GHA Workflow: Executing And Posting Benchmark Results

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GHA Workflow: Executing and Posting Benchmark Results

Let's dive into setting up a GitHub Actions (GHA) workflow that will automatically execute benchmarks and post the performance differences against the main branch as a comment on your pull requests. This is super useful for tracking performance changes and making sure your code optimizations are actually, well, optimizing things! Think of it as your personal performance watchdog, guys!

Why Automate Benchmarks with GHA?

In the world of software development, especially when dealing with performance-critical applications, benchmarking is key. We need to measure how our code performs, identify bottlenecks, and ensure that changes don't negatively impact performance. Manually running benchmarks can be time-consuming and prone to errors. That's where GitHub Actions comes to the rescue! Automating this process with GHA offers several benefits:

  • Consistency: Automated benchmarks ensure consistent testing conditions, reducing variability and making results more reliable.
  • Early Detection: By running benchmarks on every pull request, you can catch performance regressions early in the development cycle, before they make their way into production.
  • Time Savings: Automation frees up developers from the tedious task of manually running benchmarks, allowing them to focus on writing code.
  • Collaboration: Posting benchmark results as PR comments facilitates collaboration and discussion among team members about performance implications.

In essence, GHA workflows for benchmarking are about shifting left on performance concerns, baking them into your development process from the get-go. This proactive approach can save you headaches down the line, leading to more robust and efficient software.

Setting Up the GHA Workflow

Now, let's get into the nitty-gritty of setting up the GHA workflow. We'll break this down into steps, explaining each part along the way.

1. Create a Workflow File

First things first, you'll need to create a workflow file in your repository. GHA workflows are defined using YAML files and live in the .github/workflows directory. Create a new file, for example, benchmark.yml, in this directory. This is where the magic will happen!

2. Define the Workflow Trigger

The on section of your workflow file specifies when the workflow should run. In our case, we want the workflow to run after all other jobs have finished on a pull request. This ensures that the code changes are built and ready for benchmarking. Here's how you can define the trigger:

on:
  pull_request:
    types: [closed]
    branches: [main]

This configuration tells GHA to run the workflow when a pull request is closed and the target branch is main. We're using the closed event type here because we want the workflow to run after all the checks and tests have passed, ensuring that the code is in a mergeable state.

3. Define the Jobs

The jobs section defines the tasks that the workflow will execute. We'll need a job to run the benchmarks and another job to post the results as a PR comment. Let's start by defining the benchmark job:

jobs:
  benchmark:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Set up Node.js
        uses: actions/setup-node@v3
        with:
          node-version: '16'
      - name: Install dependencies
        run: npm install
      - name: Run benchmarks
        run: npm run benchmark
        env:
          CI: true

In this job:

  • runs-on: ubuntu-latest specifies that the job should run on a GitHub-hosted Ubuntu runner.
  • steps defines the sequence of actions to be performed.
  • actions/checkout@v3 checks out the code from the repository.
  • actions/setup-node@v3 sets up Node.js with version 16 (you can adjust this based on your project's requirements).
  • npm install installs the project dependencies.
  • npm run benchmark executes the benchmark script (you'll need to define this script in your package.json file).
  • CI: true is set as an environment variable to indicate that the job is running in a CI environment.

4. Define the Post-Results Job

Now, let's define the job that will post the benchmark results as a PR comment. This job will need to:

  1. Get the benchmark results from the previous job.
  2. Compare the results against the main branch.
  3. Post the diff as a PR comment.

Here's how you can define the post-results job:

  post-results:
    needs: benchmark
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Get benchmark results
        run: echo "BENCHMARK_RESULTS=$(cat benchmark-results.txt)" >> $GITHUB_ENV
      - name: Compare against main
        run: |
          git checkout main
          git pull origin main
          MAIN_BENCHMARK_RESULTS=$(cat benchmark-results.txt)
          DIFF=$(echo "$BENCHMARK_RESULTS" | diff -u --from-file <(echo "$MAIN_BENCHMARK_RESULTS") -)
          echo "DIFF<<EOF" >> $GITHUB_ENV
          echo "$DIFF" >> $GITHUB_ENV
          echo "EOF" >> $GITHUB_ENV
      - name: Post PR comment
        uses: actions/github-script@v6
        with:
          script: |
            const github = context.github;
            const context = github.context;
            if (process.env.DIFF) {
              const comment = `Benchmark Results Diff:\n\`\`\`\n${process.env.DIFF}\n\`\`\``;
              await github.rest.issues.createComment({
                issue_number: context.issue.number,
                owner: context.repo.owner,
                repo: context.repo.repo,
                body: comment
              });
            }

Let's break down this job:

  • needs: benchmark specifies that this job depends on the benchmark job and will only run after it completes successfully.
  • runs-on: ubuntu-latest specifies that the job should run on a GitHub-hosted Ubuntu runner.
  • actions/checkout@v3 checks out the code from the repository.
  • Get benchmark results reads the benchmark results from a file (assuming you're saving the results to benchmark-results.txt in the benchmark job) and stores them in an environment variable.
  • Compare against main checks out the main branch, pulls the latest changes, reads the benchmark results from the main branch, and calculates the diff between the two sets of results.
  • Post PR comment uses the actions/github-script action to post the diff as a comment on the pull request. This action allows you to run JavaScript code within your workflow, making it easy to interact with the GitHub API.

5. Complete Workflow File

Putting it all together, your workflow file (.github/workflows/benchmark.yml) should look something like this:

name: Benchmark

on:
  pull_request:
    types: [closed]
    branches: [main]

jobs:
  benchmark:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Set up Node.js
        uses: actions/setup-node@v3
        with:
          node-version: '16'
      - name: Install dependencies
        run: npm install
      - name: Run benchmarks
        run: npm run benchmark
        env:
          CI: true
  post-results:
    needs: benchmark
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Get benchmark results
        run: echo "BENCHMARK_RESULTS=$(cat benchmark-results.txt)" >> $GITHUB_ENV
      - name: Compare against main
        run: |
          git checkout main
          git pull origin main
          MAIN_BENCHMARK_RESULTS=$(cat benchmark-results.txt)
          DIFF=$(echo "$BENCHMARK_RESULTS" | diff -u --from-file <(echo "$MAIN_BENCHMARK_RESULTS") -)
          echo "DIFF<<EOF" >> $GITHUB_ENV
          echo "$DIFF" >> $GITHUB_ENV
          echo "EOF" >> $GITHUB_ENV
      - name: Post PR comment
        uses: actions/github-script@v6
        with:
          script: |
            const github = context.github;
            const context = github.context;
            if (process.env.DIFF) {
              const comment = `Benchmark Results Diff:\n\`\`\`\n${process.env.DIFF}\n\`\`\``;
              await github.rest.issues.createComment({
                issue_number: context.issue.number,
                owner: context.repo.owner,
                repo: context.repo.repo,
                body: comment
              });
            }

6. Configure Your Benchmark Script

You'll need to configure your benchmark script to save the results to a file (e.g., benchmark-results.txt). This file will be used by the post-results job to compare the results against the main branch. The specifics of how you do this will depend on your benchmarking tool and setup.

For example, if you're using a Node.js benchmarking library like benchmark.js, you might modify your benchmark script to write the results to a file like this:

const Benchmark = require('benchmark');
const fs = require('fs');

const suite = new Benchmark.Suite;

// Add your benchmark tests here
suite.add('String#concat', function() {
  'hello' + ' world';
})
.add('String#join', function() {
  ['hello', ' world'].join('');
})
// add listeners
.on('cycle', function(event) {
  console.log(String(event.target));
})
.on('complete', function() {
  console.log('Fastest is ' + this.filter('fastest').map('name'));
  // Save results to file
  fs.writeFileSync('benchmark-results.txt', JSON.stringify(this.map(benchmark => ({
    name: benchmark.name,
    mean: benchmark.stats.mean,
    rme: benchmark.stats.rme
  }))));
})
// run async
.run({ 'async': true });

This script saves the benchmark results as a JSON string to benchmark-results.txt. The post-results job will then read this file and compare the results.

Best Practices for Benchmarking

Before we wrap up, let's touch on some best practices for benchmarking. These tips will help you get the most out of your automated benchmarking setup:

  • Isolate Benchmarks: Run benchmarks in an isolated environment to minimize external factors that could affect results. This is where CI environments like GHA are super useful!
  • Warm-up: Include a warm-up phase in your benchmarks to allow the code to be optimized by the runtime environment. This will give you more accurate results.
  • Multiple Runs: Run benchmarks multiple times and calculate the average result to reduce the impact of outliers.
  • Statistical Significance: Consider using statistical methods to determine if performance differences are statistically significant. This can help you avoid making decisions based on random fluctuations.
  • Monitor Over Time: Track benchmark results over time to identify long-term performance trends and potential regressions.

Conclusion

Setting up a GHA workflow to execute benchmarks and post results as PR comments is a fantastic way to automate performance testing and ensure the quality of your code. By catching performance regressions early, you can save time and effort in the long run, and deliver more efficient software. So, go ahead, give it a try, guys! Your future self will thank you for it.