What Is Distributed Architecture and How Does It Evolve?

What Is Distributed Architecture

A distributed system is a software system built on a network.

Cohesion : each database node is highly autonomous with its own DBMS.

Transparency : each node appears transparent to the user, who cannot tell whether it is local or remote.

In a distributed data system, users perceive the data as non‑distributed; they do not need to know about partitions, replicas, locations, or where transactions execute.

Simply put, a group of independent computers presents a unified whole to the user, similar to a single MySQL instance.

The system provides services as a whole, while internal collaboration remains transparent to the user, just like using a single MySQL.

Example: the distributed MySQL middleware MyCat handles high concurrency and large data volumes.

Applications of Distributed Architecture

1. Distributed File Systems : Hadoop HDFS, Google GFS, Taobao TFS, etc.

2. Distributed Caching Systems : Memcached, HBase, MongoDB, etc.

3. Distributed Databases : MySQL, MariaDB, PostgreSQL, etc.

4. Distributed Web Services

5. Distributed Computing

Example with MyCat: it is widely used in e‑commerce for handling billions of records per day, such as China Mobile billing and IoT projects, providing real‑time query interfaces.

Studying MyCat deepens understanding of distributed architecture, ZooKeeper consistency service, HAProxy/keepalived high availability, clustering, load balancing, and related concepts.

Resource Recommendations

Large‑Scale Distributed Website Architecture Design and Practice

Core Principles and Case Studies of Large‑Scale Website Architecture

Large‑Scale Website Systems and Java Middleware Practice

Distributed Java Applications: Fundamentals and Practice

Evolution of Distributed Architecture

1. Initial Stage

All resources (applications, databases, files) reside on a single server.

2. Separation of Application, Data, and File Services

As traffic grows, a second web server is added to share load.

3. Caching to Improve Performance

80% of accesses target 20% of data; local and remote distributed caches reduce database pressure.

4. Application Server Clustering

Multiple servers provide services via load balancing, alleviating single‑server limits.

5. Database Read‑Write Separation

Read‑write separation mitigates write‑heavy contention.

6. Reverse Proxy and CDN Acceleration

CDN and reverse proxy cache content to speed up access and reduce backend load.

7. Distributed File Systems and Distributed Databases

When data volume grows, both file systems and databases are distributed.

8. NoSQL and Search Engines

Introduce NoSQL databases and search engines to handle complex storage and retrieval needs.

9. Business Splitting

Vertical splitting creates independent small applications; horizontal splitting extracts reusable services for independent deployment.

10. Distributed Services

Common modules are extracted and deployed on distributed servers for other applications to call.

Problems Faced by Distributed Service Applications

Managing an increasing number of service URLs and hardware load balancer pressure.

Complex inter‑service dependencies and unclear startup order.

Scaling service capacity and determining when to add machines.

Rising communication cost and difficulty locating responsible owners.

Ensuring service quality for multiple business consumers.

Unexpected failures (e.g., cache errors causing memory overflow) and the need for graceful degradation.