Key Techniques for Designing High‑Concurrency Systems

When designing a high‑concurrency system, developers must consider a wide range of techniques that improve performance, reliability, and scalability.

1. Page static‑generation reduces server load by rendering pages to static HTML using template engines such as Freemarker or Velocity , then deploying the generated files via shell scripts.

2. CDN acceleration brings static assets (images, CSS, JS) closer to users by replicating them across geographically distributed edge nodes, thereby lowering latency and network congestion.

3. Caching is essential; two main types are in‑process (second‑level) caches and distributed caches like Redis or Memcached . While second‑level caches are faster, distributed caches avoid data inconsistency across multiple server instances.

4. Asynchronous processing separates core business logic (synchronous) from non‑critical tasks (notifications, logging) that can be handled asynchronously using thread pools or message queues (MQ).

4.1 Thread‑pool example: submit notification and logging jobs to dedicated thread pools, improving request latency. However, thread‑pool failures may cause data loss if the server restarts.

4.2 MQ example: push tasks to an MQ broker; consumers process them later, providing reliable retry mechanisms.

5. Multi‑threaded processing speeds up heavy workloads such as bulk message consumption by configuring core and maximum thread counts and queue sizes.

6. Sharding (分库分表) splits data across multiple databases or tables to overcome connection limits, I/O bottlenecks, and large‑table query latency. Routing can be based on modulo, range, or consistent‑hash algorithms, with vertical (business) and horizontal (data) split strategies.

7. Pooling technologies (e.g., DB connection pools like Druid, Hikari) reuse expensive resources, reducing creation overhead.

8. Read‑write separation directs writes to a master database and reads to one or more slaves, improving throughput and isolating write load.

9. Index optimization balances query speed against insert overhead; techniques include creating composite indexes, removing unused indexes, using EXPLAIN , and forcing index usage when necessary.

10. Batch processing reduces remote calls by grouping queries. Example Java code:

public List
queryUser(List
searchList) {
    if (CollectionUtils.isEmpty(searchList)) {
        return Collections.emptyList();
    }
    List
ids = searchList.stream().map(User::getId).collect(Collectors.toList());
    return userMapper.getUserByIds(ids);
}

11. Clustering ensures high availability by deploying multiple application, database, or middleware nodes (e.g., Redis master‑slave clusters) and handling node failures gracefully.

12. Load balancing distributes traffic across servers using algorithms such as round‑robin, weighted, IP‑hash, least‑connections, or shortest‑response‑time, with tools like Nginx, LVS, HAProxy, Ribbon, or Spring Cloud LoadBalancer.

13. Rate limiting protects the system from overload via per‑user, per‑IP, per‑endpoint limits, or captchas, implemented with Nginx or Redis.

14. Service degradation disables non‑essential features (e.g., comments during a flash‑sale) via feature flags in a configuration center (Apollo) to preserve core functionality under stress.

15. Failover automatically routes traffic away from unhealthy instances using health checks, timeout settings, and circuit‑breaker tools like Hystrix or Sentinel.

16. Multi‑active deployment replicates services across data centers, using DNS‑based routing and data‑sync mechanisms to maintain availability during site failures.

17. Stress testing estimates required capacity (e.g., QPS) using tools like JMeter, Locust, or Alibaba PTS, and validates system behavior under peak loads.

18. Monitoring tracks metrics such as response times, CPU/memory usage, DB connection pools, and slow queries with Prometheus, enabling alerts and root‑cause analysis.

Additional security considerations include handling IP‑fluctuating attacks, cache‑snowball effects, DDoS mitigation, and dynamic auto‑scaling to cope with sudden traffic spikes.