implementing-database-caching

# Database Cache Layer

This skill provides automated assistance for database cache layer tasks.

## Prerequisites

Before using this skill, ensure:
- Redis server available or ability to deploy Redis container
- Understanding of application data access patterns and hotspots
- Knowledge of which queries/data benefit most from caching
- Monitoring tools to measure cache hit rates and performance
- Development environment for testing caching implementation
- Understanding of cache invalidation requirements for data consistency

## Instructions

### Step 1: Analyze Caching Requirements
1. Profile database queries to identify slow or frequently executed queries
2. Determine which data is read-heavy vs write-heavy
3. Identify data that can tolerate eventual consistency
4. Calculate expected cache size and Redis memory requirements
5. Document current database load and target performance metrics

### Step 2: Choose Caching Strategy
1. **Cache-Aside (Lazy Loading)**: Application checks cache first, loads from DB on miss
   - Best for: Read-heavy workloads, unpredictable access patterns
   - Pros: Only caches requested data, simple to implement
   - Cons: Cache misses incur database hit, stale data possible
2. **Write-Through**: Application writes to cache and database simultaneously
   - Best for: Write-heavy workloads needing consistency
   - Pros: Cache always consistent, no stale data
   - Cons: Write latency, unnecessary caching of rarely-read data
3. **Write-Behind (Write-Back)**: Application writes to cache, async writes to database
   - Best for: High write throughput requirements
   - Pros: Low write latency, batched database writes
   - Cons: Risk of data loss, complexity in implementation

### Step 3: Design Cache Architecture
1. Set up Redis as distributed cache layer (L2 cache)
2. Implement in-memory LRU cache in application (L1 cache)
3. Configure CDN for static assets (images, CSS, JS)
4. Design cache key naming convention (e.g., `user:123:profile`)
5. Define