Cache Invalidation Strategy

Caching trades freshness for speed. The strategy makes that trade explicit per data class instead of leaving it to defaults. Each class gets a staleness budget, an invalidation mechanism, and a defence against races and stampedes.

How it works

In cache-aside, the application reads the cache, falls back to the source on a miss, and stores the result with a time-to-live. On writes, the application updates the source and then deletes the cache entry, so the next read reloads fresh data. Event-driven invalidation publishes change events that every cache holder consumes. Versioned keys embed a version or content hash, so new data lands under a new key and old entries simply age out.

Implementation steps

1. Classify data: static, slowly changing, user-specific, and near-real-time. Set a maximum acceptable staleness for each.
2. Choose the mechanism: time-to-live alone for tolerant data, delete-on-write for single-service ownership, event-driven for shared data, versioned keys for immutable content.
3. Always set a time-to-live, even with event invalidation, as a safety net.
4. Add jitter to expirations so many keys do not expire together.
5. Coalesce concurrent misses for one key so only one request hits the source.
6. Store a version with each value and refuse to overwrite a newer entry with an older one.
7. Cache negative results briefly to protect the source from repeated misses.

Common pitfalls

• Updating the cache and the database in the wrong order, leaving a window for stale repopulation.
• Invalidating on write but not on bulk imports or administrative changes.
• A thundering herd after a cache restart or mass expiry.
• Caching authorised responses under keys that omit the user or tenant.
• Treating cache failure as fatal instead of degrading to the source.

Metrics to watch

• Hit ratio per data class.
• Stale read rate where measurable through sampling.
• Origin request rate on misses and during stampedes.
• Invalidation event lag.
• Cache latency percentiles and eviction rate.

Example configuration

cache:
  product-detail:
    pattern: cache-aside
    ttlSeconds: 300
    jitterPercent: 20
    invalidateOn: product.updated
    coalesceMisses: true
  price-list:
    pattern: versioned-key
    key: "prices:v{version}"
    ttlSeconds: 3600
  session-profile:
    pattern: write-through
    ttlSeconds: 60
    keyIncludes: [tenantId, userId]

Practical rollout

Before adding a cache, try to measure the staleness that users actually notice, because it is often larger than engineers assume. Start with the simplest mechanism, a time-to-live, and add event-driven invalidation only for classes where staleness causes real harm. Keep cache keys and policies in one reviewed configuration file, so changes are visible, and load test the cold-start path to confirm the origin survives an empty cache.

Key takeaways

• Define a staleness budget for every cached data class.
• Delete on change, and always keep a time-to-live as a backstop.
• Prevent stampedes with coalescing and jitter.
• Use versions to defeat stale-overwrite races.