Caching

Explore how high-speed data storage improves performance, and the challenges of invalidation and eviction.

Cache Architect

Can you achieve a 99% hit rate?

CLIENTS

REDIS CACHE

EMPTY SLOT

PostgreSQL

Data Explorer

Click a key to simulate a client read request.

Cache Config

CACHE SIZE (SLOTS)4

DATA TTL (SECONDS)30s

HIT RATIO

0.0%

AVG LATENCY

0ms

System Design Insight

Caching leverages the temporal locality principle: data accessed once is likely to be accessed again soon. By storing "hot" data in RAM, we bypass slow Disk I/O, reducing DB load by up to 90%. However, caching introduces the Consistency Challenge: ensuring the cache doesn't serve "stale" (old) values after a DB update.

Detailed explanation about Caching goes here. This section would explain the algorithms, trade-offs, and real-world use cases.