Caching

What a cache is, why it exists, and where it sits in a system. The mental model before you touch any strategy.

Read-through, write-through, write-back, write-around. When data changes, these strategies decide which copy gets updated first.

LRU, LFU, FIFO, TTL. When the cache is full, eviction policy decides what gets thrown out — and getting it wrong is a performance cliff.

Refresh-ahead and cache warming. How you pre-fill a cache so users never hit a cold miss on a hot path.

TTL, event-driven, write-through, versioning, stale-while-revalidate. The hardest problem in caching — keeping the cache honest without killing performance.

Why a single cache node fails at scale. Consistent hashing, cache coherence, replication, two-level caching, and node failure handling.

Stampede, cold start, penetration, avalanche. The failure modes that hit hardest when traffic spikes or the cache restarts.

Data structures, patterns, persistence, streams, and the single-threaded event loop. Redis internals that explain why it's fast and where it breaks.