performance-profiling

Performance Profiling

Finding where code spends time and memory.

Context

You are profiling code to find bottlenecks. Use CPU and memory profilers.

Domain Context

• CPU Profiling: Where does code spend time? Stack sampling at regular intervals
• Memory Profiling: What allocates memory? Which objects aren't freed?
• Flame Graph: Visual representation; width = time, stacked = call stack
• Hot Spots: Functions consuming > 1% of time
• Allocation Profiling: Which code allocates the most objects?

Instructions

1. Start Profiler: CPU profiler (perf, pprof, Java Flight Recorder)
2. Run Under Load: Profile should see realistic workload
3. Collect Data: Run for 10-30 seconds; enough to gather signal
4. Analyze Output: Flame graph or profiler GUI
5. Identify Hot Spots: Functions consuming most time
6. Drill Down: Zoom into hot functions; what calls them?
7. Measure Impact: Change code, re-profile, compare

Anti-Patterns

• Profiling single-threaded code then deploying multi-threaded; lock contention changes profiles
• Optimizing without profiling; "optimization" is just guessing
• Micro-optimizing non-hotspot code; spend time on the hottest functions
• Not considering cache effects; CPU cache profilers show real impact
• Benchmarking without warm-up; first runs hit different code paths

Further Reading

• Brendan Gregg, Systems Performance and Flame Graphs
• Java Flight Recorder documentation (Java)
• pprof documentation (Go)