Hazelcast Jet is a fast and low-latency stream processing engine for big data processing and real-time analytics. It provides several powerful transformations and aggregations that can be leveraged in Java applications to process streaming data efficiently.

In this blog post, we will explore how to use Hazelcast Jet transformations and aggregations in Java applications.

Transformations

Transformations in Hazelcast Jet allow you to modify, enrich, or filter the input data stream. Here are some commonly used transformations:

Map

The map transformation applies a mapping function to each element in the input stream and produces a new stream of transformed elements. For example:

StreamStage<String> input = pipeline.readFrom(source);
StreamStage<Integer> transformed = input.map(String::length);

FlatMap

The flatMap transformation applies a mapping function to each element in the input stream and produces zero or more elements in the output stream. For example:

StreamStage<String> input = pipeline.readFrom(source);
StreamStage<String> transformed = input.flatMap(str -> Arrays.asList(str.split(" ")).stream());

Filter

The filter transformation applies a predicate to each element in the input stream and produces a stream of elements that satisfy the predicate. For example:

StreamStage<Integer> input = pipeline.readFrom(source);
StreamStage<Integer> filtered = input.filter(num -> num % 2 == 0);

Aggregations

Aggregations in Hazelcast Jet allow you to perform computations on the input stream and produce a summarized result. Here are some commonly used aggregations:

Count

The count aggregation counts the number of elements in the input stream. For example:

StreamStage<String> input = pipeline.readFrom(source);
StreamStage<Long> count = input.map(String::length).aggregate(AggregateOperations.count());

Sum

The sum aggregation calculates the sum of elements in the input stream. For example:

StreamStage<Integer> input = pipeline.readFrom(source);
StreamStage<Long> sum = input.aggregate(AggregateOperations.sum());

Average

The average aggregation calculates the average of elements in the input stream. For example:

StreamStage<Integer> input = pipeline.readFrom(source);
StreamStage<Double> average = input.aggregate(AggregateOperations.averagingDouble());

Conclusion

Hazelcast Jet provides powerful transformations and aggregations that can be used to process streaming data in Java applications. By leveraging these features, developers can build efficient and scalable stream processing applications.