End to End Testing with Docker

January 24, 2015 at 11:10 PM | categories: Docker, Mozilla, testing | View Comments

I've written an extensive testing framework for Mozilla's version control tools. Despite it being a little rough around the edges, I'm a bit proud of it.

When you run tests for MozReview, Mozilla's heavily modified Review Board code review tool, the following things happen:

  • A MySQL server is started in a Docker container.
  • A Bugzilla server (running the same code as bugzilla.mozilla.org) is started on an Apache httpd server with mod_perl inside a Docker container.
  • A RabbitMQ server mimicking pulse.mozilla.org is started in a Docker container.
  • A Review Board Django development server is started.
  • A Mercurial HTTP server is started

In the future, we'll likely also need to add support for various other services to support MozReview and other components of version control tools:

  • The Autoland HTTP service will be started in a Docker container, along with any other requirements it may have.
  • An IRC server will be started in a Docker container.
  • Zookeeper and Kafka will be started on multiple Docker containers

The entire setup is pretty cool. You have actual services running on your local machine. Mike Conley and Steven MacLeod even did some pair coding of MozReview while on a plane last week. I think it's pretty cool this is even possible.

There is very little mocking in the tests. If we need an external service, we try to spin up an instance inside a local container. This way, we can't have unexpected test successes or failures due to bugs in mocking. We have very high confidence that if something works against local containers, it will work in production.

I currently have each test file owning its own set of Docker containers and processes. This way, we get full test isolation and can run tests concurrently without race conditions. This drastically reduces overall test execution time and makes individual tests easier to reason about.

As cool as the test setup is, there's a bunch I wish were better.

Spinning up and shutting down all those containers and processes takes a lot of time. We're currently sitting around 8s startup time and 2s shutdown time. 10s overhead per test is unacceptable. When I make a one line change, I want the tests to be instantenous. 10s is too long for me to sit idly by. Unfortunately, I've already gone to great pains to make test overhead as short as possible. Fig wasn't good enough for me for various reasons. I've reimplemented my own orchestration directly on top of the docker-py package to achieve some significant performance wins. Using concurrent.futures to perform operations against multiple containers concurrently was a big win. Bootstrapping containers (running their first-run entrypoint scripts and committing the result to be used later by tests) was a bigger win (first run of Bugzilla is 20-25 seconds).

I'm at the point of optimizing startup where the longest pole is the initialization of the services inside Docker containers themselves. MySQL takes a few seconds to start accepting connections. Apache + Bugzilla has a semi-involved initialization process. RabbitMQ takes about 4 seconds to initialize. There are some cascading dependencies in there, so the majority of startup time is waiting for processes to finish their startup routine.

Another concern with running all these containers is memory usage. When you start running 6+ instances of MySQL + Apache, RabbitMQ, + ..., it becomes really easy to exhaust system memory, incur swapping, and have performance fall off a cliff. I've spent a non-trivial amount of time figuring out the minimal amount of memory I can make services consume while still not sacrificing too much performance.

It is quite an experience having the problem of trying to minimize resource usage and startup time for various applications. Searching the internet will happily give you recommended settings for applications. You can find out how to make a service start in 10s instead of 60s or consume 100 MB of RSS instead of 1 GB. But what the internet won't tell you is how to make the service start in 2s instead of 3s or consume as little memory as possible. I reckon I'm past the point of diminishing returns where most people don't care about any further performance wins. But, because of how I'm using containers for end-to-end testing and I have a surplus of short-lived containers, it is clearly I problem I need to solve.

I might be able to squeeze out a few more seconds of reduction by further optimizing startup and shutdown. But, I doubt I'll reduce things below 5s. If you ask me, that's still not good enough. I want no more than 2s overhead per test. And I don't think I'm going to get that unless I start utilizing containers across multiple tests. And I really don't want to do that because it sacrifices test purity. Engineering is full of trade-offs.

Another takeaway from implementing this test harness is that the pre-built Docker images available from the Docker Registry almost always become useless. I eventually make a customization that can't be shoehorned into the readily-available image and I find myself having to reinvent the wheel. I'm not a fan of the download and run a binary model, especially given Docker's less-than-stellar history on the security and cryptography fronts (I'll trust Linux distributions to get package distribution right, but I'm not going to be trusting the Docker Registry quite yet), so it's not a huge loss. I'm at the point where I've lost faith in Docker Registry images and my default position is to implement my own builder. Containers are supposed to do one thing, so it usually isn't that difficult to roll my own images.

There's a lot to love about Docker and containerized test execution. But I feel like I'm foraging into new territory and solving problems like startup time minimization that I shouldn't really have to be solving. I think I can justify it given the increased accuracy from the tests and the increased confidence that brings. I just wish the cost weren't so high. Hopefully as others start leaning on containers and Docker more for test execution, people start figuring out how to make some of these problems disappear.

blog comments powered by Disqus