Software development has always involved repetition. Over time, these repetitive processes have been documented, leading to the development of methodologies, specifications, principles, and patterns. From this, popular patterns, such as design patterns or enterprise integration patterns, have emerged.
Design patterns, supported by principles such as SOLID, have remained highly relevant, even in the era of cloud-native and AI technologies. And when it comes to cloud-native development, microservices are the standard. The only problem is that developing microservices is complicated. You’ll face numerous challenges related to service-to-service communication, transactions, events, and security.
This is where microservices design patterns can help. These patterns simplify your development process by offering established and effective practices. Additionally, microservices design patterns offer you new ways to develop and improve your microservices, especially if you’re in the process of migrating from a monolith application.
In this guide, you’ll learn all about microservices design patterns and how they solve some of the most common problems you’ll experience when breaking down monolithic applications into microservices.
Why You Need Microservices Design Patterns
As microservices gained popularity, developers recognized that certain principles, such as those outlined in The Twelve-Factor App, aligned well with the requirements of microservices and addressed issues such as configuration, logs, and networking. Over time, as these recurring problems found common solutions, they evolved into patterns that developers could readily apply, now known as microservices design patterns.
While some microservices design patterns are directly related to microservices, solving problems unique to them, others are more general and applicable to different architectures.
For instance, the strangler pattern is directly associated with microservices because it solves the transformation problem of migrating to microservices. Meanwhile, the service discovery (aka service registry) pattern is not exclusive to microservices because it’s also used in other contexts.
Some other microservices patterns include the following:
- API gateway pattern
- Change data capture (CDC) pattern
- Distributed tracing pattern
- Circuit breaker pattern
You’ll learn more about each of these patterns in the following sections.
Implementing Microservices Design Patterns with Java
Java is a widely adopted programming language known for its strong support of object-oriented programming (OOP). Due to its OOP capabilities, it’s often the language of choice for showcasing and implementing design patterns.
There are two ways to implement a microservices design pattern in Java:
- Customizing the pattern’s implementation
- Using a framework/library that implements the pattern
If you want to use a framework to implement your patterns with Java, frameworks such as Spring Boot and Quarkus make it easy. They offer seamless integration with libraries and frameworks, making it effortless to configure and utilize third-party tools.
These frameworks also provide prebuilt implementations of microservices patterns, allowing you to easily incorporate them into your Java-based applications. With just a single annotation or configuration, you can effortlessly apply microservices patterns.
For instance, if you want to create an API gateway, you can utilize the Zuul library and create a Zuul proxy server as a Spring Boot application. Alternatively, you can leverage the capabilities of OpenTelemetry to trace your messages.
In the following sections, you’ll explore how you can implement microservices patterns in your Java-based microservices applications.
Strangler Pattern
The strangler pattern is a microservices pattern that helps you migrate a monolith application to a microservice application. Implementing the pattern requires an incremental process for developing new microservices while keeping the monolith running.
As previously mentioned, transitioning from a monolithic system to a microservices architecture comes with inherent risks that can negatively impact reliability, security, and performance. For instance, altering the functionality of a service during the transformation may inadvertently lead to incorrect data being returned to customers. Similarly, the process of converting a service into a microservice could unintentionally compromise its security measures, making it less secure than before.
The strangler pattern reduces these risks by placing the older systems behind a facade. It involves implementing the same functionality in the microservices first and then gradually adding new features. This approach enables you to compare the behavior of the microservices with the older systems, providing a clear understanding of how the microservices impact the overall system:
The strangler pattern offers a strategic approach to architectural transformation by mitigating risks associated with large-scale changes all at once. While implementing this pattern requires a lot of attention to routing and network management, it provides a way to implement an architectural transformation in smaller chunks or episodes of change rather than one large update.
Additionally, implementing the strangler pattern isn’t limited to a specific programming language. Regardless of your technology stack, you can leverage frameworks like Spring Boot or Quarkus to develop your microservices.
Service Discovery Pattern
The service discovery pattern, also known as the service registry pattern, is a microservices pattern that provides discovery of the service locations in an environment where all microservices live. A service registry is simply a database of services that the other service accesses to find the address data of the other services:
Ordinarily, clients of a service, whether other services or frontend applications, need to know the address information of the service in order to send requests. This implementation is straightforward when dealing with a small number of clients or services.
However, as the system grows more complex with a larger number of services, identifying and connecting to the appropriate services becomes challenging. By implementing a service registry pattern, microservices can discover and connect to each other as needed.
To successfully transition from a monolithic application to microservices, a robust service discovery mechanism is essential. Whether you opt for a complete transformation or adopt patterns such as the strangler pattern, your microservices need to connect to each other as well as with the existing monolith.
There are all kinds of ways you can implement the service discovery pattern. For instance, you could implement it with a framework such as Eureka or via a cloud platform such as Consul or Amazon Web Service (AWS) Cloud Map.
For instance, if you want to implement the service discovery pattern using Eureka, you could simply start a registry server with the following Java–Spring Boot code snippet:
@SpringBootApplication
@EnableEurekaServer
public class MyEurekaServer {
public static void main(String[] args) {
new SpringApplicationBuilder(MyEurekaServer.class).web(true).run(args);
}
}You could also use a cloud platform such as Kubernetes, which has an internal service discovery mechanism for the applications running on it. Kubernetes uses etcd for a service registry.
API Gateway Pattern
When working with microservices, you shouldn’t directly expose the individual API endpoints of each microservice. Instead, it’s best to have a single interface that provides access to the application’s API as a whole. This is where the API gateway pattern can help. This pattern provides a single entry point for all clients of a microservice-based application.
One of the benefits of the API gateway pattern is that it enables you to create a data aggregation mechanism that gets data from different services and then aggregates it. This enables you to request data from any API. Moreover, an API gateway enables you to easily implement or integrate security features, including authentication or encryption.
Additionally, using an API gateway means that you can also have an API model that’s built on a specification. This is especially useful when you’re exposing parts of your application as an API:
As an API gateway, you can use tools such as Zuul or applications such as 3scale if you need more complex functionality.
The following is a Java code snippet of a Spring Boot application that runs as a Zuul API gateway server:
@EnableZuulProxy
@EnableDiscoveryClient
@SpringBootApplication
public class MyZuulApiGatewayServer {
public static void main(String[] args)
{
SpringApplication.run(MyZuulApiGatewayServer.class, args);
}
}You can also choose to develop your own API gateway with a framework like Apache Camel. It supports synchronous or asynchronous integration of software components and has various features such as data aggregation and filtering.
The following code snippet creates a Camel route for an API gateway as a Spring Boot application:
@Component
@ConfigurationProperties(prefix="api-gateway")
public class MyCamelAPIGateway extends RouteBuilder {
...codes omitted...
@Override
public void configure() {
...other routes are omitted...
rest()
.get("/gateway").enableCORS(true)
.route()
.multicast(AggregationStrategies.flexible()
.accumulateInCollection(ArrayList.class))
.parallelProcessing()
.to("direct:accountservice")
.to("direct:paymentservice")
.end()
.marshal().json(JsonLibrary.Jackson)
.convertBodyTo(String.class)
.endRest();
}
...codes omitted...
}Change Data Capture Pattern
The change data capture (CDC) pattern is a data integration pattern for tracking data related to changes in database systems. It allows you to listen to any modifications in a database and generate corresponding events.
If you’re utilizing microservices and aiming for event-driven communication across your system, using the CDC pattern is an excellent choice, especially if you’re using a database per your microservice.
Database per service is another microservices pattern that’s designed to decrease the coupling of microservices in terms of data, enabling each microservice to have its own dedicated database:
However, using a database per service model for your microservices comes with drawbacks, such as potential data inconsistencies or the dual write problem, where data needs to be written to multiple databases.
The CDC pattern solves this aforementioned problem because it prevents any data inconsistency by establishing an event-driven system based on data changes. As each event is triggered by a database event, you can ensure data integrity and propagate the changes to other microservices effectively:
CDC is not only an important pattern for creating an event-driven microservices architecture but also a useful pattern for transforming your applications from monolith to microservices.
You can use CDC along with the strangler pattern to create an event-driven communication between the monolith and the newly created microservices that consumes the captured events of the monolithic application’s database and acts upon those events.
Some databases already implement CDC, so you won’t need any extra tools to implement. There are also tools such as Debezium that support numerous types of databases, such as PostgreSQL, MongoDB, and Cassandra.
Distributed Tracing Pattern
When you create a set of microservices, you need to build a synchronous or asynchronous communication system between them. This communication can become complex when the number of microservices and the number of connections increase, making it harder to inspect errors or latency issues.
There are systems that aggregate logs as an implementation of the log aggregation pattern, but logs don’t show the behavioral details of the microservices.
The distributed tracing pattern, part of the microservices chassis framework, which gathers some microservices patterns and introduces them as a framework, solves this issue by providing a tracing system with the microservice requests. This is usually done via setting a request ID and passing it to all services that are involved in handling the request:
There are standards that define specifications for distributed tracing. One of the most popular is a Cloud Native Computing Foundation (CNCF) project called OpenTelemetry, which is a tracing, metric, and logging framework.
There are many kinds of implementations of distributed tracing. For example, there are service mesh frameworks, such as Istio, that implement tracing at the Kubernetes network level. Apart from frameworks, there are also tools such as Jaeger and older tracing implementations that are implemented in Java, such as Zipkin.
For example, if you’re interested in implementing the distributed tracing pattern, the following code snippet creates a Zipkin server in a Spring Boot application:
@SpringBootApplication
@EnableZipkinStreamServer
public class MyZipkinServer {
public static void main(String[] args) {
SpringApplication.run(MyZipkinServer.class, args);
}
}Additionally, the MicroProfile specification provides some capabilities for tracing Java applications. The following Quarkus application code snippet uses the @Traced annotation for tracing a public method:
import jakarta.enterprise.context.ApplicationScoped;
import org.eclipse.microprofile.opentracing.Traced;
@Traced
@ApplicationScoped
public class MessageService {
public String message() {
return "a message";
}
}
Circuit Breaker Pattern
Distributed tracing implementation can help you detect cascading failures. However, you may still want more control over failure situations, commonly known as fault tolerance, to guarantee uninterrupted system operation despite failures. The circuit breaker pattern can help you with this.
The circuit breaker pattern, a microservices pattern inspired by electrical circuit breakers, helps microservices prevent cascading network or service failure. When an issue arises in an electrical system, the circuit breaker intervenes by cutting off the electricity in a specific area until the problem is resolved.
A circuit breaker pattern implementation functions in a similar way. In the event of a failure within your microservices, based on your configuration, the circuit breaker mechanism activates and provides a fallback response:
Circuit breaking can be beneficial, especially if you’re in the middle of a monolith to microservices transformation. When you start separating services from a monolith, you’re likely to experience issues within your system.
Hystrix is probably the most popular circuit breaker pattern implementation as a framework. Service mesh frameworks, such as Istio, also have mechanisms for circuit breaking internally, so you can set up a fault tolerance configuration for any microservice within the mesh system.
The following is a code snippet of a Spring Boot application that uses Hystrix:
@Service
public class GreetingService {
@HystrixCommand(fallbackMethod = "getFallbackMessage")
public String getMessage(Integer id) {
return new RestTemplate()
.getForObject("http://localhost:9090/message/{id}", Integer.class, id);
}
private String getFallbackMessage(Integer id) {
return "Message for id: " + id;
}
}Notice that the getMessage() method has a fallback method defined as getFallbackMessage().
Conclusion
The transition from monolith to microservices is a critical process that can cause numerous problems. Microservices design patterns come to the rescue to tackle these challenges effectively. They offer solutions for discovering services, implementing a common API gateway, and they create reliable, traceable, and fault-tolerant event-driven systems. In this article, you learned all about various microservices design patterns and even saw how to implement them in Java.
To successfully navigate this transition, it’s important to have a good transformation strategy backed by microservices patterns and a robust refactoring strategy. Refactoring can be one of the pain points in the journey from monolith to microservices, but with the right approach and pattern adoption, you can significantly ease the process and achieve success.
If you don’t have a good refactoring strategy, regardless of what microservices patterns you use, failure during transformation is likely. And in order to have a solid refactoring strategy, you need efficient tests.
Diffblue is a software company that specializes in automated software testing using artificial intelligence. It can help you define a strategy for refactoring and testing your microservices. It provides an AI-powered unit testing framework that creates unit tests for your Java applications. Try Diffblue Cover today and learn how it can help you on your microservices journey.
