When Cache-Aside Becomes Your Bottleneck

Cache-aside (lazy loading) hands control to your application to fetch and populate cache entries on demand. This sounds ideal until your cache is cold or a key expires after a spike. Your application threads block waiting for database queries, creating a thundering herd scenario. The pattern shifts responsibility entirely to you, which is powerful but requires careful coordination.

The real cost emerges under high contention. When multiple requests miss the same key simultaneously, they all hit your database. This cascading effect is manageable with circuit breakers or request coalescing, but adds operational complexity.

Three Caching Writes Compared

Write-through guarantees consistency but doubles latency for every write since your application waits for both cache and database. Write-behind decouples the two, batching database updates asynchronously, but a cache failure means losing unwritten data. Your choice depends entirely on your consistency requirements and tolerance for data loss.

TTL Expiration and Cache Stampedes

Time-to-live sounds simple: set an expiration window and let entries vanish. The problem surfaces at scale. When bulk data expires simultaneously (e.g., all hourly aggregations at the top of the hour), thousands of threads rush to recompute cache entries. Databases spike, query latency explodes, and this cascading failure spreads through dependent services.

Solutions include staggered TTLs (randomizing expiration times), probabilistic early revalidation (refreshing entries before expiration based on access patterns), and cache warming to preload frequently accessed data. None are free, but they're necessary in production systems handling millions of transactions.

Applied Insight

Choose cache-aside for read-heavy workloads where you can tolerate brief cache misses and manage invalidation explicitly. Use write-through when data consistency is non-negotiable, accepting the performance penalty. Reserve write-behind for high-volume, less critical data like metrics or event logs. Always implement staggered TTLs in multi-tenant systems and warm your cache proactively before traffic spikes to avoid stampedes. Monitoring cache hit rates, miss patterns, and database load will reveal which pattern actually works for your access patterns, not which looks good in theory.