How can Bazel build performance be diagnosed using measurements and profiling while minimizing measurement overhead?

Key Metrics and Profiling Techniques

To diagnose Bazel build performance effectively, focus on the following metrics:

• CPU Usage: Identify CPU-bound tasks that may be inefficiently implemented.
• Memory Usage: Detect memory leaks or excessive allocations that slow down the build.
• I/O Operations: Pinpoint I/O-bound actions that may benefit from caching or parallelization.
• Action Duration: Measure the time taken by individual build actions to identify the most time-consuming steps.

Use Bazel's built-in profiling tools to collect these metrics:

bazel build --profile=profile.log --experimental_profile_benchmark

Minimizing Profiling Overhead

To ensure profiling does not significantly distort build performance:

1. Use Low Sampling Rates: Start with a conservative sampling rate to reduce overhead. For example:

bazel build --profile=profile.log --experimental_profile --experimental_profile_sample_rate=10

2. Leverage Remote Execution: Use remote execution to isolate performance issues related to local build execution. This can help differentiate between local and remote bottlenecks.

bazel build --profile=profile.log --experimental_profile --experimental_remote_profile

3. Compare Local and Remote Builds: Analyze profiles from both local and remote builds to identify environment-specific issues.

Thresholds and Heuristics

To flag actions for optimization without generating excessive noise:

• CPU Usage: Flag actions that use more than 50% of CPU time for a significant portion of the build.
• Memory Usage: Investigate actions that allocate more than a certain threshold (e.g., 100 MB) of memory.
• I/O Operations: Focus on actions with high I/O wait times or frequent disk accesses.
• Action Duration: Prioritize actions that take longer than a certain threshold (e.g., 1 second) to complete.

Correlating Changes with Profiling Data

To correlate changes in the profile with specific modifications:

1. Version Control: Ensure that each build is associated with a specific commit or version of the BUILD files and toolchain.
2. Build Logs: Compare build logs and profiles from different versions to identify changes in performance.
3. Benchmarking: Use the `--experimental_profile_benchmark` flag to collect benchmarking data and analyze trends over time.

Missing Diagnostic Detail

To further refine the diagnosis, it would be helpful to know the specific version of Bazel being used, as newer versions may have additional profiling features or optimizations.