Hazelcast is an open-source, in-memory data grid platform that provides distributed computing capabilities. One of its key components is Hazelcast Jet, a powerful data processing engine that allows you to perform high-performance, scalable computations on distributed data.

In this blog post, we will explore how to use Hazelcast Jet in Java applications to achieve efficient distributed data processing.

Setting Up Hazelcast Jet

To get started with Hazelcast Jet, you need to add the necessary dependencies to your Java project. If you are using Maven, add the following dependency to your pom.xml file:

<dependency>
    <groupId>com.hazelcast.jet</groupId>
    <artifactId>hazelcast-jet</artifactId>
    <version>4.5.1</version>
</dependency>

If you are using Gradle, add the following dependency to your build.gradle file:

implementation 'com.hazelcast.jet:hazelcast-jet:4.5.1'

Building a Distributed Data Processing Pipeline

Hazelcast Jet allows you to build a distributed data processing pipeline using the concept of a DAG (Directed Acyclic Graph). The pipeline consists of several stages, with each stage representing a step in the data processing workflow.

Here’s an example of how to create a simple distributed data processing pipeline using Hazelcast Jet:

import com.hazelcast.jet.Jet;
import com.hazelcast.jet.JetInstance;
import com.hazelcast.jet.pipeline.Pipeline;
import com.hazelcast.jet.pipeline.Sources;
import com.hazelcast.jet.pipeline.Stage;

public class DistributedDataProcessingExample {

    public static void main(String[] args) {
        JetInstance jet = Jet.newJetInstance();

        Pipeline pipeline = Pipeline.create();

        Stage sourceStage = pipeline.readFrom(Sources.<String>list("inputList"));

        Stage transformStage = sourceStage.map(item -> item.toUpperCase());

        transformStage.writeTo(Sinks.logger());

        jet.newJob(pipeline).join();
    }
}

In the above example, we create a Jet instance and define a pipeline that reads from a source stage, transforms the input by mapping each element to uppercase, and finally writes the transformed elements to the logger.

Executing the Distributed Data Processing Job

To execute the distributed data processing job, we need to submit the pipeline to the Jet cluster. This can be done by invoking the newJob() method on the Jet instance, passing in the pipeline configuration.

jet.newJob(pipeline).join();

The join() method will block until the job execution is complete.

Conclusion

Hazelcast Jet provides a powerful framework for performing distributed data processing in Java applications. By setting up a distributed data processing pipeline and executing it using Hazelcast Jet, you can leverage Hazelcast’s in-memory capabilities to achieve high-performance and scalable computations.

With the ability to easily scale out with additional Jet instances, Hazelcast Jet is a great choice for applications that require efficient distributed data processing.

#jet #distributedcomputing