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 Patterns
These are the foundational read and write strategies every caching system is built on — how data gets into the cache and how writes flow through to the backing store. The big decision is who owns the logic (your application or the cache itself) and whether writes are synchronous or deferred, which is the trade-off between strong consistency and write speed.
| Pattern | Example | Description |
|---|---|---|
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. | |
data = cache.get(key) | • Cache automatically loads from DB on miss using registered loader • abstracts cache logic from application code. | |
cache.set(key, data)db.write(key, data) | • Writes update both cache and DB synchronously • ensures strong consistency but adds write latency. | |
cache.set(key, data)queue.push(db_write_task) | • Writes update cache immediately and DB asynchronously after delay • optimizes write performance with eventual consistency. | |
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. |