Optimize test dependencies

Oct 13, 2025 by talynt.org 75 views

Introduction

Parallel testing is a powerful feature in Mill, a build tool designed for the Scala ecosystem. It allows you to run multiple tests in parallel, significantly reducing the overall testing time. However, as you've discovered, running too many jobs or threads in parallel can lead to performance issues and unexpected behavior. In this article, we'll delve into the issue of too many jobs/threads with parallel testing, explore the possible causes, and provide a step-by-step guide to resolving the problem.

Understanding the Issue

When you run tests in parallel using Mill, the tool creates multiple jobs or threads to execute the tests concurrently. The number of jobs created depends on the number of logical CPU cores available on your system and the --jobs option specified in the .mill-opts file. In your case, you've set --jobs=0.5C, which means Mill will create up to 50% of the available CPU cores as jobs.

However, as you've observed, the actual number of jobs created is much higher than expected. In your example, you have 16 logical CPU cores, but the output shows over 310 jobs running in parallel. This is a clear indication that something is amiss.

Possible Causes

There are several possible reasons why you're experiencing too many jobs/threads with parallel testing:

Blocked or Paused Jobs: When a job is blocked or paused, it can lead to the creation of additional jobs to compensate for the blocked or paused job. This can result in a higher number of jobs than expected.
Test Dependencies: If your tests have dependencies on other tests or resources, it can lead to the creation of additional jobs to resolve these dependencies.
Resource Contention: When multiple jobs contend for the same resources, it can lead to performance issues and the creation of additional jobs to resolve the contention.
Configuration Issues: Misconfigured settings in the .mill-opts file or other Mill configuration files can lead to the creation of too many jobs.

Resolving the Issue

To resolve the issue of too many jobs/threads with parallel testing, follow these steps:

Step 1: Review the `.mill-opts` File

Review the contents of the .mill-opts file to ensure that the --jobs option is set correctly. In your case, you've set --jobs=0.5C, which means Mill will create up to 50% of the available CPU cores as jobs. Verify that this setting is correct for your system.

Step 2: Identify Blocked or Paused Jobs

Use the Mill debug logs to identify any blocked or paused jobs. You can do this by running Mill with the --debug option and reviewing the logs for any job-related issues.

Step 3: Optimize Test Dependencies

Review your test dependencies to ensure that they are optimized for parallel testing. You can do this by using Mill's --test-parallelism option to specify the number of tests to run in parallel.

Step 4: Resolve Resource Contention

Use Mill's --resource-contention option to specify how to handle resource contention. You can choose from several strategies, including "fail-fast," "wait," or "retry."

Step 5: Review Mill Configuration Files

Review all Mill configuration files to ensure that they are correctly configured. Misconfigured settings can lead to the creation of too many jobs.

Step 6: Run Mill with Debugging Options

Run Mill with debugging options enabled to gather more information about the issue. You can do this by running Mill with the --debug option and reviewing the logs for any job-related issues.

Step 7: Optimize Mill Configuration

Optimize Mill configuration settings to improve performance and reduce the number of jobs created. You can do this by adjusting the --jobs option, --test-parallelism option, and --resource-contention option.

Conclusion

Too many jobs/threads with parallel testing can lead to performance issues and unexpected behavior. By understanding the possible causes and following the steps outlined in this article, you can resolve the issue and improve the performance of your Mill-based testing setup.

Best Practices

To avoid the issue of too many jobs/threads with parallel testing, follow these best practices:

Set the --jobs option correctly: Ensure that the --jobs option is set correctly in the .mill-opts file to match the number of logical CPU cores available on your system.
Optimize test dependencies: Review your test dependencies to ensure that they are optimized for parallel testing.
Resolve resource contention: Use Mill's --resource-contention option to specify how to handle resource contention.
Review Mill configuration files: Review all Mill configuration files to ensure that they are correctly configured.
Run Mill with debugging options: Run Mill with debugging options enabled to gather more information about the issue.

Q: What is the recommended approach to setting the `--jobs` option in the `.mill-opts` file?

A: The recommended approach is to set the --jobs option to a value that matches the number of logical CPU cores available on your system. This will ensure that Mill creates the optimal number of jobs for parallel testing.

Q: How can I identify blocked or paused jobs in Mill?

A: You can use the Mill debug logs to identify blocked or paused jobs. Run Mill with the --debug option and review the logs for any job-related issues.

Q: What is the difference between `--test-parallelism` and `--jobs` options in Mill?

A: The --test-parallelism option specifies the number of tests to run in parallel, while the --jobs option specifies the number of jobs to create for parallel testing. The --test-parallelism option is used to optimize test dependencies, while the --jobs option is used to control the number of jobs created.

Q: How can I resolve resource contention in Mill?

A: You can use Mill's --resource-contention option to specify how to handle resource contention. Choose from several strategies, including "fail-fast," "wait," or "retry."

Q: What are some common mistakes to avoid when configuring Mill for parallel testing?

A: Some common mistakes to avoid include:

Setting the --jobs option to a value that is too high, leading to performance issues.
Failing to optimize test dependencies, leading to resource contention.
Not reviewing Mill configuration files for misconfigured settings.
Not running Mill with debugging options enabled to gather more information about the issue.

Q: How can I optimize Mill configuration settings for better performance?

A: To optimize Mill configuration settings, follow these steps:

Review the .mill-opts file to ensure that the --jobs option is set correctly.
Optimize test dependencies using the --test-parallelism option.
Resolve resource contention using the --resource-contention option.
Review Mill configuration files for misconfigured settings.
Run Mill with debugging options enabled to gather more information about the issue.

Q: What are some best practices for parallel testing with Mill?

A: Some best practices for parallel testing with Mill include:

Setting the --jobs option correctly to match the number of logical CPU cores available on your system.
Optimizing test dependencies using the --test-parallelism option.
Resolving resource contention using the --resource-contention option.
Reviewing Mill configuration files for misconfigured settings.
Running Mill with debugging options enabled to gather more information about the issue.

Q: How can I troubleshoot issues with parallel testing in Mill?

A: To troubleshoot issues with parallel testing in Mill, follow these steps:

Review the Mill debug logs for any job-related issues.
Use the --debug option to gather more information about the issue.
Review Mill configuration files for misconfigured settings.
Optimize test dependencies using the --test-parallelism option.
Resolve resource contention using the --resource-contention option.

By following these best practices and troubleshooting steps, you can resolve issues with parallel testing in Mill and improve the performance of your testing setup.