Redis Caching Patterns
Trigger Boundary
- •Use when Redis is introduced or tuned for cache/session/queue-like workloads.
- •Do not use for primary relational schema design.
- •Do not use for in-memory caching that does not involve Redis.
Goal
Improve latency and throughput with explicit cache correctness and invalidation policy.
Inputs
- •Target read/write hotspots and latency objectives
- •Data freshness and consistency requirements
- •Eviction policy and memory constraints
Outputs
- •Key namespace and TTL strategy
- •Cache invalidation and degradation behavior plan
- •Observability checklist for hit rate, memory, and evictions
Workflow
- •Identify cache candidates by read amplification and recomputation cost.
- •Design key schema with clear namespace and versioning.
- •Define TTL, invalidation triggers, and stale-read tolerance.
- •Add protection against cache stampede and hot-key concentration.
- •Monitor hit rate, latency, and eviction behavior to iterate policy.
Quality Gates
- •Key naming and ownership are consistent across services.
- •Invalidation strategy matches freshness requirements.
- •Cache failures do not silently corrupt correctness expectations.
- •Metrics allow quick detection of cache regressions.
Failure Handling
- •Stop when invalidation policy cannot guarantee correctness bounds.
- •Escalate when cache behavior increases tail latency or error rates.