How does map, filter, reduce behave in multithreaded code?

In Java, the operations map, filter, and reduce can be used in a multithreaded context particularly when utilizing the Stream API. These operations are designed to facilitate parallel processing, thus enhancing performance by leveraging multi-core processors.

When using parallel streams, the map, filter, and reduce operations are executed concurrently across multiple threads. This means that each of these operations can significantly increase throughput by dividing the workload among available CPUs. However, developers must be cautious about shared state and ensure that operations are stateless or use appropriate synchronization techniques.

import java.util.Arrays;
        import java.util.List;
        import java.util.stream.Collectors;

        public class Main {
            public static void main(String[] args) {
                List numbers = Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);

                // Parallel stream example
                List squared = numbers.parallelStream()
                    .map(n -> n * n)                 // Map: square each number
                    .filter(n -> n > 20)             // Filter: only keep numbers greater than 20
                    .collect(Collectors.toList());    // Reduce: collect results into a list

                System.out.println(squared); // Output: [25, 36, 49, 64, 81, 100]
            }
        }

Java multithreading map filter reduce Stream API parallel processing

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