Microservice architectures are the ‘new normal’. Building small, self-contained, ready to run applications can bring great flexibility and added resilience to your code. Spring Boot’s many purpose-built features make it easy to build and run your microservices in production at scale. And don’t forget, no microservice architecture is complete without Spring Cloud ‒ easing administration and boosting your fault-tolerance.
What are microservices?
Microservices are a modern approach to software whereby application code is delivered in small, manageable pieces, independent of others.
Why build microservices?
Their small scale and relative isolation can lead to many additional benefits, such as easier maintenance, improved productivity, greater fault tolerance, better business alignment, and more.
Microservices with Spring Boot
With Spring Boot, your microservices can start small and iterate fast. That’s why it has become the de facto standard for Java™ microservices. Quickstart your project with Spring Initializr and then package as a JAR. With Spring Boot’s embedded server model, you’re ready to go in minutes.
Microservice resilience with Spring Cloud
The distributed nature of microservices brings challenges. Spring helps you mitigate these. With several ready-to-run cloud patterns, Spring Cloud can help with service discovery, load-balancing, circuit-breaking, distributed tracing, and monitoring. It can even act as an API gateway.
Build streaming data microservices with Spring Cloud Stream
Spring Cloud Stream makes it easy to consume and produce events, no matter which messaging platform you choose. Spring Cloud Stream connects your microservices with real-time messaging in just a few lines of code, to help you build highly scalable, event-driven systems.
Spring Boot’s optional instrumentation framework, Micrometer, sends metrics straight to Prometheus, Atlas, and more to provide valuable insights. With Micrometer Tracing, you can ship your spans to different backends (e.g. OpenZipkin or Wavefront) so that you can follow along with what’s happening in real-time.
The small, stateless nature of microservices makes them ideal for horizontal scaling. Platforms like TAS and PKS can provide scalable infrastructure to match, with and greatly reduce your administrative overhead. Using cloud connectors, you can also consume multiple backend services with ease.
The order in which these components should typically be used or implemented while working with microservices, along with the rationale for their placement:
Order
Component
Description
Use Case
1
GitHub
Stores configuration for distributed systems.
Centralized and versioned configuration management.
2
Eureka
Service registry for microservices discovery.
Enables dynamic discovery of microservices.
3
Ribbon
Client-side load balancer for service requests.
Distributes requests across multiple service instances.
4
Zuul
API Gateway for routing and pre/post filters.
Handles API routing, monitoring, and security.
5
Feign
Declarative REST client for inter-service calls.
Simplifies REST API calls between microservices.
6
OAuth2
Secure authorization framework for servers.
Provides secure access control for APIs and users.
7
Hystrix
Provides circuit breaker pattern for resilience.
Prevents cascading failures during service downtime.
8
Kafka
Distributed message broker for event streaming.
Ensures reliable and scalable message communication.
9
Camel
Integrates and routes data between services.
Manages data flow in complex service ecosystems.
10
Actuator
Exposes production-ready monitoring endpoints.
Provides insights into service health and metrics.
11
Zipkin + Sleuth
Distributed tracing and logging for microservices.
Tracks service calls for debugging and monitoring.
12
Admin (Server/Client)
UI for real-time service monitoring and metrics.
Visualizes health and metrics of running services.
13
PCF, Docker
Platforms for cloud-based app deployment and scaling.
Simplifies app deployment and scaling in the cloud.
Rationale:
GitHub: Configuration must be established before starting services.
Eureka: Services need to register and discover one another.
Ribbon: Load balancing is critical for handling requests efficiently.
Zuul: Gateway ensures controlled access and routing to microservices.
Feign: Inter-service communication simplifies once routing and discovery are in place.
OAuth2: Security layers are added to protect services and APIs.
Hystrix: Resilience and fault tolerance ensure the system's stability.
Kafka: Asynchronous communication is integrated next for scalability.
Camel: Complex workflows and data integration are added later.
Actuator: Health monitoring becomes essential in production environments.
Principle: A microservices architecture is built around the concept of small, independent services that can be deployed, scaled, and managed separately. Explanation: Each microservice should have a single, well-defined responsibility and operate independently. These services should be loosely coupled, meaning changes to one service should have minimal or no impact on others. This allows for better maintainability, flexibility, and scalability as each service can evolve independently based on its own requirements without affecting the entire system.
2. Single Responsibility Principle (SRP)
Principle: A service should only have one reason to change. Explanation: Each microservice should focus on a single business capability or domain, encapsulating all related logic, data, and processes. This makes services simpler, easier to test, and maintain. For example, a payment service should only handle payment transactions and related logic, not customer management or order processing.
3. Decentralized Data Management
Principle: Each service should manage its own data store, and should not directly access the data of another service. Explanation: Microservices should have their own database or storage system to ensure data consistency and prevent tight coupling between services. This enables each service to operate independently and reduces the risk of system-wide failures. Data replication or eventual consistency patterns can be used to handle cross-service data sharing.
4. API First Development
Principle: Services should be designed based on the API contracts first, ensuring clear communication between services. Explanation: Before writing implementation code, design the API that each service will expose. This approach helps in defining clear and agreed-upon interfaces for communication between services. It makes integration easier, facilitates service discovery, and promotes faster iteration of each microservice. API specifications like OpenAPI (Swagger) can be used to define these contracts.
5. Loose Coupling and High Cohesion
Principle: Microservices should be loosely coupled (minimal interdependencies) but highly cohesive (strong internal relationships). Explanation: Loose coupling ensures that changes in one service do not have a significant impact on others. High cohesion means that the elements within a service should be closely related and perform a well-defined task. Together, this principle improves system resilience, maintainability, and scalability. A service should be self-contained with as few dependencies on others as possible.
6. Building for Failure (Resilience)
Principle: Design microservices to be resilient and handle failures gracefully. Explanation: Since microservices often communicate over the network, there’s always a risk of failures due to timeouts, service crashes, or network issues. To handle this, services should be built with failure tolerance in mind. Techniques like retries, circuit breakers, fallbacks, and timeouts are implemented to ensure the system remains responsive and doesn’t fail completely if one service is unavailable. Tools like Netflix’s Hystrix or resilience libraries in Spring Boot can help implement this principle.
These principles help ensure that the microservices architecture is scalable, maintainable, and adaptable to changing business needs.
9 min read
Nov 18, 2024
By Nitesh Synergy
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