Exploring the Performance of stream() and parallelStream() in Java

Java 8 introduced the Stream API, a powerful new abstraction for working with sequences of data in a functional programming style. Among its many features, two that often cause confusion are stream() and parallelStream(). In this blog post, we’ll explore the differences between these two methods and their impact on performance.

What are stream() and parallelStream()?

The stream() method creates a sequential stream, where the elements are processed in the order they appear in the source collection. The operations on the elements are performed one after the other.

On the other hand, parallelStream() creates a parallel stream, where the elements can be processed concurrently, potentially utilizing multiple CPU cores. The operations on the elements can be performed at the same time, in no particular order.

A Performance Comparison

Let’s consider an example where we generate a large list of integers and calculate their sum using both stream() and parallelStream():

import java.util.List;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.stream.Collectors;
import java.util.stream.Stream;

public class StreamExample {
    public static void main(String[] args) {
        List<Integer> numbers = Stream.generate(new AtomicInteger()::getAndIncrement)
                                      .limit(100000000)
                                      .collect(Collectors.toList());

        long startTime1 = System.nanoTime();
        int sum1 = numbers.stream()
                          .mapToInt(Integer::intValue)
                          .sum();
        long endTime1 = System.nanoTime();
        System.out.println("Time Taken by Stream: "+ (endTime1 - startTime1));

        long startTime2 = System.nanoTime();
        int sum2 = numbers.parallelStream()
                          .mapToInt(Integer::intValue)
                          .sum();
        long endTime2 = System.nanoTime();
        System.out.println("Time Taken by ParallelStream: "+ (endTime2 - startTime2));
    }
}

In this example, we first generate a list of 100 million integers using Stream.generate(). We then calculate the sum of these integers using both stream() and parallelStream(), measuring the time taken by each operation.

Interpreting the Results

You might expect the parallelStream() to be faster due to its ability to process elements concurrently. However, the actual performance can depend on several factors:

• Overhead of Parallelism: Parallel streams use multiple threads, and there is a certain amount of overhead associated with managing these threads and dividing the work between them. For smaller data sets or simpler operations, this overhead can outweigh the benefits of parallelism, making the parallel stream slower than the sequential one.
• CPU Utilization: Parallel streams can effectively utilize multiple CPU cores. However, if the CPU is already heavily loaded, or if the number of threads exceeds the number of available cores, then the performance may not improve and could even degrade.
• Data Characteristics: Certain data structures and operations are more amenable to parallelism than others. For example, operations on ArrayList are typically faster in parallel than operations on LinkedList.

In general, whether to use a sequential or parallel stream depends on the specific circumstances, including the size and nature of the data, the complexity of the operations being performed, and the characteristics of the system on which the code is running. It’s always a good idea to benchmark your code under realistic conditions to determine which approach is faster.

Conclusion

While stream() and parallelStream() provide powerful tools for processing sequences of data, understanding the differences between them and when to use each can be crucial for writing efficient Java code. By carefully considering the characteristics of your data and operations, you can choose the right tool for the job and make the most of the Java Stream API.

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