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title

Ad-hoc Optimization

매 한 줄

"매 measure-then-fix-locally tactic". 매 system-wide 매 architectural improvement 의 opposite — 매 single profiler hot-spot 의 surgical fix. 매 effective when bounded, dangerous when systemic problem masked.

매 핵심

매 mechanism

매 profiler → bottleneck → patch → re-profile loop.
매 80/20 rule — 매 20% code 의 80% time 의 surgical strike.

매 vs systematic

Ad-hoc: caching one query, inlining one loop.
Systematic: index strategy, algorithm change, architecture refactor.

매 응용

Performance bug regressions (single function got slow).
Hot path tuning post-profiling.
Pre-launch polish.

💻 패턴

Profile-first (Node.js)

import { performance } from 'perf_hooks';

const t0 = performance.now();
const result = expensiveFunction(input);
console.log(`took ${performance.now() - t0}ms`);

Memoize one hot function

const memo = new Map<string, Result>();
function compute(key: string, input: Input): Result {
  if (memo.has(key)) return memo.get(key)!;
  const r = expensiveFunction(input);
  memo.set(key, r);
  return r;
}

Batch one N+1 query

// Before: O(N) DB roundtrips
for (const u of users) u.posts = await db.posts.where({ userId: u.id });

// After: 1 roundtrip
const posts = await db.posts.whereIn('userId', users.map(u => u.id));
const byUser = groupBy(posts, 'userId');
users.forEach(u => u.posts = byUser[u.id] ?? []);

Hot loop unroll (tight CPU path)

// Before
for (let i = 0; i < n; i++) sum += arr[i];

// After (4x unrolled)
let i = 0;
for (; i + 3 < n; i += 4) {
  sum += arr[i] + arr[i+1] + arr[i+2] + arr[i+3];
}
for (; i < n; i++) sum += arr[i];

Cache HTTP response (1-line fix)

app.get('/api/feed', cacheMiddleware({ ttl: 60 }), async (req, res) => {
  res.json(await buildFeed(req.user));
});

매 결정 기준

상황	Approach
1 hot function, rest 매 fine	Ad-hoc fix
매 systemic — many slow paths	Architectural refactor
Pre-launch perf polish	Ad-hoc 매 first, systematic later

기본값: Profile → ad-hoc fix → re-profile. 매 escalate to systematic only if 매 ad-hoc 매 insufficient.

🔗 Graph

부모: Performance-Optimization
변형: Memoization
Adjacent: Profiling · Premature-Optimization

🤖 LLM 활용

언제: measured bottleneck, bounded scope. 언제 X: 매 system-wide perf issue (architectural fix needed); 매 unmeasured guess (premature optimization).

❌ 안티패턴

Optimizing without profiling: 매 wrong target.
Local fix masking systemic issue: e.g., caching to hide N+1 query.
Ad-hoc until 매 spaghetti: 매 too many patches → architectural debt.

🧪 검증 / 중복

Verified (Knuth — premature optimization is the root of all evil; Brendan Gregg — Systems Performance).
신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — Ad-hoc Optimization FULL with profile-first patterns

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