distributed-caching-and-invalidation

Distributed Caching And Invalidation

Use When

• Read-heavy paths need lower latency
• Cache coherence issues are causing stale user data

Workflow

1. Classify cacheable data and staleness tolerance.
2. Define cache keys, TTL, and ownership.
3. Implement write-through or invalidation strategy.
4. Add fallback behavior for cache misses and outages.
5. Observe hit ratio, stale reads, and stampede risk.

Required Checks

• Invalidation path exists for every mutating action
• Cache outage does not break core user flows
• Metrics include hit rate, miss rate, and stale-read incidents

Decision Framework

• Prefer explicit contracts and compatibility rules before implementation.
• If dependency risk is high, require timeout, retry, and fallback strategy per call path.
• If async messaging is used, require idempotency, replay, and dead-letter handling.
• If traffic patterns are volatile, require load, failover, and scaling validation before ship.

Common Rationalizations And Rebuttals

• "Retries will handle failures automatically." -> Unbounded retries can amplify outages; use budgets.
• "We can skip runbooks for now." -> Operational ambiguity delays incident recovery.
• "Contract changes are minor." -> Small breaking changes cause broad downstream regressions.

Evidence Pack

• Contract compatibility note and migration strategy (if applicable)
• Failure-mode test evidence for dependency degradation and recovery
• Observability snapshot (latency, error, saturation, or queue health)
• Rollout and rollback steps with clear trigger thresholds

Exit Criteria

• Performance improves without correctness regressions
• Cache behavior is predictable under failure conditions