Caching Strategies

Caching Strategies

Cache effectively to improve performance and reduce load.

Cache Layers

| Layer | Latency | Storage | Examples |

|-------|---------|---------|----------|

| Browser | Local | Small | localStorage, HTTP cache |

| CDN | Regional | Large | CloudFront, CloudFlare, Fastly |

| Application | In-process | Small | L1 cache (heap) |

| Distributed | Network | Large | Redis, Memcached |

| Database | Same host | Very large | Buffer pool, query cache |

Caching Patterns

Cache-Aside (Lazy Loading)

1. Check cache → miss
2. Query database
3. Store in cache
4. Return

Best for: Read-heavy, general purpose.

Write-Through

1. Write to cache
2. Cache writes to DB (sync)
3. Return

Best for: Consistency critical, write-heavy.

Write-Behind

1. Write to cache
2. Return immediately
3. Cache writes to DB (async)

Best for: High throughput writes, eventual consistency OK.

Refresh-Ahead

• Cache proactively refreshes before expiry
• Reduces latency spikes on cache miss
• Requires predictive logic or scheduled refresh

Cache Invalidation

Two hard things: naming, cache invalidation, off-by-one errors.

| Strategy | How | Risk |

|----------|-----|------|

| TTL | Expire after X seconds | Stale data during TTL |

| Event-based | Invalidate on data change | Missed events |

| Version keys | user_42_v3 | Unused old keys accumulate |

| Write-through | Update cache on write | Write amplification |

Redis Patterns

• Use EXPIRE on every SET
• Use SCAN not KEYS in production
• Distributing: Redis Cluster for sharding
• Backup: RDB (snapshot) + AOF (append-only log)
• Monitor: hit rate, memory, evictions, latency