Microservices Architecture

常见问题

What are the main differences between microservices architecture and monolithic architecture?

A monolithic architecture builds and deploys the entire application as a single unit, with all functional modules sharing the same process and database. A microservices architecture, on the other hand, splits the application into multiple independent services, each with its own database and process, communicating via APIs. Key differences include: deployment independence (microservices can be deployed independently, while a monolith requires full deployment), scaling granularity (microservices allow scaling individual services on demand, whereas a monolith scales as a whole), technology stack (microservices enable heterogeneous technologies, while a monolith typically uses a unified stack), fault impact (microservices isolate failures, whereas a failure in a monolith may render the entire system unavailable), and team organization (microservices suit autonomous small teams, while a monolith often requires large team collaboration).

What types of projects are suitable for microservices architecture?

Microservices architecture is particularly suitable for large, complex business systems that require continuous iteration and rapid delivery, such as e-commerce platforms, financial trading systems, education management platforms, and IoT backends. It delivers maximum value when the project is large-scale, the team is sizable, business modules have low coupling, and independent scaling is needed. For small projects or prototype validation stages, a monolithic architecture may be simpler and more efficient. Mangxu Software adopts microservices in its education informatization products like the comprehensive assessment system precisely because these systems have numerous business modules, high user volumes, and require frequent feature updates.

What are the main challenges in implementing a microservices architecture?

Key challenges include: 1) Complexity of distributed systems, such as service discovery, load balancing, and configuration management; 2) Data consistency, where distributed transactions are far more complex than monolithic database transactions; 3) Communication latency and network failure handling between services; 4) Monitoring and observability, requiring aggregated logs, metrics, and distributed tracing; 5) Increased difficulty in testing and deployment, necessitating robust CI/CD pipelines and contract testing; 6) Team organization needs to adapt, typically guided by Conway's Law for service decomposition. Overcoming these challenges requires a mature DevOps culture, automated toolchains, and architectural governance capabilities.

How does microservices architecture ensure data consistency?

Microservices architecture typically adopts an eventual consistency model rather than strong consistency. Common strategies include: 1) The Saga pattern, which breaks a distributed transaction into a series of local transactions and uses compensating actions to handle failures; 2) Event-driven architecture, where services asynchronously synchronize data through publish/subscribe events; 3) Two-phase commit (2PC) is rarely used in microservices due to high performance overhead and blocking. In practice, the appropriate consistency strategy should be chosen based on business scenarios, such as using Saga in order systems and event-driven approaches for user information synchronization. Mangxu Software combines event-driven methods with local transactions in its comprehensive assessment system to balance consistency and performance.

What specific practices does Mangxu Software have in microservices architecture?

Mangxu Software deeply practices microservices architecture in products such as the comprehensive assessment system and the student management integrated information system. Specific practices include: 1) Service decomposition based on business domains, such as user service, assessment service, and grade service; 2) Using containerization technologies (Docker/Kubernetes) for automated deployment and elastic scaling of services; 3) Deploying an API gateway to centrally manage external requests, enabling authentication, rate limiting, and routing; 4) Adopting message queues (e.g., RabbitMQ) for asynchronous communication between services to reduce coupling; 5) Establishing centralized logging and monitoring systems to ensure observability; 6) Implementing continuous integration and continuous deployment through CI/CD pipelines, supporting multiple releases per week. These practices significantly enhance system availability and iteration efficiency.