Mastering Cache: Concepts, Types, and Best Practices for High‑Performance Systems

Traditional relational databases like MySQL cannot handle all business scenarios such as e‑commerce flash‑sale or app homepage traffic spikes; caching solves this by moving hot data to memory.

1. What is Cache?

Cache stores frequently accessed hot data in memory so subsequent reads bypass the database, reducing load and preventing crashes under high concurrency.

2. Why Use Cache (Why Redis?)

Cache addresses two aspects: high‑performance and high‑concurrency requirements.

1) High‑Performance

On first access, data is fetched from the database (disk), which is slow; after storing it in cache, subsequent accesses retrieve it from memory, which is fast.

2) High‑Concurrency

Cache read/write throughput far exceeds that of databases; for example, Redis can read 110,000 ops/s and write 81,000 ops/s, allowing the system to serve many more concurrent requests.

3. Cache Classifications

Caches are generally divided into three categories: local cache, distributed cache, and multi‑level cache.

1) Local Cache

Concept

Local cache resides in the same process memory as the application, with reads and writes performed within the process.

Advantages

Fast read speed, no network overhead, but limited storage capacity.

Disadvantages

Data inconsistency across multiple application instances.

Data loss on application restart.

Implementation typically uses key‑value structures such as HashMap or ConcurrentHashMap, or libraries like Guava, Ehcache, and Caffeine.

2) Distributed Cache

Concept

Distributed cache runs as an independent service process, accessed over the network, suitable for clustered deployments.

Advantages

Supports large data volumes.

Data persists across application restarts.

Centralized storage ensures consistency across nodes.

Read‑write separation and high availability via replication.

Disadvantages

Network latency makes read/write slower than local cache.

Typical implementations include Memcached and Redis.

3) Multi‑Level Cache

Combines local (L1) and distributed (L2) caches: the system first checks L1, then L2, and finally the database, updating caches along the way.

Implementation can use Guava or Caffeine for L1 and Redis for L2; consistency across nodes can be achieved with Redis pub/sub.