Key Techniques for Building High‑Concurrency Systems: Load Balancing, Microservices, Caching, Sharding, Message Queues, and CDN

1. Load Balancing

In the era of distributed systems, optimizing a single machine's memory , CPU , disk and network bandwidth is no longer sufficient; horizontal scaling is required.

Load balancing distributes network requests evenly across multiple machines, preventing overload on any single server.

Common load‑balancing algorithms include random, round‑robin, weighted round‑robin, consistent hashing, least connections, and adaptive algorithms. Typical tools are LVS, Nginx, and HAProxy, often combined as DNS + L4 + L7 layers for large systems.

2. Distributed Microservices

Instead of a monolithic system, the "divide‑and‑conquer" approach splits the application into independent microservices using SOA architecture.

Each microservice is deployed separately and communicates via lightweight protocols such as HTTP or private RPC.

Key characteristics of microservices:

Business‑oriented, small codebase, easy to maintain.

Independent resources (e.g., databases) per service.

Fault‑tolerant communication.

Loose coupling with service governance.

Scalable deployment with built‑in load balancing.

Comprehensive security (authentication, authorization, resource protection).

Traceability and real‑time logging.

Popular microservice frameworks include Spring Cloud, Dubbo, Kubernetes, gRPC, and Thrift.

3. Caching Mechanism

Caching dramatically improves performance; Memcached can achieve >50,000 TPS, while Redis can exceed 100,000 QPS.

Two main cache types:

Local cache (in‑process) – short TTL to avoid stale data.

Distributed cache – cluster‑managed, horizontally scalable, with typical network overhead around 1ms .

Common cache update strategies:

Cache‑aside: update DB then delete cache.

Read/Write‑through: a cache provider abstracts read/write.

Write‑behind: batch write‑back to DB.

4. Distributed Relational Databases

MySQL tables with B‑tree indexes should keep rows under ten million for optimal I/O; when limits are reached, sharding is considered.

Sharding can be vertical (splitting columns) or horizontal (splitting rows). Key techniques include SQL rewriting, database routing, and result merging.

Open‑source sharding solutions fall into two models:

Proxy mode – a middle‑layer handles SQL composition, routing, and merging (supports multiple languages, but adds a potential bottleneck).

Client mode – e.g., sharding-jdbc library embedded in the application (lightweight, but language‑specific).

5. Distributed Message Queues

Asynchronous processing decouples producers and consumers via a message broker, improving scalability and reliability.

Typical roles: producer, message queue, consumer.

Common MQ frameworks: ActiveMQ, RabbitMQ, ZeroMQ, Kafka, MetaQ, RocketMQ, Pulsar.

Typical use cases: async processing, traffic shaping, application decoupling, and real‑time communication.

6. CDN

Content Delivery Networks (CDN) combine mirroring, caching, and global load balancing (GSLB) to serve static assets from edge locations, reducing latency and bandwidth usage.

Local cache acceleration

Mirroring services

Remote acceleration

Bandwidth optimization

Cluster‑level DDoS mitigation

Typical scenarios include website acceleration, media streaming, large file distribution, video live streaming, and mobile app acceleration.

7. Other Complementary Technologies

Additional components such as distributed file systems, big data platforms, NoSQL, and NewSQL databases further enrich the ecosystem for high‑concurrency architectures.