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caching-strategy

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Implement Redis/Memcached patterns and invalidation strategies.

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caching-strategy Skill

This skill defines how to implement caching to improve performance without serving stale data.

1. Caching Patterns

Read Patterns

  1. Cache-Aside (Lazy Loading) - Most Common

    • App checks Cache.
    • If hit: return.
    • If miss: fetch DB -> write to Cache -> return.
    • Pros: Resilient to cache failure. Cons: First request is slow.

  2. Read-Through

    • App asks Cache. Cache itself fetches from DB if missing.
    • Pros: App logic simple. Cons: Requires specific library/provider support.

Write Patterns

  1. Write-Through

    • App writes to Cache and DB synchronously.
    • Pros: Consistency. Cons: Write latency.

  2. Write-Behind (Write-Back)

    • App writes to Cache. Cache writes to DB asynchronously.
    • Pros: Fast writes. Cons: Data loss risk if cache crashes.

  3. Write-Around

    • App writes to DB directly. Cache is only populated on Read miss.
    • Pros: Reduces cache churn for write-heavy data.

2. Invalidation Strategy (The Hard Part)

  • Time To Live (TTL): Always set a TTL. No key lives forever.
  • Event-Based Invalidation: On UserUpdated event, delete user:{id} key.
  • Versioned Keys: user:{id}:v2. Increment version to bust cache.

3. Keys & Values

  • Naming: namespace:entity:id:attribute (e.g., app:users:123:profile).
  • Serialization: Compress large JSON payloads (zlib/snappy) before caching.
  • Hot Keys: If one key gets 100k req/s, replicate it or use local in-memory caching (L1) + Redis (L2).

4. Implementation Checklist

  • Fallbacks: Wrap cache calls in try/catch. If Redis is down, fetch from DB.
  • Metrics: Track cache_hit_rate (Target: >96%).
  • Consistency: Is eventual consistency acceptable? If no, do not cache.
  • Eviction Policy: Configure Redis maxmemory-policy (usually allkeys-lru or volatile-lru).

5. Anti-Patterns

  • Caching Lists: Hard to update. Better to cache individual items and IDs.
  • Long TTLs for Dynamic Data: Users will see old profiles.
  • Thundering Herd: 1000 processes ensuring the same cache key at once. (Use locking or "probabilistic early expiration").