Using Docker Compose for Python Development
Using Docker Compose for Python Development
Let's see how you can use Docker Compose for local app development (with Python as the language of choice), all leading up to a CI/CD pipeline.
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Docker is an amazing tool for developers. It allows us to build and replicate images on any host, removing the inconsistencies of dev environments and reducing onboarding timelines considerably.
To provide an example of how you might move to containerized development, I built a simple
todo API with Python, Django REST Framework, and PostgreSQL using Docker Compose for development, testing, and eventually in my CI/CD pipeline.
In a two-part series, I will cover the development and pipeline creation steps. In this post, I will cover the first part: developing and testing with Docker Compose.
Requirements for This Tutorial
This tutorial requires you to have a few items before you can get started.
- Install Docker Community Edition
- Install Docker Compose
- Download Todo app example — Non-Docker branch
The todo app here is essentially a stand-in, and you could replace it with your own application. Some of the setup here is specific for this application. The needs of your application may not be covered, but it should be a good starting point for you to get the concepts needed to Dockerize your own applications.
Once you have everything set up, you can move on to the next section.
Creating the Dockerfile
At the foundation of any Dockerized application, you will find a
Dockerfile contains all of the instructions used to build out the application image. You can set this up by installing Python and all of its dependencies. However, the Docker ecosystem has an image repository with a Python image already created and ready to use.
In the root directory of the application, create a new
/> touch Dockerfile
Open the newly created
Dockerfile in your favorite editor. The first instruction,
FROM, will tell Docker to use the prebuilt Python image. There are several choices, but this project uses the
python:3.6.1-alpine image. For more details about why I’m using
alpine here over the other options, you can read this post.
If you run
docker build ., you will see something similar to the following:
Sending build context to Docker daemon 10.53MB Step 1/1 : FROM python:3.6.1-alpine 3.6.1-alpine: Pulling from library/python 90f4dba627d6: Pull complete 19bc0bb0be9f: Pull complete e05eff433916: Pull complete e70196200a87: Pull complete a6d780959950: Pull complete Digest: sha256:0945574465b917d524ce9b748479a286c2ed3c5a97311ac5950464907d4d8b53 Status: Downloaded newer image for python:3.6.1-alpine ---> ddd6300d05a3 Successfully built ddd6300d05a3
With only one instruction in the Dockerfile, this doesn’t do too much, but it does show you the build process without too much happening. At this point, you now have an image created, and running
docker images will show you the images you have available:
REPOSITORY TAG IMAGE ID CREATED SIZE python 3.6.1-alpine ddd6300d05a3 5 weeks ago 88.7MB
Dockerfile needs more instructions to build out the application. Currently, it’s only creating an image with Python installed, but we still need our application code to run inside the container. Let’s add some more instructions to do this and build this image again.
Let’s add the instructions to the
FROM python:3.6.1-alpine RUN apk update \ && apk add \ build-base \ postgresql \ postgresql-dev \ libpq RUN mkdir /usr/src/app WORKDIR /usr/src/app COPY ./requirements.txt . RUN pip install -r requirements.txt ENV PYTHONUNBUFFERED 1 COPY . .
Here is what is happening:
apkpackages and then install a few additional requirements
- Make the directory
- Set the working directory
requirements.txtto the working directory
- Run pip install with the
- Set the environment variable
- Copy all the files from the project’s root to the working directory
You can now run
docker build . again and see the results:
Sending build context to Docker daemon 10.53MB Step 1/8 : FROM python:3.6.1-alpine ---> ddd6300d05a3 Step 2/8 : RUN apk update && apk add build-base postgresql postgresql-dev libpq ---> Running in 22bfdcf8a0dd fetch http://dl-cdn.alpinelinux.org/alpine/v3.4/main/x86_64/APKINDEX.tar.gz fetch http://dl-cdn.alpinelinux.org/alpine/v3.4/community/x86_64/APKINDEX.tar.gz v3.4.6-165-g6b9a79f [http://dl-cdn.alpinelinux.org/alpine/v3.4/main] v3.4.6-160-g14ad2a3 [http://dl-cdn.alpinelinux.org/alpine/v3.4/community] OK: 5974 distinct packages available Executing busybox-1.24.2-r13.trigger OK: 215 MiB in 65 packages ---> 782357cafece Removing intermediate container 22bfdcf8a0dd Step 3/8 : ENV PYTHONUNBUFFERED 1 ---> Running in 0d6de64f5f8b ---> 609106526013 Removing intermediate container 0d6de64f5f8b Step 4/8 : RUN mkdir /usr/src/app ---> Running in b30ac1098156 ---> 17def88f6c5f Removing intermediate container b30ac1098156 Step 5/8 : WORKDIR /usr/src/app ---> 158d43ac2a47 Removing intermediate container fef446ea4ed0 Step 6/8 : COPY ./requirements.txt . ---> 2751d1f4c313 Removing intermediate container a49b090cb8e3 Step 7/8 : RUN pip install -r requirements.txt ---> Running in eaf9912e4810 Installing collected packages: pytz, Django, djangorestframework, psycopg2, django-cors-headers, dj-database-url, gunicorn, virtualenv Successfully installed Django-1.11.3 dj-database-url-0.4.2 django-cors-headers-2.1.0 djangorestframework-3.6.3 gunicorn-19.7.1 psycopg2-2.7.2 pytz-2017.2 virtualenv-15.1.0 ---> dfcca42a45b0 Removing intermediate container eaf9912e4810 Step 8/8 : COPY . . ---> 61f356fba3ea Removing intermediate container fda87c3db8cf Successfully built 61f356fba3ea
You have now successfully created the application image using Docker. Currently, however, our app won’t do much since we still need a database, and we want to connect everything together. This is where Docker Compose will help us out.
Docker Compose Services
Now that you know how to create an image with a
Dockerfile, let’s create an application as a service and connect it to a database. Then we can run some setup commands and be on our way to creating that new todo list.
Create the file
/> touch docker-compose.yml
The Docker Compose file will define and run the containers based on a configuration file. We are using compose file version 3 syntax, and you can read up on it on Docker’s site.
An important concept to understand is that Docker Compose works at “runtime.” Up until now, we have been building images using
docker build . — this is “buildtime.” This is especially important when we add things like
command because they will override what is set up at “buildtime.”
For example, the
usr/src/app directory will be created during the build. We then map that directory to the host machine, and the host machine application code will be used during “runtime.” This allows us to make changes locally, and those are then accessible in the container.
docker-compose.yml file in your editor, and copy paste the following lines:
version: '3' services: web: build: . command: gunicorn -b 0.0.0.0:8000 todosapp.wsgi:application depends_on: - postgres volumes: - .:/usr/src/app ports: - "8000:8000" environment: DATABASE_URL: postgres://todoapp@postgres/todos postgres: image: postgres:9.6.2-alpine environment: POSTGRES_USER: todoapp POSTGRES_DB: todos
This will take a bit to unpack, but let’s break it down by service.
The Web Service
The first directive in the web service is to
build the image based on our
Dockerfile. This will recreate the image we used before, but it will now be named according to the project we are in,
name. After that, we are giving the service some specific instructions on how it should operate:
command: gunicorn -b 0.0.0.0:8000 todosapp.wsgi:application– Once the image is built, and the container is running, this command will start the application.
depends_on:– This will tell Docker Compose to start up the
postgresservice when the
volumes:– This section will mount paths between the host and the container.
.:/usr/src/app– This will mount the root directory to our working directory in the container.
environment:– The application itself expects the environment variable
ports:– This will publish the container’s port, in this case
8000, to the host as port
DATABASE_URL is the connection string.
postgres://todoapp@postgres/todos connects using the
todoapp user, on the host
postgres, using the database
The Postgres Service
Like the Python image we used, the Docker Store has a pre-built image for PostgreSQL. Instead of using a
build directive, we can use the name of the image, and Docker will grab that image for us and use it. In this case, we are using
postgres:9.6.2-alpine. We could leave it like that, but it has
environment variables to let us customize it a bit.
environment:– This particular image accepts a couple environment variables so we can customize things to our needs.
POSTGRES_USER: todoapp– This creates the user
todoappas the default user for PostgreSQL.
POSTGRES_DB: todos– This will create the default database as
Running the Application
Now that we have our services defined, we can build the application using
docker-compose up. This will show the images being built and eventually starting. After the initial build, you will see the names of the containers being created.
Pulling postgres (postgres:9.6.2-alpine)... 9.6.2-alpine: Pulling from library/postgres cfc728c1c558: Pull complete b749e72b24f9: Pull complete 0abdb8c9c36b: Pull complete 90ca3848ef7e: Pull complete 3ecf037a5034: Pull complete 9327e3c5554c: Pull complete 3133782bad17: Pull complete 143bac6c8910: Pull complete d6da9f4bd18e: Pull complete Digest: sha256:f88000211e3c682e7419ac6e6cbd3a7a4980b483ac416a3b5d5ee81d4f831cc9 Status: Downloaded newer image for postgres:9.6.2-alpine Building web ... Creating pythondjangotodoapp_postgres_1 ... Creating pythondjangotodoapp_postgres_1 ... done Creating pythondjangotodoapp_web_1 ... Creating pythondjangotodoapp_web_1 ... done ... web_1 | [2017-08-03 13:23:18 +0000] () [INFO] Starting gunicorn 19.7.1 web_1 | [2017-08-03 13:23:18 +0000] () [INFO] Listening at: http://0.0.0.0:8000 (1)
At this point, the application is running, and you will see log output in the console. You can also run the services as a background process, using
docker-compose up -d. During development, I prefer to run without
-d and create a second terminal window to run other commands. If you want to run it as a background process and view the logs, you can run
At a new command prompt, you can run
docker-compose ps to view your running containers. You should see something like the following:
Name Command State Ports ------------------------------------------------------------------------------------------------ pythondjangotodoapp_postgres_1 docker-entrypoint.sh postgres Up 5432/tcp pythondjangotodoapp_web_1 gunicorn -b 0.0.0.0:8000 t ... Up 0.0.0.0:8000->8000/tcp
This will tell you the name of the services, the command used to start it, its current state, and the ports. Notice
pythondjangotodoapp_web_1 has listed the port as
0.0.0.0:8000->8000/tcp. This tells us that you can access the application using
localhost:8000/todos on the host machine.
Migrate the Database Schema
A small but important step not to overlook is the schema migration for the database. Compose comes with an
exec command that will execute a one-off command on a running container. The typical function to migrate schemas is
python manage.py migrate. We can run that on the web service using
/> docker-compose exec web python manage.py migrate Operations to perform: Apply all migrations: admin, auth, contenttypes, sessions, todos Running migrations: Applying contenttypes.0001_initial... OK Applying auth.0001_initial... OK Applying admin.0001_initial... OK Applying admin.0002_logentry_remove_auto_add... OK Applying contenttypes.0002_remove_content_type_name... OK Applying auth.0002_alter_permission_name_max_length... OK Applying auth.0003_alter_user_email_max_length... OK Applying auth.0004_alter_user_username_opts... OK Applying auth.0005_alter_user_last_login_null... OK Applying auth.0006_require_contenttypes_0002... OK Applying auth.0007_alter_validators_add_error_messages... OK Applying auth.0008_alter_user_username_max_length... OK Applying sessions.0001_initial... OK Applying todos.0001_initial... OK
Now we can try out the API:
/> curl localhost:8000/todos 
The schema and all of the data in the container will persist as long as the
postgres:9.6.2-alpine image is not removed. Eventually, however, it would be good to check how your app will build with a clean setup. You can run
docker-compose down, which will clear things that are built and let you see what is happening with a fresh start.
Feel free to check out the source code, play around a bit, and see how things go for you.
Testing the Application
The application itself includes some integration tests. There are various ways to go about testing with Docker, including creating
docker-compose.test.yml files specific for the test environment. That’s a bit beyond the current scope of this article, but I want to show you how to run the tests using the current setup.
The current containers are running using the project name
pythondjangotodoapp. This is a default from the directory name. If we attempt to run commands, it will use the same project, and containers will restart. This is what we don’t want.
Instead, we will use a different project name to run the application, isolating the tests into their own environment. Since containers are ephemeral (short-lived), running your tests in a separate set of containers makes certain that your app is behaving exactly as it should in a clean environment.
In your terminal, run the following command:
/> docker-compose -p tests run -p 8000 --rm web python manage.py test Starting tests_postgres_1 ... done Creating test database for alias 'default'... System check identified no issues (0 silenced). ........ ---------------------------------------------------------------------- Ran 8 tests in 0.086s OK Destroying test database for alias 'default'...
docker-compose command accepts several options, followed by a command. In this case, you are using
-p tests to run the services under the
tests project name. The command being used is
run, which will execute a one-time command against a service.
docker-compose.yml file specifies a port, we use
-p 8000 to create a random port to prevent port collision. The
--rm option will remove the containers when we stop the containers. Finally, we are running in the
python manage.py test.
At this point, you should have a solid start using Docker Compose for local app development. In the next part of this series about using Docker Compose for Python development, I will cover integration and deployments of this application using Codeship.
Is your team using Docker in its development workflow? If so, I would love to hear about what you are doing and what benefits you see as a result.
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