Distributed computing is a powerful technique used to solve complex problems by distributing the workload across multiple nodes in a network. It enables parallel processing and improved performance by utilizing multiple machines working together.

One popular framework for implementing distributed computing is Hazelcast Jet, which is an in-memory computing platform built on top of Hazelcast IMDG (In-Memory Data Grid). Jet provides a high-level API for building and executing distributed data processing pipelines.

Getting started with Hazelcast Jet

Before we start, make sure you have Hazelcast Jet and Java installed on your machine. You can download Jet from the official Hazelcast website and install Java from the Oracle website or any other trusted source.

To get started, create a new Maven project in your favorite IDE and add the Hazelcast Jet dependency to your pom.xml file:

<dependencies>
    <dependency>
        <groupId>com.hazelcast.jet</groupId>
        <artifactId>hazelcast-jet</artifactId>
        <version>4.5</version>
    </dependency>
</dependencies>

Now, let’s write some code to demonstrate the power of distributed computing with Hazelcast Jet.

Example: Distributed Computing with Hazelcast Jet

Let’s say we have a collection of integers and we want to find the sum of all the even numbers using distributed computing. We can achieve this using a Hazelcast Jet pipeline as follows:

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.StreamStage;

public class DistributedComputingExample {

    public static void main(String[] args) {
        JetInstance jet = Jet.newJetInstance(); // creates a new Jet instance
        Pipeline pipeline = Pipeline.create(); // create a new pipeline

        StreamStage<Integer> numbers = pipeline.drawFrom(Sources.<Integer>list("numbers")); // source stage

        StreamStage<Integer> evenNumbers = numbers.filter(number -> number % 2 == 0); // intermediate stage

        StreamStage<Integer> summedNumbers = evenNumbers.aggregate(summing()); // sink stage

        summedNumbers
            .map(sum -> "Sum of even numbers: " + sum)
            .drainTo(Sinks.logger()); // sink stage to log the result

        jet.newJob(pipeline).join(); // execute the pipeline

        Jet.shutdownAll(); // shutdown all Jet instances
    }
}

In this example, we create a Jet instance, define the pipeline stages, execute the pipeline, and finally, shut down the Jet instance.

Using the drawFrom method, we define a source stage that retrieves the numbers from a distributed list. Then, we filter the numbers in the intermediate stage to keep only the even numbers. In the sink stage, we aggregate the even numbers using the summing function. Finally, we log the result using the drainTo method.

Conclusion

Distributed computing with Hazelcast Jet provides a simple yet powerful way to process large datasets in a distributed manner, enabling parallel processing and improved performance.

By utilizing the high-level API provided by Jet, developers can easily build distributed data processing pipelines and execute them on a cluster of Hazelcast Jet instances.

To learn more about Hazelcast Jet and its capabilities, check out the official documentation and explore the various features and extensions available.

#distributedcomputing #hazelcastjet