Скачать или смотреть Understanding Why Your Spliterator Stream Isn't Processing in Parallel

Learn how to troubleshoot your Java `Spliterator` streams and ensure they process in parallel effectively.
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Why Your Spliterator Stream Isn't Processing in Parallel

Java provides powerful tools for parallel processing, especially through its Stream API. However, using Spliterators can sometimes lead to unexpected results, particularly when it comes to parallel processing. If you've noticed that your stream created with a Spliterator is not being processed in parallel, don't worry! In this guide, we'll delve into why this might be happening and how to resolve it.

The Problem: Custom Iterator and Parallel Streams

You might have sets of code where a standard parallel stream works as intended, while a custom iterator created using a Spliterator does not. Here is a summary of the situation:

Working Parallel Stream Example

The following code successfully creates a parallel stream from an IntStream:

[[See Video to Reveal this Text or Code Snippet]]

In this case, the output shows interleaved processing and finishing messages, indicating effective parallel execution:

[[See Video to Reveal this Text or Code Snippet]]

Non-Working Custom Iterator Example

However, when you use a custom Iterator to create a stream, the parallel processing does not occur:

[[See Video to Reveal this Text or Code Snippet]]

The output appears sequentially instead:

[[See Video to Reveal this Text or Code Snippet]]

This raises a question: what is going wrong with the custom iterator version?

The Solution: Estimating the Size of the Stream

Understanding Size Estimation

The key issue with the custom Spliterator not processing in parallel lies in the way it estimates the size of the stream. Here’s what you need to know:

Size Matters

When creating a Spliterator, you can provide an estimate of the size of the stream. This allows the Stream API to effectively determine how to divide the work between available threads. Without a proper estimate, the API defaults to a single thread, leading to sequential processing.

Implementing the Solution

To fix the issue, you should explicitly provide an estimate of the size in your custom Spliterator. Here’s how you can do it:

Modify the creation of your Spliterator like this:

[[See Video to Reveal this Text or Code Snippet]]

In this snippet, the number 3 serves as the estimate of the maximum size of the stream that will be processed by the Iterator.

Why Does This Work?

Batch Processing: The estimate helps the processing engine decide how many tasks to send to each thread at once. If your estimate is too high relative to the actual number of elements, it may result in under-utilization of threads.

Effective Thread Allocation: Providing a more reasonable estimate allows the stream processing engine to allocate tasks across multiple threads rather than funneling all tasks through a single one.

Important Considerations

The estimate does not need to be exact but should be a reasonable approximation of the number of elements in your Iterator.

If you provide a significantly larger estimate compared to actual elements, it might lead to suboptimal performance.

Conclusion

By understanding the importance of size estimates in Spliterator, you can ensure that your custom iterator streams utilize parallel processing capabilities effectively. Always ensure to tweak these estimates to optimize the performance of your Java applications!

Now, the next time you encounter issues with parallel processing in Java streams, especially with custom iterators, you'll be equipped with the knowledge to troubleshoot and resolve the problem effectively.