implementing-database-caching

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1.9k2593 months ago

Process use when you need to implement multi-tier caching to improve database performance. This skill sets up Redis, in-memory caching, and CDN layers to reduce database load. Trigger with phrases like "implement database caching", "add Redis cache layer", "improve query performance with caching", or "reduce database load".

Quick Install

npxskills add jeremylongshore/claude-code-plugins-plus-skills--skill implementing-database-caching

Instructions

Database Cache Layer

Overview

Implement multi-tier caching strategies using Redis, application-level in-memory caches, and query result caching to reduce database load and improve read latency. This skill covers cache-aside, write-through, and write-behind patterns with proper invalidation strategies, TTL configuration, and cache stampede prevention.

Prerequisites

Redis server (6.x+) available or Docker for running docker run redis:7-alpine
redis-cli installed for cache inspection and debugging
Application framework with Redis client library (ioredis, redis-py, Jedis, go-redis)
Database query profiling data identifying read-heavy and slow queries
Understanding of data freshness requirements (how stale can cached data be)
Monitoring tools for cache hit rate and Redis memory usage

Instructions

Profile database queries to identify caching candidates. Focus on queries that: execute more than 100 times per minute, take longer than 50ms, return data that changes less frequently than every 5 minutes, and produce results smaller than 1MB. Use pg_stat_statements or MySQL slow query log.
Design the cache key schema with a consistent naming convention: service:entity:identifier:variant. Examples: , . Include a version prefix to enable bulk invalidation: .

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Tags & Topics

aiautomationclaude-codedevopsmcpai-agents

Database Cache Layer

Overview

Prerequisites

Redis server (6.x+) available or Docker for running docker run redis:7-alpine
redis-cli installed for cache inspection and debugging
Application framework with Redis client library (ioredis, redis-py, Jedis, go-redis)
Database query profiling data identifying read-heavy and slow queries
Understanding of data freshness requirements (how stale can cached data be)
Monitoring tools for cache hit rate and Redis memory usage

Instructions

Profile database queries to identify caching candidates. Focus on queries that: execute more than 100 times per minute, take longer than 50ms, return data that changes less frequently than every 5 minutes, and produce results smaller than 1MB. Use pg_stat_statements or MySQL slow query log.
Design the cache key schema with a consistent naming convention: service:entity:identifier:variant. Examples: , . Include a version prefix to enable bulk invalidation: .

Error	Cause	Solution
Redis connection refused	Redis server down or network issue	Implement circuit breaker pattern; fall through to database on cache unavailability; retry with exponential backoff
Cache stampede on popular key expiration	Many concurrent requests hit cache miss simultaneously	Use distributed locking or probabilistic early refresh; extend TTL with jitter (`TTL + random(0, TTL*0.1)`)
Stale data served after database update	Cache invalidation missed or delayed	Audit invalidation paths; use publish/subscribe for cache invalidation events; reduce TTL for sensitive data
Redis out of memory (OOM)	Cache size exceeds `maxmemory` setting	Enable `allkeys-lru` eviction; reduce TTLs; audit large keys with `redis-cli --bigkeys`; increase maxmemory
Cache key collision	Different data stored under the same key pattern	Include all discriminating parameters in the cache key; add content hash to key for variant detection

implementing-database-caching

Database Cache Layer

Overview

Prerequisites

Instructions

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Database Cache Layer

Overview

Prerequisites

Instructions

Output

Error Handling

Examples

Resources

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