How to Implement Caching with Redis

Cache patterns that actually work - without the stale data bugs and cache stampedes.

Redis caching can reduce database load by 90% for read-heavy applications. It can also introduce subtle bugs: stale data served after a write, cache stampedes under load, and silent failures when Redis goes down. This guide covers the patterns that capture the performance benefits while avoiding the pitfalls.

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Which Cache Pattern to Use - And When

Cache-aside (lazy loading): application checks cache first fetches from DB on miss stores in cache. Good for read-heavy data that changes infrequently. Downside: cache miss on first access potential stale data. Write-through: write to cache and DB simultaneously. Data always fresh in cache higher write latency. Good for data thats read immediately after write. Write-behind: write to cache immediately async write to DB. Only when write performance is critical and some loss is acceptable.

At Valletta Software, we focus on:

Cache-aside: check Redis miss fetch DB store in Redis with TTL - most common pattern

TTL strategy: base TTL on acceptable staleness - not a universal 5 minutes

Key naming: {service}:{entity}:{id} - predictable scannable avoids collisions

Invalidation on write: delete cache key on mutation - simpler than update avoids inconsistency

Cache stampede: use probabilistic early expiration or Redis locks to prevent concurrent miss/rebuild

Serialization: JSON.stringify/parse or MessagePack - benchmark for your payload sizes

Redis failure: fallback to DB log the miss - never crash the application on Redis unavailability

What Not to Cache (The Expensive Mistakes)

Caching the wrong things is worse than not caching at all.

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Do not cache: frequently mutating data without a short TTL - stale data is a bug not a feature

Do not cache: user-specific data in a shared key - serves one user data to another

Do not cache: large objects that exceed Redis memory limits - use S3/blob storage instead

Do not cache: security-sensitive data without encryption - Redis is not a secrets store

Do not cache: everything - measure DB load first cache the top 10 queries by load

Monitor: cache hit rate (target 80%+) memory usage eviction rate - Redis INFO command

Eviction policy: allkeys-lru for pure cache volatile-lru if Redis stores non-cached data too

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How to Implement Caching with Redis - With Engineers Who've Done It in Production

Our backend engineers implement Redis caching with appropriate patterns per data type proper TTL strategy stampede prevention and graceful Redis failure handling.

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Our backend engineers implement Redis caching with appropriate patterns per data type, proper TTL strategy, stampede prevention, and graceful Redis failure handling.

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