Optimizing Performance
Core principle: Readable code that's "fast enough" beats complex code that's "optimal". Measure first.
Focus area: Use an explicitly named target if one was given. Otherwise, run git diff and focus on unstaged changes. If no unstaged changes exist, ask the user what to optimize.
The Golden Rule
IF optimization reduces complexity AND improves performance → ALWAYS DO IT IF optimization increases complexity → Only if 10x faster OR fixes critical UX (>16ms UI, >100ms input)
Win-Win Optimizations (Always Do)
Multiple loops → Single loop:
javascript// ❌ Three passes const ids = users.map(u => u.id); const active = users.filter(u => u.active); // ✅ One pass const { ids, active } = users.reduce((acc, u) => { acc.ids.push(u.id); if (u.active) acc.active.push(u); return acc; }, { ids: [], active: [] });
Nested loops → Hash map (O(n²) → O(n)):
javascript// ❌ O(n²) const matched = orders.filter(o => users.some(u => u.id === o.userId)); // ✅ O(n) const userIds = new Set(users.map(u => u.id)); const matched = orders.filter(o => userIds.has(o.userId));
High-Value Optimizations
| Pattern | When | Fix |
|---|---|---|
| Virtualization | Lists >1000 items | react-window, tanstack-virtual |
| Memoization | >5ms calc OR unnecessary re-renders | useMemo, React.memo |
| Batching | Multiple state updates | Single setState, bulk INSERT |
| Lazy loading | Large dependencies | import('./heavy-lib') |
Red Flags
- Optimizing without benchmark data
- Micro-optimizing <16ms code
- Adding complexity for minimal gain
- Optimizing infrequently-run code
