Month: March 2024

Java 21 introduces a game-changer for concurrent programming: virtual threads. This article explores what virtual threads are and how they can revolutionize the way you build high-performance applications.

Traditional Threads vs. Virtual Threads

Java developers have long relied on platform threads, the fundamental unit of processing that runs concurrently. However, creating and managing a large number of platform threads can be resource-intensive. This becomes a bottleneck for applications handling high volumes of requests.

Virtual threads offer a lightweight alternative. They are managed by the Java runtime environment, allowing for a much larger number to coexist within a single process compared to platform threads. This translates to significant benefits:

• Reduced Overhead: Creating and managing virtual threads requires fewer resources, making them ideal for applications that thrive on high concurrency.
• Efficient Hardware Utilization: Virtual threads don't directly map to operating system threads, enabling them to better leverage available hardware cores. This translates to handling more concurrent requests and improved application throughput.
• Simpler Concurrency Model: Virtual threads adhere to the familiar "one thread per request" approach used with platform threads. This makes the transition for developers already comfortable with traditional concurrency patterns much smoother. There's no need to learn entirely new paradigms or complex APIs.

Creating Virtual Threads

Java 21 offers two primary ways to create virtual threads:

1. Thread.Builder Interface: This approach provides a familiar interface for creating virtual threads. You can use a static builder method or a builder object to configure properties like thread name before starting it.

   Here's an example of using the Thread.Builder interface:

   Runnable runnable = () -> {
      var name = Thread.currentThread().getName();
      System.out.printf("Hello, %s!%n", name.isEmpty() ? "anonymous" : name);
   };
   
   try {
      // Using a static builder method
      Thread virtualThread = Thread.startVirtualThread(runnable);
   
      // Using a builder with a custom name
      Thread namedThread = Thread.ofVirtual()
              .name("my-virtual-thread")
              .start(runnable);
   
      // Wait for the threads to finish (optional)
      virtualThread.join();
      namedThread.join();
   } catch (InterruptedException e) {
      throw new RuntimeException(e);
   }

2. ExecutorService with Virtual Threads: This method leverages an ExecutorService specifically designed to create virtual threads for each submitted task. This approach simplifies thread management and ensures proper cleanup of resources.

   Here's an example of using an ExecutorService with virtual threads:

   try (ExecutorService myExecutor = Executors.newVirtualThreadPerTaskExecutor()) {
      Future future = myExecutor.submit(() -> System.out.println("Running thread"));
      future.get(); // Wait for the task to complete
      System.out.println("Task completed");
   } catch (ExecutionException | InterruptedException e) {
      throw new RuntimeException(e);
   }

Embrace a New Era of Concurrency

Virtual threads represent a significant leap forward in Java concurrency. Their efficiency, better hardware utilization, and familiar approach make them a powerful tool for building high-performance and scalable applications.

Instead of simply matching against constant values, switch type patterns allow you to match against the types and their specific characteristics of the evaluated expression. This translates to cleaner, more readable code compared to traditional if-else statements or cumbersome instanceof checks.

Key Features

• Type patterns: These match against the exact type of the evaluated expression (e.g., case String s).
• Deconstruction patterns: These extract specific elements from record objects of a certain type (e.g., case Point(int x, int y)).
• Guarded patterns: These add additional conditions to be met alongside the type pattern, utilizing the when clause (e.g., case String s when s.length() > 5).
• Null handling: You can now explicitly handle the null case within the switch statement.

Benefits

• Enhanced Readability: Code becomes more intuitive by directly matching against types and extracting relevant information.
• Reduced Boilerplate: Eliminate the need for extensive instanceof checks and type casting, leading to cleaner code.
• Improved Type Safety: Explicit type checks within the switch statement prevent potential runtime errors.
• Fine-grained Control Flow: The when clause enables precise matching based on both type and additional conditions.

Examples in Action

1. Type Patterns:

   Number number = 10l;
   
   switch (number) {
      case Integer i -> System.out.printf("%d is an integer!", i);
      case Long l -> System.out.printf("%d is a long!", l);
      default -> System.out.println("Unknown type");
   }

   In this example, the switch statement checks the exact type of number using the Long type pattern.

2. Deconstruction Patterns:

   record Point(int x, int y) {}
   
   Point point = new Point(2, 3);
   
   switch (point) {
      case Point(var x, var y) -> System.out.println("Point coordinates: (" + x + ", " + y + ")");
      default -> System.out.println("Unknown object type");
   }

   Here, the deconstruction pattern extracts the x and y coordinates from the Point record object and assigns them to variables within the case block.

3. Guarded Patterns with the when Clause:

   String name = "John Doe";
   
   switch (name) {
      case String s when s.length() > 5 -> System.out.println("Long name!");
      case String s -> System.out.println("It's a string.");
   }

   This example demonstrates a guarded pattern. The first case checks if the evaluated expression is a String and its length is greater than 5 using the when clause.

4. Null Handling:

   Object object = null;
   
   switch (object) {
    case null -> System.out.println("The object is null.");
    case String s -> System.out.println("It's a string!");
    default -> System.out.println("Unknown object type");
   }

   Finally, this example showcases the ability to explicitly handle the null case within the switch statement, improving code safety.

Conclusion

Switch type patterns in Java 21 offer a powerful and versatile way to write concise, readable, and type-safe code. By leveraging its features, including the when clause for guarded patterns, you can significantly enhance the maintainability and expressiveness of your Java applications.

Java 21 introduced a significant enhancement to the collection framework: SequencedCollection. This new interface brings order to the world of collections, providing standardized ways to interact with elements based on their sequence.

What are Sequenced Collections?

Imagine a list where the order of elements matters. That's the essence of a SequencedCollection. It extends the existing Collection interface, offering additional functionalities specific to ordered collections.

Key Features:

• Accessing first and last elements: Methods like getFirst() and getLast() grant direct access to the first and last elements in the collection, respectively.
• Adding and removing elements at ends: Efficiently manipulate the beginning and end of the sequence with methods like addFirst(), addLast(), removeFirst(), and removeLast().
• Reversed view: The reversed() method provides a view of the collection in reverse order. Any changes made to the original collection are reflected in the reversed view.

Benefits:

• Simplified code: SequencedCollection provides clear and concise methods for working with ordered collections, making code easier to read and maintain.
• Improved readability: The intent of operations becomes more evident when using methods like addFirst() and removeLast(), leading to better understanding of code.

Example Usage:

Consider a Deque (double-ended queue) implemented using ArrayDeque:

import java.util.ArrayDeque;
import java.util.Deque;

public class SequencedCollectionExample {
    public static void main(String ... args) {
        Deque<String> tasks = new ArrayDeque<>();

        // Add tasks (FIFO order)
        tasks.addLast("Buy groceries");
        tasks.addLast("Finish homework");
        tasks.addLast("Call mom");

        // Access and process elements
        System.out.println("First task: " + tasks.getFirst());

        // Process elements in reverse order
        Deque<String> reversedTasks = tasks.reversed();
        for (String task : reversedTasks) {
            System.out.println("Reversed: " + task);
        }
    }
}

This example demonstrates how SequencedCollection allows for efficient access and manipulation of elements based on their order, both forward and backward.

Implementation Classes:

While SequencedCollection is an interface, existing collection classes automatically become SequencedCollection by virtue of inheriting from Collection. Here's a brief overview:

• Lists: ArrayList, LinkedList, and Vector
• Sets: Not directly applicable, but LinkedHashSet maintains order within sets.
• Queues: ArrayDeque and LinkedList
• Maps: Not directly applicable, but LinkedHashMap and TreeMap (based on key order) maintain order for key-value pairs.

Remember, specific functionalities and behaviors might vary within these classes. Refer to the official Java documentation for detailed information.

Conclusion:

SequencedCollection is a valuable addition to the Java collection framework, offering a structured and efficient way to work with ordered collections. By understanding its features and functionalities, you can write more readable, maintainable, and expressive code when dealing with ordered data structures in Java 21 and beyond.

Java's Fork-Join Framework, introduced in Java 7 as part of the java.util.concurrent package, offers a powerful mechanism for parallelizing and efficiently handling divide-and-conquer-style algorithms. At its core is the ForkJoinPool, a sophisticated executor service designed for managing parallel tasks.

Overview of Fork-Join Framework

The Fork-Join Framework is particularly well-suited for recursive and divide-and-conquer algorithms. It provides two main classes: RecursiveTask for tasks that return a result, and RecursiveAction for tasks that perform an action without returning a result.

ForkJoinPool and Parallelism

The ForkJoinPool manages a set of worker threads and facilitates the parallel execution of tasks. The default pool size is dynamically determined based on the available processors on the machine. This adaptive sizing allows for efficient resource utilization.

// Creating a ForkJoinPool with default parallelism
ForkJoinPool forkJoinPool = new ForkJoinPool();

Limiting the Size of the Pool

It is possible to limit the size of the ForkJoinPool by specifying the parallelism level during its creation. This can be useful to control resource usage and adapt the pool to specific requirements.

// Creating a ForkJoinPool with a limited parallelism level
int parallelismLevel = 4;
ForkJoinPool limitedPool = new ForkJoinPool(parallelismLevel);

Work-Stealing Strategy

The heart of the Fork-Join Framework's efficiency lies in its work-stealing strategy. Here's a breakdown of how it works:

• Task Splitting: Tasks can recursively split into smaller subtasks during execution.
• Deque Structure: Each worker thread has its own deque (double-ended queue) for storing tasks.
• Stealing Tasks: When a worker thread's deque is empty, it steals tasks from other worker threads' deques, minimizing contention.
• Load Balancing: The strategy ensures efficient load balancing by redistributing tasks among available threads.
• Task Affinity: Threads tend to execute tasks they have recently created or stolen, optimizing cache usage.

Handling Blocking Situations

If all worker threads are blocked, for instance, due to tasks waiting on external resources, the pool can become stalled. In such cases, the efficiency of the Fork-Join Pool might be compromised. It's crucial to be mindful of blocking operations within tasks and consider alternative concurrency mechanisms if needed.

6. Managing the Pool's Lifecycle

Once a ForkJoinPool is created, it should be explicitly shut down when no longer needed to prevent resource leaks and ensure a clean application exit.

// Shutting down the ForkJoinPool
forkJoinPool.shutdown();

7. Sample Usage

Let's consider a simple example of calculating the sum of an array using a Fork-Join task:

import java.util.concurrent.RecursiveTask;
import java.util.concurrent.ForkJoinPool;

public class SumTask extends RecursiveTask<Integer> {
    private final int[] data;
    private final int start;
    private final int end;

    public SumTask(int[] data, int start, int end) {
        this.data = data;
        this.start = start;
        this.end = end;
    }

    @Override
    protected Integer compute() {
        if (end - start <= 10) {
            // Solve the problem sequentially
            int sum = 0;
            for (int i = start; i < end; i++) {
                sum += data[i];
            }
            return sum;
        } else {
            // Split the task into subtasks
            int mid = (start + end) / 2;
            SumTask leftTask = new SumTask(data, start, mid);
            SumTask rightTask = new SumTask(data, mid, end);

            // Fork the subtasks
            leftTask.fork();
            rightTask.fork();

            // Join the results
            int leftResult = leftTask.join();
            int rightResult = rightTask.join();

            // Combine the results
            return leftResult + rightResult;
        }
    }

    public static void main(String[] args) {
        int[] data = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
        ForkJoinPool forkJoinPool = new ForkJoinPool();
        int result = forkJoinPool.invoke(new SumTask(data, 0, data.length));
        System.out.println("Result: " + result);

        // Don't forget to shut down the pool when it's no longer needed
        forkJoinPool.shutdown();
    }
}

In this example, we use a ForkJoinPool to calculate the sum of an array efficiently by dividing the task into subtasks and utilizing the work-stealing strategy.