Virtual Threads (Project Loom) – Revolutionizing Concurrency in Java

Introduction

Concurrency has always been a cornerstone of Java, but as applications scale and demands for high throughput and low latency increase, traditional threading models show their limitations. Project Loom and its groundbreaking introduction of virtual threads redefines how we approach concurrency in Java, making applications more scalable and development more straightforward.

In this post, we’ll go deep into virtual threads, exploring how they work, their impact on scalability, and how they simplify backend development. We’ll provide both simple and complex code examples to illustrate these concepts in practice.

Project Loom Virtual Threads in Java

The Limitations of Traditional Threads

In Java, each thread maps to an operating system (OS) thread. While this model is straightforward, it comes with significant overhead:

  • Resource Consumption: OS threads are heavy-weight, consuming considerable memory (~1MB stack size by default).
  • Context Switching: The OS has to manage context switching between threads, which can degrade performance when thousands of threads are involved.
  • Scalability Issues: Blocking operations (e.g., I/O calls) tie up OS threads, limiting scalability.

Traditional solutions involve complex asynchronous programming models or reactive frameworks, which can make code harder to read and maintain.

Introducing Virtual Threads

Virtual threads are “lightweight” threads that aim to solve these problems:

  • Lightweight: Thousands of virtual threads can be created without significant overhead.
  • Efficient Scheduling: Managed by the JVM rather than the OS, leading to more efficient context switching.
  • Simplified Concurrency: Enable writing straightforward, blocking code without sacrificing scalability.

Virtual threads decouple the application thread from the OS thread, allowing the JVM to manage threading more efficiently.

How Virtual Threads Work

Under the hood, virtual threads are scheduled by the JVM onto a pool of OS threads. Key aspects include:

  • Continuation-Based: Virtual threads use continuations to save and restore execution state.
  • Non-Blocking Operations: When a virtual thread performs a blocking operation, it yields control, allowing the JVM to schedule another virtual thread.
  • Efficient Utilization: The JVM reuses OS threads, minimizing the cost of context switches.

Here’s a simplified diagram:

[plant uml diagram]

Benefits of Virtual Threads

  • Scalability: Handle millions of concurrent tasks with minimal resources.
  • Simplified Code: Write blocking code without complex asynchronous patterns.
  • Performance: Reduced context switching overhead and better CPU utilization.
  • Integration: Works seamlessly with existing Java code and libraries.

Simple Examples

Example 1: Spawning Virtual Threads

public class VirtualThreadExample {
    public static void main(String[] args) throws InterruptedException {
        Thread.startVirtualThread(() -> {
            System.out.println("Hello from a virtual thread!");
        });

        // Alternatively, using Thread.Builder
        Thread thread = Thread.builder()
                .virtual()
                .task(() -> System.out.println("Another virtual thread"))
                .start();

        thread.join();
    }
}

Explanation:

  • Thread.startVirtualThread creates and starts a virtual thread.
  • Virtual threads behave like regular threads but are lightweight.

Example 2: Migrating from Traditional to Virtual Threads

Traditional threading:

ExecutorService executor = Executors.newFixedThreadPool(10);

for (int i = 0; i < 100; i++) {
    executor.submit(() -> {
        // Perform task
    });
}

executor.shutdown();

Using virtual threads:

ExecutorService executor = Executors.newVirtualThreadPerTaskExecutor();

for (int i = 0; i < 100; i++) {
    executor.submit(() -> {
        // Perform task
    });
}

executor.shutdown();

Explanation:

  • Executors.newVirtualThreadPerTaskExecutor() creates an executor that uses virtual threads.
  • We can submit a large number of tasks without worrying about thread exhaustion.

Complex Examples

Example 1: High-Throughput Server with Virtual Threads

Let’s build a server that handles a massive number of connections using virtual threads.

import java.io.IOException;
import java.net.ServerSocket;
import java.net.Socket;

public class VirtualThreadServer {
    public static void main(String[] args) throws IOException {
        try (ServerSocket serverSocket = new ServerSocket(8080)) {
            while (true) {
                Socket clientSocket = serverSocket.accept();

                Thread.startVirtualThread(() -> handleClient(clientSocket));
            }
        }
    }

    private static void handleClient(Socket clientSocket) {
        try (clientSocket) {
            // Read from and write to the client
            clientSocket.getOutputStream().write("HTTP/1.1 200 OK\r\n\r\nHello World".getBytes());
        } catch (IOException e) {
            e.printStackTrace();
        }
    }
}

Explanation:

  • Each incoming connection is handled by a virtual thread.
  • The server can handle a vast number of simultaneous connections efficiently.

Performance Considerations:

  • Blocking I/O operations in virtual threads do not block OS threads.
  • The JVM efficiently manages the scheduling of virtual threads.

Example 2: Custom Virtual Thread Executor Service

Creating a custom executor service that manages virtual threads with specific configurations.

import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.ThreadFactory;

public class CustomVirtualThreadExecutor {
    public static void main(String[] args) {
        ThreadFactory factory = Thread.builder()
                .virtual()
                .name("virtual-thread-", 0)
                .factory();

        ExecutorService executor = Executors.newThreadPerTaskExecutor(factory);

        for (int i = 0; i < 1000; i++) {
            int taskNumber = i;
            executor.submit(() -> {
                System.out.println(Thread.currentThread().getName() + " executing task " + taskNumber);
                // Simulate work
                try {
                    Thread.sleep(100);
                } catch (InterruptedException e) {
                    Thread.currentThread().interrupt();
                }
            });
        }

        executor.shutdown();
    }
}

Explanation:

  • Using Thread.builder(), we create a custom thread factory for virtual threads with a naming pattern.
  • The executor service uses this factory to create virtual threads per task.
  • This setup allows for customized thread creation and better debugging.

Example 3: Structured Concurrency with Virtual Threads

Structured concurrency helps manage multiple concurrent tasks as a single unit.

import java.time.Duration;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.StructuredTaskScope;

public class StructuredConcurrencyExample {
    public static void main(String[] args) {
        try (var scope = new StructuredTaskScope.ShutdownOnFailure()) {
            var future1 = scope.fork(() -> fetchDataFromServiceA());
            var future2 = scope.fork(() -> fetchDataFromServiceB());

            scope.join();           // Wait for all tasks
            scope.throwIfFailed();  // Propagate exceptions

            String resultA = future1.resultNow();
            String resultB = future2.resultNow();

            System.out.println("Results: " + resultA + ", " + resultB);
        } catch (InterruptedException | ExecutionException e) {
            e.printStackTrace();
        }
    }

    private static String fetchDataFromServiceA() throws InterruptedException {
        Thread.sleep(Duration.ofSeconds(2));
        return "Data from Service A";
    }

    private static String fetchDataFromServiceB() throws InterruptedException {
        Thread.sleep(Duration.ofSeconds(3));
        return "Data from Service B";
    }
}

Explanation:

  • StructuredTaskScope allows grouping tasks and managing them collectively.
  • ShutdownOnFailure ensures that if one task fails, all others are canceled.
  • Virtual threads make this pattern efficient and practical.

Benefits:

  • Simplifies error handling in concurrent code.
  • Improves readability and maintainability.

Impact on Backend Development

Virtual threads have profound implications for backend development:

Simplified Codebases

In traditional Java, we often use non-blocking I/O to achieve concurrency, which can complicate code structure. With virtual threads, we can use blocking code without the performance penalties associated with OS threads.

Example Without Virtual Threads (Using Asynchronous I/O):

CompletableFuture<Void> future = CompletableFuture.runAsync(() -> {
    try {
        HttpClient client = HttpClient.newHttpClient();
        HttpRequest request = HttpRequest.newBuilder(URI.create("https://example.com")).build();
        client.sendAsync(request, HttpResponse.BodyHandlers.ofString())
              .thenAccept(response -> System.out.println(response.body()));
    } catch (Exception e) {
        e.printStackTrace();
    }
});

Simplified with Virtual Threads:

Thread.startVirtualThread(() -> {
    try {
        HttpClient client = HttpClient.newHttpClient();
        HttpRequest request = HttpRequest.newBuilder(URI.create("https://example.com")).build();
        HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());
        System.out.println(response.body());
    } catch (Exception e) {
        e.printStackTrace();
    }
});

With virtual threads, we can use synchronous client.send() directly, making the code simpler and more readable, while still benefiting from concurrency.

Elimination of Callback Hell

Asynchronous programming often leads to nested callbacks, which make the code harder to read and debug. Virtual threads allow us to write code in a linear, blocking style, avoiding callback hell.

Example Using Callbacks (Without Virtual Threads):

fetchDataAsync("https://example.com/data", result -> {
    processAsync(result, processed -> {
        saveAsync(processed, saved -> {
            System.out.println("Data saved successfully!");
        });
    });
});

Simplified with Virtual Threads:

Thread.startVirtualThread(() -> {
    String data = fetchData("https://example.com/data");
    String processed = process(data);
    save(processed);
    System.out.println("Data saved successfully!");
});

With virtual threads, we can write sequential, synchronous code while retaining concurrency, eliminating the need for nested callbacks.

Enhanced Performance

Handling many concurrent requests with traditional threads can quickly lead to memory exhaustion. Virtual threads allow us to handle a large number of connections concurrently with minimal resource overhead.

Example: High-Concurrency Server with Virtual Threads

try (var serverSocket = new ServerSocket(8080)) {
    while (true) {
        var clientSocket = serverSocket.accept();
        Thread.startVirtualThread(() -> handleClient(clientSocket));
    }
}

private static void handleClient(Socket clientSocket) {
    try (clientSocket) {
        clientSocket.getOutputStream().write("HTTP/1.1 200 OK\r\n\r\nHello, World!".getBytes());
    } catch (IOException e) {
        e.printStackTrace();
    }
}

This server can handle thousands of simultaneous connections without exhausting system resources, as each connection runs on a virtual thread.

Compatibility with Existing Libraries and Frameworks

Since virtual threads are part of the standard Java threading API, they are compatible with most existing libraries and frameworks, allowing developers to integrate virtual threads without extensive refactoring.

Example: Using Virtual Threads with ExecutorService

You can replace traditional thread pools with virtual thread-based executors to use existing code with minimal changes.

ExecutorService executor = Executors.newVirtualThreadPerTaskExecutor();

for (int i = 0; i < 100; i++) {
    executor.submit(() -> {
        System.out.println("Running task on a virtual thread.");
    });
}

executor.shutdown();

Any code that works with ExecutorService will continue to work seamlessly with virtual threads, enhancing compatibility.

Reduced Need for Reactive Frameworks

Virtual threads allow developers to use blocking code patterns without the overhead associated with OS threads, making it possible to achieve high concurrency with simpler code structures, reducing the need for reactive frameworks.

Example: Synchronous Data Fetching with Virtual Threads Instead of Reactive Patterns

Reactive (Without Virtual Threads):

Mono<String> data = WebClient.create("https://example.com")
    .get()
    .retrieve()
    .bodyToMono(String.class);

data.subscribe(System.out::println);

Simplified with Virtual Threads:

Thread.startVirtualThread(() -> {
    HttpClient client = HttpClient.newHttpClient();
    HttpRequest request = HttpRequest.newBuilder(URI.create("https://example.com")).build();
    String response = client.send(request, HttpResponse.BodyHandlers.ofString()).body();
    System.out.println(response);
});

Virtual threads allow us to use blocking code directly, making reactive patterns unnecessary for some use cases. This reduces complexity, especially for applications that don’t require the full power of reactive programming.

Considerations

When implementing virtual threads with Project Loom, developers must consider various technical and architectural implications. Below are some detailed considerations to keep in mind:

Memory Usage and Stack Management

Virtual threads are lightweight compared to traditional OS threads, but they still consume memory, especially if the virtual threads are highly stacked (deep call stacks).

  • Stack Size: Virtual threads start with a small stack and can expand as needed, which can potentially reduce memory consumption compared to OS threads. However, developers should monitor stack usage to avoid excessive memory consumption.
  • Memory Monitoring: Although virtual threads are efficient, monitoring the JVM’s memory usage becomes essential as thousands of virtual threads may be active concurrently.
  • JVM Configuration: Tuning the JVM’s garbage collection and memory settings is important when handling millions of threads, as they may put unexpected pressure on the heap.

Blocking vs. Non-Blocking Code Patterns

Virtual threads make blocking I/O efficient, but there are some nuances:

  • Blocking I/O Operations: With virtual threads, you can use blocking calls like Socket or File I/O without performance penalties. However, the JVM handles only traditional blocking I/O efficiently, so libraries must be updated for Loom support.
  • Non-Blocking I/O: If your project is already using non-blocking I/O, switching to virtual threads might simplify the code structure but won’t necessarily bring significant performance gains, as non-blocking code is already optimized.
  • Thread Pool Alternatives: In traditional models, a common technique is to use a pool of threads to limit the number of concurrent operations. With virtual threads, this might no longer be necessary, allowing a model where each task gets its own virtual thread without causing bottlenecks.

Concurrency Limitations

While virtual threads allow for a high degree of concurrency, they are not a silver bullet. Certain scenarios, such as CPU-bound tasks, still require careful handling to avoid performance degradation.

  • CPU-Bound Tasks: Virtual threads are designed for I/O-bound workloads. If the application has CPU-intensive tasks, virtual threads might not yield the same benefits, as they do not reduce CPU time requirements.
  • Parallelism Control: For tasks that require controlled parallelism, developers may still benefit from combining virtual threads with task-limiting mechanisms (e.g., limiting the number of CPU-bound threads).
  • Thread Priority and Scheduling: Virtual threads are managed by the JVM and may not respect OS-level thread priorities. If your application requires fine-grained control over thread priority, virtual threads might not be ideal.

Error Handling and Exception Propagation

Error handling becomes crucial, especially with the simplicity of launching thousands of threads.

  • Propagating Exceptions: Virtual threads handle exceptions differently; uncaught exceptions in a virtual thread do not terminate the JVM process but are logged or can be handled asynchronously.
  • Graceful Shutdowns: Virtual threads simplify concurrency, but managing error states across thousands of threads can be challenging. Structured concurrency (a model for grouping and managing threads introduced alongside Loom) helps manage error propagation and task cancellation.
  • Task Scopes: When using structured concurrency with virtual threads, grouping tasks with scopes (e.g., ShutdownOnFailure in Java’s StructuredTaskScope) ensures that if one task in a group fails, other tasks can be canceled or handled appropriately.

Impact on Debugging and Profiling

With potentially millions of threads, debugging and profiling virtual-threaded applications introduce unique challenges.

  • Thread Explosion in Debuggers: Debuggers might struggle with applications using millions of virtual threads, leading to overwhelming output. It may be helpful to add application-level logging or selectively enable virtual threads for debugging.
  • Profiling Complexity: Traditional thread profilers may not provide granular insights for virtual threads. Consider using JVM flight recording or Loom-aware profiling tools to trace virtual thread usage accurately.
  • Stack Trace Analysis: Virtual threads make it possible to have more granular and descriptive stack traces, but interpreting large volumes of stack traces could require additional tooling or filtering strategies.

Interplay with Synchronization and Locks

Though virtual threads alleviate many concurrency issues, developers still need to be cautious with shared resources.

  • Contention on Shared Resources: Virtual threads do not inherently solve issues related to contention on shared resources. If two virtual threads try to acquire the same lock, they may still face contention, potentially leading to bottlenecks.
  • Thread Safety: Existing synchronized code will generally work with virtual threads. However, in a highly concurrent environment, developers should consider using java.util.concurrent locks (e.g., ReentrantLock with try-lock mechanisms) or lock-free data structures to avoid contention.
  • Deadlock Risks: While virtual threads reduce many resource-related problems, deadlocks can still occur if resources are mismanaged. Deadlock analysis tools can help in identifying potential deadlock situations.

Structured Concurrency for Task Management

Structured concurrency in Project Loom allows developers to group threads and manage them collectively, making error handling and task cancellation more intuitive.

  • Parent-Child Relationships: Structured concurrency introduces a parent-child relationship between tasks, simplifying lifecycle management and error propagation.
  • Graceful Cancellation: If a parent task is canceled, all child tasks are automatically canceled, making it easier to handle scenarios where one task failure requires the cancellation of other related tasks.
  • Scope Lifecycle Management: With StructuredTaskScope, developers can define a task group’s lifecycle. This ensures that resources are managed properly, and all tasks in the scope are completed, failed, or canceled together.

Interfacing with Existing Thread-Based Libraries

Virtual threads integrate well with many libraries but may require attention with those heavily reliant on OS-level threads or specialized thread management.

  • OS-Threaded Libraries: Libraries that rely on low-level OS-thread management (e.g., JNI-based libraries) may not benefit directly from virtual threads, as they may require actual OS threads for certain operations.
  • External Thread Pools: If your application integrates with external thread pools (e.g., database connection pools), consider switching to Loom-compatible connection handling, as some third-party libraries may not yet support virtual threads.
  • Task Executors: Replacing ThreadPoolExecutor with Executors.newVirtualThreadPerTaskExecutor() allows easier adaptation to existing thread-based code, but testing is recommended to ensure compatibility and performance stability.

Performance Profiling and Resource Management

Virtual threads reduce context-switching overhead and make high-concurrency applications more feasible, but monitoring and optimizing performance remain crucial.

  • Avoiding Thread Overuse: Although virtual threads are lightweight, overusing them (e.g., starting a new thread for every small task) can still degrade performance. Consider batching or grouping tasks when feasible.
  • Heap Pressure and Garbage Collection: Large numbers of virtual threads may generate considerable garbage, adding pressure on the JVM’s garbage collection. Profiling and tuning the GC for high-throughput applications with virtual threads is crucial.
  • Application Profiling: Java Flight Recorder (JFR) or other profiling tools with virtual-thread awareness can help understand the application’s runtime characteristics, especially in production environments.

Testing and Migration Strategies

Testing and planning migration from traditional to virtual threads require thorough analysis and validation.

  • Gradual Migration: Virtual threads allow a gradual migration. Developers can begin by converting specific thread-heavy sections of the application to virtual threads while retaining traditional threads elsewhere.
  • Testing for Loom Compatibility: While most Java libraries are expected to be compatible, rigorous testing is recommended, particularly for libraries with complex threading requirements or blocking operations.
  • Load Testing and Performance Validation: Applications utilizing virtual threads should undergo load testing to validate that the new threading model provides the expected concurrency improvements without introducing regressions or bottlenecks.

Patterns and Anti-Patterns

After all beeing said lets examine some common patterns and antipatterns

Patterns

Task-Per-Thread Pattern

One of the primary use cases for virtual threads is to simplify concurrency by using a “task-per-thread” model. In this pattern, each concurrent task is assigned to a separate virtual thread, which avoids the complex management typically needed for thread pools with OS threads.

Example: A server handling multiple incoming connections, with each connection running in its own virtual thread.

try (ServerSocket serverSocket = new ServerSocket(8080)) {
    while (true) {
        Socket clientSocket = serverSocket.accept();
        Thread.startVirtualThread(() -> handleClient(clientSocket));
    }
}

private static void handleClient(Socket clientSocket) {
    try (clientSocket) {
        clientSocket.getOutputStream().write("HTTP/1.1 200 OK\r\n\r\nHello, World!".getBytes());
    } catch (IOException e) {
        e.printStackTrace();
    }
}

Each connection is handled by a virtual thread, providing simple and scalable concurrency without the need for a complex thread pool. This pattern would be inefficient with OS threads but is efficient with virtual threads.

Structured Concurrency Pattern

Structured concurrency organizes concurrent tasks as a structured unit, making it easier to manage lifecycles, cancellations, and exceptions. This pattern is particularly useful in request-based applications where tasks are interdependent.

Example: Fetching data from multiple services concurrently and consolidating the results.

try (var scope = new StructuredTaskScope.ShutdownOnFailure()) {
    var future1 = scope.fork(() -> fetchDataFromServiceA());
    var future2 = scope.fork(() -> fetchDataFromServiceB());

    scope.join();           // Wait for all tasks to complete
    scope.throwIfFailed();  // Handle exceptions

    String resultA = future1.resultNow();
    String resultB = future2.resultNow();
    System.out.println("Results: " + resultA + ", " + resultB);
} catch (Exception e) {
    e.printStackTrace();
}

Structured concurrency ensures that if any task fails, all other tasks in the same scope are canceled. It simplifies error handling and improves resource management, providing better control over concurrent flows.

Blocking Code in Virtual Threads

With virtual threads, developers can safely use blocking calls, such as Thread.sleep() or blocking I/O operations, as the JVM handles scheduling efficiently. This pattern contrasts with the traditional approach of avoiding blocking calls to prevent thread starvation.

Example: Running blocking I/O operations without impacting overall performance.

Thread.startVirtualThread(() -> {
    try {
        Thread.sleep(1000);  // Blocking call
        System.out.println("Virtual thread finished sleeping");
    } catch (InterruptedException e) {
        Thread.currentThread().interrupt();
    }
});

Virtual threads make blocking operations efficient, as the JVM schedules other virtual threads to run while the blocking call is in progress. This allows developers to write simpler, more readable code without performance trade-offs.

Task-Based Executors with Virtual Threads

Using Executors.newVirtualThreadPerTaskExecutor() creates a virtual-thread-based executor that simplifies parallel task execution. This pattern allows developers to leverage the ExecutorService interface, making it easy to transition from traditional threads to virtual threads.

Example: Running multiple tasks concurrently with a virtual-thread-based executor.

ExecutorService executor = Executors.newVirtualThreadPerTaskExecutor();

for (int i = 0; i < 10; i++) {
    executor.submit(() -> {
        System.out.println("Running task on virtual thread: " + Thread.currentThread().getName());
    });
}

executor.shutdown();

This executor allows each task to run on a virtual thread, making it efficient to create a new thread per task without the need for traditional thread pooling.

Anti-Patterns

Overuse of Virtual Threads

While virtual threads are lightweight, they are not “free.” Creating excessive numbers of virtual threads for very short-lived tasks can introduce overhead in terms of scheduling and garbage collection, which may impact performance.

Anti-Pattern Example: Creating a new virtual thread for every small task, such as iterating over a list.

List<String> items = List.of("A", "B", "C");
for (String item : items) {
    Thread.startVirtualThread(() -> processItem(item));  // Inefficient
}

Better Approach: Instead, batch tasks together if they are very short-lived to avoid excessive thread creation.

Thread.startVirtualThread(() -> items.forEach(VirtualThreadsExample::processItem));

By batching the tasks within a single virtual thread, you avoid creating unnecessary threads, optimizing resource usage and reducing scheduling overhead.

Blocking OS Resources in Virtual Threads

Blocking calls that involve resources controlled by the OS, such as file locks or certain low-level network operations, may still tie up OS threads when used with virtual threads, leading to potential bottlenecks.

Anti-Pattern Example: Locking a file for extended periods in a virtual thread.

Thread.startVirtualThread(() -> {
    try (var fileChannel = FileChannel.open(Path.of("file.txt"), StandardOpenOption.WRITE)) {
        fileChannel.lock();  // This can block an OS thread, not recommended
    } catch (IOException e) {
        e.printStackTrace();
    }
});

Better Approach: Avoid blocking virtual threads on OS resources that do not release quickly. Use asynchronous approaches for tasks involving external resources.

Improper Exception Handling in Virtual Threads

Exceptions in virtual threads do not terminate the JVM, and uncaught exceptions in virtual threads may not be logged as prominently as in traditional threads, leading to undetected errors.

Anti-Pattern Example: Ignoring exceptions in virtual threads.

Thread.startVirtualThread(() -> {
    int result = 10 / 0;  // Unhandled exception
    System.out.println("Result: " + result);
});

Better Approach: Use structured concurrency or set up explicit exception handling within virtual threads to capture and handle errors effectively.

Thread.startVirtualThread(() -> {
    try {
        int result = 10 / 0;
        System.out.println("Result: " + result);
    } catch (Exception e) {
        System.err.println("Error in virtual thread: " + e.getMessage());
    }
});

Proper error handling in virtual threads helps in identifying and managing issues without causing untracked failures.

Manual Management of Thread Lifecycles

With virtual threads, the need for manually managing thread lifecycles or using traditional thread pooling mechanisms decreases. Creating a custom virtual-thread pool or managing virtual threads directly as a group is often unnecessary and counterproductive.

Anti-Pattern Example: Manually creating a virtual-thread pool.

List<Thread> virtualThreadPool = new ArrayList<>();
for (int i = 0; i < 10; i++) {
    virtualThreadPool.add(Thread.startVirtualThread(() -> System.out.println("Task in virtual pool")));
}
virtualThreadPool.forEach(t -> {
    try {
        t.join();
    } catch (InterruptedException e) {
        Thread.currentThread().interrupt();
    }
});

Better Approach: Use Executors.newVirtualThreadPerTaskExecutor() to manage tasks rather than manually handling virtual threads.

ExecutorService executor = Executors.newVirtualThreadPerTaskExecutor();

for (int i = 0; i < 10; i++) {
    executor.submit(() -> System.out.println("Task in virtual thread executor"));
}

executor.shutdown();

Manually creating and managing virtual-thread pools contradicts the efficiency of built-in virtual-thread executors, which are optimized for this purpose.

Over-Reliance on Virtual Threads for CPU-Bound Tasks

Virtual threads excel in I/O-bound tasks, but for CPU-bound tasks, they offer limited benefits. Virtual threads do not reduce the CPU time required, so heavy reliance on virtual threads for CPU-bound operations can lead to high contention and degraded performance.

Anti-Pattern Example: Running a CPU-intensive task on a high number of virtual threads.

ExecutorService executor = Executors.newVirtualThreadPerTaskExecutor();
for (int i = 0; i < 100; i++) {
    executor.submit(() -> performCPUIntensiveTask());
}

Better Approach: Use a fixed-size thread pool for CPU-bound tasks to limit the number of concurrent CPU-intensive operations.

ExecutorService cpuExecutor = Executors.newFixedThreadPool(4);  // Adjust pool size for CPU cores
for (int i = 0; i < 100; i++) {
    cpuExecutor.submit(() -> performCPUIntensiveTask());
}
cpuExecutor.shutdown();

Using a fixed-size pool for CPU-bound tasks helps manage CPU usage and avoids overwhelming the CPU with excessive task scheduling.

Relying on Global State in Virtual Threads

Virtual threads can be short-lived and numerous, so relying on shared global state can lead to contention and potential race conditions, especially when many virtual threads attempt to access or modify the state concurrently.

Anti-Pattern Example: Modifying shared global state from multiple virtual threads.

public static int counter = 0;

for (int i = 0; i < 1000; i++) {
    Thread.startVirtualThread(() -> counter++);
}

Better Approach: Use thread-safe data structures or local variables to reduce contention. For counters, consider using AtomicInteger or other concurrent collections.

AtomicInteger counter = new AtomicInteger();

for (int i = 0; i < 1000; i++) {
    Thread.startVirtualThread(() -> counter.incrementAndGet());
}

Avoiding shared global state or using thread-safe structures reduces contention and prevents data corruption, especially in high-concurrency environments.

Conclusion

Project Loom’s virtual threads bring a groundbreaking shift to Java’s concurrency model, allowing developers to write more intuitive, efficient, and scalable concurrent code. By making virtual threads lightweight and capable of handling blocking operations without tying up OS resources, Project Loom simplifies complex concurrency patterns, allowing developers to write straightforward, blocking code that performs well under high concurrency.

Key patterns, such as task-per-thread, structured concurrency, and asynchronous handling of OS-bound tasks, demonstrate how virtual threads can enhance both code simplicity and application performance. These patterns, combined with new APIs, like StructuredTaskScope, make it easier to handle interdependent tasks, manage cancellations, and propagate exceptions in a cohesive way. At the same time, understanding anti-patterns—such as avoiding excessive thread creation for short-lived tasks or blocking on OS-level resources—is essential to prevent bottlenecks and ensure efficient resource usage.

Virtual threads encourage developers to rethink their approach to concurrency, moving away from complex reactive frameworks or callback-heavy asynchronous code toward a more synchronous and readable model. However, for CPU-bound tasks or specific I/O operations that require OS thread involvement, traditional approaches like fixed-thread pools and asynchronous task delegation remain relevant.

In essence, virtual threads make concurrency accessible and manageable, even for complex applications, while allowing developers to focus on the core logic rather than threading intricacies. As virtual threads become standard, Java developers can embrace a more flexible and high-performing concurrency model that scales efficiently and integrates smoothly with existing libraries and frameworks, setting the stage for a new era in Java application development.

Passionate Archer, Runner, Linux lover and JAVA Geek! That's about everything! Alexius Dionysius Diakogiannis is a Senior Java Solutions Architect and Squad Lead at the European Investment Bank. He has over 20 years of experience in Java/JEE development, with a strong focus on enterprise architecture, security and performance optimization. He is proficient in a wide range of technologies, including Spring, Hibernate and JakartaEE. Alexius is a certified Scrum Master and is passionate about agile development. He is also an experienced trainer and speaker, and has given presentations at a number of conferences and meetups. In his current role, Alexius is responsible for leading a team of developers in the development of mission-critical applications. He is also responsible for designing and implementing the architecture for these applications, focusing on performance optimization and security.

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