Building Efficient Parallel Testing Platforms with Docker
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The document discusses the development of efficient parallel testing platforms using Docker to optimize automated testing processes, enabling faster deployments and better test management. It highlights the advantages of containers over VMs, emphasizing flexibility, resource management, and the ability to run multiple tests in parallel. The implementation allows developers to define their testing environments, manage dependencies, and leverage Docker's ecosystem for improved testing workflows.
Building Efficient Parallel Testing Platforms with Docker
- 1. Building Efficient Parallel Testing Platforms with Docker Laura Frank @rhein_wein Software Engineer @codeship laura@codeship.com
- 3. Automated builds take too long. PROBLEM
- 5. Optimize automated tests by running them in parallel. PROPOSED SOLUTION
- 6. You’ll quickly reach the limit of how much you can optimize a test itself. PROPOSED SOLUTION
- 7. Instead, let’s build a more performant system. PROPOSED SOLUTION
- 8. And let’s do it with containers 💃 PROPOSED SOLUTION
- 9. Create a customizable, flexible test environment that enables us to run tests in parallel. GOAL
- 10. codeship.com/resources • The why and how of parallel testing • DIY with LXC • Using Docker and the Docker ecosystem Agenda
- 11. ParallelTesting
- 12. Spend less time waiting around for your automated testing steps and deployments to finish. • Ship newest code to production faster • Be alerted quickly when tests fail Why? If you’re still not sure why testing is important… let’s have a different conversation. 😊
- 13. Developers should have full autonomy over testing environments, and the way tests are executed. • Move testing commands to separate pipelines • Designate commands to be run serially or in parallel • Declare specific dependencies for each service Why?
- 14. • For local testing, e.g. unit and integration tests run by a development team • On internal CI/CD systems • As part of a hosted CI/CD solution (like Codeship) Where?
- 15. • Split up testing tasks • Give developer full control of dependencies per task • Use containers to run multiple tests at once How?
- 17. Run tasks across multiple processors in parallel computing environments TASK PARALLELISM
- 18. Distributed Task Parallelism A distributed system of containerized computing environments takes the place of a single multiprocessor machine A container is a process, not a small VM
- 19. Why not VMs? • Isolation of running builds on infrastructure • Challenges with dependency management • No clean interface for imposing resource limits • Infrastructure is underutilized which makes it expensive • People use containers!
- 20. Containers, duh! • Impose resource limits and increase virtualization density • Run customer code in isolation • Provide consistent build environment across many build runs • Run testing tasks in parallel 👍
- 21. DIYwith LXC
- 22. Codeship has been powered by containers since the very beginning
- 23. codeship.com/resources Codeship’s LXC-based Platform 40K builds per day 8M builds per year
- 24. Architecture • Universal container with provided dependencies • Fixed amount of available containers per VM • Implement parallel testing pattern using pipelines with ParallelCI • Users can have N pipelines running in isolation during a build
- 25. User Commands Universal Container Pipeline Heroku Deployment Provider Capistrano AppEngine Elastic Beanstalk etc… User Commands Universal Container Pipeline User Commands Universal Container Pipeline High-level Build Workflow test pipelines deploy pipeline
- 26. commit deploy Not just a test runner, but a complete system
- 27. build worker build dispatcher account/project service infrastructure service new code!
- 28. build worker build dispatcher • receives webhooks • transforms payload (keep the good bits) • send payload to account/project service • read config and start workload • sends updates upstream account/project service • receives payload • identifies build configuration • sends config to build machines infrastructure service • keep track of infra utilization • upscale/downscale
- 29. worker Compute Instance container container container container container container container container container container worker Compute Instance container container container container container container container container container container worker Compute Instance container container container container container container container container container container
- 30. worker Compute Instance container container container container container container container container container container worker Compute Instance container container container container container container container container container container worker Compute Instance container container container container container container container container container container
- 31. Good Stuff • Extremely simple implementation (no extra scheduler) • Just Works™ • Pretty okay virtualization density • No allocation time for builds — slots are always open
- 32. Not So Good Stuff • Parity between dev and test • Can’t really debug locally • No useable interface between user and container • All pipelines run all services and dependencies
- 33. We weren’t able to provide the best, most efficient product to our customers (or ourselves)
- 35. Create a customizable, flexible test environment that enables us to run tests in parallel GOAL
- 36. Big Wins with Docker Even before 1.0, Docker was a clear choice • Support and tooling • Standardization • Community of motivated developers
- 37. Using Docker allowed us to build a much more flexible testing platform than with LXC alone
- 38. A Docker-based Testing Platform • Different enough from the LXC-based platform to be a new tool • Development started in 2014, beta in 2015 • Official launch February 2016 • Written in golfing (LXC-based platform is in Ruby)
- 40. Managing containers with Docker allowed us to improve our parallel testing workflow
- 41. A New Parallel Workflow • Introducing services adds additional layer of flexibility • Loosen coupling between steps and services — execute N steps against M services • Parallel and serial steps can be grouped and ordered in any way
- 42. Services • Pull image from any registry or build from Dockerfile • Optimize service for testing tasks • Fully customizable by the user
- 43. Steps • Each step is executed in an independent environment • Has own set of containers (services) • Can be nested in serial and parallel groups
- 44. Steps • Two functions • Run: execute a command against a service • Push: push image to registry • Tag matching (simple string or regex) to run steps on certain branches or tagged releases
- 45. T1 T1 T1 User Commands Universal Container Pipeline User Commands Universal Container Pipeline User Commands Universal Container Pipeline High-Level Build Workflow: The “Old” Way
- 46. Step postgres redis command web Step command ruby T1 T2 T3 Step postgres redis command web Step postgres redis command web High-Level Build Workflow: The New Way
- 47. build worker build dispatcher account/project service build orchestrator new code! SQS queue
- 48. build worker build dispatcher • receives webhooks • transforms payload (keep the good bits) • send payload to central service • run build steps and services via Docker • sends updates upstream account/project service • identifies build configuration build orchestrator • build machine allocation and provisioning • handles logs • starts build and sends config to build machines SQS queue
- 51. Docker WorkflowTools • Docker Compose: service and step definition syntax • Docker Registry: storage for images; previously used for remote caching • Docker for Mac and Windows: give users ability to reproduce CI environments locally
- 52. services.yml db: image: postgres:9.5 app: encrypted_dockercfg_path: dockercfg.encrypted build: image: user/some-image dockerfile: Dockerfile.test cached: true links: - db deploy: encrypted_dockercfg_path: dockercfg.encrypted build: dockerfile: Dockerfile.deploy
- 53. - type: serial steps: - type: parallel steps: - name: rspec service: app command: bin/ci spec - name: rubocop service: app command: rubocop - name: haml-lint service: app command: haml-lint app/views - name: rails_best_practices service: app command: bin/railsbp - service: deploy type: push image_name: rheinwein/notes-app tag: ^master$ registry: https://index.docker.io/v1/ encrypted_dockercfg_path: dockercfg.encrypted 1 2 steps.yml
- 54. Docker for Mac and Windows • All users can test locally • Jet CLI is available at bit.ly/codeship-jet-tool • Free, and you don’t need a Codeship account • Big advantage over the LXC implementation
- 55. codeship.com/resources Your push or deploy step should never be part of a parallel step group PRO TIP
- 57. Infrastructure • Build allocation • Customers can choose specs for their build machines • Machine provisioning used to be part of the build process • Now we pool build machines • Allocation time is ~1 second!
- 58. Performance • Image Caching • Old way: rely on the registry for caching • A pull gave us access to each parent layer; rebuilding the image used the local cache • 1.10 content addressable breaking change
- 59. Performance • Image Caching • Great news: 1.11 restored parent/child relationship when you save the images via docker save • 1.13 will include --cache-from flag • Double-edged sword of relying on external tools ¯_( )_/¯
- 60. What’s Missing?
- 61. libcompose • Currently use APIs directly for container-level operations (Jet was also born before Fig was popular) • Minimal change for our users and builds, but much easier for our engineers • Work is underway 🐳
- 62. Compose V2 syntax • Will come with libcompose
- 63. Swarm! • Jet was born pre-Swarm • We manage build machines on AWS via our own service • Previous concerns about security — single tenancy • Swarm (and services like Carina) are promising for the future
- 64. Swarm! • Instead of using a single machine per build, we should schedule containers across a cluster • Use the best parts of the LXC platform, but with Docker behind it
- 65. codeship.com/resources You can create a highly efficient parallel testing platform with LXC alone, but using Docker tools makes it more flexible TL;DR
- 66. Wednesday — October 5th How Secure is your Container? A Docker Engine Security Update - Phil Estes Docker Orchestration: Beyond the Basics - Aaron Lehmann When the Going gets Tough, get TUF Going - Riyaz Faizullabhoy and Lily Guo Thursday — October 6th Orchestrating Linux Containers while Tolerating Failures - Drew Erny Unikernels: When you Should and When you Shouldn’t - Amir Chaudhry Berlin Docker Meetup
- 67. thanks!