Monitoring CPU Performance of Lyft’s Android Applications
Article Summary
Lyft's Android app serves millions of users daily, but how do you track CPU performance when hundreds of engineers ship code constantly? The team built a custom monitoring system from scratch.
Pavlo Stavytskyi from Lyft Engineering breaks down their approach to measuring CPU usage across Android releases. Instead of relying on third-party tools, they went deep into Linux system files and Android APIs to build precise, time-based metrics.
Key Takeaways
- Reads /proc/[pid]/stat files to calculate CPU time in clock ticks
- Tracks relative average CPU usage between any two time points
- Divides results by core count to get accurate percentages under 100%
- Uses time-based metrics instead of frequency for better aggregation
- Validates measurements against Android Studio profiler in real-time
Lyft built a custom CPU monitoring system using Linux system files and native APIs to track performance regressions across releases with precision down to one-minute intervals.
About This Article
Lyft's Android apps grow quickly with hundreds of contributors working on the codebase. This makes it hard to spot CPU performance regressions between product releases without a proper way to measure them.
Pavlo Stavytskyi's team pulled intermediate metrics from /proc/[pid]/stat files, specifically utime, stime, cutime, and cstime values. They converted clock ticks to seconds using CPU clock speed to calculate relative average CPU usage between two points in time.
Lyft can now measure CPU load at one-minute intervals with precision. The system compares average values across releases and tracks performance patterns on different device architectures and core counts.
