Java Project Loom: Understand the new Java concurrency model

The other part is that it uses a simple, blocking, thread-per-request model with Java 1.0 networking APIs. So this is “achieving 5M persistent connections with 26-year-old code that’s fully debuggable and observable by the platform.” This stresses both the OS and the Java runtime. There is no need for a split world of APIs, some designed for threads and others for coroutines (so-called “function colouring”). Existing APIs, third-party libraries, and programs — even those dating back to Java 1.0 (just as this experiment does with Java 1.0’s — just work on millions of virtual threads. It helped me think of virtual threads as tasks, that will eventually run on a real thread⟨™) AND that need the underlying native calls to do the heavy non-blocking lifting. A thread supports the concurrent execution of instructions in modern high-level programming languages and operating systems.

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.

  • Preview releases are available and show what’ll be possible.
  • The main focus of the tests is checking if there is any difference in TPS, RAM consumption, CPU, or general results between platform threads and virtual threads.
  • Also, the profile of your garbage collection will be much different.
  • This is just a basic unit of scheduling in the operating system.
  • I’d love to test machine stitching the body together rather than hand stitching.
  • But if you don’t need ReentrantLock’s more advanced features then, as before, the solution requires a little more code and unit testing, although not much.

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. Eventually, a lightweight concurrency construct is direly needed that does not make use of these traditional threads that are dependent on the Operating system. When the clock is pushed forward, the interpreter makes sure that all effects on all currently running are fully evaluated before returning control to the test code. That way, we can write fast (in a couple of milliseconds we can cover seconds, minutes or hours of test-clock time!), predictable, reproducible tests. The points at which interruption might happen are also quite different.

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Since it runs on its own thread, it can complete successfully. But now we have an issue with a mismatch in inventory and order. Suppose the updateOrder() is an expensive operation. In that case, we are just wasting the resources for nothing, and we will have to write some sort of guard logic to revert the updates done to order as our overall operation has failed. I hope these tips will come in handy the next time you work on a pin-loom project—especially when you need to get creative with equipment. Because this was going to a young child, I used a YKK® zipper to be sure it would be safe and stay secure.

The utility of those other uses is, however, expected to be much lower than that of fibers. In fact, continuations don’t add expressivity on top of that of fibers (i.e., continuations can be implemented on top of fibers). This is a software construct that’s built into the JVM, or that will be built into the JVM. To give you a sense of how ambitious the changes in Loom are, current Java threading, even with hefty servers, is counted in the thousands of threads . Loom proposes to move this limit towards million of threads.

Project Loom Solution

I’d suggest gluing them in place first, so they stay where you want as you sew them on. By the time I completed my caterpillar, I had made a few other minor adjustments to adapt this project to the tools I had and its recipient. Months ago, we were in our preview meeting for Easy Weaving with Little Looms Fall 2022, and one of the projects presented was the Cute as a Bug Pencil Case by Deborah Bagley.

My machine is Intel Core i H with 8 cores, 16 threads, and 64GB RAM running Fedora 36. Migrating applications to use virtual threads requires relatively few changes because of those reasons, and because we designed them with easy adoption in mind. This particular experiment is about simple, “legacy” Java 1.0 code enjoying terrific scalability.

To date it’s only been possible for a Java app to have a few thousand active platform threads. With virtual threads, the Java runtime can easily support hundreds of thousands of active threads. Virtual threads are the mechanism that enables the developers to preserve the clarity of thread-per-request programming and significantly increase a number of threads. Virtual threads are not tied to OS threads for the whole lifetime of the code. Instead, they capture an OS thread only while they perform their calculations, i.e. multiple virtual threads can share one OS thread. This is a user thread, but there’s also the concept of a kernel thread.

User Threads and Kernel Threads

If you’ve already heard of Project Loom a while ago, you might have come across the term fibers. In the first versions of Project Loom, fiber was the name for the virtual thread. It goes back to a previous project of the current Loom project leader Ron Pressler, the Quasar Fibers. However, the name fiber was discarded at the end of 2019, as was the alternative coroutine, and virtual thread prevailed.

Project Loom Solution

It more or less voluntarily can give up the CPU and other threads may use that CPU. It’s much easier when you have multiple CPUs, but most of the time, this is almost always the case, you will never have as many CPUs as many kernel threads are running. This mechanism happens in the operating system level.

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In order to suspend a computation, a continuation is required to store an entire call-stack context, or simply put, store the stack. To support native languages, the memory storing the stack must be contiguous and remain at the same memory address. While virtual memory does offer some flexibility, there are still limitations on just how lightweight and flexible such kernel continuations (i.e. stacks) can be. Ideally, we would like stacks to grow and shrink depending on usage.

I see both engineers and projects have a great deal of themselves invested in their Rx position, and I already sense that they’re not going to give it up without a fight. But the title of this article might as well have been “5M persistent connections with Linux” because that’s where the magic 5M connections happen. If you use Spring Boot with Kotlin for example, rather than dealing with Spring’s Flux, you simply define your asynchronous resources as suspend functions. That’s the whole point of Truffle and why it’s such a big leap forward.

We no longer have to think about this low level abstraction of a thread, we can now simply create a thread every time for every time we have a business use case for that. There is no leaky abstraction of expensive threads because they are no longer expensive. As you can probably tell, it’s fairly easy to implement an actor system like Akka using virtual threads, because essentially what you do is you create a new actor, which is backed by a virtual thread.

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In our case, I just create an executor with just one thread. Even with just a single thread, single carriers, or single kernel thread, you can run millions of threads as long as they don’t consume the CPU all the time. Because, after all, Project Loom will not magically scale your CPU so that it can perform more work. It’s just a different API, it’s just a different way of defining tasks that for most of the time are not doing much. They are sleeping blocked on a synchronization mechanism, or waiting on I/O. It’s just a different way of performing or developing software.

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I don’t think this part can be retrofitted on top of simple BlockingQueues. I just want to see to what scale it would get without threading issues. Would love to have a more realistic sample with Spring Boot that would demonstrate the real world scale. I saw a few but nothing remotely as ambitious as that. This could be done with mere bytes for the message itself and some very dumb anycast-to-s3 services in different data centers.

Java fibers in action

I maintain some skepticism, as the research typically shows a poorly scaled system, which is transformed into a lock avoidance model, then shown to be better. I have yet to see one which unleashes some experienced developers to analyze the synchronization behavior of the system, transform it for scalability, then measure the result. But, even if that were a win experienced developers are a rare and expensive commodity; the heart of scalability is project loom java really financial. What we potentially will get is performance similar to asynchronous, but with synchronous code. As the number of threads increases, we see an expected increment in deviation from ideal TPS due to the higher load imposed on the machine. Since running tests from a local machine to a remote server may include fluctuations caused by network or local machine issues , we are now going to test with two servers in the same network.

Project Loom Solution

With the rise of powerful and multicore CPUs, more raw power is available for applications to consume. In Java, threads are used to make the application work on multiple tasks concurrently. A developer starts a Java thread in the program, and tasks are assigned to this thread to get processed.

Why Use Project Loom

Of course there’s also trying to make easy projects more challenging, resume-driven development etc too. So while you could achieve 5M in other ways, those ways would not only be more complex, but also not really observable/debuggable by Java platform tools. While impressive, I don’t really see it as something practical – I think scaling across processes/VMs is a much more realistic approach. Does it show that Project Loom meets the goal of being able to do large client count thread per server workloads? Does the name remind me of a best selling point and click adventure game? But Go Channels come also with the superpower of „select“, which allows to wait for multiple objects to become ready and atomic execution of actions.

Regarding results, in general, there is not much difference in CPU or RAM usage in usual scenarios between virtual and platform threads. Even though virtual threads might be “lighter” than platform ones, it is nothing compared to the resources required by JMeter Thread Variables, JMeter Thread test plan trees , Sample Results, etc. We have seen a lower TPS and higher CPU usage in virtual threads when hitting really fast services, but that might be a “temporal” difference since virtual threads have a margin of improvement per OpenJDK team words. A difference does appear when using a dummy sampler, but that is a pretty fictional scenario. The main focus of the tests is checking if there is any difference in TPS, RAM consumption, CPU, or general results between platform threads and virtual threads. We are not going to experiment much with JVM settings, try to tune code, or check actual limitations of the load generator or service under test to keep the tests simple and reduced scope.

Beyond virtual threads

If it gets the expected response, the preview status of the virtual threads will then be removed by the time of the release of JDK21. A point to be noted is this suspension or resuming occurs in the application runtime instead of the OS. As a result, it prevents the expensive context switch between kernel threads. Before proceeding, it is very important to understand the difference between parallelism and concurrency.

The lack of tail call optimisation on the JRE means recursion is a lot less safe than in functional language runtimes which guarantee stacks don’t overflow when you make tail calls. Imagine that we concurrently start 100k tasks that just sleep for 100ms and then reply to the main calling thread. Once the main thread collected all the responses, it assumes the job done.