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My Moment Of Docker Clarity

I was, for a long time, a Docker skeptic. I was in the audience for the now-famous lightning talk at PyCon 2013 where it was first introduced, and I was puzzled. I had my development and deployment workflows worked out and because I had full control over my production environment, Docker seemed of little use to me. Why learn yet another new deployment tool when Fabric and a bit of manual fiddling with servers works perfectly fine?

I continued to be even more mystified by one of the supposed great benefits of Docker: having my development environment exactly match my production environment. Again, all my tests were passing in both so I didn't see the need for an extra layer of tooling.

What ended up partially changing my mind about Docker was, of all things, a great blog post about making maps with Python. You see, getting all of the necessary libraries needed for that blog post (IPython, Numpy, Basemap, etc.) installed and updated on a Mac is surprisingly difficult and I was getting increasingly frustrated trying to install them with pip, and then while reading the miniconda documentation I saw these magic words:

docker run -t -i continuumio/miniconda /bin/bash

And then, I was enlightened.

Docker was not just for deploying my apps, I saw, but was a general purpose software packaging, distribution, and deployment solution that was far easier to use and more approachable than it seemed at first glance.

Finding My Docker Path

I am still not ready to dive in 100%. I believe Docker, useful as it is, is optimized for horizontal scaling of stateless microservices. I understand that there are technology companies out there that benefit greatly from this, but for what I do (highly tailored, bespoke web applications for a small-ish group of users at a research university) I don't need scaling. Moreover, my apps tend to be highly stateful and I love few things more than a beautifully normalized Postgres schema. Docker and databases don't play well together.

(You of course can run your database in a Docker container, and many quickstarts have you doing just this, but I believe it is a Bad Idea that diminishes some of the benefits of Dockerizing your app in the first place.)

Furthermore, I believe that what is important about my app is the behavior of the app, not the deployment method I choose to use at any given time. I may use Docker now, but I may switch to a pure push-to-deploy PaaS in the future, so I don't want to have to restructure my repository just to accommodate the limitations of Dockerfiles.

So, my current approach is to write very simple shell scripts to build docker images for the stateless parts of my app (namely, the nginx frontend and the app code running in gunicorn), wire them together and deploy them with docker-compose, and have them use databases that I manage "out of band" with Ansible.

This approach gives me

  • the benefits of being able to redeploy my app anywhere, insulating me from the shifting infrastructure landscape
  • an easy way to distribute my application
  • the benefits of packaging my dependencies with my code.

While allowing me to avoid

  • having to fight Docker to containerize an inherently stateful service
  • disrupting my preferred local development workflow, using virtualenv, pip, and Django's runserver command.
  • having Docker infect my repository layout by forcing all app code to live in the context of a Dockerfile.

No doubt my approach will continue to evolve and my build scripts can be refined, but this method is a useful and effective middle ground.


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