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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.
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
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)
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