performance-engineering

Profiling identifies bottlenecks by measuring where code spends the most time or memory. Establish a baseline with realistic workload data, use language-specific profiling tools to locate hot paths, apply targeted optimizations, then re-measure to confirm gains and document tradeoffs.

Benchmarking measures latency, throughput, and memory before and after optimization. Start with baseline metrics under realistic conditions, optimize specific bottlenecks identified through profiling, then re-benchmark to validate that improvements match your goals.

Memory leak detection uses profiling tools to track allocations and identify unreleased memory over time. Profile your application under sustained load, analyze retention patterns, optimize cleanup logic, then re-profile to confirm the leak is resolved.

Database query tuning optimizes latency by profiling slow queries, analyzing execution plans, and applying targeted changes like indexing or query restructuring. Measure baseline performance, profile the hot paths, optimize queries, then benchmark improvements in staging or production.

Performance profiling tools vary by language and runtime. TypeScript and Bun have specialized profiling tooling, while Rust offers dedicated performance analysis tools. Choose profilers that match your language and runtime, establish baselines, and measure bottlenecks before optimizing.

Measurement establishes baselines and reveals actual bottlenecks, preventing wasted effort on guessed problems. Without measurement, optimizations may target the wrong code path or introduce complexity without real gains; profiling ensures optimization targets the highest-impact areas.