2nd/10_Wiki/Topics/Frontend/Cache miss rates.md

---
id: wiki-2026-0508-cache-miss-rates
title: Cache Miss Rates
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Cache Miss Rate, Cache Hit Rate, Cache Efficiency]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [performance, cache, profiling, cpu, memory]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: agnostic
  framework: agnostic
---

# Cache Miss Rates

## 매 한 줄
> **"매 cache lookup 의 fail (data 의 cache 외 fetch) ratio"**. CPU L1/L2/L3 cache, browser HTTP cache, application-level memoization, CDN edge cache 의 universal metric — `miss / (hit + miss)`. 2026 perspective: Apple Silicon M4 의 huge L2 (16MB+), 매 Cloudflare/Fastly tiered cache, 매 LLM prompt-cache (Anthropic) 의 cost-driven optimization.

## 매 핵심

### 매 hierarchy (CPU)
- **L1**: 32-128KB / core, ~1-4 cycles.
- **L2**: 256KB-2MB / core, ~10-15 cycles.
- **L3**: 8-128MB shared, ~40 cycles.
- **DRAM**: >100ns, ~200-300 cycles. 매 miss 의 huge cost.

### 매 miss types (3C)
- **Compulsory** (cold): 매 first access.
- **Capacity**: 매 working set > cache size.
- **Conflict**: 매 set-associative collision.
- (+) **Coherence**: multi-core invalidation.

### 매 measurement
- **CPU**: `perf stat -e cache-misses,cache-references` (Linux).
- **HTTP**: `cf-cache-status: HIT/MISS` headers.
- **App**: hit/miss counter on cache wrapper.

### 매 응용
1. 매 hot loop 의 data layout (SoA vs AoS) tuning.
2. 매 CDN cache key strategy.
3. 매 LLM prompt-cache 의 prefix stability.

## 💻 패턴

### Pattern 1 — 매 cache-friendly layout (SoA)
```ts
// 매 BAD (AoS) — 매 padding / strided access
const particles = [{ x: 0, y: 0, vx: 1, vy: 1 }, /* ... */];

// 매 GOOD (SoA) — 매 sequential, cache-line dense
const xs = new Float32Array(N), ys = new Float32Array(N);
const vxs = new Float32Array(N), vys = new Float32Array(N);
for (let i = 0; i < N; i++) {
  xs[i] += vxs[i];
  ys[i] += vys[i];
}
```

### Pattern 2 — 매 LRU cache (memoization)
```ts
class LRU<K, V> {
  private map = new Map<K, V>();
  constructor(private capacity: number) {}

  get(key: K): V | undefined {
    if (!this.map.has(key)) return;
    const v = this.map.get(key)!;
    this.map.delete(key);
    this.map.set(key, v); // refresh
    return v;
  }
  set(key: K, val: V) {
    if (this.map.has(key)) this.map.delete(key);
    else if (this.map.size >= this.capacity) {
      this.map.delete(this.map.keys().next().value!);
    }
    this.map.set(key, val);
  }
}
```

### Pattern 3 — 매 hit rate metric
```ts
class CountingCache<K, V> {
  private hits = 0;
  private misses = 0;
  constructor(private inner: Map<K, V>, private load: (k: K) => V) {}

  get(k: K): V {
    if (this.inner.has(k)) { this.hits++; return this.inner.get(k)!; }
    this.misses++;
    const v = this.load(k);
    this.inner.set(k, v);
    return v;
  }
  hitRate(): number { return this.hits / (this.hits + this.misses || 1); }
}
```

### Pattern 4 — 매 HTTP cache header
```http
GET /api/posts/123
Cache-Control: public, max-age=300, stale-while-revalidate=86400
ETag: "abc123"

# CDN response
cf-cache-status: HIT
age: 42
```

### Pattern 5 — 매 stable key (CDN)
```ts
// 매 BAD — query order varies
fetch(`/api/list?b=2&a=1`);
fetch(`/api/list?a=1&b=2`); // 매 different cache key

// 매 GOOD — canonical
const params = new URLSearchParams();
[...Object.entries(args)].sort().forEach(([k, v]) => params.set(k, v));
fetch(`/api/list?${params}`);
```

### Pattern 6 — 매 LLM prompt cache (Anthropic)
```python
# 매 stable system prefix → cache hit (90% cost reduction)
client.messages.create(
    model="claude-opus-4-7",
    system=[
        {"type": "text", "text": LARGE_STABLE_PREFIX,
         "cache_control": {"type": "ephemeral"}},
    ],
    messages=[{"role": "user", "content": query}],
)
```

### Pattern 7 — 매 CPU perf measure
```bash
perf stat -e cache-references,cache-misses,L1-dcache-load-misses ./app
# 매 miss rate = misses / references
```

### Pattern 8 — 매 prefetch hint
```ts
// 매 link prefetch (browser)
<link rel="prefetch" href="/next-page" as="document">

// 매 software prefetch (WASM/native via SIMD intrinsics)
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| 매 hot loop slow | profile cache-misses, switch to SoA. |
| 매 CDN low hit rate | normalize key, raise max-age. |
| 매 LLM cost high | prompt-cache stable prefix. |
| 매 working set > cache | partition (tiling), reduce. |
| 매 frequent-but-changing | LRU + TTL. |

**기본값**: 매 measure first (perf, CDN headers, app counter), 매 90%+ hit rate target on hot caches.

## 🔗 Graph
- 부모: [[Performance]]
- 변형: [[CDN]] · [[Memoization]]
- 응용: [[Prompt Cache]]
- Adjacent: [[CPU Overhead]] · [[Buffer Allocation]] · [[Batching]]

## 🤖 LLM 활용
**언제**: 매 cache strategy design, hit rate target setting, prompt-cache prefix structuring.
**언제 X**: 매 hardware-specific cache line size assumption — vendor docs 의.

## ❌ 안티패턴
- **매 cache everything**: 매 cold data cache space waste.
- **매 unstable cache key**: 매 hit rate near zero.
- **매 no eviction**: unbounded memory.
- **매 measuring without baseline**: 매 hit rate alone meaningless — 매 latency / cost outcome 의.
- **매 caching mutable data without invalidation**: stale read bug.

## 🧪 검증 / 중복
- Reference (Hennessy & Patterson, Cloudflare cache docs, Anthropic prompt caching docs).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — cache hierarchy + LRU + HTTP + prompt-cache patterns |