name: caching
description: >-
Framework-agnostic caching strategies including cache selection, TTL design,
invalidation patterns, and anti-patterns.
Use when designing or reviewing cache logic.

Caching Rules

1. Cache Strategy Selection

When to Cache

Cache Placement

2. TTL Design Guidelines

Recommended TTL by Data Type

TTL Rules

• Always set a TTL — never cache indefinitely
• Shorter TTL for data that affects user experience when stale
• Longer TTL for reference data that changes infrequently
• Use explicit eviction on writes in addition to TTL
• Consider TTL jitter to avoid cache stampede

3. Cache Invalidation Patterns

Write-Through

Update the cache entry simultaneously when writing to the data store. Ensures cache is always consistent but adds write latency.

Evict on Write

Evict the cache entry on write and let the next read repopulate it. Simpler to implement and avoids stale data from failed cache updates.

Event-Based Invalidation

Invalidate cache entries via events (message queue, application events) for cross-service consistency. Best for distributed systems where multiple services share cached data.

Invalidation Rules

• Prefer explicit eviction over TTL-only for mutable data
• Invalidate on all write paths (update, delete, bulk operations)
• Use event-based invalidation for cross-service cache consistency
• Test cache invalidation paths — stale cache bugs are hard to diagnose

4. Anti-Patterns

• Caching without TTL (memory leak risk)
• Caching mutable objects (caller modifies cached reference)
• Cache key collisions (using only entity ID without type prefix)
• Caching null values without explicit handling
• Ignoring cache in delete/update paths (stale reads)
• Over-caching (caching everything "just in case")
• No monitoring of cache hit/miss rates
• Using distributed cache for data that only needs local caching