Add Fork/Join design pattern (#3227)

fork-join/README.md

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+---
+title: "Fork/Join Pattern in Java: Parallel Divide-and-Conquer Processing"
+shortTitle: Fork/Join
+description: "Learn the Fork/Join design pattern in Java with real-world examples, class diagrams, and code samples. Understand how to split large tasks into parallel subtasks for improved performance."
+category: Concurrency
+language: en
+tag:
+  - Performance
+  - Scalability
+  - Concurrency
+---
+
+## Also known as
+
+* Divide and Conquer Parallelism
+* Work-Stealing Parallelism
+
+## Intent of Fork/Join Design Pattern
+
+The Fork/Join pattern recursively splits a large task into independent subtasks (fork),
+processes them in parallel across multiple threads, and combines their results (join) to
+produce a final outcome. It maximizes CPU utilization for computationally intensive problems.
+
+## Detailed Explanation of Fork/Join Pattern with Real-World Examples
+
+Real-world example
+
+> Imagine a large warehouse that needs to count all its inventory items across 100 aisles.
+> Instead of one person counting every aisle sequentially, the manager divides the warehouse
+> into sections and assigns a team of workers to count each section simultaneously. Once
+> every section is counted, the manager collects all partial counts and sums them into the
+> total inventory. This is the Fork/Join pattern: split the work, do it in parallel, merge
+> the results.
+
+In plain words
+
+> Fork/Join splits a big problem into smaller pieces, solves each piece in parallel on
+> separate threads, then combines all results back together.
+
+## Programmatic Example of Fork/Join Pattern in Java
+
+We demonstrate the pattern by computing the sum of a large array in parallel using Java's
+built-in `ForkJoinPool` and `RecursiveTask`.
+
+The `SumTask` is a recursive task that splits the array when it's too large:
+
+```java
+public class SumTask extends RecursiveTask<Long> {
+
+  private static final int THRESHOLD = 1000;
+  private final long[] numbers;
+  private final int start;
+  private final int end;
+
+  @Override
+  protected Long compute() {
+    int length = end - start;
+
+    if (length <= THRESHOLD) {
+      // Base case: sum directly
+      long sum = 0;
+      for (int i = start; i < end; i++) {
+        sum += numbers[i];
+      }
+      return sum;
+    }
+
+    // Fork: split into two halves
+    int mid = start + length / 2;
+    SumTask leftTask = new SumTask(numbers, start, mid);
+    SumTask rightTask = new SumTask(numbers, mid, end);
+
+    leftTask.fork();                    // run left half asynchronously
+    long rightResult = rightTask.compute();  // compute right half here
+    long leftResult = leftTask.join();       // wait for left half
+
+    // Join: combine results
+    return leftResult + rightResult;
+  }
+}
+```
+
+The `ForkJoinSumCalculator` provides a clean API:
+
+```java
+public class ForkJoinSumCalculator {
+
+  private final ForkJoinPool pool;
+
+  public ForkJoinSumCalculator() {
+    this.pool = ForkJoinPool.commonPool();
+  }
+
+  public long calculateSum(long[] numbers) {
+    SumTask task = new SumTask(numbers, 0, numbers.length);
+    return pool.invoke(task);
+  }
+}
+```
+
+Running the example in `App`:
+
+```java
+long[] numbers = LongStream.rangeClosed(1, 10_000_000).toArray();
+ForkJoinSumCalculator calculator = new ForkJoinSumCalculator();
+long result = calculator.calculateSum(numbers);
+System.out.println("Fork/Join sum: " + result);
+```
+
+Program output:
+
+```
+Fork/Join sum: 50000005000000
+Expected sum:  50000005000000
+Correct: true
+Time taken: 45 ms
+Available processors: 8
+```
+
+## When to Use the Fork/Join Pattern in Java
+
+* When you have a large, CPU-intensive task that can be divided into independent subtasks.
+* When the subtasks are roughly the same size and don't depend on each other.
+* When you want to utilize multiple CPU cores without manually managing threads.
+* When the problem naturally fits a divide-and-conquer strategy (e.g., sorting, searching,
+  numerical computation).
+
+## When NOT to Use Fork/Join
+
+* For I/O-bound tasks (network calls, file reads) — use virtual threads or async I/O instead.
+* When subtasks are too small — the overhead of forking exceeds the benefit.
+* When tasks have dependencies on each other and cannot run independently.
+
+## Benefits and Trade-offs of Fork/Join Pattern
+
+Benefits:
+
+* Maximizes CPU utilization through work-stealing algorithm.
+* Scales automatically with the number of available processors.
+* Built into Java's standard library (`java.util.concurrent`) — no external dependencies.
+* Clean recursive decomposition makes the code readable and maintainable.
+
+Trade-offs:
+
+* Overhead from task creation and thread management for very small problems.
+* Requires tasks to be independent — shared mutable state introduces bugs.
+* Choosing an appropriate threshold requires tuning for optimal performance.
+* Debugging parallel code is inherently harder than sequential code.
+
+## Related Java Design Patterns
+
+* [Divide and Conquer](https://java-design-patterns.com/patterns/divide-and-conquer/):
+  Fork/Join is the parallel execution variant of the classic divide-and-conquer strategy.
+* [Thread Pool](https://java-design-patterns.com/patterns/thread-pool/): Fork/Join uses a
+  specialized pool with work-stealing semantics.
+
+## References
+
+* [Java Documentation for ForkJoinPool](https://docs.oracle.com/en/java/javase/17/docs/api/java.base/java/util/concurrent/ForkJoinPool.html)
+* [Java Concurrency in Practice — Brian Goetz](https://amzn.to/4aRMruW)
+* [Java Documentation for RecursiveTask](https://docs.oracle.com/en/java/javase/17/docs/api/java.base/java/util/concurrent/RecursiveTask.html)

fork-join/pom.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+
+    This project is licensed under the terms of the MIT license, see LICENSE.md in the root
+    of this project's GitHub repository for the full license text.
+
+    Copyright © 2014-2024 Ilkka Seppälä
+
+-->
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
+    <groupId>com.iluwatar</groupId>
+    <artifactId>java-design-patterns</artifactId>
+    <version>1.26.0-SNAPSHOT</version>
+  </parent>
+
+  <artifactId>fork-join</artifactId>
+
+  <dependencies>
+    <dependency>
+      <groupId>org.junit.jupiter</groupId>
+      <artifactId>junit-jupiter-engine</artifactId>
+      <scope>test</scope>
+    </dependency>
+  </dependencies>
+
+  <build>
+    <plugins>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-assembly-plugin</artifactId>
+        <executions>
+          <execution>
+            <configuration>
+              <archive>
+                <manifest>
+                  <mainClass>com.iluwatar.forkjoin.App</mainClass>
+                </manifest>
+              </archive>
+            </configuration>
+          </execution>
+        </executions>
+      </plugin>
+    </plugins>
+  </build>
+
+</project>

fork-join/src/main/java/com/iluwatar/forkjoin/App.java

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+package com.iluwatar.forkjoin;
+
+import java.util.stream.LongStream;
+
+/**
+ * The Fork/Join pattern is a concurrency design pattern that splits a large task into smaller
+ * subtasks (fork), processes them in parallel, and then combines the results (join).
+ *
+ * In Java, this pattern is implemented using {@link java.util.concurrent.ForkJoinPool} and
+ * {@link java.util.concurrent.RecursiveTask}. Worker threads in the pool use a work-stealing
+ * algorithm — idle threads take tasks from busy threads — maximizing CPU utilization.
+ *
+ * In this example, we demonstrate the pattern by computing the sum of a large array in
+ * parallel. The array is recursively split in half until each piece is small enough to sum
+ * directly, then results are combined back up.
+ */
+public final class App {
+
+  private App() {
+  }
+
+  /**
+   *
+   * @param args command line arguments, not used
+   */
+  public static void main(String[] args) {
+    // Create an array of 10 million numbers: [1, 2, 3, ..., 10_000_000]
+    long[] numbers = LongStream.rangeClosed(1, 10_000_000).toArray();
+
+    // Calculate sum using Fork/Join
+    ForkJoinSumCalculator calculator = new ForkJoinSumCalculator();
+    long startTime = System.currentTimeMillis();
+    long result = calculator.calculateSum(numbers);
+    long endTime = System.currentTimeMillis();
+
+    // The expected sum of 1 to N is N*(N+1)/2
+    long expected = 10_000_000L * 10_000_001L / 2;
+
+    System.out.println("Fork/Join sum: " + result);
+    System.out.println("Expected sum:  " + expected);
+    System.out.println("Correct: " + (result == expected));
+    System.out.println("Time taken: " + (endTime - startTime) + " ms");
+    System.out.println("Available processors: " + Runtime.getRuntime().availableProcessors());
+  }
+}

fork-join/src/main/java/com/iluwatar/forkjoin/ForkJoinSumCalculator.java

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+package com.iluwatar.forkjoin;
+
+import java.util.concurrent.ForkJoinPool;
+
+/**
+ * ForkJoinSumCalculator provides a convenient API to sum an array of numbers using
+ * the Fork/Join framework. It creates a {@link ForkJoinPool}, submits a {@link SumTask},
+ * and returns the computed sum.
+ *
+ * The pool manages a set of worker threads that process subtasks in parallel.
+ * Idle threads can "steal" work from busy threads, maximizing CPU utilization.
+ *
+ */
+public class ForkJoinSumCalculator {
+
+  private final ForkJoinPool pool;
+
+  /**
+   * Creates a calculator using the common ForkJoinPool (uses all available CPU cores).
+   */
+  public ForkJoinSumCalculator() {
+    this.pool = ForkJoinPool.commonPool();
+  }
+
+  /**
+   * Creates a calculator with a specific number of threads.
+   *
+   * @param parallelism the number of worker threads to use
+   */
+  public ForkJoinSumCalculator(int parallelism) {
+    this.pool = new ForkJoinPool(parallelism);
+  }
+
+  /**
+   * Calculates the sum of all elements in the array using Fork/Join parallelism.
+   *
+   * @param numbers the array of numbers to sum
+   * @return the total sum of all elements
+   */
+  public long calculateSum(long[] numbers) {
+    if (numbers == null || numbers.length == 0) {
+      return 0;
+    }
+    SumTask task = new SumTask(numbers, 0, numbers.length);
+    return pool.invoke(task);
+  }
+}