performance-profiling

name	performance-profiling
description	CPU/memory profiling, identifying bottlenecks, flame graphs, hotspot analysis.

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)