Application-Level Caching with Redis

Explain why application-level caching improves performance, and define cache hit, cache miss, and hit ratio.

Describe Redis as an in-memory data store and its primary data structures (strings, hashes, lists, sets).

Compare caching patterns: cache-aside, write-through, and read-through.

Explain common cache eviction policies (e.g., LRU, LFU, TTL).

Describe cache invalidation strategies (e.g., TTL-based, event-based) and the challenges of keeping caches fresh.

Implement application-level caching in a Python backend to cache database query results using Redis.

Analyze the trade-offs of caching, including data consistency issues and increased system complexity.

Explain the 'thundering herd' problem and strategies to mitigate it, such as using cache locks.