multithreading What exactly makes Java Virtual Threads better

Let’s use a simple Java example, where we have a thread that kicks off some concurrent work, does some work for itself, and then waits for the initial work to finish. The “Using virtual threads vs. platform threads” section contains example code for using virtual threads. (Note – virtual threads are only available as a preview in Java 19, and there may well be further preview releases as the feature’s APIs and implementation are refined, and feedback from users is addressed).

• Because after all, you do have to store the stack trace somewhere.
• This makes it more likely that the author will find bugs that they were not specifically looking for.
• The operating system recognizes that the thread is waiting for I/O, and the scheduler switches directly to the next one.
• Continuation is a programming construct that was put into the JVM, at the very heart of the JVM.
• If you do not do anything exotic, it does not matter, in terms of performance, if you submit all tasks with one executor or with two.
• The answer is both to make it easier for developers to understand, and to make it easier to move the universe of existing code.

Project Loom includes an API for working with continuations, but it’s not meant for application development and is locked away in the jdk.internal.vm package. It’s the low-level construct that makes virtual threads possible. However, those who want to experiment with it have the option, see listing 3. Things become interesting when all these virtual threads only use the CPU for a short time. Most server-side applications aren’t CPU-bound, but I/O-bound. There might be some input validation, but then it’s mostly fetching data over the network, for example from the database, or over HTTP from another service.

Synchronous vs Asynchronous

Some 37% of the Perforce’s 876 Java users planned to move to Java 17 LTS. Oracle has released Java 19, which adds a port for the open-source RSIC-V chip architecture plus other developments. At the time of writing this readme, the latest jdk version was jdk-17. Blocking a thread is is huge waste of this resource.

That’s the piece of code that you can run even right now. You can download Project Loom with Java 18 or Java 19, if you’re cutting edge at the moment, and just see how it works. If you put 1 million, it will actually start 1 million threads, and your laptop will not melt and your system will not hang, it will simply just create these millions of threads. Because project loom java what actually happens is that we created 1 million virtual threads, which are not kernel threads, so we are not spamming our operating system with millions of kernel threads. The only thing these kernel threads are doing is actually just scheduling, or going to sleep, but before they do it, they schedule themselves to be woken up after a certain time.

A single execution flow handling a single connection is a lot simpler to understand and reason. While many frameworks today, in particular reactive frameworks, hide a lot of this complexity from the developer, a different mindset is needed for asynchronous I/O. 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.

Project Loom makes no changes to the Java language itself, so all of the benefits are available to other JVM languages such as Scala, Kotlin, etc. If the thread executing handleOrder() is interrupted, the interruption is not propagated to the subtasks. In this case updateInventory() and updateOrder() will leak and continue to run in the background. Already, Java and its primary server-side competitor Node.js are neck and neck in performance. An order of magnitude boost to Java performance in typical web app use cases could alter the landscape for years to come.

In between calling the sleep function and actually being woken up, our virtual thread no longer consumes the CPU. At this point, the carrier thread is free to run another virtual thread. Technically, you can have millions of virtual threads that are sleeping without really paying that much in terms of the memory consumption.

The advantage Of Project Loom

The source code in this article was run on build 19-loom+6-625. So now we can start a million threads at the same time. This may be a nice effect to show off, but is probably of little value for the programs we need to write.

Only when the data arrives, the JVM will wake up your virtual thread. However, you just have to be aware of the fact that the kernel threads of your thread pools were actually just natural like limit to concurrency. Just blindly switching from platform threads, the old ones, to virtual threads will change the semantics of your application. The main goal of this project is to add a lightweight thread construct, which we call fibers, managed by the Java runtime, which would be optionally used alongside the existing heavyweight, OS-provided, implementation of threads. Fibers are much more lightweight than kernel threads in terms of memory footprint, and the overhead of task-switching among them is close to zero. Millions of fibers can be spawned in a single JVM instance, and programmers need not hesitate to issue synchronous, blocking calls, as blocking will be virtually free.

Usage in a real database (Raft)

This means that idiomatic Java fits in well, and the APIs that Loom exposes3 makes it straightforward to experiment with this kind of approach. This significantly broadens the scope for FoundationDB like implementation patterns, making it much easier for a large class of software to utilize this mechanism of building and verifying distributed systems. Adopting the use of virtual threads will therefore require using one or more of the above APIs.

Most java servers use thread pools with certain limit of threads (200, 300 ..), however you are not limited by the os to spawn many more, I’ve read that with special configurations for linux you can reach huge numbers. 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.

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Library and framework developers are likely to make the heaviest use of these new APIs, rather than application developers, particularly the ones provided by the Thread class. With the recap out of the way, let’s get back to covering the cases where we may need to change existing code to take full advantage of the promise of virtual threads, covering both the why and the how. It was recently announced that virtual threads, a new JDK feature developed by the OpenJDK project Loom, will be available as a preview feature in the forthcoming Java 19 release.

A fiber is made of two components — a continuation and a scheduler. As Java already has an excellent scheduler in the form of ForkJoinPool, fibers will be implemented by adding continuations to the JVM. Another thing that’s not yet handled is preemption, https://globalcloudteam.com/ when you have a very CPU intensive task. Let’s say you have 4 CPU cores, and you create 4 platform threads, or 4 kernel threads that are doing very CPU intensive work, like crunching numbers, cryptography, hashing, compression, encoding, whatever.

So, that’s another problem solved by virtual threads. It turns out that user threads are actually kernel threads these days. To prove that that’s the case, just check, for example, jstack utility that shows you the stack trace of your JVM. Besides the actual stack, it actually shows quite a few interesting properties of your threads.

Listing 2. Creating a virtual thread

Also, the continuations discussed here are non-reentrant, meaning that any invocation of the continuation may change the “current” suspension point. In other words, the continuation object is stateful. It is the goal of this project to add a public delimited continuation construct to the Java platform. While O/S Threads have many advantages, such as preemptive scheduling, usually with O/S Kernel support, they have several disadvantages too, such as a heavy footprint. As a rule, in any Java Virtual Machine, running too many threads is not a good idea, as they require a lot of resources.

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. However, if you want to try it out, you can either check out the source code from Loom Github and build the JDK yourself, or download an early access build.

Hard to get working, hard to choose the fineness of the grain. When to use are obvious in textbook examples; a little less so in deeply nested logic. Lock avoidance makes that, for the most part, go away, and be limited to contended leaf components like malloc().

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Many applications make use of data stores, message brokers, and remote services. 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.

What exactly makes Java Virtual Threads better

When these features are production ready, it will be a big deal for libraries and frameworks that use threads or parallelism. Library authors will see huge performance and scalability improvements while simplifying the codebase and making it more maintainable. Most Java projects using thread pools and platform threads will benefit from switching to virtual threads. Candidates include Java server software like Tomcat, Undertow, and Netty; and web frameworks like Spring and Micronaut. I expect most Java web technologies to migrate to virtual threads from thread pools.

The original release of Java used ‘Green Threads’ where scheduling was handled on the JVM by the Green Threads implementation. The policy is that the JVM schedules work, and the procedure is implemented in the Java Runtime Library. Project Loom does not improve parallelism, rather it potentially improves concurrency. If you have parallel processing applications, such as using Java Parallel Streams, Loom will not help you.