Video lesson: Profile Before Rewriting the Slow Path

Lesson promise

By the end, the learner should be able to explain the core model for profile before rewriting the slow path, apply it to a concrete input, and identify when its usual shortcut or guarantee stops applying. This is a recording brief; publish it as a playable lesson after the narration and visual sequence have been produced and reviewed.

Narration draft

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.

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.

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.

Visual sequence

1. Put the input and assumptions on screen. Ask the learner to predict the next state before revealing it.
2. Animate the representation and show the operation one transition at a time.
3. Pause at the boundary case in the companion article and compare the result with the invariant.
4. End with the exercise prompt: 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.

Companion material

Use the article, trace, and interactive concept flow as the learner’s written and visual references. The video remains planned until an actual playable media URL and reviewed transcript are available.