performance-profiling

name	performance-profiling
description	Profile system performance using representative workloads, time/resource attribution, traces, profilers, query plans, and repeatable evidence to locate where execution cost actually accumulates.

Performance Profiling

Use when a measured performance symptom exists but the expensive code, query, resource, or boundary is not yet known.

Procedure

1. Define the target metric and scenario before profiling: latency percentile, throughput, startup time, frame time, CPU, I/O, allocations, or another measurable outcome.
2. Reproduce under a representative workload and environment. Record build mode, dataset size, concurrency, hardware/runtime, configuration, and warm/cold state that materially affect results.
3. Choose the profiler or trace closest to the suspected layer: CPU sampling/instrumentation, allocation/memory profiler, database plan, browser/runtime trace, I/O/network trace, application spans, or engine-specific profiler.
4. Capture enough duration/samples to represent the problem without averaging away bursts or spending most time in profiler overhead.
5. Attribute cost by call path/boundary, not just by function names. Look for hot paths, repeated work, waits, lock/contention, I/O, serialization, garbage collection, context switching, and dependency time as relevant.
6. Compare failing/slow traces with a healthy baseline or alternate workload to isolate what changes with the symptom.
7. Form a specific hypothesis about the dominant cost and design the smallest experiment that can change that cost without altering several layers at once.
8. Re-profile after the change using the same scenario and compare both the target metric and where cost moved.
9. Preserve representative profiles/traces or a summarized result with exact conditions so future regressions can be compared rather than rediscovered.

Decision rules

• A hot function is not automatically the best optimization target if it is required useful work or outside the critical path.
• Profiler overhead and development/debug builds can distort results; measure the mode relevant to the problem.
• Optimize the dominant end-to-end cost, not the most visually dramatic chart segment.
• Infrastructure or database bottlenecks should be handed to their owning specialist when the profiling evidence crosses that boundary.

Quality gate

Profiling is complete when the symptom is reproducible, cost is attributed to a specific path or resource with representative evidence, an optimization hypothesis follows from that evidence, and re-profiling demonstrates whether the cost was reduced or merely shifted.