performance-review

When to use

The user reports slowness or asks to optimize/profile code.

How

1. read_file the code; find the hot path before judging anything.
2. Look for, in impact order:
   • N+1 patterns — a query/API/file call inside a loop that could batch.
   • Wrong complexity — nested scans where a set/dict lookup works; repeated list.index/in list on large lists.
   • Needless I/O — re-reading files, re-opening connections, no caching for repeated pure calls.
   • Memory — loading whole files/datasets when streaming works; holding references that prevent cleanup.
   • Sync blocking in async code.
3. Estimate the win in plain words ("this turns 10k queries into 1").
4. Suggest measuring first when the hot path is unclear: time.perf_counter around suspects or python -m cProfile -s cumtime.

Avoid

• Micro-optimizations (f-string vs concat) while an N+1 sits in the loop.
• "Rewrite it in X" — fix the algorithm first.
• Claiming speedups you can't justify.

Done well

The few changes that matter, ranked by expected win, each with location and a concrete replacement — plus how to verify the improvement.