Chrome collects performance data both in the lab, and from end users. There are thousands of individual metrics. This is an overview of how to sort through them at a high level.
At a high level, performance work in Chrome is categorized into tracks, like loading, memory, and power. Each track has high-level metrics associated with it.
- An overview of tracks: lists the tracks and key contact points.
- **Speed Launch Metrics: the important high-level metrics we measure for each track.
Chrome has multiple performance labs in which benchmarks are run on continuous builds to pinpoint performance regressions down to individual changelists.
The main lab for performance monitoring is chromium.perf. It continuously tests chromium commits and is monitored by the perf sheriff rotation.
- What is the perf waterfall? An overview of the waterfall that runs the continuous build.
- **How telemetry works: An overview of telemetry, our performance testing harness.
- **How perf bisects work: An overview of the bisect bots, which narrow down regressions over a CL range to a specific commit.
- *Benchmarks
- Benchmark Policy: An overview of the benchmark harnesses available in Chrome, and how to find the right place to add a new test case.
- System health benchmarks: The system health benchmarks measure the speed launch metrics on real-world web use scenarios.
- How to run on perf trybots: Have an unsubmitted CL and want to run benchmarks on it? Need to try a variety of hardware and operating systems? Use the perf trybots.
- How to run telemetry locally: Instructions on running telemetry benchmarks on your local machine.
- List of platforms in the lab: Devices, configurations, and OSes the chromium.perf lab tests on.
There are several other performance labs for specialized use:
- Lab Spotlight: AV Lab (Googlers only): Learn all about audio/video quality testing.
- Lab Spotlight: Cluster telemetry: Need to run a performance test over thousands of pages? Check out cluster telemetry!
The Speed Launch Metrics doc explains metrics available in UMA for end user performance. If you want to test how your change impacts these metrics for end users, you'll probably want to Run a Finch Trial.