Of course, the bottom line is that you can run a lot of virtual threads sharing the same carrier thread. In some sense, it’s like an implementation of an actor system where we have millions of actors using a small pool of threads. All of this can be achieved using a so-called continuation.

However, if a failure occurs in one subtask, things get messy. The capitalized words Thread and Fiber would refer to particular Java classes, and will be used mostly when discussing the design of the API rather than of the implementation. Debuggers, profilers and other serviceability services would need to be aware of fibers to provide a good user experience. This means that JFR and JVMTI would need to accommodate fibers, and relevant platform MBeans may be added. My code is posted on loom-lab in case other people want to verify my conclusions. And if it does, and if it doesn’t, we’d like to hear about it.

Clone the project

It is worth mentioning that we can create a very high number of virtual threads in an application without depending on the number of platform threads. These virtual threads are managed by JVM, so they do not java project loom add extra context-switching overhead as well because they are stored in RAM as normal Java objects. Before proceeding, it is very important to understand the difference between parallelism and concurrency.

You can use this guide to understand what Java’s Project loom is all about and how its virtual threads (also called ‘fibers’) work under the hood. Many applications written for the Java Virtual Machine are concurrent — meaning, programs like servers and databases, that are required to serve many requests, occurring concurrently and competing for computational resources. Project Loom is intended to significantly reduce the difficulty of writing efficient concurrent applications, or, more precisely, to eliminate the tradeoff between simplicity and efficiency in writing concurrent programs. A few use cases that are actually insane these days, but they will be maybe useful to some people when Project Loom arrives. For example, let’s say you want to run something after eight hours, so you need a very simple scheduling mechanism.

Lower-level async with continuations

The code is much more readable, and the intent is also clear. StructuredTaskScope also ensures the following behavior automatically. We can achieve the same functionality with structured concurrency using the code below. Imagine that updateInventory() fails and throws an exception. Then, the handleOrder() method throws an exception when calling inventory.get(). So far this is fine, but what about updateOrder()?

The stacks are known as GC roots and the GC starts with them and treats them specially. And the assumption there is that you won’t have too many stacks, but of course that assumption is broken with project Loom, because you might have a million stacks. So, the stacks of virtual threads are not GC roots. They’re kind of like arrays, but their structure is still the same as that of a stack. We have to do some interesting changes in the GCs.

This uses the newThreadPerTaskExecutor with the default thread factory and thus uses a thread group. When I ran this code and timed it, I got the numbers shown here. I get better performance when I use a thread pool with Executors.newCachedThreadPool().

Custom schedulers, what they’re seeing is something very close to continuations. And in the future, we will certainly consider exposing a more limited kind of a continuation that is confined to a single thread. So with custom virtual thread schedulers in one hand, which they maintain thread identity and thread confined continuations on the other, we’ve got everything covered, but that might take a while.

• If the thread executing handleOrder() is interrupted, the interruption is not propagated to the subtasks.
• Project Loom’s mission is to make it easier to write, debug, profile and maintain concurrent applications meeting today’s requirements.
• A few more critical or skeptic points of view, mainly around the fact that Project Loom won’t really change that much.
• And the try-with-resources block does not exit as long as are any live threads in the executor.
• You can freeze your piece of code, and then you can unlock it, or you can unhibernate it, you can wake it up on a different moment in time, and preferably even on a different thread.

It is not meant to be exhaustive, but merely present an outline of the design space and provide a sense of the challenges involved. It is the goal of this project to add a public delimited continuation construct to the Java platform. These results vary because they were ran on a developer machine with other services running. Also, according to the JMH docs, to avoid blackholes . If you call a method and return void the JVM will optimize by removing dead code.

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. Error handling with short-circuiting — If either the updateInventory() or updateOrder() fails, the other is canceled unless its already completed.

1. Using Thread.startVirtualThread()

In between, we may make some constructs fiber-blocking while leaving others kernel-thread-blocking. There is good reason to believe that many of these cases can be left unchanged, i.e. kernel-thread-blocking. For example, class loading occurs frequently only during startup and only very infrequently afterwards, and, as explained above, the fiber scheduler can easily schedule around such blocking.

One of the main goals of Project Loom is to actually rewrite all the standard APIs. For example, socket API, or file API, or lock APIs, so lock support, semaphores, CountDownLatches. All of these APIs are sleep, which we already saw. All of these APIs need to be rewritten so that they play well with Project Loom.

It runs the first line, and then goes to bar method, it goes to bar function, it continues running. Then on line 16, something really exciting and interesting happens. The function bar voluntarily says it would like to suspend itself. The code says that it no longer wishes to run for some bizarre reason, it no longer wishes to use the CPU, the carrier thread. What happens now is that we jump directly back to line four, as if it was an exception of some kind.

a. Pluggable user-mode scheduler

Also, we have to adopt a new programming style away from typical loops and conditional statements. The new lambda-style syntax makes it hard to understand the existing code and write programs because we must now break our program into multiple smaller units that can be run independently and asynchronously. And so, even if we try to change the priority of a virtual thread, it will stay the same.

On my machine, the process hung after 14_625_956 virtual threads but didn’t crash, and as memory became available, it kept going slowly. It’s due to the parked virtual threads being garbage collected, and the JVM is able to create more virtual threads and assign them to the underlying platform https://globalcloudteam.com/ thread. Virtual threads are lightweight threads that are not tied to OS threads but are managed by the JVM. They are suitable for thread-per-request programming styles without having the limitations of OS threads. You can create millions of virtual threads without affecting throughput.

What about the Thread.sleep example?

Even though everything, all you’ve said, so far is perfectly sufficient to use Loom. A similar API Thread.ofPlatform() exists for creating platform threads as well. Virtual threads do not support the stop(), suspend(), or resume() methods. These methods throw an UnsupportedOperationException when invoked on a virtual thread. Virtual threads always have the normal priority and the priority cannot be changed, even with setPriority method.

I will not go into the API too much because it’s subject to change. You essentially say Thread.startVirtualThread, as opposed to new thread or starting a platform thread. A platform thread is your old typical user threads, that’s actually a kernel thread, but we’re talking about virtual threads here. You can create it using a builder method, whatever. You can also create a very weird ExecutorService. This ExecutorService doesn’t actually pull threads.

Comparing Performance of Platform Threads and Virtual Threads

In real life, what you will get normally is actually, for example, a very deep stack with a lot of data. 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.

Project Loom – Goal

You don’t pay this huge price of scheduling operating system resources and consuming operating system’s memory. This is a main function that calls foo, then foo calls bar. There’s nothing really exciting here, except from the fact that the foo function is wrapped in a continuation. Wrapping up a function in a continuation doesn’t really run that function, it just wraps a Lambda expression, nothing specific to see here. However, if I now run the continuation, so if I call run on that object, I will go into foo function, and it will continue running.

It is advised to create a new virtual thread everytime we need one. Reactive style programming solved the problem of platform threads waiting for responses from other systems. The asynchronous APIs do not wait for the response, rather they work through the callbacks. Whenever a thread invokes an async API, the platform thread is returned to the pool until the response comes back from the remote system or database.

When you want to make an HTTP call or rather send any sort of data to another server, you will open up a Socket. When you open up the JavaDoc of inputStream.readAllBytes() , it gets hammered into you that the call is blocking, i.e. won’t return until all the bytes are read – your current thread is blocked until then. At a high level, a continuation is a representation in code of the execution flow.