What is a parallel stream in Java?
Table of Contents
- Introduction
- What is a Parallel Stream?
- When to Use Parallel Streams
- Practical Example of Parallel Stream Usage
- Conclusion
Introduction
In Java, streams are a key feature introduced in Java 8 as part of the Stream API, which enables functional-style operations on data. While a sequential stream processes elements one at a time in a linear order, a parallel stream allows you to process elements in parallel across multiple threads, providing a potential performance boost, especially when working with large datasets. This guide explains what a parallel stream is, how to use it, and when it's appropriate to apply it in your Java applications.
What is a Parallel Stream?
A parallel stream is a special type of stream in Java that processes data in parallel by splitting the dataset into multiple chunks and distributing them across multiple threads for concurrent processing. Java handles the splitting, processing, and merging of the results behind the scenes, making parallel streams a convenient tool for improving performance in CPU-intensive operations.
How Parallel Streams Work
When you invoke the parallelStream() method on a collection, Java automatically divides the collection into smaller parts, each processed by a different thread. This approach speeds up data processing when operations are independent and can be performed concurrently without affecting each other.
In contrast, a sequential stream processes elements one by one in the order they appear in the collection, using a single thread.
Example of Parallel Stream:
Output:
In the above example, the parallelStream() method is used to process the list of numbers in parallel. The filter() operation checks for even numbers, and the forEach() method prints the results along with the thread name to show parallel processing.
Key Differences Between Sequential and Parallel Streams
| Aspect | Sequential Stream | Parallel Stream |
|---|---|---|
| Thread Usage | Uses a single thread for processing. | Uses multiple threads for concurrent processing. |
| Order of Processing | Elements are processed in the order they appear in the collection. | The order of processing may not be preserved. |
| Performance | May be slower for large datasets or complex operations. | Can be faster for large datasets and CPU-intensive operations, depending on the system and task. |
| Ideal Use Case | Simple operations on small datasets. | Large datasets or computationally expensive operations where parallelism can improve performance. |
When to Use Parallel Streams
Parallel streams are particularly useful in scenarios where:
- The operation can be performed independently on each element (e.g., mapping, filtering).
- You have a large dataset where processing each element in parallel would lead to a noticeable performance improvement.
- The operation is CPU-bound and can benefit from multiple cores.
However, parallel streams are not always beneficial and can even be slower for small datasets or when the operation involves significant synchronization (e.g., modifying shared resources).
When NOT to Use Parallel Streams
- Small datasets: For small collections, the overhead of splitting the data and managing multiple threads may outweigh the benefits of parallelism.
- Operations with side effects: Parallel streams may not be suitable for operations where the order of processing is important or when side effects like writing to shared resources could cause race conditions.
- Non-CPU-intensive tasks: If your task is I/O-bound (e.g., network or disk operations), parallel streams will not provide significant performance benefits.
Practical Example of Parallel Stream Usage
Example 1: Summing Large Numbers in Parallel
A common use case for parallel streams is performing aggregate operations like summing a large set of numbers.
Output:
In this example, the sum of numbers is calculated in parallel. The parallelStream() method splits the dataset across multiple threads, and the sum() operation is performed in parallel, speeding up the process for large datasets.
Example 2: Processing a Large Dataset
Parallel streams are also useful when processing large datasets, such as performing computations on a large list of objects.
Output:
In this example, the parallel stream is used to square each number in a large list. The operation is applied concurrently on multiple threads, improving performance for larger datasets.
Conclusion
A parallel stream in Java provides a way to process data concurrently across multiple threads, making it a powerful tool for speeding up computationally expensive operations on large datasets. By using the parallelStream() method, you can take advantage of multi-core processors to improve performance. However, it’s important to use parallel streams judiciously, as they may not always offer performance gains and can introduce overhead for small datasets or complex operations. Always benchmark your application to ensure parallel streams are beneficial for your specific use case.