Implement a Caching Strategy

Design and implement a caching strategy for the following application:

Application type: [web app / API / mobile backend / data pipeline]
Current performance problem: [describe what is slow and the scale of the issue]
Stack: [language, framework, database, current infrastructure]
Data characteristics: [how often data changes / read vs write ratio / data size]
Consistency requirements: [strict consistency required / eventual consistency acceptable]
Available caching infrastructure: [Redis / Memcached / CDN / in-memory / none yet]
Traffic patterns: [consistent / spiky / read-heavy / write-heavy]

Provide a complete caching strategy:

CACHING LAYER DESIGN:
- Which caching layers to implement (browser / CDN / application / database)
- Cache topology recommendation (single node / cluster / distributed)
- Cache sizing and memory allocation

CACHING PATTERNS:
For each data type in the application, recommend:
- Cache-aside vs read-through vs write-through vs write-behind
- TTL (time to live) values with justification
- Cache key naming convention
- Cache warming strategy

CACHE INVALIDATION:
- Invalidation strategy per data type (TTL / event-driven / manual)
- Handling stale data gracefully
- Cache stampede prevention (thundering herd problem)
- Tag-based invalidation where appropriate

IMPLEMENTATION CODE:
- Cache client setup and connection pooling
- Generic cache wrapper/decorator
- Cache-aside pattern implementation
- Cache invalidation helpers
- Specific implementations for the top 3 slowest operations

DATABASE QUERY CACHING:
- Query result caching approach
- ORM-level caching configuration
- Read replica routing

CDN CONFIGURATION:
- Static asset caching rules
- Dynamic content caching rules
- Cache-Control header strategy

MONITORING:
- Cache hit rate targets
- Memory usage monitoring
- Eviction rate alerting
- Key metrics dashboard

FAILURE HANDLING:
- Behaviour when cache is unavailable
- Circuit breaker for cache layer
- Graceful degradation approach