Hazelcast Jet is a distributed computing platform that allows you to process large amounts of data in a parallel and scalable manner. One of the powerful features provided by Hazelcast Jet is stateful aggregation, which enables you to perform calculations that rely on previously processed data.

In this blog post, we will explore how to use stateful aggregation in Hazelcast Jet to solve various real-world use cases in Java applications.

What is Stateful Aggregation?

Stateful aggregation refers to the ability to maintain and update the state of a computation over a streaming or batch data set. This state can be used to perform complex calculations that require information from previously processed records.

Hazelcast Jet provides a built-in mechanism for stateful aggregations, allowing you to store and update states during the processing of data. These states can be used to compute aggregations such as count, sum, average, and more.

Example Use Case: Computing Average Temperature

Let’s consider a scenario where we have a stream of temperature readings and we need to compute the average temperature over a specific time window.

Pipeline pipeline = Pipeline.create();

pipeline
    .readFrom(Sources.<TemperatureRecord>stream("temperature-source"))
    .withNativeTimestamps(0)
    .groupingKey(TemperatureRecord::getSensorId)
    .window(WindowDefinition.sliding(10_000, 1_000))
    .aggregate(toSingletonMap(TemperatureRecord::getValue, averagingDouble(TemperatureRecord::getValue)))
    .writeTo(Sinks.logger());

JetInstance jet = Jet.newJetInstance();
jet.newJob(pipeline).join();

In the above example, we create a pipeline that reads the temperature records from a stream named “temperature-source”. We enable native timestamps to ensure correct event time processing. We then group the records by sensor ID and define a sliding window of 10 seconds with a slide interval of 1 second.

Next, we use the aggregate operator to aggregate the temperature values into a map, where the key is the sensor ID and the value is the average temperature over the specified window. Finally, we write the results to a logger sink.

Benefits of Stateful Aggregation in Hazelcast Jet

Stateful aggregation in Hazelcast Jet offers several benefits:

• Efficient and scalable processing: Hazelcast Jet distributes the stateful processing across a cluster of machines, allowing for parallel execution and optimal resource utilization. This enables you to process large data sets without sacrificing performance.

• Fault tolerance: Hazelcast Jet ensures fault tolerance by backing up the states across multiple nodes. In the event of a failure, the processing can be seamlessly recovered, ensuring data consistency and reliability.

• Dynamic processing: Stateful aggregation in Hazelcast Jet supports dynamic updates to the state, allowing you to modify the computation logic without interrupting the processing. This flexibility enables you to adapt to changing business requirements and perform real-time analytics on live data streams.

Conclusion

In this blog post, we explored how to leverage the power of stateful aggregation in Hazelcast Jet to perform complex calculations in Java applications. We discussed the concept of stateful aggregation and showcased an example use case of computing the average temperature over a time window.

By utilizing Hazelcast Jet’s stateful aggregation, you can efficiently process large amounts of data, ensure fault tolerance, and perform dynamic computations. This enables you to build scalable and flexible real-time applications that can handle the most demanding data processing requirements.

#java #bigdata