Mastering High-Concurrency Architecture: Load Balancing, Microservices, Caching & More

Load Balancing

Load balancing distributes network requests across multiple servers to avoid overload on any single machine and improve resource utilization.

Common 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 + layer‑4 + layer‑7 load balancing for large systems.

Distributed Microservices

Instead of a monolithic system, the "divide‑and‑conquer" approach splits functionality into independent microservices, each with its own codebase, database, and lightweight communication (HTTP or RPC).

Key characteristics:

Business‑oriented, small, single‑purpose services that are easy to maintain.

Each service may have its own database.

Communication is fault‑tolerant via HTTP or private protocols.

Service governance enables loose coupling, dynamic addition/removal.

Each service can be clustered with its own load balancing.

Comprehensive security, tracing, and real‑time logging are required.

Common frameworks: Spring Cloud, Dubbo, Kubernetes, gRPC, Thrift.

Caching Mechanisms

Caching dramatically improves performance; a single Memcached server can handle >50,000 TPS, while Redis can exceed 100,000 QPS.

Local cache resides in the application server and is short‑lived to avoid stale data, whereas distributed cache forms a cluster, offers horizontal scaling, and adds only ~1 ms network overhead.

Typical update strategies:

Cache aside

: update DB then delete cache, often with TTL.

Read/Write through

: a cache provider abstracts read/write operations.

Write behind

: asynchronous batch writes to DB improve write throughput.

Distributed Relational Databases

MySQL uses B‑tree indexes; tables with tens of millions of rows may require sharding.

Sharding can be vertical (splitting columns) or horizontal (splitting rows). Vertical sharding separates hot and cold columns, large fields, and tightly related columns. Horizontal sharding keeps the schema unchanged while distributing rows across multiple tables or databases.

Key techniques:

SQL composition based on sharding keys.

Database routing for multi‑database setups.

Result merging for queries without explicit sharding keys.

Implementation approaches:

Proxy mode (e.g., a dedicated proxy service handling routing, SQL rewriting, and result merging).

Client mode (e.g.,

sharding‑jdbc

library embedded in the application).

Distributed Message Queues

Asynchronous processing decouples producers, the message broker, and consumers, improving scalability.

Common middleware: ActiveMQ, RabbitMQ, ZeroMQ, Kafka, MetaQ, RocketMQ, Pulsar.

Typical use cases:

Asynchronous tasks (e.g., sending SMS after user registration).

Peak‑shaving (e.g., handling flash‑sale traffic).

Application decoupling (e.g., async inventory deduction).

Message‑based communication (e.g., chat systems).

Content Delivery Network (CDN)

CDN adds an edge layer that mirrors and caches static assets (CSS, JS, images) close to users, reducing latency and bandwidth usage.

Core components: Mirror + Cache + Global Server Load Balancing (GSLB).

Key benefits: local cache acceleration, mirroring, remote acceleration, bandwidth optimization, and DDoS resistance.

Typical scenarios: website/app acceleration, video/audio streaming, large file distribution, live video, mobile app acceleration.

Other Distributed Technologies

Complementary technologies include distributed file systems, big data platforms, NoSQL, and NewSQL databases, which together enrich the ecosystem for high‑concurrency systems.