Understanding Cache: Concepts, Types, and Implementation in Backend Development

Traditional relational databases such as MySQL struggle with high‑traffic scenarios like e‑commerce flash sales or app home‑page spikes, leading to potential database failures; caching technology addresses these issues by storing hot data in memory.

1. Cache Concept (What is a Cache)

A cache stores frequently accessed hot data in memory, allowing subsequent requests to retrieve data quickly without hitting the database, thereby reducing load and preventing database crashes under high concurrency.

2. Why Use a Cache (Why Redis)

Two main reasons drive cache adoption: high performance and high concurrency. On the first access, data must be fetched from the slower disk‑based database; after storing it in the cache, subsequent accesses retrieve it from fast memory. In high‑concurrency scenarios, caches like Redis can handle far more read/write operations (e.g., 110,000 reads/s, 81,000 writes/s) than a database, dramatically increasing overall throughput.

3. Cache Classifications

Caches are generally divided into three categories: local cache, distributed cache, and multi‑level cache. The choice depends on whether the cache resides in the same process as the application.

1) Local Cache

Concept: Stored in the same process memory as the application, with read/write operations occurring locally.

Advantages: Very fast read speed because no network overhead; however, it cannot store large data volumes.

Disadvantages: Data inconsistency across multiple application instances and loss of cached data on application restart.

In clustered deployments, synchronizing updates across nodes is complex; Redis pub/sub can be used for consistency.

Cache data disappears when the application process restarts.

Implementation: Typically key‑value structures using Java's HashMap or ConcurrentHashMap , or third‑party libraries such as Guava, Ehcache, and Caffeine.

2) Distributed Cache

Concept: Deployed as an independent service process, accessed over the network, suitable for clustered application environments.

Advantages:

Supports large data volumes.

Data persists across application restarts.

Centralized storage ensures data consistency among multiple nodes.

Read/write separation and replication provide high performance and high availability.

Disadvantages: Network latency makes read/write performance slower than local cache.

Implementation: Common solutions include Memcached and Redis.

3) Multi‑Level Cache

Combines the strengths of local and distributed caches: local cache acts as the first‑level cache, distributed cache as the second‑level. The request flow is:

Attempt to read from local cache; if hit, return data.

If miss, read from distributed cache; if hit, update local cache and return data.

If still miss, query the database, then update both distributed and local caches before returning the result.

Implementation: Use Guava or Caffeine for the first‑level (local) cache and Redis for the second‑level (distributed) cache.

Note: Local caches are best for data with low update frequency but high read frequency, while distributed caches suit data that updates frequently.

When deploying in a clustered environment, cache consistency becomes challenging; using Redis's publish/subscribe mechanism can help synchronize updates across nodes.