Caching Strategies Cheat Sheet

Updated 2026-04-29

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Caching is a fundamental performance optimization technique that stores copies of frequently accessed data in fast-access storage layers, reducing latency from milliseconds to microseconds and dramatically decreasing load on backend systems. Effective caching sits at the intersection of data locality, consistency models, and eviction policies — choosing the wrong strategy can create data staleness issues or cache stampedes that bring down entire systems. In 2026, caching is no longer an optional optimization: microservices amplify latency costs, cloud spend scales with repeated computation, and AI/LLM workloads have introduced entirely new caching dimensions — from semantic similarity matching to GPU-resident KV attention caches. The key insight: caching is about intelligently deciding what to cache, when to invalidate it, how to handle failures, and increasingly, how to apply it to non-deterministic AI inference pipelines.

What This Cheat Sheet Covers

This topic spans 21 focused tables and 149 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Core Caching PatternsTable 2: Cache Eviction PoliciesTable 3: Cache Invalidation StrategiesTable 4: Cache Consistency ModelsTable 5: Distributed Caching PatternsTable 6: Cache Warming & PreloadingTable 7: Cache Problem PatternsTable 8: Cache Stampede SolutionsTable 9: Cache Penetration SolutionsTable 10: Redis-Specific FeaturesTable 11: Memcached-Specific FeaturesTable 12: HTTP Caching HeadersTable 13: Browser & Service Worker CachingTable 14: Cache Key Design Best PracticesTable 15: Cache Partitioning & ShardingTable 16: Cache Monitoring & MetricsTable 17: Redis / Valkey / Memcached ComparisonTable 18: In-Memory Cache AlternativesTable 19: Advanced Caching TechniquesTable 20: AI & Semantic CachingTable 21: Cache Security

Table 1: Core Caching Patterns

Pattern	Example	Description
Cache-Aside (Lazy Loading)	`data = cache.get(key)` `if data is None:` `data = db.query(key)` `cache.set(key, data, ttl=3600)`	• Application checks cache first on read • on miss, fetches from DB and populates cache • most common pattern providing explicit application control.
Read-Through	`data = cache.get(key)`	• Cache automatically loads from DB on miss using registered loader • abstracts cache logic from application code.
Write-Through	`cache.set(key, data)` `db.write(key, data)`	• Writes update both cache and DB synchronously • ensures strong consistency but adds write latency.
Write-Behind (Write-Back)	`cache.set(key, data)` `queue.push(db_write_task)`	• Writes update cache immediately and DB asynchronously after delay • optimizes write performance with eventual consistency.
Write-Around	`db.write(key, data)` `cache.delete(key)`	• Writes bypass the cache entirely • prevents cache pollution from write-once data but causes read misses after writes.

Table 1: Core Caching Patterns

Pattern	Example	Description
Cache-Aside (Lazy Loading)	`data = cache.get(key)` `if data is None:` `data = db.query(key)` `cache.set(key, data, ttl=3600)`	• Application checks cache first on read • on miss, fetches from DB and populates cache • most common pattern providing explicit application control.
Read-Through	`data = cache.get(key)`	• Cache automatically loads from DB on miss using registered loader • abstracts cache logic from application code.
Write-Through	`cache.set(key, data)` `db.write(key, data)`	• Writes update both cache and DB synchronously • ensures strong consistency but adds write latency.
Write-Behind (Write-Back)	`cache.set(key, data)` `queue.push(db_write_task)`	• Writes update cache immediately and DB asynchronously after delay • optimizes write performance with eventual consistency.
Write-Around	`db.write(key, data)` `cache.delete(key)`	• Writes bypass the cache entirely • prevents cache pollution from write-once data but causes read misses after writes.