I’m excited to announce that AWS CodeBuild now helps parallel take a look at execution, so you’ll be able to run your take a look at suites concurrently and cut back construct occasions considerably.

With the demo mission I wrote for this submit, the full take a look at time went down from 35 minutes to six minutes, together with the time to provision the environments. These two screenshots from the AWS Administration Console present the distinction.

Sequential execution of the take a look at suite

Parallel execution of the take a look at suite

Very lengthy take a look at occasions pose a major problem when operating steady integration (CI) at scale. As initiatives develop in complexity and crew dimension, the time required to execute complete take a look at suites can enhance dramatically, resulting in prolonged pipeline execution occasions. This not solely delays the supply of latest options and bug fixes, but in addition hampers developer productiveness by forcing them to attend for construct outcomes earlier than continuing with their duties. I’ve skilled pipelines that took as much as 60 minutes to run, solely to fail on the final step, requiring a whole rerun and additional delays. These prolonged cycles can erode developer belief within the CI course of, contribute to frustration, and finally decelerate the complete software program supply cycle. Furthermore, long-running checks can result in useful resource competition, elevated prices due to wasted computing energy, and diminished general effectivity of the event course of.

With parallel take a look at execution in CodeBuild, now you can run your checks concurrently throughout a number of construct compute environments. This characteristic implements a sharding strategy the place every construct node independently executes a subset of your take a look at suite. CodeBuild supplies setting variables that determine the present node quantity and the full variety of nodes, that are used to find out which checks every node ought to run. There is no such thing as a management construct node or coordination between nodes at construct time—every node operates independently to execute its assigned portion of your checks.

To allow take a look at splitting, configure the batch fanout part in your buildspec.xml, specifying the specified parallelism degree and different related parameters. Moreover, use the codebuild-tests-run utility in your construct step, together with the suitable take a look at instructions and the chosen splitting methodology.

The checks are break up based mostly on the sharding technique you specify. codebuild-tests-run affords two sharding methods:

• Equal-distribution. This technique kinds take a look at information alphabetically and distributes them in chunks equally throughout parallel take a look at environments. Adjustments within the names or amount of take a look at information would possibly reassign information throughout shards.
• Stability. This technique fixes the distribution of checks throughout shards through the use of a constant hashing algorithm. It maintains present file-to-shard assignments when new information are added or eliminated.

CodeBuild helps automated merging of take a look at experiences when operating checks in parallel. With automated take a look at report merging, CodeBuild consolidates checks experiences right into a single take a look at abstract, simplifying outcome evaluation. The merged report contains aggregated cross/fail statuses, take a look at durations, and failure particulars, decreasing the necessity for guide report processing. You may view the merged ends in the CodeBuild console, retrieve them utilizing the AWS Command Line Interface (AWS CLI), or combine them with different reporting instruments to streamline take a look at evaluation.

Let’s have a look at the way it works
Let me reveal how one can implement parallel testing in a mission. For this demo, I created a really fundamental Python mission with a whole lot of checks. To hurry issues up, I requested Amazon Q Developer on the command line to create a mission and 1,800 take a look at instances. Every take a look at case is in a separate file and takes one second to finish. Working all checks in a sequence requires half-hour, excluding the time to provision the setting.

On this demo, I run the take a look at suite on ten compute environments in parallel and measure how lengthy it takes to run the suite.

To take action, I added a buildspec.yml file to my mission.

model: 0.2

batch:
  fast-fail: false
  build-fanout:
    parallelism: 10 # ten runtime environments 
    ignore-failure: false

phases:
  set up:
    instructions:
      - echo 'Putting in Python dependencies'
      - dnf set up -y python3 python3-pip
      - pip3 set up --upgrade pip
      - pip3 set up pytest
  construct:
    instructions:
      - echo 'Working Python Assessments'
      - |
         codebuild-tests-run 
          --test-command 'python -m pytest --junitxml=report/test_report.xml' 
          --files-search "codebuild-glob-search 'checks/test_*.py'" 
          --sharding-strategy 'equal-distribution'
  post_build:
    instructions:
      - echo "Check execution accomplished"

experiences:
  pytest_reports:
    information:
      - "*.xml"
    base-directory: "report"
    file-format: JUNITXML 

There are three components to focus on within the YAML file.

First, there’s a build-fanout part below batch. The parallelism command tells CodeBuild what number of take a look at environments to run in parallel. The ignore-failure command signifies if failure in any of the fanout construct duties might be ignored.

Second, I take advantage of the pre-installed codebuild-tests-run command to run my checks.

This command receives the entire checklist of take a look at information and decides which of the checks should be run on the present node.

• Use the sharding-strategy argument to decide on between equally distributed or steady distribution, as I defined earlier.
• Use the files-search argument to cross all of the information which can be candidates for a run. We advocate to make use of the offered codebuild-glob-search command for efficiency causes, however any file search instrument, resembling discover(1), will work.
• I cross the precise take a look at command to run on the shard with the test-command argument.

Lastly, the experiences part instructs CodeBuild to gather and merge the take a look at experiences on every node.

Then, I open the CodeBuild console to create a mission and a batch construct configuration for this mission. There’s nothing new right here, so I’ll spare you the main points. The documentation has all the main points to get you began.  Parallel testing works on batch builds. Ensure to configure your mission to run in batch.

Now, I’m able to set off an execution of the take a look at suite. I can commit new code on my GitHub repository or set off the construct within the console.

After a couple of minutes, I see a standing report of the totally different steps of the construct; with a standing for every take a look at setting or shard.

When the take a look at is full, I choose the Studies tab to entry the merged take a look at experiences.

The Studies part aggregates all take a look at knowledge from all shards and retains the historical past for all builds. I choose my most up-to-date construct within the Report historical past part to entry the detailed report.

As anticipated, I can see the aggregated and the person standing for every of my 1,800 take a look at instances. On this demo, they’re all passing, and the report is inexperienced.

The 1,800 checks of the demo mission take one second every to finish. Once I run this take a look at suite sequentially, it took 35 minutes to finish. Once I run the take a look at suite in parallel on ten compute environments, it took 6 minutes to finish, together with the time to provision the environments. The parallel run took 17.9 p.c of the time of the sequential run. Precise numbers will differ together with your initiatives.

Extra issues to know
This new functionality is suitable with all testing frameworks. The documentation contains examples for Django, Elixir, Go, Java (Maven), Javascript (Jest), Kotlin, PHPUnit, Pytest, Ruby (Cucumber), and Ruby (RSpec).

For take a look at frameworks that don’t settle for space-separated lists, the codebuild-tests-run CLI supplies a versatile various by the CODEBUILD_CURRENT_SHARD_FILES setting variable. This variable comprises a newline-separated checklist of take a look at file paths for the present construct shard. You should utilize it to adapt to totally different take a look at framework necessities and format take a look at file names.

You may additional customise how checks are break up throughout environments by writing your individual sharding script and utilizing the CODEBUILD_BATCH_BUILD_IDENTIFIER setting variable, which is robotically set in every construct. You should utilize this method to implement framework-specific parallelization or optimization.

Pricing and availability
With parallel take a look at execution, now you can full your take a look at suites in a fraction of the time beforehand required, accelerating your growth cycle and bettering your crew’s productiveness.

Parallel take a look at execution is offered on all three compute modes provided by CodeBuild: on-demand, reserved capability, and AWS Lambda compute.

This functionality is offered right this moment in all AWS Areas the place CodeBuild is obtainable, with no extra price past the usual CodeBuild pricing for the compute sources used.

I invite you to attempt parallel take a look at execution in CodeBuild right this moment. Go to the AWS CodeBuild documentation to study extra and get began with parallelizing your checks.

— seb

PS: Right here’s the immediate I used to create the demo software and its take a look at suite: “I’m writing a weblog submit to announce codebuild parallel testing. Write a quite simple python app that has a whole lot of checks, every take a look at in a separate take a look at file. Every take a look at takes one second to finish.”

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