03

Concepts

Caching

The fastest database query is one that never hits the database. Everything here is about making that work reliably at scale.

What a cache is, where it lives, and the core trade-offs between hit rate, freshness, and memory cost.

Hit RateCache MissFreshnessMemory Cost

Writing Strategies

Read-through, write-through, write-behind, and write-around — how data gets into the cache and what breaks when it doesn't.

Write-ThroughWrite-BehindRead-ThroughWrite-Around

Eviction Policies

LRU, LFU, FIFO, and TTL. What gets evicted when memory fills up and how to pick the right policy for your workload.

Population Strategies

Refresh-ahead and cache warming — how to keep caches populated without a thundering herd on every cold start.

Refresh-AheadCache WarmingLazy LoadingPre-Population

Cache Invalidation

TTL-based, event-driven, write-through, and versioning strategies. The hardest problem in caching is knowing when to throw data away.

TTLEvent-DrivenVersioningStale-While-Revalidate

Distributed Caching

Consistent hashing, cache coherence, replication, two-level caching, and what happens when a cache node dies mid-traffic.

Consistent HashingCache CoherenceReplicationNode Failure

Cache stampede, cold start, penetration, and avalanche — the failure modes that turn a cache from a fix into the cause of the outage.

StampedeCold StartPenetrationAvalanche

Data structures, patterns, persistence options, streams, and the single-threaded event loop that makes Redis predictably fast.

Data StructuresPersistenceStreamsEvent Loop