Redis Caching Patterns That Actually Work
When your API starts getting thousands of requests per second, you quickly realize that hitting your database for every request isn't sustainable. That's where Redis comes in.
Why Caching Matters
Every database query has overhead - connection pooling, query parsing, disk I/O, network latency. For frequently accessed data that doesn't change often, this overhead is wasteful.
Redis sits in memory, responds in microseconds, and can handle millions of operations per second. But using it effectively requires understanding the right patterns.
Pattern 1: Cache-Aside (Lazy Loading)
This is the most common pattern. Your application checks the cache first. If it's a miss, it fetches from the database and populates the cache.
**Pros:** Only caches data that's actually requested. Simple to implement.
**Cons:** First request is always slow. Potential for stale data.
Pattern 2: Write-Through
Update the cache whenever you update the database. This ensures cache consistency but adds latency to writes.
Pattern 3: Write-Behind (Write-Back)
Write to cache immediately, then asynchronously update the database. Great for high-write scenarios.
**Warning:** This risks data loss if Redis crashes before the database is updated. Use with caution.
Cache Invalidation Strategies
The hardest problem in computer science (after naming things). Here's what works:
- **TTL-Based Expiration** - Set reasonable expiration times based on how often data changes.
- **Event-Driven Invalidation** - Invalidate cache entries when the underlying data changes.
- **Version Keys** - Include a version number in your cache keys. Increment it when data changes.
Key Takeaways
- Start simple with cache-aside pattern
- Set appropriate TTLs - don't cache forever
- Monitor hit rates - aim for 90%+
- Have a fallback - gracefully handle Redis downtime
Caching is powerful, but remember: premature optimization is the root of all evil. Profile first, then cache what matters.