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Docker for development

Below we lay out recommendations for using Docker to wrap development dependencies. Unfortunately, this is a relatively new space and we don’t have a consistent, end-to-end setup at the moment. Instead, we’ll provide pointers, partial solutions, and examples. We intend to flesh out complete solutions as they become apparent in addition to a comprehensive introduction.


Consider your app’s production environment. You’re likely running on (which runs Ubuntu Linux) with specific versions of databases, app language (e.g. Go/Node/Python/Ruby), and required libraries (you are pinning your dependencies, right?). Now consider your local environment; if you’re an TTS engineer, you likely have OS X, language version management tools, and an assortment of strategies for running databases, worker queues, etc. If you’re a designer, open source contributor, or working at a partner agency, we should assume even less. Not only do we have significant mismatches between these environments, we also require significant setup for new and infrequent contributors.

Docker promises a partial solution to this problem by wrapping dependencies into a consistent, reproducible environment. While we don’t generally support Docker in production, we can create a setup that matches relatively closely and which makes running our app painless.


We hope to have an end-to-end recommendation in the future, but for now we provide these scoped recommendations.

Use Docker-Compose

Docker introductions tend to highlight the Dockerfile, a procedural description of how to build a Docker image, but that’s generally not encompassing enough for development needs. We will likely want to run a database (or other service) separate from our application, and provide different environments (e.g. one for building, one for development, one for production-like); while it’s possible to include all of that in a single image, it’s generally better to use separate, focused images. Moreover, while Dockerfiles provide hints around which ports should be exposed, which files should be shared with the host, etc., the docker-compose file instantiates specific values. This is needed when we run the app locally.

Docker-compose files solve both of these problems by allowing us to specify different services, dependencies between those services, and specific values for exposed ports, mount points, etc. Though it feels more complex at first, we recommend using docker-compose even when a single Dockerfile would suffice so that we have a consistent tool across TTS engineering. One of the bigger “aha” moments you will encounter is transitioning from thinking of Docker as a VM build script (a la vagrant) to thinking of it as a way to configure (“orchestrate”) several, single-purpose containers (i.e. microservices).


There’s a balance to be struck between mirroring (e.g. through the cflinuxfs3 base image based on Ubuntu 18.04 LTS, aka Bionic Beaver) and using official Docker images for Node, Postgres, Nginx, etc. We’re waiting on Docker-based buildpacks to stabilize, but in the meantime we recommend using the Ubuntu build (where available) of the official images, such as python:3.5, ruby:2.4, etc. Avoid the slim and alpine images as they aren’t likely to closely match the production environment.

Mounting the working directory

Demanding the developers rebuild their Docker images after every modification won’t fly and isn’t necessary. Instead, we’ll want to mount the source code into a shared volume, meaning edits on the host file system are visible within the container and vice versa. This will likely look something like:

# docker-compose.yml
  - $PWD:/usr/src/app
  working_dir: /usr/src/app

where /usr/src/app is the mount point within the container (different Docker images will assume the project’s code is at different spots within the file system; be sure to verify).

By doing this, you guarantee that the container sees your changes without needing to rebuild the Docker image (which can take a considerable amount of time).

Does this mean that your Dockerfile shouldn’t include the application’s source code? It certainly means that whatever the container had stored at the mount point is ignored during local development. That doesn’t rule out all of the benefits of including a checkpoint of the application source within the docker image, however. See Are Dockerfiles needed? for further discussion.


We’d like our local environment to match production, so let’s mirror our manifest files in docker-compose.yml. This way, we’ll be accessing our configuration variables the same way (e.g. via cf-env) in our two environments.

Adding fixed environment variables (e.g. DJANGO_SETTINGS_MODULE) is straight forward, just add an environment key to your service definition. We’ll also want to include settings provided within like PORT, DATABASE_URL, and (potentially) TMPDIR. See the full list from Cloud Foundry. We’ll also want to duplicate the serialized JSON of the VCAP_APPLICATION and VCAP_SERVICES variables, which is pretty straight forward using a “folded” string literal (>):

    DATABASE_URL: postgres://postgres@my-database-service/postgres
    PORT: 8000
    TMPDIR: /tmp
      {"uris": ["localhost", "", ""]}
      {"my-user-provided-service": [{"credentials": {"SOME_KEY": "VALUE"}}],
       "my-elastic-service": [{"more": "settings"}]}

For further debate

As with any new tool, we need time to derive best practices. Below we catalog a few of the more important debates and solutions we’re actively using. We’ll see which of the competing strategies resonates the most at a future date.

Library storage

One of our primary needs from Docker is to wrap all of our application’s runtime (and development) libraries – how do we do that?

One strategy would be to create a Dockerfile which instructed Docker to include a description of these libraries (requirements.txt, package.json, Gemfile.lock, etc.) and to download those requirements into the constructed Docker image (e.g. via pip install, gem install, npm install -g). Any commands which use this constructed image would have the libraries already at their disposal. The benefit to this approach is that we could ship this image around and all users would have access to the full environment (this is the promise of Docker in production). The cost is that every change to our requirements forces us to rebuild our images.

A different strategy would store the libraries somewhere in the current directory, as we’ve come to expect from npm install and node_modules. This can be recreated in other languages through careful configuration of virtualenvs, bundler, etc. In this scenario, we don’t need to worry about rebuilding images based on requirements (which are effectively dynamically included). Further, we can destroy our Docker containers and images with little fear as all the libraries are actually stored in the host’s file system. This approach requires a bit more planning and configuration and diverges from the majority of Docker’s use cases (meaning documentation is harder to find). It also has confusion points around Linux binaries being stored on OS X/Windows/etc.

As a third strategy, we can try storing these libraries within a Docker volume, meaning they stay with the project between restarts but are not (generally) accessible from the host. We’d more commonly use volumes like this for database storage, but there’s nothing to stop us from storing our runtime libraries the same way. This approach has the benefit of being very flexible – our docker-compose configurations aren’t closely tied to our specific projects, but requires significant planning and configuration.

One-off commands

Apps generally require more than a single “run-everything” command – we’ll want to get to the database console, run tests, migrate data, etc. etc. We have several strategies at our disposal.

Before we dive too deep, it’s important to first discuss docker-compose run. Consider

docker-compose run --rm my-service py.test --pdb

This starts your my-service (as defined in your docker-compose manifest), including any necessary dependencies, such as databases. It doesn’t execute my-service’s startup command, however; instead it runs py.test within the my-service container. Once that command finished, Docker stops and deletes (--rm) the running container (any services it depended on will continue to run).

As a first approach, we can run these one-off commands directly as described above. This works well, but requires engineers keep track of the relevant commands and which image they should be ran within. We can soften the burden by writing wrapping shell scripts or command aliases.

A second strategy places those commands within the docker-compose manifest as pseudo-services, e.g.

    image: thing-that-contains-pytest
    - $PWD:/apps-dir
    entrypoint: py.test

These would be executed via

docker-compose run --rm py.test --pdb

This approach defines a concise list of the entry points to your software suite, but may require additional image rebuilding and can be confusing when combined with docker-compose up. If taking this approach, be sure to use YAML anchors to reduce duplication (search within this example for more details).

If using Django, Atul has written a library which intelligently selects between docker-compose and the local environment. If we can describe all of our one-time commands via Django, this tactic may get you going faster.

Are Dockerfiles needed?

Describing our application setup in a Dockerfile is a great way to create a standard platform for the team. We aren’t actually shipping this image around, however, and, in many cases, we’re overriding much of the work it performed (e.g. by replacing the working directory with the local version of the files). Can we get by without an application image, then?

For example, consider a docker-compose manifest that referred only to official images but shared a Docker volume:

    image: python:3.5
    - dependencies:/path/to/dependency/storage

Then we could execute all of our application setup without an application image:

docker-compose run --rm my-app pip install
docker-compose run --rm my-app gunicorn   # start app

This approach isn’t described often (it seems the community is still settling), but it’s worth considering.


Docker as primary dev env

Docker as alternative dev env

Additional reading