The benefits of using microservices to replace monolith architectures and how microservices communicate with each other in Java are becoming more evident in the development world but in this article, we will shed light over some of the other aspects of the microservices Java architecture, too.
What Are Microservices
Very simply, as it is implied in its name, microservices are smaller or larger services able to function on their own to serve as solutions for a service.
Microservices are a form of service-oriented architecture style wherein applications are built as a collection of different smaller services rather than one whole app. Instead of a monolithic app, you have several independent applications that can run on their own and may be created using different coding or programming languages. Big and complicated applications can be made up of simpler and independent programs that are executable by themselves. These smaller programs are grouped together to deliver all the functionalities of the big, monolithic app.
Microservices captures your business scenario, answering the question “What problem are you trying to solve?” It is usually developed by an engineering team with only a few members and can be written in any programming language as well as utilize any framework. Each of the involved programs is independently versioned, executed, and scaled. These microservices can interact with other microservices and can have unique URLs or names while being always available and consistent even when failures are experienced.
What Are the Benefits of Microservices
There are several benefits to using microservices. For one, because these smaller applications are not dependent on the same coding language, the developers can use the programming language that they are most familiar with. That helps developers come up with a program faster with lower costs and fewer bugs. The agility and low costs can also come from being able to reuse these smaller programs on other projects, making it more efficient.
How Microservices Work in Java
Java microservices are a set of software applications written in the Java programming language (and typically leverage the vast ecosystem of Java tools and frameworks), designed for limited scope that work with each other to form a bigger solution. Each microservice, as the name implies, has minimal capabilities for the sake of creating a very modularized overall architecture. A microservices architecture is analogous to a manufacturing assembly line, where each microservice is like a station in the assembly line. Just as each station is responsible for one specific task, the same holds true for microservices. Each station and microservice are “experts” in their given responsibilities, thus promoting efficiency, consistency, and quality in the workflow and the outputs. Contrast that to a manufacturing environment in which each station is responsible for building the entire product itself. This is analogous to a monolithic software application that performs all tasks within the same process.
Examples of Microservices Frameworks for Java
There are several microservices frameworks that you can use for developing for Java. Some of these are:
- Spring Boot: This is probably the best Java microservices framework that works on top of languages for Inversion of Control, Aspect Oriented Programming, and others.
- Jersey: This open-source framework supports JAX-RS APIs in Java is very easy to use.
- Swagger: Helps you in documenting API as well as gives you a development portal, which allows users to test your APIs.
Others that you can consider include: Dropwizard, Ninja Web Framework, Play Framework, RestExpress, Restlet, Restx, and Spark Framework.
How Microservices Communicate with Each Other in Java
You basically have two choices: synchronous communication or asynchronous communication.
(HTTP)/REST – Synchronous Communication
Synchronous microservice communication is usually done via HTTP and REST-like services that return XML or JSON – though this is by no means required (have a look at Google’s Protocol Buffers for example).
Use REST communication when you need an immediate response, which we do in our case, as risk-checking is mandatory before opening an account: No risk check, no account.
Messaging – Asynchronous Communication
Asynchronous microservice communication is usually done through messaging with a JMS implementation and/or with a protocol like AMQP. Usually, because the number of, for example, email/SMTP-driven integrations is not to be underestimated in practice.
Use it when you do not need an immediate response, say the users presses the ‘buy-now’ button and you want to generate an invoice, which certainly does not have to happen as part of the user’s purchase request-response cycle.
Example Use Cases
Many use cases are applicable for Java microservices, especially those that involve an extensive data pipeline. For example, a microservices-based system would be ideal for a reporting system on a company’s retail store sales. Each step in the data preparation process would be handled by a microservice: data collection, cleansing, normalization, enrichment, aggregation, reporting, etc. The microservices workflow naturally creates a trackable lineage so if any problems are encountered in the data, it is relatively easy to trace back which microservice might need to be updated.
Another use case entails machine learning (ML). An microservices-based ML environment collects, aggregates, and analyzes a data flow so that the ML framework can determine an outcome. In such an environment, the data runs through a workflow that has many steps, and each step is handled by a microservice. Several great ML frameworks can be used with Java, including TensorFlow, Apache Mahout, and Apache Singa. One advantage of using a microservices architecture for machine learning is that multiple machine learning frameworks can be included in the workflow so that multiple models can be created on the same data flow. This is useful when the machine learning frameworks are targeting a predicted outcome, and having multiple frameworks running simultaneously provides a comparison around which model is providing the best results.
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