Cache Strategies

What is Caching?

Caching stores copies of frequently accessed data in a faster storage layer (like memory) to reduce latency and database load.

Cache Layers

User Request
     │
     ▼
┌─────────────┐
│ Browser     │ ← Client-side cache
│ Cache       │
└──────┬──────┘
       │
┌──────▼──────┐
│    CDN      │ ← Edge cache
└──────┬──────┘
       │
┌──────▼──────┐
│ Application │ ← In-memory cache
│ Cache       │
└──────┬──────┘
       │
┌──────▼──────┐
│  Database   │ ← Query cache
└─────────────┘

Caching Patterns

Cache-Aside (Lazy Loading)

Application manages cache explicitly:

Read:
1. Check cache
2. If miss, read from DB
3. Store in cache
4. Return data

Write:
1. Write to DB
2. Invalidate cache

Pros: Only requested data cached, resilient to cache failures Cons: Initial requests slow (cache miss)

Read-Through

Cache sits between app and DB:

Read:
1. Request to cache
2. Cache fetches from DB on miss
3. Cache returns data

Pros: Simpler application code Cons: Cache must understand data source

Write-Through

Writes go through cache to DB:

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

Pros: Cache always consistent, data not lost Cons: Higher write latency

Write-Behind (Write-Back)

Writes to cache, async to DB:

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

Pros: Low write latency Cons: Risk of data loss if cache fails

Cache Eviction Policies

Cache Invalidation

"There are only two hard things in Computer Science: cache invalidation and naming things."

Strategies

1. TTL (Time-to-Live): Auto-expire after duration
2. Event-based: Invalidate on data change
3. Version-based: Include version in cache key

Common Caching Solutions

Interview Tips

• Know the four main patterns and trade-offs
• Explain cache invalidation strategies
• Discuss eviction policies (LRU vs LFU)
• Mention cache stampede and how to prevent it
• Give examples: Redis, Memcached, CDN