caching-and-distributed-state

What problem does it solve?

Designs safe caching and distributed state with TTLs, invalidation, staleness rules, Redis-style patterns, locks, sessions, hot-key controls, and consistency trade-offs.

Core Features & Use Cases

• Cache patterns & strategies: cache-aside, read-through, write-through, and write-behind to balance latency, durability, and complexity.
• Invalidation, TTLs, and staleness: explicit invalidation rules and freshness guarantees to avoid stale reads.
• Distributed coordination: locks, sessions, and stampede protections to guard critical sections and hot keys.
• Design boundaries: clear source of truth, tenancy considerations, and safe fallback behavior for degraded modes.

Quick Start

Configure a cache-aside pattern with a TTL and explicit invalidation for a read-heavy dataset.