Implementing High-Concurrency Performance Testing and Practical Solutions Based on Server Architecture

Introduction

In operating systems, high concurrency refers to a period during which multiple programs are in the running state on the same processor, although only one program executes at any given instant.

Server Architecture for High Concurrency

A robust server architecture is essential for supporting high concurrency. It should include load balancing (e.g., Nginx, Alibaba Cloud SLB), resource monitoring, distributed deployment, master‑slave clustered databases, NoSQL caches (Redis, MongoDB, Memcached) with clustering, and a CDN for static assets.

Concurrency Testing

High‑concurrency services require performance testing to evaluate the maximum load the architecture can sustain. Third‑party services (e.g., Alibaba Cloud performance testing) or self‑hosted test servers can be used together with tools such as Apache JMeter, Visual Studio Load Test, and Microsoft Web Application Stress Tool.

Practical Solutions

1) General Scheme

For typical daily traffic that is dispersed but may spike during events (e.g., user sign‑in, user center, order queries), the recommended pattern is to read from cache first and fall back to the database only when the cache misses, then populate the cache.

Examples:

User sign‑in: compute a Redis hash key, check the sign‑in record, if missing query the DB, update Redis, and return the result.

User orders: cache only the first page (40 items); subsequent pages are read directly from the DB.

User center: similar cache‑first approach with Redis hash.

When caching shared data, consider using a management backend to refresh the cache or applying DB‑level locks to avoid stampedes.

2) Message Queue

For bursty write‑heavy scenarios such as flash sales or timed red‑packet distribution, a message queue (e.g., Redis list) can decouple user requests from database writes. Users push their participation info into the queue, and a multithreaded consumer processes the queue to grant rewards, preventing DB overload.

Additional use cases include scheduled SMS sending using a sorted set (ZSET) where timestamps determine processing order.

Source: http://server.51cto.com/sOS-567016.htm

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