Java Parallel Streams

Introduction

In this chapter, we want to talk more about parallel streams in Java. Java streams can be executed in parallel, which can significantly improve performance. Learning how to use parallel streams effectively is an important topic for Java developers. It is most often used when there are large data sets, which need to be processed

What are parallel streams in Java?

Processing datasets at once, with multiple threads dividing the streams into sub-streams, is a parallel stream. This allows for faster processing of large data sets, as the processing can be distributed across multiple processors and executed in parallel.

To create a parallel stream, you simply call the parallel() method on a stream. This creates a parallel stream that can be processed in parallel.

In this example, we summed up all numbers in the numbers list. First, we create parallel streams with parellelStream() and then convert with the method mapToInt the Numbers to primitive integers and then just use sum.

Benefits of using parallel streams

The main benefit is faster processing. We utilize multiple processors and threads. Also, the syntax is really easy for you (like you just saw). The syntax is very similar to regular streams and therefore it is really easy to start.

Considerations when using parallel streams

• Parallel processing adds overhead: The overhead of parallel processing can outweigh the benefits if the data set is small. In general, parallel streams are best suited for large data sets that can be processed in parallel.
• Thread safety: It is important to ensure that the code being executed in parallel is thread-safe. If the code is not thread-safe, it can lead to unexpected results and data corruption.
• Ordered vs unordered streams: Parallel processing can change the order of the elements in the stream. If the order of the elements is important, you may need to use an ordered stream to ensure that the order is preserved.

Conclusion

Parallel streams are a powerful tool for improving the performance of processing large datasets. For simpler use cases, I would not recommend it, since you can have many bugs with thread safety and the order of the streams. Still it is good to have parallel streams always in mind.