Hazelcast Jet is a fast, distributed in-memory computing platform that allows you to process large amounts of data in real-time. One of the powerful features of Hazelcast Jet is its streaming aggregations, which enable you to perform real-time aggregations on large data sets.

In this blog post, we will explore how to use Hazelcast Jet streaming aggregations in Java applications and demonstrate its capabilities with examples.

Setting up the Environment

Before we can start using Hazelcast Jet streaming aggregations, we need to set up our development environment. Here are the steps to follow:

1. Download Hazelcast Jet: First, download and install Hazelcast Jet from the official website or include the required dependency in your project.

2. Create a Hazelcast Jet Pipeline: Next, we create a Hazelcast Jet pipeline to define the data processing operations. This is where we will specify our streaming aggregations.

import com.hazelcast.jet.Jet;
import com.hazelcast.jet.JetInstance;
import com.hazelcast.jet.datamodel.Tuple2;
import com.hazelcast.jet.pipeline.Pipeline;
import com.hazelcast.jet.pipeline.Sources;
import com.hazelcast.jet.pipeline.WindowDefinition;
import com.hazelcast.jet.pipeline.WindowGroupAggregateBuilder;
import com.hazelcast.jet.pipeline.WindowResult;
import com.hazelcast.jet.pipeline.WindowResultBuilder;

public class StreamingAggregationsExample {

    public static void main(String[] args) {
        // Create a Hazelcast Jet instance
        JetInstance jet = Jet.newJetInstance();

        // Create a pipeline
        Pipeline pipeline = Pipeline.create();

        // Read the stream of data from a source
        pipeline
            .readFrom(Sources.<SensorData>mapJournal("sensor-data"))
            .window(WindowDefinition.tumbling(Time.seconds(5)))
            .groupingKey(SensorData::getSensorId)
            .aggregate(WindowGroupAggregateBuilder
                .<SensorData, SensorAggregate>builder()
                .add(SensorAggregate::sumTemperature, SensorData::getTemperature)
                .build());

        // Run the pipeline
        jet.newJob(pipeline).join();
    }
}

Streaming Aggregations in Action

In the above example, we create a Hazelcast Jet pipeline and configure it to read from a source of sensor data. We then define a tumbling window of 5 seconds, which will aggregate the sensor data into 5-second intervals.

Next, we specify the key to group the data by, which in this case is the sensor ID. Finally, we define the aggregation function using the WindowGroupAggregateBuilder. In our example, we sum the temperature values of the sensor data.

When we run the pipeline using jet.newJob(pipeline).join(), the aggregations will be calculated for each window of sensor data.

Conclusion

Hazelcast Jet streaming aggregations allow you to perform real-time aggregations on large data sets with ease. In this blog post, we explored how to set up the environment for using Hazelcast Jet and demonstrated how to use streaming aggregations in a Java application.

By leveraging Hazelcast Jet’s powerful streaming processing capabilities, you can efficiently process and analyze large volumes of data in real-time. This opens up new possibilities for building scalable and high-performance applications.

Give it a try in your Java projects and experience the power of Hazelcast Jet streaming aggregations!

#HazelcastJet #StreamingAggregations