High‑Performance Architecture: Caching, Single‑Server Models, and Cluster Load Balancing

High‑Performance Caching Architecture

In read‑heavy scenarios such as online forums or social media, using MySQL alone cannot meet real‑time requirements; caching stores frequently accessed data in memory to reduce storage load, but introduces complexities like cache penetration, avalanche, and hotspots, each requiring specific mitigation strategies.

Cache Penetration

Occurs when a request misses the cache and repeatedly queries the storage, e.g., for non‑existent data or when cache generation is expensive. Solutions include caching default values for missing keys and pre‑computing expensive pages.

Cache Avalanche

When many cached items expire simultaneously, the sudden surge of storage queries can overload the database. Common remedies are update‑lock mechanisms (often using distributed locks such as ZooKeeper) and background refresh strategies.

Cache Hotspot

Highly popular keys can overload a single cache node; replicating multiple cache copies with staggered expiration times distributes the load.

Single‑Server High‑Performance Model

Performance depends on I/O models (blocking, non‑blocking, synchronous, asynchronous) and process/thread models. Models discussed include:

PPC (Process Per Connection)

Each connection spawns a new process; simple but costly in CPU and memory, limited to a few hundred concurrent connections.

Prefork

Processes are pre‑created and accept connections, reducing fork overhead but still suffering from “thundering herd” and inter‑process communication complexities.

TPC (Thread Per Connection)

Each connection gets a thread; lighter than processes but introduces thread‑synchronization and potential deadlocks.

Prethread

Threads are pre‑created and reuse a shared listening socket, improving latency and scalability.

Reactor

Uses non‑blocking I/O with an event loop (select/epoll/kqueue) and dispatches events to handlers, often combined with a thread pool for processing.

Proactor

Leverages asynchronous I/O where the OS completes operations and notifies the application, offering higher throughput on platforms that support true async I/O (e.g., Windows IOCP).

High‑Performance Cluster Architecture

When a single server reaches its performance ceiling, clustering with load balancers distributes traffic. Load‑balancing techniques include DNS‑based geographic routing, hardware appliances (F5, A10), and software solutions (Nginx, LVS). Algorithms such as round‑robin, weighted round‑robin, least‑load, performance‑based, and hash‑based routing are explained with their trade‑offs.

Typical Load‑Balancing Stack

Combines DNS for region selection, hardware balancers for cluster‑level distribution, and software balancers for per‑machine routing.

Conclusion

The article summarizes key cache design pitfalls, single‑server concurrency models, and cluster load‑balancing strategies for building high‑performance backend systems.