how to run github actions steps in parallel

How can I specify the parallelism of GitHub Actions steps?

GitHub Actions allows you to specify the parallelism of steps within a job using the parallelism keyword. By setting the parallelism level, you can control the maximum number of steps that can run concurrently within a job.

To specify the parallelism, use the following syntax within your .github/workflows/<workflow-file>.yml</workflow-file> file:

jobs:
  <job_id>:
    steps:
      - name: Step 1
        run: echo "Step 1"
      - name: Step 2
        run: echo "Step 2"
      - name: Step 3
        run: echo "Step 3"
    steps:
      - name: Parallel Steps
        run: |
          echo "Running steps in parallel"
          echo "Step 1"
          echo "Step 2"
          echo "Step 3"
        parallelism: 3

In this example, the parallelism value is set to 3, indicating that a maximum of three steps can run concurrently within the Parallel Steps step.

Is there a way to configure the number of parallel jobs in GitHub Actions?

Yes, it is possible to configure the number of parallel jobs that can run within a workflow using the jobs.concurrency property. By specifying a concurrency group, you can limit the number of jobs that can run simultaneously, preventing resource contention and optimizing workflow performance.

To configure the number of parallel jobs, add the following to your .github/workflows/<workflow-file>.yml</workflow-file> file:

jobs:
  <job_id>:
    concurrency:
      group: <concurrency-group-name>
      cancel-in-progress: true

In this example, the concurrency property specifies a concurrency group named <concurrency-group-name></concurrency-group-name>. The cancel-in-progress property is set to true, indicating that any in-progress jobs will be canceled if the concurrency limit is reached.

How do I optimize the performance of GitHub Actions by running steps concurrently?

Optimizing the performance of GitHub Actions by running steps concurrently can greatly improve workflow execution times. Here are some best practices to follow:

1. Identify independent steps: Determine which steps in your workflow can run independently without requiring outputs from other steps. These steps are ideal candidates for parallelization.
2. Use the parallelism keyword: Specify the parallelism level for steps that can run concurrently. Consider setting appropriate parallelism values to maximize resource utilization while avoiding bottlenecks.
3. Utilize concurrency groups: Configure concurrency groups to limit the number of jobs that can run simultaneously within a workflow. This prevents resource contention and ensures optimal performance.
4. Avoid sequential dependencies: Minimize dependencies between steps to allow maximum parallelism. If possible, restructure your workflow to eliminate unnecessary sequential execution.
5. Monitor resource usage: Regularly monitor the resource usage of your workflows to identify any performance bottlenecks. Adjust the parallelism and concurrency settings accordingly to optimize performance.

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