Understand images, containers, and storage drivers

Images and layers

Each Docker image references a list of read-only layers that represent filesystem differences. Layers are stacked on top of each other to form a base for a container’s root filesystem. The diagram below shows the Ubuntu 15.04 image comprising 4 stacked image layers.

The Docker storage driver is responsible for stacking these layers and providing a single unified view.

When you create a new container, you add a new, thin, writable layer on top of the underlying stack. This layer is often called the “container layer”. All changes made to the running container - such as writing new files, modifying existing files, and deleting files - are written to this thin writable container layer. The diagram below shows a container based on the Ubuntu 15.04 image.

Content addressable storage

Docker 1.10 introduced a new content addressable storage model. This is a completely new way to address image and layer data on disk. Previously, image and layer data was referenced and stored using a randomly generated UUID. In the new model this is replaced by a secure content hash.

The new model improves security, provides a built-in way to avoid ID collisions, and guarantees data integrity after pull, push, load, and save operations. It also enables better sharing of layers by allowing many images to freely share their layers even if they didn’t come from the same build.

The diagram below shows an updated version of the previous diagram, highlighting the changes implemented by Docker 1.10.

As can be seen, all image layer IDs are cryptographic hashes, whereas the container ID is still a randomly generated UUID.

There are several things to note regarding the new model. These include:

1. Migration of existing images
2. Image and layer filesystem structures

Existing images, those created and pulled by earlier versions of Docker, need to be migrated before they can be used with the new model. This migration involves calculating new secure checksums and is performed automatically the first time you start an updated Docker daemon. After the migration is complete, all images and tags will have brand new secure IDs.

Although the migration is automatic and transparent, it is computationally intensive. This means it can take time if you have lots of image data. During this time your Docker daemon will not respond to other requests.

A migration tool exists that allows you to migrate existing images to the new format before upgrading your Docker daemon. This means that upgraded Docker daemons do not need to perform the migration in-band, and therefore avoids any associated downtime. It also provides a way to manually migrate existing images so that they can be distributed to other Docker daemons in your environment that are already running the latest versions of Docker.

The migration tool is provided by Docker, Inc., and runs as a container. You can download it from https://github.com/docker/v1.10-migrator/releases.

While running the “migrator” image you need to expose your Docker host’s data directory to the container. If you are using the default Docker data path, the command to run the container will look like this

$ sudo docker run --rm -v /var/lib/docker:/var/lib/docker docker/v1.10-migrator

If you use the devicemapper storage driver, you will need to include the --privileged option so that the container has access to your storage devices.

MIGRATION EXAMPLE

The following example shows the migration tool in use on a Docker host running version 1.9.1 of the Docker daemon and the AUFS storage driver. The Docker host is running on a t2.micro AWS EC2 instance with 1 vCPU, 1GB RAM, and a single 8GB general purpose SSD EBS volume. The Docker data directory (/var/lib/docker) was consuming 2GB of space.

$ docker images

REPOSITORY          TAG                 IMAGE ID            CREATED             SIZE
jenkins             latest              285c9f0f9d3d        17 hours ago        708.5 MB
mysql               latest              d39c3fa09ced        8 days ago          360.3 MB
mongo               latest              a74137af4532        13 days ago         317.4 MB
postgres            latest              9aae83d4127f        13 days ago         270.7 MB
redis               latest              8bccd73928d9        2 weeks ago         151.3 MB
centos              latest              c8a648134623        4 weeks ago         196.6 MB
ubuntu              15.04               c8be1ac8145a        7 weeks ago         131.3 MB

$ sudo du -hs /var/lib/docker

2.0G    /var/lib/docker

$ time docker run --rm -v /var/lib/docker:/var/lib/docker docker/v1.10-migrator

Unable to find image 'docker/v1.10-migrator:latest' locally
latest: Pulling from docker/v1.10-migrator
ed1f33c5883d: Pull complete
b3ca410aa2c1: Pull complete
2b9c6ed9099e: Pull complete
dce7e318b173: Pull complete
Digest: sha256:bd2b245d5d22dd94ec4a8417a9b81bb5e90b171031c6e216484db3fe300c2097
Status: Downloaded newer image for docker/v1.10-migrator:latest
time="2016-01-27T12:31:06Z" level=debug msg="Assembling tar data for 01e70da302a553ba13485ad020a0d77dbb47575a31c4f48221137bb08f45878d from /var/lib/docker/aufs/diff/01e70da302a553ba13485ad020a0d77dbb47575a31c4f48221137bb08f45878d"
time="2016-01-27T12:31:06Z" level=debug msg="Assembling tar data for 07ac220aeeef9febf1ac16a9d1a4eff7ef3c8cbf5ed0be6b6f4c35952ed7920d from /var/lib/docker/aufs/diff/07ac220aeeef9febf1ac16a9d1a4eff7ef3c8cbf5ed0be6b6f4c35952ed7920d"
<snip>
time="2016-01-27T12:32:00Z" level=debug msg="layer dbacfa057b30b1feaf15937c28bd8ca0d6c634fc311ccc35bd8d56d017595d5b took 10.80 seconds"

real    0m59.583s
user    0m0.046s
sys     0m0.008s

The Unix time command prepends the docker run command to produce timings for the operation. As can be seen, the overall time taken to migrate 7 images comprising 2GB of disk space took approximately 1 minute. However, this included the time taken to pull the docker/v1.10-migrator image (approximately 3.5 seconds). The same operation on an m4.10xlarge EC2 instance with 40 vCPUs, 160GB RAM and an 8GB provisioned IOPS EBS volume resulted in the following improved timings:

real    0m9.871s
user    0m0.094s
sys     0m0.021s

This shows that the migration operation is affected by the hardware spec of the machine performing the migration.

Container and layers

The major difference between a container and an image is the top writable layer. All writes to the container that add new or modify existing data are stored in this writable layer. When the container is deleted the writable layer is also deleted. The underlying image remains unchanged.

Because each container has its own thin writable container layer, and all changes are stored in this container layer, this means that multiple containers can share access to the same underlying image and yet have their own data state. The diagram below shows multiple containers sharing the same Ubuntu 15.04 image.

The Docker storage driver is responsible for enabling and managing both the image layers and the writable container layer. How a storage driver accomplishes these can vary between drivers. Two key technologies behind Docker image and container management are stackable image layers and copy-on-write (CoW).

The copy-on-write strategy

Sharing is a good way to optimize resources. People do this instinctively in daily life. For example, twins Jane and Joseph taking an Algebra class at different times from different teachers can share the same exercise book by passing it between each other. Now, suppose Jane gets an assignment to complete the homework on page 11 in the book. At that point, Jane copies page 11, completes the homework, and hands in her copy. The original exercise book is unchanged and only Jane has a copy of the changed page 11.

Copy-on-write is a similar strategy of sharing and copying. In this strategy, system processes that need the same data share the same instance of that data rather than having their own copy. At some point, if one process needs to modify or write to the data, only then does the operating system make a copy of the data for that process to use. Only the process that needs to write has access to the data copy. All the other processes continue to use the original data.

Docker uses a copy-on-write technology with both images and containers. This CoW strategy optimizes both image disk space usage and the performance of container start times. The next sections look at how copy-on-write is leveraged with images and containers through sharing and copying.

Sharing promotes smaller images

This section looks at image layers and copy-on-write technology. All image and container layers exist inside the Docker host’s local storage area and are managed by the storage driver. On Linux-based Docker hosts this is usually located under /var/lib/docker/.

The Docker client reports on image layers when instructed to pull and push images with docker pull and docker push. The command below pulls the ubuntu:15.04 Docker image from Docker Hub.

$ docker pull ubuntu:15.04

15.04: Pulling from library/ubuntu
1ba8ac955b97: Pull complete
f157c4e5ede7: Pull complete
0b7e98f84c4c: Pull complete
a3ed95caeb02: Pull complete
Digest: sha256:5e279a9df07990286cce22e1b0f5b0490629ca6d187698746ae5e28e604a640e
Status: Downloaded newer image for ubuntu:15.04

From the output, you’ll see that the command actually pulls 4 image layers. Each of the above lines lists an image layer and its UUID or cryptographic hash. The combination of these four layers makes up the ubuntu:15.04 Docker image.

Each of these layers is stored in its own directory inside the Docker host’s local storage area.

Versions of Docker prior to 1.10 stored each layer in a directory with the same name as the image layer ID. However, this is not the case for images pulled with Docker version 1.10 and later. For example, the command below shows an image being pulled from Docker Hub, followed by a directory listing on a host running version 1.9.1 of the Docker Engine.

$  docker pull ubuntu:15.04

15.04: Pulling from library/ubuntu
47984b517ca9: Pull complete
df6e891a3ea9: Pull complete
e65155041eed: Pull complete
c8be1ac8145a: Pull complete
Digest: sha256:5e279a9df07990286cce22e1b0f5b0490629ca6d187698746ae5e28e604a640e
Status: Downloaded newer image for ubuntu:15.04

$ ls /var/lib/docker/aufs/layers

47984b517ca9ca0312aced5c9698753ffa964c2015f2a5f18e5efa9848cf30e2
c8be1ac8145a6e59a55667f573883749ad66eaeef92b4df17e5ea1260e2d7356
df6e891a3ea9cdce2a388a2cf1b1711629557454fd120abd5be6d32329a0e0ac
e65155041eed7ec58dea78d90286048055ca75d41ea893c7246e794389ecf203

Notice how the four directories match up with the layer IDs of the downloaded image. Now compare this with the same operations performed on a host running version 1.10 of the Docker Engine.

$ docker pull ubuntu:15.04
15.04: Pulling from library/ubuntu
1ba8ac955b97: Pull complete
f157c4e5ede7: Pull complete
0b7e98f84c4c: Pull complete
a3ed95caeb02: Pull complete
Digest: sha256:5e279a9df07990286cce22e1b0f5b0490629ca6d187698746ae5e28e604a640e
Status: Downloaded newer image for ubuntu:15.04

$ ls /var/lib/docker/aufs/layers/
1d6674ff835b10f76e354806e16b950f91a191d3b471236609ab13a930275e24
5dbb0cbe0148cf447b9464a358c1587be586058d9a4c9ce079320265e2bb94e7
bef7199f2ed8e86fa4ada1309cfad3089e0542fec8894690529e4c04a7ca2d73
ebf814eccfe98f2704660ca1d844e4348db3b5ccc637eb905d4818fbfb00a06a

See how the four directories do not match up with the image layer IDs pulled in the previous step.

Despite the differences between image management before and after version 1.10, all versions of Docker still allow images to share layers. For example, If you pull an image that shares some of the same image layers as an image that has already been pulled, the Docker daemon recognizes this, and only pulls the layers it doesn’t already have stored locally. After the second pull, the two images will share any common image layers.

You can illustrate this now for yourself. Starting with the ubuntu:15.04 image that you just pulled, make a change to it, and build a new image based on the change. One way to do this is using a Dockerfile and the docker build command.

1. In an empty directory, create a simple Dockerfile that starts with the ubuntu:15.04 image.

    FROM ubuntu:15.04
   

2. Add a new file called “newfile” in the image’s /tmp directory with the text “Hello world” in it.
   When you are done, the Dockerfile contains two lines:

    FROM ubuntu:15.04
   
    RUN echo "Hello world" > /tmp/newfile
   

3. Save and close the file.
4. From a terminal in the same folder as your Dockerfile, run the following command:

    $ docker build -t changed-ubuntu .
   
    Sending build context to Docker daemon 2.048 kB
    Step 1 : FROM ubuntu:15.04
     ---> 3f7bcee56709
    Step 2 : RUN echo "Hello world" > /tmp/newfile
     ---> Running in d14acd6fad4e
     ---> 94e6b7d2c720
    Removing intermediate container d14acd6fad4e
    Successfully built 94e6b7d2c720
   

   Note: The period (.) at the end of the above command is important. It tells the docker build command to use the current working directory as its build context.

   The output above shows a new image with image ID 94e6b7d2c720.
5. Run the docker images command to verify the new changed-ubuntu image is in the Docker host’s local storage area.

    REPOSITORY       TAG      IMAGE ID       CREATED           SIZE
    changed-ubuntu   latest   03b964f68d06   33 seconds ago    131.4 MB
    ubuntu           15.04    013f3d01d247   6 weeks ago       131.3 MB
   

6. Run the docker history command to see which image layers were used to create the new changed-ubuntu image.

    $ docker history changed-ubuntu
    IMAGE               CREATED              CREATED BY                                      SIZE        COMMENT
    94e6b7d2c720        2 minutes ago       /bin/sh -c echo "Hello world" > /tmp/newfile    12 B
    3f7bcee56709        6 weeks ago         /bin/sh -c #(nop) CMD ["/bin/bash"]             0 B
    <missing>           6 weeks ago         /bin/sh -c sed -i 's/^#\s*\(deb.*universe\)$/   1.879 kB
    <missing>           6 weeks ago         /bin/sh -c echo '#!/bin/sh' > /usr/sbin/polic   701 B
    <missing>           6 weeks ago         /bin/sh -c #(nop) ADD file:8e4943cd86e9b2ca13   131.3 MB
   

   The docker history output shows the new 94e6b7d2c720 image layer at the top. You know that this is the new image layer added because it was created by the echo "Hello world" > /tmp/newfile command in your Dockerfile. The 4 image layers below it are the exact same image layers that make up the ubuntu:15.04 image.

Note: Under the content addressable storage model introduced with Docker 1.10, image history data is no longer stored in a config file with each image layer. It is now stored as a string of text in a single config file that relates to the overall image. This can result in some parent image layers showing as missing in the output of the docker history command. This is normal behavior and can be ignored. missing means that there is no local image associated with this history chain.

You may hear images like these referred to as flat images.

Notice the new changed-ubuntu image does not have its own copies of every layer. As can be seen in the diagram below, the new image is sharing its four underlying layers with the ubuntu:15.04 image.

The docker history command also shows the size of each image layer. As you can see, the 94e6b7d2c720 layer is only consuming 12 Bytes of disk space. This means that the changed-ubuntu image we just created is only consuming an additional 12 Bytes of disk space on the Docker host - all layers below the 94e6b7d2c720 layer already exist on the Docker host and are shared by other images.

This sharing of image layers is what makes Docker images and containers so space efficient.

Copying makes containers efficient

You learned earlier that a container is a Docker image with a thin writable, container layer added. The diagram below shows the layers of a container based on the ubuntu:15.04 image:

All writes made to a container are stored in the thin writable container layer. The other layers are read-only (RO) image layers and can’t be changed. This means that multiple containers can safely share a single underlying image. The diagram below shows multiple containers sharing a single copy of the ubuntu:15.04 image. Each container has its own thin RW layer, but they all share a single instance of the ubuntu:15.04 image:

When an existing file in a container is modified, Docker uses the storage driver to perform a copy-on-write operation. The specifics of operation depends on the storage driver. For the AUFS and OverlayFS storage drivers, the copy-on-write operation is pretty much as follows:

• Search through the image layers for the file to update. The process starts at the top, newest layer and works down to the base layer one layer at a time.
• Perform a “copy-up” operation on the first copy of the file that is found. A “copy up” copies the file up to the container’s own thin writable layer.
• Modify the copy of the file in container’s thin writable layer.

Btrfs, ZFS, and other drivers handle the copy-on-write differently. You can read more about the methods of these drivers later in their detailed descriptions.

Containers that write a lot of data will consume more space than containers that do not. This is because most write operations consume new space in the container’s thin writable top layer. If your container needs to write a lot of data, you should consider using a data volume.

A copy-up operation can incur a noticeable performance overhead. This overhead is different depending on which storage driver is in use. However, large files, lots of layers, and deep directory trees can make the impact more noticeable. Fortunately, the operation only occurs the first time any particular file is modified. Subsequent modifications to the same file do not cause a copy-up operation and can operate directly on the file’s existing copy already present in the container layer.

Let’s see what happens if we spin up 5 containers based on our changed-ubuntu image we built earlier:

1. From a terminal on your Docker host, run the following docker run command 5 times.

    $ docker run -dit changed-ubuntu bash
   
    75bab0d54f3cf193cfdc3a86483466363f442fba30859f7dcd1b816b6ede82d4
   
    $ docker run -dit changed-ubuntu bash
   
    9280e777d109e2eb4b13ab211553516124a3d4d4280a0edfc7abf75c59024d47
   
    $ docker run -dit changed-ubuntu bash
   
    a651680bd6c2ef64902e154eeb8a064b85c9abf08ac46f922ad8dfc11bb5cd8a
   
    $ docker run -dit changed-ubuntu bash
   
    8eb24b3b2d246f225b24f2fca39625aaad71689c392a7b552b78baf264647373
   
    $ docker run -dit changed-ubuntu bash
   
    0ad25d06bdf6fca0dedc38301b2aff7478b3e1ce3d1acd676573bba57cb1cfef
   

   This launches 5 containers based on the changed-ubuntu image. As each container is created, Docker adds a writable layer and assigns it a random UUID. This is the value returned from the docker run command.
2. Run the docker ps command to verify the 5 containers are running.

    $ docker ps
    CONTAINER ID    IMAGE             COMMAND    CREATED              STATUS              PORTS    NAMES
    0ad25d06bdf6    changed-ubuntu    "bash"     About a minute ago   Up About a minute            stoic_ptolemy
    8eb24b3b2d24    changed-ubuntu    "bash"     About a minute ago   Up About a minute            pensive_bartik
    a651680bd6c2    changed-ubuntu    "bash"     2 minutes ago        Up 2 minutes                 hopeful_turing
    9280e777d109    changed-ubuntu    "bash"     2 minutes ago        Up 2 minutes                 backstabbing_mahavira
    75bab0d54f3c    changed-ubuntu    "bash"     2 minutes ago        Up 2 minutes                 boring_pasteur
   

   The output above shows 5 running containers, all sharing the changed-ubuntu image. Each CONTAINER ID is derived from the UUID when creating each container.
3. List the contents of the local storage area.

    $ sudo ls /var/lib/docker/containers
   
    0ad25d06bdf6fca0dedc38301b2aff7478b3e1ce3d1acd676573bba57cb1cfef
    9280e777d109e2eb4b13ab211553516124a3d4d4280a0edfc7abf75c59024d47
    75bab0d54f3cf193cfdc3a86483466363f442fba30859f7dcd1b816b6ede82d4
    a651680bd6c2ef64902e154eeb8a064b85c9abf08ac46f922ad8dfc11bb5cd8a
    8eb24b3b2d246f225b24f2fca39625aaad71689c392a7b552b78baf264647373
   

Docker’s copy-on-write strategy not only reduces the amount of space consumed by containers, it also reduces the time required to start a container. At start time, Docker only has to create the thin writable layer for each container. The diagram below shows these 5 containers sharing a single read-only (RO) copy of the changed-ubuntu image.

If Docker had to make an entire copy of the underlying image stack each time it started a new container, container start times and disk space used would be significantly increased.

Data volumes and the storage driver

When a container is deleted, any data written to the container that is not stored in a data volume is deleted along with the container.

A data volume is a directory or file in the Docker host’s filesystem that is mounted directly into a container. Data volumes are not controlled by the storage driver. Reads and writes to data volumes bypass the storage driver and operate at native host speeds. You can mount any number of data volumes into a container. Multiple containers can also share one or more data volumes.

The diagram below shows a single Docker host running two containers. Each container exists inside of its own address space within the Docker host’s local storage area (/var/lib/docker/...). There is also a single shared data volume located at /data on the Docker host. This is mounted directly into both containers.

Data volumes reside outside of the local storage area on the Docker host, further reinforcing their independence from the storage driver’s control. When a container is deleted, any data stored in data volumes persists on the Docker host.

Building image with tomcat7 on Docker IO

Docker IO is evolving light weight  virtualization technology.

Take a look at Docker and play with it. This solves the problem of code shipment and makes the developer's life easy.

 Following paragraph will help you to build an image with apache installed in it. 

There are two ways to make it. 

1. Using Dockerfile  (you need to check the above link if you are surprised "Dockerfile")

2. By executing command one after another.

Though first method is simple, I will discuss the second method. 

You can do method one if you are comfortable with Docker's allowed command.

#Get a container

sudo docker run -t -i ubuntu /bin/bash

#install wget you need it
apt-get update
apt-get -f install
apt-get install wget

#Get the tomcat tar file

#if following mirror doesn't work check the page version might have changed 

wget mirror.atlanticmetro.net/apache/tomcat/tomcat-7/v7.0.54/bin/apache-tomcat-7.0.54.tar.gz

#Extract it
tar xvzf apache-tomcat-7.0.54.tar.gz

#move to the location you want 

# I move to root directory      mv apache-tomcat-7.0.54/   /tomcat7

mv apache-tomcat-7.0.54/  <any_directory>/tomcat7

#remove tar.gz

rm apache-tomcat-7.0.54.tar.gz

#Lets install java now, will come back to tomcat again
sudo apt-get install default-jdk

#update the path of java home in bashrc
sudo vi ~/.bashrc
export JAVA_HOME=/usr/lib/jvm/default-java
export CATALINA_HOME=~/path/to/tomcat  

#Save the bashrc  and restart it, there is . in the front
. ~/.bashrc

# Update the path of java in tomcat as well
sudo gedit <path to tomcat>/tomcat7/bin/catalina.sh

#add the following lines at the end, version might be different so use your not mine :) 
JAVA_HOME="/usr/lib/jvm/default-java"
JRE_HOME="/usr/lib/jvm/default-java/jre"

#now lets start the tomcat
$CATALINA_HOME/bin/startup.sh

if it runs you are good to go, sometime it might says "touch, can't touch .../logs/catalina.out

This might be the problem related to permission so go ahead create a directory called logs under tomcat7 and create a file catalina.out

using other terminal find the ip address (docker inspect container_id) of your container  and enter following line in your browser

<ip address of container>:8080

it should so

It works !

Voila, you have a container running apache inside of it. :)
 I am new to blog writing so bear with me for a while. Let me know if you face any issue 

HOW ANSIBLE MAKES DOCKER-COMPOSE BETTER

1. If you know docker-compose, you know Ansible (almost).

Here's a simple docker-compose file for launching two containers:

wordpress:

 image: wordpress

 links:

   - db:mysql

 ports:

   - 8080:80

db:

 image: mariadb

 environment:

   MYSQL_ROOT_PASSWORD: example

And here's an Ansible playbook that does exactly the same thing:

---

# tasks file for ansible-dockerized-wordpress

- name: "Launch database container"

 docker:

   name: db

   image: mariadb

   env:

     MYSQL_ROOT_PASSWORD: example

- name: "Launch wordpress container"

 docker:

   name: wordpress

   image: wordpress

   links:

   - db:mysql

   ports:

   - 8080:80

Both the docker-compose file and the Ansible playbook are YAML files, and the syntax is nearly identical. This is no accident: the docker-compose tool is written in Python, and it uses the docker-py API client.  Ansible is also written in Python, and the Docker module uses the exact same docker-py API client that docker-compose uses.

The key difference is that docker-compose can only do one thing: manage Docker containers. Ansible can do that too, and it can also do everything else that Ansible does, all in the same playbook.

2.  Because you need to configure the system that your containers are running on.

Every Docker container is ultimately running on a Linux system somewhere. When you download the suite of Docker tools to your Windows or Mac system, the key element is docker-machine -- which is basically a tiny Linux distro.  But people choose lots of different Linux distros for lots of different reasons, and people may not want to use the same Linux distro that Docker chooses to put under the hood. There are perfectly valid reasons to choose another Linux distro than the one provided by default.

For example: let's say that you want the strong security features provided to you by SELinux -- a reasonable desire if you're going to be running your containers in production. That means setting SELinux contexts on your host machines properly. There's no concept of such a thing in docker-compose -- but Ansible makes it simple:

- name: "Set SELinux context properly"

 command: chcon -R system_u:object_r:admin_home_t:s0 your-app

(Of course, it's only simple once you know how to write SELinux policy. Fortunately, there's acoloring book to help you with that.) 

3. Because you want to call out to other systems to configure things.

Not everything is in a container, and not everything is easily containerized. Sure, in development you can hook your application up to a mocked-up database, but what if you've got an actual production database that requires authorization for your application to talk to it? Or what if you need to change Route 53 settings because you're doing a blue-green deployment of containers in AWS? What if you need to set up networking between two containers on different hosts, but the two hosts can't even talk to one another?

The truth is that the laptop, where containers are all easily set up and linked together by developers, is always different than the production environment -- and Ansible is an ideal tool for managing complex production environments. With hundreds of modules to handle cloud tasks or networking tasks or plain ol' boring UNIX-y tasks, Ansible can provide the glue that can make it easier to deploy your containers into any environment.

4. Because you want to build testing directly into your container deployment process.

Containers are immutable -- but only if you're always referring to the same container.  If you tell docker-compose that you want to use the "latest" version of some container from Dockerhub, that "latest" version can and generally will change! Which means that deploying with docker-compose could leave you with an application stack that no longer works.  It's always possible to pin the versions of the containers that you're using, but then you have no way of consuming necessary changes, like key functionality additions or security updates. Immutability is a double-edged sword: you don't consume bad changes, but you don't get to consume good changes either.

One of the less obvious advantages of Ansible is the ability to roll testing directly into your deployment process. Using the assert module, you can easily build functional checks directly into your deployment playbook, giving you the ability to know immediately whether a container has changed in a way that breaks your application.

Take a look at the following snippet, in which Ansible runs a simple test to see if the playbook worked as expected, and throws an assert failure if not:

- name: "Wait for services to start"

  pause: minutes=1

- name: "Check the status of kibana UI"

  command: curl localhost:5601/app/kibana

  register: curl_result

- name: "Ensure that proper data is present in stdout"

  assert:

    that:

      - "'kibana.bundle.js' in curl_result.stdout"

The ability to know instantly when a deployment breaks is a fundamental requirement for continuous deployment. Ansible can give you that knowledge.

5. Because you want to integrate containers into your Ansible Tower workflow.

For organizations with more complex orchestration needs, Ansible Tower is a leading choice for managing that complexity. It provides the safeguards to help ensure that the right people are in charge of deploying the right things to the right places, and it tells you exactly who deployed what, when, where and how.

By using Ansible as the control mechanism for container deployment, you can get the benefits of Tower when container orchestration eventually becomes a key piece of your overall IT deployment strategy.

6. Because Ansible now speaks docker-compose!

Maybe you've already written hundreds of lines of docker-compose, and you don't want to be bothered to rewrite it, but you also want all of the advantages afforded by Ansible.

That's fine! In Ansible 2.1, we are introducing the docker_service module, which allows Ansible users to consume docker-compose files directly. Just call the docker_service module from any Ansible playbook, and specify either an external docker-compose file, or put the docker-compose syntax directly into the Ansible playbook itself.

Dockerize your Python Application

Creating a Dockerfile

First, start with a fresh empty directory. In our example, we call this my_new_docker_build – but feel free to use whatever name you like. This directory defines the context of your build, meaning it contains all of the things you need to build your image.

Create a new text file in my_new_docker_build called Dockerfile (note no extension; on Windows, you may need to save the file as “All types” and put the filename in quotes to avoid automatically appending an extension); use whatever text file editor you already know (you might use Sublime, Notepad++, emacs, nano, or even vi). In our example, we use the basic Python 3 image as our launching point. Add the following line to your Dockerfile:

FROM python:3

We want to run a basic Python script which we’ll call my_script.py. First, we need to add the script to the Dockerfile:

 ADD my_script.py /

Our script depends on the Python pyStrich library (pyStrich generates 1D and 2D barcodes), so we need to make sure we install that before we run my_script.py! Add this line to your Dockerfile to install random:

RUN pip install pystrich

Add this line to your Dockerfile to execute the script:

CMD [ "python", "./my_script.py" ]

Your Dockerfile should look like this:

FROM python:3

ADD my_script.py /

RUN pip install pystrich

CMD [ "python", "./my_script.py" ]

• FROM tells Docker which image you base your image on (in the example, Python 3).
• RUN tells Docker which additional commands to execute.
• CMD tells Docker to execute the command when the image loads.

The Python script my_script.py looks like the following:

# Sample taken from pyStrich GitHub repository
# https://github.com/mmulqueen/pyStrich
from pystrich.datamatrix import DataMatrixEncoder

encoder = DataMatrixEncoder('This is a DataMatrix.')
encoder.save('./datamatrix_test.png')
print(encoder.get_ascii())

Now you are ready to build an image from this Dockerfile. Run:

docker build -t python-barcode .

Run Your Image

After your image has been built successfully, you can run it as a container. In your terminal, run the command docker images to view your images. You should see an entry for “python-barcode”. Run the new image by entering:

docker run python-barcode

You should see what looks like a large ASCII QR code.

Alternatives

If you only need to run a simple script (with a single file), you can avoid writing a complete Dockerfile. In the examples below, assume you store my_script.py in /usr/src/widget_app/, and you want to name the container my-first-python-script:

Python 3:

docker run -it --rm --name my-first-python-script -v "$PWD":/usr/src/widget_app python:3 python my_script.py

Python 2:

docker run -it --rm --name my-first-python-script -v "$PWD":/usr/src/widget_app python:2 python my_script.py

Further information

Creating a Dockerfile

Make sure you do not append an extension to the Dockerfile (i.e., Docker does not recognize Dockerfile.txt).

You do not have to read the contents of every Dockerfile you base yours on, but make sure to at least familiarize yourself with them; you can avoid trying to install redundant software (e.g., installing pip when the Python image already loads it), and you can make sure you write your RUN commands appropriately. Docker Hub does not enforce basing all images off only one distribution of Linux; if you use a Debian-based distribution (Debian, Ubuntu, Mint, etc.) you need to call apt-get to install software, and if you use a Red Hat-based distribution (Red Hat Enterprise Linux/RHEL, CentOS) you need to use yum. Gaining familiarity early prevents redoing your work and saves time.

You might end up starting with an unfamiliar base image (i.e., if you primarily use CentOS and want to run a Python installation, the Python image extends Debian Jessie, so you need to use caution in how you write your RUN directives). If you maintain familiarity with Ubuntu, using Debian does not offer too many challenges (Ubuntu came from an offshoot of Debian Linux).

Avoid putting any unused files in your build directory. Docker makes tarballs of everything in the current directory and sends that to the Docker daemon, so if you have unnecessary files, those are included.

Alternatives

Do not attempt to run a script requiring dependencies using the Alternative method, unless those dependencies come with the bare Python installation.

Deleting Docker Containers

Run the following command from your docker console to see a list of your containers:

docker ps

# OR #

docker ps -a  # to see all containers, including those not running

Note: Removing a Container is FINAL.

Delete a Single Container

1. Run docker ps -a and retrieve the container ID (an alphanumeric string, something like a39c259df462).
2. Run docker rm a39c259df462 to remove just that container.

Delete All Your Containers

To delete all your containers, run:

$ docker ps -q -a | xargs docker rm

• -q prints only the container ID’s
• -a prints all containers
• passing all container IDs to xargs, docker rm deletes all containers

Deleting Docker Images

Delete a Single Image

1. Retrieve the Image ID using docker images (The Image IDs should be in the third column.)
2. Run docker rmi <image_id>

For example:

$ docker rmi 60959f29de3a

Delete All Untagged Images

This requires a little bit of Linux magic (like deleting all containers above). Docker marks images without tags with "<none>" so we need to process only those images. Run the following command from your terminal (the awk programming language gives you text manipulation tools):

docker rmi $(docker images | grep "<none>" | awk '{print $3}')

Delete All Images

To delete all of your images, you can simplify the command above:

docker rmi $(docker images | awk '{print $3}')