Virtual Threads: JMeter meets Project Loom

Doing it this way without Project Loom is actually just crazy. Creating a thread and then sleeping for eight hours, because for eight hours, you are consuming system project loom java resources, essentially for nothing. With Project Loom, this may be even a reasonable approach, because a virtual thread that sleeps consumes very little resources.

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• In between calling the sleep function and actually being woken up, our virtual thread no longer consumes the CPU.
• Instead, you can think of the JVM as managing the thread pool for you.
• The most basic way to use a virtual thread is with Thread.startVirtualThread.
• The handleOrder() task will be blocked on inventory.get() even though updateOrder() threw an error.
• While they all make far more effective use of resources, developers need to adapt to a somewhat different programming model.

If you suspend such a virtual thread, you do have to keep that memory that holds all these stack lines somewhere. The cost of the virtual thread will actually approach the cost of the platform thread. Because after all, you do have to store the stack trace somewhere. Most of the time it’s going to be less expensive, you will use less memory, but it doesn’t mean that you can create millions of very complex threads that are doing a lot of work.

Despite its wide application, vert.x cannot balance the legacy code and lock blocking logic in the code. WISP supports coroutine scheduling by using non-blocking methods and event recovery coroutines in all blocking calls in JDK. While providing users with the greatest convenience, this ensures compatibility with the existing code. While the main motivation for this goal is to make concurrency easier/more scalable, a thread implemented by the Java runtime and over which the runtime has more control, has other benefits. For example, such a thread could be paused and serialized on one machine and then deserialized and resumed on another. A fiber would then have methods like parkAndSerialize, and deserializeAndUnpark.

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Developers can anticipate a feature freeze in mid-December 2022. In this podcast, Jim Barton explains some of the fundamentals of modern service meshes, and provides an overview of Istio Ambient Mesh and the benefits it will provide in the future. Your personal data collected in this form will be used only to contact you and talk about your project. We’re just at the start of a discussion as to how to further evolve our effect systems. There’s been a hot exchange on that topic on the Scala Contributors forum, you can find the summary over here.

I/O-intensive applications are the primary ones that benefit from Virtual Threads if they were built to use blocking I/O facilities such as InputStream and synchronous HTTP, database, and message broker clients. Running such workloads on Virtual Threads helps reduce the memory footprint compared to Platform Threads and in certain situations, Virtual Threads can increase concurrency. It’s worth mentioning that virtual threads are a form of “cooperative multitasking”. Native threads are kicked off the CPU by the operating system, regardless of what they’re doing . Even an infinite loop will not block the CPU core this way, others will still get their turn. On the virtual thread level, however, there’s no such scheduler – the virtual thread itself must return control to the native thread.

Performance Comparison: Manual Asynchronous Programming vs WISP Programming

Virtual Threads impact not only Spring Framework but all surrounding integrations, such as database drivers, messaging systems, HTTP clients, and many more. Many of these projects are aware of the need to improve their synchronized behavior to unleash the full potential of Project Loom. // The scope is a tool for creating nested continuations.

Also, my personal opinion, that’s not going to be the case, we will still need some higher level abstraction. JEP 428, Structured Concurrency , proposes to simplify multithreaded programming by introducing a library to treat multiple tasks running in different threads as a single unit of work. This can streamline error handling and cancellation, improve reliability, and enhance observability.

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Due to the heaviness of threads, there is a limit to how many threads an application can have, and thus also a limit to how many concurrent connections the application can handle. This constraint means threads do not scale very well. Traditional threads in Java are very heavy and bound one-to-one with an OS thread, making it the OS’ job to schedule threads. This means threads’ execution time depends on the CPU. Virtual threads, also referred to as green threads or user threads, moves the responsibility of scheduling from the OS to the application, in this case the JVM. This allows the JVM to take advantage of its knowledge about what’s happening in the virtual threads when making decision on which threads to schedule next.

Note that writeQuery returns the result after the asynchronous write operation is called. Therefore, if the code to be run after write must logically be contained in the callback, «write» must be in the last row of the function. If the function has other callers, CPS conversion is required. Since kernel switching and context switching are fast, it’s crucial to understand what produces multithreading overhead.

Since then and still with the release of Java 19, a limitation was prevalent, leading to Platform Thread pinning, effectively reducing concurrency when using synchronized. The use of synchronized code blocks is not in of itself a problem; only when those blocks contain blocking code, generally speaking I/O operations. In fact, the same blocking code in synchronized blocks can lead to performance issues even without Virtual Threads.

Why project Loom will make Java a better cloud language than Go

Another common use case is parallel processing or multi-threading, where you might split a task into subtasks across multiple threads. Here you have to write solutions to avoid data corruption and data races. In some cases, you must also ensure thread synchronization when executing a parallel task distributed over multiple threads. https://globalcloudteam.com/ The implementation becomes even more fragile and puts a lot more responsibility on the developer to ensure there are no issues like thread leaks and cancellation delays. We don’t run any further tests since the service under load is already overloaded and we have already identified a difference between virtual and platform threads.

1) The number of active threads is approximately equal to the number of CPUs. As shown in the above figure, the hotspot produces a large amount of scheduling overhead. When the preceding program is tested on the ECS Bare Metal Instance server, each pipe operation only takes about 334 ns. WISP 2 is completely compatible with the existing Java code and, therefore, easy to use. See the Executors documentation for more about the executor methods. Listing 1 shows the changes I made to the Maven archetype’s POM file.

If our users’ program is based on the Fiber API, we ensure that the code behaves exactly the same on Project Loom and WISP. Project Loom serializes the context and then save it, which saves memory but reduces the switching efficiency. Project Loom is a standard coroutine implementation on OpenJDK. In WISP 2, all threads are converted into coroutines, and no adaption is needed. However, coroutine still has an advantage as WISP correctly switches the scheduling of ubiquitous synchronized blocks in JDK. Almost no coroutine runtime and scheduling overhead (about 1%) is generated.

Virtual Threads: JMeter meets Project Loom

I’m experimenting with Project Loom for quite some time already. Uncover emerging trends and practices from domain experts. The formal release date for JDK 20 has not yet been announced, but it is expected to be delivered in mid-March 2023 as per the six-month release cadence.

In addition, business intent is blurred by the extra verbosity of Java. Project Loom is keeping a very low profile when it comes to in which Java release the features will be included. At the moment everything is still experimental and APIs may still change.

You can reach us directly at or you can also ask us on the forum. Check out these additional resources to learn more about Java, multi-threading, and Project Loom. Cancellation propagation — If the thread running handleOrder() is interrupted before or during the call to join(), both forks are canceled automatically when the thread exits the scope. For these situations, we would have to carefully write workarounds and failsafe, putting all the burden on the developer. If the thread executing handleOrder() is interrupted, the interruption is not propagated to the subtasks.

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Represent fibers as a Fiber class, and factor out the common API for Fiber and Thread into a common super-type, provisionally called Strand. Thread-implementation-agnostic code would be programmed against Strand, so that Strand.currentStrand would return a fiber if the code is running in a fiber, and Strand.sleep would suspend the fiber if the code is running in a fiber. As there are two separate concerns, we can pick different implementations for each. Currently, the thread construct offered by the Java platform is the Thread class, which is implemented by a kernel thread; it relies on the OS for the implementation of both the continuation and the scheduler. JDK libraries making use of native code that blocks threads would need to be adapted to be able to run in fibers.

Nesting subtasks in a parent’s block induces a hierarchy that can be represented at run time when structured concurrency builds a tree-shaped hierarchy of tasks. This tree is the concurrent counterpart to a single thread’s call stack and tools can use it to present subtasks as children of their parent tasks. Spring Framework makes a lot of use of synchronized to implement locking, mostly around local data structures. Over the years, before Virtual Threads were available, we have revised synchronized blocks which might potentially interact with third-party resources, removing lock contention in highly concurrent applications.

While virtual threads won’t magically run everything faster, benchmarks run against the current early access builds do indicate that you can obtain similar scalability, throughput, and performance as when using asynchronous I/O. Developing using virtual threads are near identical to developing using traditional threads. The enhancement proposal adds several API methods for this. Loom’s other big play is introducing structured concurrency to Java. Its principle is that if a task splits into concurrent subtasks, they all return to the task’s code block. Consequently, the lifetimes of all concurrent subtasks are confined to a single syntactic block, which means they can be reasoned-about and managed as a unit.

Hello World: Kotlin style

Not all these will be gone with Project Loom , but for sure we’ll have to rethink our approach to concurrency in a number of places. When these features are production ready, it should not affect regular Java developers much, as these developers may be using libraries for concurrency use cases. But it can be a big deal in those rare scenarios where you are doing a lot of multi-threading without using libraries. Virtual threads could be a no-brainer replacement for all use cases where you use thread pools today. This will increase performance and scalability in most cases based on the benchmarks out there. Structured concurrency can help simplify the multi-threading or parallel processing use cases and make them less fragile and more maintainable.