Walk the stages in order for Profile Before Rewriting the Slow Path: start from the contract, trace the representation through the operation, then confirm the boundary. Worked case: If a CPU profile shows most samples inside JSON parsing, tuning database indexes will not fix the measured CPU bottleneck. For a worked explanation, continue to the topic article and execution trace.
Stage-by-stage execution walkthrough
This request flow decomposes Profile Before Rewriting the Slow Path into 5 sequential stages so you can trace where latency accumulates, which state transitions are irreversible, and where production failure modes occur.
Stage 1: Contract
Profiling attributes resource use to parts of a running program; benchmarking compares defined workloads under controlled conditions. A useful optimization starts with a hypothesis about the bottleneck and gathers evidence at the level where the cost occurs. During this transition, the runtime enforces strict state invariants before handing control to the next stage: inputs are validated at the boundary, state mutations are ordered deterministically, and latency/resource costs are bounded so downstream consumers never observe a torn or partially initialized state.
Stage 2: Representation
If a CPU profile shows most samples inside JSON parsing, tuning database indexes will not fix the measured CPU bottleneck. A benchmark should use representative data, warm-up behavior, stable machine conditions, and enough repetitions to show variation rather than one favorable run. During this transition, the runtime enforces strict state invariants before handing control to the next stage: inputs are validated at the boundary, state mutations are ordered deterministically, and latency/resource costs are bounded so downstream consumers never observe a torn or partially initialized state.
Stage 3: Operation
A new implementation is twice as fast in a microbenchmark but the endpoint latency is unchanged. Name three possible reasons and the next measurement you would collect. During this transition, the runtime enforces strict state invariants before handing control to the next stage: inputs are validated at the boundary, state mutations are ordered deterministically, and latency/resource costs are bounded so downstream consumers never observe a torn or partially initialized state.
Stage 4: Boundary
Instrumentation adds overhead and can distort timing, so use the least intrusive tool that answers the question. Microbenchmarks isolate operations but may fail to predict full-system behavior. Preserve correctness checks and compare end-to-end latency after any local speedup. During this transition, the runtime enforces strict state invariants before handing control to the next stage: inputs are validated at the boundary, state mutations are ordered deterministically, and latency/resource costs are bounded so downstream consumers never observe a torn or partially initialized state.
Stage 5: Result
A new implementation is twice as fast in a microbenchmark but the endpoint latency is unchanged. Name three possible reasons and the next measurement you would collect. During this transition, the runtime enforces strict state invariants before handing control to the next stage: inputs are validated at the boundary, state mutations are ordered deterministically, and latency/resource costs are bounded so downstream consumers never observe a torn or partially initialized state.
Production invariants and failure modes
Done when the worked check holds: A new implementation is twice as fast in a microbenchmark but the endpoint latency is unchanged. When debugging or capacity-planning this flow in production, instrument each stage boundary separately (measuring both p50 median and p99.9 tail latency), verify what happens when an intermediate stage times out or drops packets, and ensure retry or fallback paths preserve end-to-end idempotency.