Why Caching Is the Secret Weapon for High‑Performance Systems

What is Cache?

Cache is a collection of data duplicating original values stored elsewhere on a computer, usually for easier access. In simple terms, it is a copy of data kept in memory to enable fast subsequent reads.

Why Use Cache?

Caching solves performance bottlenecks by reducing response time, latency, and network traffic, thereby improving user experience and overall system throughput. It is especially valuable in read‑heavy, write‑light scenarios where data freshness requirements are low.

Common Performance Metrics

System response time (average and max)

Latency (client rendering time and server processing time)

Throughput (requests processed per unit time)

Concurrent users

Resource utilization

Advantages of Cache

Proper cache usage dramatically enhances user experience, increases throughput, and reduces load on backend services, embodying the "space‑for‑time" optimization principle.

Where Does Cache Exist?

Cache Classification

Caches can be categorized by their position in the request chain, deployment architecture, and memory region.

Location: client cache, network cache, server cache

Deployment: single‑node cache, cache cluster, distributed cache

Memory region: local/process cache, inter‑process cache, remote cache

Client Cache

Client‑side caches are closest to the user and include page cache, browser cache (leveraging ETag, Expires, Cache‑Control), and app cache (in‑memory or local database).

Network Cache

Network caches sit between client and server, such as web proxy caches (forward proxies) and edge caches (CDNs) that store static resources near users.

Server Cache

Server‑side caches are a primary performance‑optimization point for backend developers. They include database query caches (e.g., MySQL query cache), cache frameworks (e.g., Ehcache, Guava), and application‑level caches (e.g., Redis, MongoDB).

When to Use Cache?

Cache should be introduced only when a system faces performance bottlenecks, especially in high‑concurrency, read‑heavy workloads. Misusing cache can increase complexity and maintenance cost.

How to Use Cache Correctly?

Consider language runtime effects (e.g., Java GC), aim for high hit rates, and carefully configure expiration, consistency, and eviction policies.

Key Design Considerations

Business traffic volume and application scale

Choice of cache technology (Redis, Memcached, Tair, etc.)

Accurate assessment of cache impact factors (value size, QPS, expiration, hit rate, read/write strategy, key distribution, consistency)

High‑availability architecture for distributed caches

Comprehensive monitoring to observe cache health and detect hot spots

Placing cache as close to the user as possible (multi‑level cache design)

References

《深入分布式缓存》

Cache penetration, breakdown, and avalanche discussions

Cache update strategies

Data final consistency approaches

Cache design articles and tutorials