caching-and-distributed-state

Cache invalidation requires explicit rules and freshness guarantees to avoid stale reads. You can define TTLs and staleness handling rules to ensure data integrity across latency-sensitive workloads while maintaining clear source boundaries.

Choosing caching patterns depends on balancing latency, durability, and complexity. Cache-aside offers simplicity for read-heavy datasets, while write-through and write-behind provide stronger durability guarantees for regulated workloads requiring strict consistency.

Prevent cache stampedes by applying distributed coordination patterns like locks, sessions, and stampede protections. These mechanisms guard critical sections and hot keys, ensuring safe fallback behavior during degraded modes.

Yes, it supports regulated workloads by defining safe fallbacks, isolation, and distributed coordination patterns. It establishes a clear source of truth and tenancy considerations to maintain data integrity across distributed state boundaries.

Avoid write-behind caching when strict consistency and immediate durability are required. Write-behind introduces complexity and potential data loss risks during failures, making write-through or read-through more suitable for regulated workloads.