-prod
local               pgdata-dev
local               13b59c9936d78d109d094693446e174e5480d973...
```

Some of those volume names were explicit (pgdata-prod, pgdata-dev).

The others (the hex IDs) were generated automatically by Docker.

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## Naming volumes

* Volumes can be created without a container, then used in multiple containers.

Let's create a couple of volumes directly.

```bash
$ docker volume create webapps
webapps
```

```bash
$ docker volume create logs
logs
```

Volumes are not anchored to a specific path.

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## Using our named volumes

* Volumes are used with the `-v` option.

* When a host path does not contain a /, it is considered to be a volume name.

Let's start a web server using the two previous volumes.

```bash
$ docker run -d -p 1234:8080 \
         -v logs:/usr/local/tomcat/logs \
         -v webapps:/usr/local/tomcat/webapps \
         tomcat
```

Check that it's running correctly:

```bash
$ curl localhost:1234
... (Tomcat tells us how happy it is to be up and running) ...
```

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## Using a volume in another container

* We will make changes to the volume from another container.

* In this example, we will run a text editor in the other container.

(But this could be a FTP server, a WebDAV server, a Git receiver...)

Let's start another container using the `webapps` volume.

```bash
$ docker run -v webapps:/webapps -w /webapps -ti alpine vi ROOT/index.jsp
```

Vandalize the page, save, exit.

Then run `curl localhost:1234` again to see your changes.

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## Managing volumes explicitly

In some cases, you want a specific directory on the host to be mapped
inside the container:

* You want to manage storage and snapshots yourself.

(With LVM, or a SAN, or ZFS, or anything else!)

* You have a separate disk with better performance (SSD) or resiliency (EBS)
  than the system disk, and you want to put important data on that disk.

* You want to share your source directory between your host (where the
  source gets edited) and the container (where it is compiled or executed).

Wait, we already met the last use-case in our example development workflow!
Nice.

```bash
$ docker run -d -v /path/on/the/host:/path/in/container image ...
```

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## Migrating data with `--volumes-from`

The `--volumes-from` option tells Docker to re-use all the volumes
of an existing container.

* Scenario: migrating from Redis 2.8 to Redis 3.0.

* We have a container (`myredis`) running Redis 2.8.

* Stop the `myredis` container.

* Start a new container, using the Redis 3.0 image, and the `--volumes-from` option.

* The new container will inherit the data of the old one.

* Newer containers can use `--volumes-from` too.

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## Data migration in practice

Let's create a Redis container.

```bash
$ docker run -d --name redis28 redis:2.8
```

Connect to the Redis container and set some data.

```bash
$ docker run -ti --link redis28:redis alpine telnet redis 6379
```

Issue the following commands:

```bash
SET counter 42
INFO server
SAVE
QUIT
```

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## Upgrading Redis

Stop the Redis container.

```bash
$ docker stop redis28
```

Start the new Redis container.

```bash
$ docker run -d --name redis30 --volumes-from redis28 redis:3.0
```

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## Testing the new Redis

Connect to the Redis container and see our data.

```bash
docker run -ti --link redis30:redis alpine telnet redis 6379
```

Issue a few commands.

```bash
GET counter
INFO server
QUIT
```

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## What happens when you remove containers with volumes?

* Volumes are kept around.

* You can list them with `docker volume ls`.

* You can access them by creating a container with `docker run -v`.

* You can remove them with `docker volume rm` or `docker system prune`.

Ultimately, _you_ are the one responsible for logging,
monitoring, and backup of your volumes.

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## Checking volumes defined by an image

Wondering if an image has volumes? Just use `docker inspect`:

```bash
$ # docker inspect training/datavol
[{
  "config": {
    . . .
    "Volumes": {
        "/var/webapp": {}
    },
    . . .
}]
```

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## Checking volumes used by a container

To look which paths are actually volumes, and to what they are bound,
use `docker inspect` (again):

```bash
$ docker inspect <yourContainerID>
[{
 "ID": "<yourContainerID>",
. . .
 "Volumes": {
 "/var/webapp": "/var/lib/docker/vfs/dir/f4280c5b6207ed531efd4cc673ff620cef2a7980f747dbbcca001db61de04468"
 },
 "VolumesRW": {
 "/var/webapp": true
 },
}]
```

* We can see that our volume is present on the file system of the Docker host.

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## Sharing a single file between the host and a container

The same `-v` flag can be used to share a single file.

One of the most interesting examples is to share the Docker control socket.

```bash
$ docker run -it -v /var/run/docker.sock:/var/run/docker.sock docker sh
```

Warning: when using such mounts, the container gains root-like access to the host.
It can potentially do bad things.

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## Volume plugins

You can install plugins to manage volumes backed by particular storage systems,
or providing extra features. For instance:

* [dvol](https://github.com/ClusterHQ/dvol) - allows to commit/branch/rollback volumes;
* [Flocker](https://clusterhq.com/flocker/introduction/), [REX-Ray](https://github.com/emccode/rexray) - create and manage volumes backed by an enterprise storage system (e.g. SAN or NAS), or by cloud block stores (e.g. EBS);
* [Blockbridge](http://www.blockbridge.com/), [Portworx](http://portworx.com/) - provide distributed block store for containers;
* and much more!

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

## Section summary

We've learned how to:

* Create and manage volumes.

* Share volumes across containers.

* Share a host directory with one or many containers.

.debug[[intro/Working_With_Volumes.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Working_With_Volumes.md)]
---

Compose For Development Stacks

---

# Compose For Development Stacks

Dockerfiles are great to build container images.

But what if we work with a complex stack made of multiple containers?

Eventually, we will want to write some custom scripts and automation to build, run, and connect
our containers together.

There is a better way: using Docker Compose.

In this section, you will use Compose to bootstrap a development environment.

.debug[[intro/Compose_For_Dev_Stacks.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Compose_For_Dev_Stacks.md)]
---

## What is Docker Compose?

Docker Compose (formerly known as `fig`) is an external tool.

Unlike the Docker Engine, it is written in Python. It's open source as well.

The general idea of Compose is to enable a very simple, powerful onboarding workflow:

1. Checkout your code.

2. Run `docker-compose up`.

3. Your app is up and running!

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---

## Compose overview

This is how you work with Compose:

* You describe a set (or stack) of containers in a YAML file called `docker-compose.yml`.

* You run `docker-compose up`.

* Compose automatically pulls images, builds containers, and starts them.

* Compose can set up links, volumes, and other Docker options for you.

* Compose can run the containers in the background, or in the foreground.

* When containers are running in the foreground, their aggregated output is shown.

Before diving in, let's see a small example of Compose in action.

.debug[[intro/Compose_For_Dev_Stacks.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Compose_For_Dev_Stacks.md)]
---

## Compose in action

![composeup](images/composeup.gif)

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---

## Checking if Compose is installed

If you are using the official training virtual machines, Compose has been
pre-installed.

You can always check that it is installed by running:

```bash
$ docker-compose --version
```

.debug[[intro/Compose_For_Dev_Stacks.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Compose_For_Dev_Stacks.md)]
---

## Launching Our First Stack with Compose

First step: clone the source code for the app we will be working on.

```bash
$ cd
$ git clone git://github.com/jpetazzo/trainingwheels
...
$ cd trainingwheels
```

Second step: start your app.

```bash
$ docker-compose up
```

Watch Compose build and run your app with the correct parameters,
including linking the relevant containers together.

.debug[[intro/Compose_For_Dev_Stacks.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Compose_For_Dev_Stacks.md)]
---

## Launching Our First Stack with Compose

Verify that the app is running at `http://<yourHostIP>:8000`.

![composeapp](images/composeapp.png)

.debug[[intro/Compose_For_Dev_Stacks.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Compose_For_Dev_Stacks.md)]
---

## Stopping the app

When you hit `^C`, Compose tries to gracefully terminate all of the containers.

After ten seconds (or if you press `^C` again) it will forcibly kill
them.

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---

## The `docker-compose.yml` file

Here is the file used in the demo:

```yaml
version: "2"

services:
  www:
    build: www
    ports:
      - 8000:5000
    user: nobody
    environment:
      DEBUG: 1
    command: python counter.py
    volumes:
      - ./www:/src

redis:
    image: redis
```

.debug[[intro/Compose_For_Dev_Stacks.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Compose_For_Dev_Stacks.md)]
---

## Compose file versions

Version 1 directly has the various containers (`www`, `redis`...) at the top level of the  file.

Version 2 has multiple sections:

* `version` is mandatory and should be `"2"`.

* `services` is mandatory and corresponds to the content of the version 1 format.

* `networks` is optional and indicates to which networks containers should be connected.
 (By default, containers will be connected on a private, per-app network.)

* `volumes` is optional and can define volumes to be used and/or shared by the containers.

Version 3 adds support for deployment options (scaling, rolling updates, etc.)

.debug[[intro/Compose_For_Dev_Stacks.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Compose_For_Dev_Stacks.md)]
---

## Containers in `docker-compose.yml`

Each service in the YAML file must contain either `build`, or `image`.

* `build` indicates a path containing a Dockerfile.

* `image` indicates an image name (local, or on a registry).

* If both are specified, an image will be built from the `build` directory and named `image`.

The other parameters are optional.

They encode the parameters that you would typically add to `docker run`.

Sometimes they have several minor improvements.

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---

## Container parameters

* `command` indicates what to run (like `CMD` in a Dockerfile).

* `ports` translates to one (or multiple) `-p` options to map ports.
 You can specify local ports (i.e. `x:y` to expose public port `x`).

* `volumes` translates to one (or multiple) `-v` options.
 You can use relative paths here.

For the full list, check: https://docs.docker.com/compose/compose-file/

.debug[[intro/Compose_For_Dev_Stacks.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Compose_For_Dev_Stacks.md)]
---

## Compose commands

We already saw `docker-compose up`, but another one is `docker-compose build`.

It will execute `docker build` for all containers mentioning a `build` path.

It can also be invoked automatically when starting the application:

```bash
docker-compose up --build
```

Another common option is to start containers in the background:

```bash
docker-compose up -d
```

.debug[[intro/Compose_For_Dev_Stacks.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Compose_For_Dev_Stacks.md)]
---

## Check container status

It can be tedious to check the status of your containers with `docker ps`,
especially when running multiple apps at the same time.

Compose makes it easier; with `docker-compose ps` you will see only the status of the
containers of the current stack:

```bash
$ docker-compose ps
Name                      Command             State           Ports          
----------------------------------------------------------------------------
trainingwheels_redis_1   /entrypoint.sh red   Up      6379/tcp               
trainingwheels_www_1     python counter.py    Up      0.0.0.0:8000->5000/tcp 
```

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---

## Cleaning up (1)

If you have started your application in the background with Compose and
want to stop it easily, you can use the `kill` command:

```bash
$ docker-compose kill
```

Likewise, `docker-compose rm` will let you remove containers (after confirmation):

```bash
$ docker-compose rm
Going to remove trainingwheels_redis_1, trainingwheels_www_1
Are you sure? [yN] y
Removing trainingwheels_redis_1...
Removing trainingwheels_www_1...
```

.debug[[intro/Compose_For_Dev_Stacks.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Compose_For_Dev_Stacks.md)]
---

## Cleaning up (2)

Alternatively, `docker-compose down` will stop and remove containers.

It will also remove other resources, like networks that were created for the application.

```bash
$ docker-compose down
Stopping trainingwheels_www_1 ... done
Stopping trainingwheels_redis_1 ... done
Removing trainingwheels_www_1 ... done
Removing trainingwheels_redis_1 ... done
```

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---

## Special handling of volumes

Compose is smart. If your container uses volumes, when you restart your
application, Compose will create a new container, but carefully re-use
the volumes it was using previously.

This makes it easy to upgrade a stateful service, by pulling its
new image and just restarting your stack with Compose.

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---

## Compose project name

* When you run a Compose command, Compose infers the "project name" of your app.

* By default, the "project name" is the name of the current directory.

* For instance, if you are in `/home/zelda/src/ocarina`, the project name is `ocarina`.

* All resources created by Compose are tagged with this project name.

* The project name also appears as a prefix of the names of the resources.

E.g. in the previous example, service `www` will create a container `ocarina_www_1`.

* The project name can be overridden with `docker-compose -p`.

.debug[[intro/Compose_For_Dev_Stacks.md](https://github.com/jpetazzo/container.training/tree/lisa17m7/slides/intro/Compose_For_Dev_Stacks.md)]
---

## Running two copies of the same app

If you want to run two copies of the same app simultaneously, all you have to do is to
make sure that each copy has a different project name.

You can:

* copy your code in a directory with a different name

* start each copy with `docker-compose -p myprojname up`

Each copy will run in a different network, totally isolated from the other.

This is ideal to debug regressions, do side-by-side comparisons, etc.

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---

Course Conclusion

---
class: title

# Course Conclusion

![end](images/end.jpg)

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---

## Questions & Next Steps

A bunch of useful links:

* Docker homepage - http://www.docker.com/
* Docker Hub - https://hub.docker.com
* Docker blog - http://blog.docker.com/
* Docker documentation - http://docs.docker.com/
* Docker code on GitHub - https://github.com/docker/docker
* Docker mailing list - [https://groups.google.com/forum/#!forum/docker-user
* Docker on IRC: irc.freenode.net and channels `#docker` and `#docker-dev`
* Docker on Twitter - http://twitter.com/docker
* Get Docker help on Stack Overflow - http://stackoverflow.com/search?q=docker
* Play With Docker Hands-On Labs - http://training.play-with-docker.com/

These slides are at: http://container.training/

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---

Thank You!

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