Database caching is a critical performance optimization technique that stores frequently accessed data in high-speed memory to reduce database query latency and prevent overload. Caches sit between the application and persistent storage, dramatically improving response times by serving data from RAM instead of disk. The primary challenge in caching is maintaining data consistency while balancing speed, durability, and scalability — choosing the wrong invalidation strategy or eviction policy can lead to stale data, cache stampedes, or database overwhelm when the cache fails.
What This Cheat Sheet Covers
This topic spans 9 focused tables and 53 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 decisions about how data flows between your application, the cache, and the database — who reads on a miss, and whether writes hit one store or both. Cache-aside and read-through govern the read path, while the write-through/write-behind/write-around trio trade off durability against write speed, and refresh-ahead keeps hot keys warm before they expire. Get this layer right and almost everything else is tuning.
| Pattern | Example | Description |
|---|---|---|
data = cache.get(key)if data is None: data = db.query(key) cache.set(key, data) | • Application checks cache first, loads from database on miss, then updates cache • most common pattern with full application control over cache logic | |
data = cache.get(key)# Cache automatically# fetches from DB | • Cache layer transparently fetches from database on miss and returns data • simplifies application code by abstracting cache management into the cache provider | |
cache.set(key, data)db.write(key, data)# Both synchronous | • Writes to cache and database simultaneously • ensures strong consistency but adds write latency since both operations must complete |