optimizing-performance

name	optimizing-performance
description	Measure-first performance optimization that balances gains against complexity. Use when addressing slow code, profiling issues, or evaluating optimization trade-offs.

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:

// ❌ 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)):

// ❌ 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