Performance optimization

Performance optimization

The rule that outranks every technique: measure, change one thing, measure again. Optimization without a profile is guessing with extra steps, and the guess is usually wrong.

Method

1. Define fast enough before starting. A target with units: "page loads under 800 ms on the median device", "the import handles 100k rows in under a minute". Without a target, optimization never ends and every trade-off is unjustifiable.
2. Measure the real workload. Profile the actual slow case with realistic data volume, not a toy benchmark. Capture where time or memory actually goes; the bottleneck is nearly always one or two spots, and nearly never where intuition points.
3. Fix in order of leverage:
   • Do less work: cache repeated computation, skip work whose result is unused, exit early, paginate instead of loading everything.
   • Do work fewer times: hoist the query out of the loop (the N+1 pattern hides everywhere), batch requests, debounce repeated triggers.
   • Do work with the right complexity: the list scan inside a loop is quadratic; a set or map makes it linear. Algorithmic wins dwarf micro-tuning.
   • Do work at a better time: precompute, defer off the hot path, move it to the background, stream instead of buffering.
   • Only then micro-optimize, and only with the profiler confirming the hot spot.
4. Verify against the target with the same measurement, then check what the change cost: memory for speed, freshness for caching, complexity for everything. State the trade honestly.
5. Guard the win. A performance fix without a regression check (a benchmark, a budget in CI, an alert) is a temporary loan.

Rules

• Caching gets an invalidation story before it ships, or it is a correctness bug with good latency.
• Never trade correctness for speed silently; if a fast path returns slightly different results, that is a decision for the requester.
• Readable-but-fast beats clever-but-fragile; note when the optimized version costs clarity and quarantine the cleverness behind a well-named function.
• Report numbers, not adjectives: before, after, workload, machine.