Caching is a fundamental performance optimization technique in backend systems that stores frequently accessed data in temporary, fast-access storage layers closer to the application. By reducing redundant database queries and expensive computations, caching can cut response times from seconds to milliseconds and reduce database load by 90% or more. The critical challenge lies not just in storing data, but in choosing the right caching pattern, managing invalidation, and preventing common failure modes like cache stampedes and stale data — all while maintaining consistency across distributed systems.
What This Cheat Sheet Covers
This topic spans 13 focused tables and 89 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Core Caching Patterns
The six foundational read/write patterns define how your application coordinates between the cache and the database; nearly every caching bug stems from choosing the wrong pattern for a given access profile.
| Pattern | Example | Description |
|---|---|---|
data = cache.get(key)if not data: data = db.query() cache.set(key, data) | • Application checks cache first on read • on miss, loads from DB and populates cache. Most common pattern — gives full application control over caching logic. | |
data = cache.get(key) | • Cache itself handles DB fetch on miss • application treats cache as primary data source. Centralizes cache logic and simplifies application code. | |
cache.set(key, data)db.write(data) | • Every write goes to both cache and DB synchronously • ensures strong consistency but adds latency to writes. Best for read-heavy workloads needing fresh data. |