caching-strategy

To reduce database load and latency, implement intelligent caching layers using cache-aside patterns. This offloads read-heavy application paths by storing frequently accessed data, smoothing out traffic spikes and improving response times.

A cache stampede occurs when many concurrent requests overwhelm the database simultaneously. Prevent it using stampede protection mechanisms like single-flight locks and stale-while-revalidate to ensure database stability under high concurrency.

Choose caching patterns based on your specific read and write requirements. Cache-aside optimizes read-heavy paths, while write-through and write-behind strategies manage data consistency and write latency differently depending on your application needs.

Ensure data consistency in Redis caching by implementing proper invalidation strategies and versioned key namespaces. These mechanisms prevent stale data issues by systematically invalidating outdated cache entries when underlying database records change.

The best way to manage TTL settings under high traffic is to implement jittered TTLs. This smooths out load by preventing synchronized cache expirations, reducing p99 latency spikes and distributing database queries evenly over time.