What Are Microservices?
When building a microservices application, Java is a great choice, due to its mature ecosystem with lightweight frameworks and robust libraries. Java can help you simplify the process of breaking down more complex, monolithic systems into independent, high-performance services. This article explains microservices, microservices cloud architecture, and the specifics of building a microservices application in Java.
Microservices is a style of building applications with small, independent, and loosely coupled services. Unlike the traditional monolithic architecture, microservices break down the application into smaller and more manageable parts. This way, you can build, deploy, update, scale, and remove an individual service without having to shut down operations on your application.
For example, you could have a user-authentication service that manages logins and verifies what users have permission to access within the application. You could have a product catalog service that manages the inventory in your catalog along with all the data about each product. Or, you could have an order-processing service that manages each step when a user places an order.
What Is Microservices Cloud Architecture?
Microservices cloud architecture is when you use the microservices design pattern to build your application with independent services. You host, manage, and optimize your services on a cloud-computing platform, which includes AWS, Microsoft Azure, and Google Cloud. The distinction of microservices cloud architecture is to integrate the core principles of microservices with cloud-native services to gain increased elasticity, scalability, operational efficiency, global distribution, and resilience:
- Elasticity: Cloud platforms provide on-demand infrastructure, which allows you to spin up and shut down services based on your current traffic load. The result is that you only pay for the resources that you actually need and use.
- Independent scalability: You can use cloud orchestration tools like Kubernetes to scale only the services that need capacity. For example, you could scale your payment service during a promotional event.
- Developer productivity: Managed services allow developers to focus on writing business logic and creating new features, rather than spending time on patching and managing their own software infrastructure.
- Global distribution: Deploy your application across availability zones and regions in major cloud providers. This increases your application’s resilience and decreases your latency within regions.
- Resilience: If you deploy your application’s components across availability zones, then it prevents a single point of failure. For example, if one datacenter has a power outage, the system can instantly fail over and continue running your application.
Microservices in Java
Microservices in Java refers to the development process of using the microservices design pattern when building applications with the Java programming language, including the Java ecosystem of frameworks, tools, and platforms. You decouple your architectural components into independent microservices that communicate using your network.
Java is a great language to use when building a microservices application because of its robust tooling and development ecosystem. You’ll find lightweight Java frameworks, like Spring Boot, which allows you to quickly create standalone, executable Java files. Other lightweight frameworks, such as Quarkus and Micronaut, help you build high-performance Java applications in containerized environments.
When developing a microservices application, the Java Virtual Machine (JVM) you select will determine your application’s stability. A JVM like Azul Prime offers high performance, automatic memory management through Garbage Collection, and platform independence.
Java provides a rich ecosystem with robust tools, including observability libraries like Micrometer for metrics, Spring Security to protect API endpoints, and tight integration with Docker and Kubernetes.
What Are the Unique Considerations of Java-Based Microservices?
Microservices often require an overhead of resource consumption. The JVM can consume a lot of resources, which can increase substantially if you run dozens of microservices. The overall memory consumption for the entire fleet of microservices can become high, as the baseline memory for the JVM and the framework metadata is multiplied across each service. This can result in higher cloud costs, due to running larger instances.
Another potential problem is slow startup and warmup times. A service could take several seconds to boot up, due to scanning, dependencies, and the compilation process.
Managing distributed data with Object Relational Mapping (ORM) might cause challenges in a distributed microservices environment. ORMs map your Java objects to a relational database schema. If multiple services share a database (which you should avoid in microservices whenever possible), then changes to the data model in one service create cascading change requirements across all the other services. Similarly, ORM often relies on caches, which introduces statefulness—something you want to avoid for cloud-native services to get stateless benefits like scalability and resiliency.
You also might find challenges with the complexity of observability and monitoring in the Java ecosystem. First, you should centralize your logging, which can be a challenge if you’re getting logs from dozens of separate Java services. You’ll need a consistent log format and structure (likely using JSON) across all your services. You’ll also likely need to implement distributed tracing to assign a unique correlation ID to track each request.
You also might need to address the complexity of resilience patterns in Java microservices. When communicating over a network, you might run into network-related failures, such as latency and timeouts. Circuit breakers ensure that you stop calling a failing service and prevent cascading failures. Timeouts and retries help you handle slow or transient failures.
How Do You Create Microservices in Java?
First, you’ll need to define the boundaries of your services. You can use Domain-Driven Design (DDD) to identify the logical units of your business, called bounded contexts. Each bounded context becomes a single microservice. For example, you might have a user authentication service to handle login and security, and then a separate customer profile service to handle personal details.
Second, select a lightweight framework that specializes in helping you build small, fast-acting services. Examples include Spring Boot, Micronaut, and Quarkus. You’ll want your microservices to be lightweight so that they start quickly and use minimal memory, avoiding heavy enterprise application servers.
Third, implement the service by defining what APIs your service will expose. For example, you might use REST via JSON for high-performance communication. Keep your service small and only include the libraries necessary for its primary business functions.
Fourth, build resilience and communication into your solution. Because microservices communicate over a network, you’ll want to build your Java code to handle any introduced failure points.
Fifth, use containerization and orchestration to package and deploy your Java microservices. Package your executable JAR file into a Docker image to keep the image size as small as possible. Kubernetes can help you manage deployment, scaling, and networking of all your Java services.
Finally, integrate observability and monitoring into your system. A distributed tracing library can track a request’s full journey by using a single correlation ID. A logging framework can help you output logs and send them to a centralized logging system. Collect performance metrics (such as CPU and memory usage and latency) and export them to a monitoring system.
Azul Prime: Addressing the Challenges of Java-Based Microservices
Azul Prime is engineered to address the unique challenges and considerations of Java microservices—introduced from the operational overhead, startup latency, and memory inefficiencies of the traditional JVM.
First, Prime resolves resource and startup overhead. The slow startup and warmup times of applications using standard JVMs can lead to higher cloud resource costs and difficulty scaling your services. The Azul Cloud Native Compiler (CNC) offloads JIT compilation from your local microservice JVM to a centralized service. Azul’s ReadyNow! Technology records the optimized code state after a service runs and instantly loads that profile upon future restarts or scale-out events, eliminating the Java “warmup” period and providing immediate peak performance.
Second, Prime resolves latency spikes and inconsistent performance. Unpredictable performance and latency spikes are often caused by garbage collection (GC) pauses, which can violate strict latency requirements and service-level agreements (SLAs). Azul’s C4 (Continuously Concurrent Compacting Collector) Garbage Collector reduces GC pauses regardless of your heap size, resulting in predictably low latency and much more stable response times.
Third, Prime’s GC Log Analyzer provides deeper information, which improves your debugging process and insights.
To learn more about how Prime can provide you with a high-performance JVM, see Azul Prime: High Performance JVM.
