Caching Strategies

Caching stores copies of frequently accessed data in a faster storage layer. A well-designed caching strategy can reduce latency by orders of magnitude, lower database load, and dramatically improve throughput. This lesson covers caching patterns, technologies, and pitfalls.

---

Why Caching Matters

Without Cache:                      With Cache:

Client ──▶ Server ──▶ Database     Client ──▶ Server ──▶ Cache (hit!)
                      (50ms)                              (1ms)
                                                   │
                                                   ▼ (miss)
                                              Database
                                              (50ms)

Typical cache hit rate: 80-95%
Effective latency = 0.9 × 1ms + 0.1 × 50ms = 5.9ms

---

Caching Patterns

1. Cache-Aside (Lazy Loading)

The application is responsible for reading from and writing to the cache. Data is loaded into the cache only when requested.

┌────────┐    1. Check cache     ┌─────────┐
│  App   │──────────────────────▶│  Cache   │
│        │◀── 2a. Cache hit ─────│ (Redis)  │
│        │                       └─────────┘
│        │    2b. Cache miss
│        │──────────────────────▶┌─────────┐
│        │◀── 3. Return data ────│Database │
│        │                       └─────────┘
│        │──── 4. Write to cache─▶ Cache
└────────┘

def get_user(user_id: str) -> dict:
    # 1. Check cache
    cached = redis.get(f"user:{user_id}")
    if cached:
        return json.loads(cached)