Antigravity Agent
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10_Wiki/Topics 대규모 정리: - 오류 캡처/미완성 stub 문서 227개 제거 - 교차폴더 중복 43클러스터 병합 (63파일 → redirect) - 링크명 정규화: 깨진 링크 수정·redirect 직결·개념 매핑 ~2,400건 - 카테고리 MOC 6개 신규 생성 - Graph 섹션 미해결 related-keyword 링크 10,058건 제거 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
126 lines
3.5 KiB
Markdown
126 lines
3.5 KiB
Markdown
---
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id: wiki-2026-0508-ad-hoc-optimization
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title: Ad-hoc Optimization
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Local Optimization, Point Optimization]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [performance, optimization, anti-pattern]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: any
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framework: any
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---
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# Ad-hoc Optimization
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## 매 한 줄
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> **"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.
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## 매 핵심
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### 매 mechanism
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- 매 profiler → bottleneck → patch → re-profile loop.
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- 매 80/20 rule — 매 20% code 의 80% time 의 surgical strike.
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### 매 vs systematic
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- **Ad-hoc**: caching one query, inlining one loop.
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- **Systematic**: index strategy, algorithm change, architecture refactor.
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### 매 응용
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1. Performance bug regressions (single function got slow).
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2. Hot path tuning post-profiling.
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3. Pre-launch polish.
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## 💻 패턴
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### Profile-first (Node.js)
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```ts
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import { performance } from 'perf_hooks';
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const t0 = performance.now();
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const result = expensiveFunction(input);
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console.log(`took ${performance.now() - t0}ms`);
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```
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### Memoize one hot function
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```ts
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const memo = new Map<string, Result>();
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function compute(key: string, input: Input): Result {
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if (memo.has(key)) return memo.get(key)!;
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const r = expensiveFunction(input);
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memo.set(key, r);
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return r;
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}
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```
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### Batch one N+1 query
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```ts
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// Before: O(N) DB roundtrips
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for (const u of users) u.posts = await db.posts.where({ userId: u.id });
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// After: 1 roundtrip
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const posts = await db.posts.whereIn('userId', users.map(u => u.id));
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const byUser = groupBy(posts, 'userId');
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users.forEach(u => u.posts = byUser[u.id] ?? []);
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```
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### Hot loop unroll (tight CPU path)
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```ts
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// Before
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for (let i = 0; i < n; i++) sum += arr[i];
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// After (4x unrolled)
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let i = 0;
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for (; i + 3 < n; i += 4) {
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sum += arr[i] + arr[i+1] + arr[i+2] + arr[i+3];
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}
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for (; i < n; i++) sum += arr[i];
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```
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### Cache HTTP response (1-line fix)
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```ts
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app.get('/api/feed', cacheMiddleware({ ttl: 60 }), async (req, res) => {
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res.json(await buildFeed(req.user));
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});
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| 1 hot function, rest 매 fine | Ad-hoc fix |
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| 매 systemic — many slow paths | Architectural refactor |
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| Pre-launch perf polish | Ad-hoc 매 first, systematic later |
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**기본값**: Profile → ad-hoc fix → re-profile. 매 escalate to systematic only if 매 ad-hoc 매 insufficient.
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## 🔗 Graph
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- 부모: [[Performance-Optimization]]
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- 변형: [[Memoization]]
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- Adjacent: [[Profiling]] · [[Premature-Optimization]]
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## 🤖 LLM 활용
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**언제**: measured bottleneck, bounded scope.
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**언제 X**: 매 system-wide perf issue (architectural fix needed); 매 unmeasured guess (premature optimization).
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## ❌ 안티패턴
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- **Optimizing without profiling**: 매 wrong target.
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- **Local fix masking systemic issue**: e.g., caching to hide N+1 query.
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- **Ad-hoc until 매 spaghetti**: 매 too many patches → architectural debt.
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## 🧪 검증 / 중복
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- Verified (Knuth — premature optimization is the root of all evil; Brendan Gregg — Systems Performance).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — Ad-hoc Optimization FULL with profile-first patterns |
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