If a virtual thread is blocked due to a delay by an I/O task, it still won’t block the thread as the virtual threads can be managed by the application instead of the operating system. This could easily eliminate scalability issues due to blocking I/O. The structured concurrency API is also designed to preserve order in multi-threaded environments by treating multiple tasks running in individual threads as a single logical unit of work. Without it, multi-threaded applications are more error-prone when subtasks are shut down or canceled in the wrong order, and harder to understand, he said. This is far more performant than using platform threads with thread pools. Of course, these are simple use cases; both thread pools and virtual thread implementations can be further optimized for better performance, but that’s not the point of this post.
However, if a failure occurs in one subtask, things get messy. But now that threads are lightweight, thanks to Loom, ExecutorServices can be used differently as well. This all means that not every thread needs a corresponding one within the operating system, as it is in the case of traditional threading. That’s why the traditional threading system needs improvements. This is exactly what Java’s Project Loom is offering.
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Unlike traditional threads, which require a separate stack for each thread, fibers share a common stack. This significantly reduces memory overhead, allowing you to have a large number of concurrent tasks without exhausting system resources. Project Loom addresses just a tiny fraction of the problem, it addresses asynchronous programming. However, it doesn’t address quite a few other features that are supported by reactive programming, namely backpressure, change propagation, composability. These are all features or frameworks like Reactor, or Akka, or Akka streams, whatever, which are not addressed by Loom because Loom is actually quite low level. After all, it’s just a different way of creating threads.
Another question is whether we still need reactive programming. If you think about it, we do have a very old class like RestTemplate, which is like this old school blocking HTTP client. With Project Loom, technically, you can start using RestTemplate again, and you can use it to, very efficiently, run multiple concurrent connections. Because RestTemplate underneath uses HTTP client from Apache, which uses sockets, and sockets are rewritten so that every time you block, or wait for reading or writing data, you are actually suspending your virtual thread. It seems like RestTemplate or any other blocking API is exciting again. At least that’s what we might think, you no longer need reactive programming and all these like WebFluxes, RxJavas, Reactors, and so on.
What does this mean to regular Java developers?
However, you just have to remember on the back of your head, that there is something special happening there, that there is a whole variety of threads that you don’t see, because they are suspended. As far as JVM is concerned, they do not exist, because they are suspended. Should you just blindly install the new version of Java whenever it comes out and just switch to virtual threads? First of all, the semantics of your application change.
All of these are actually very similar concepts, which are finally brought into the JVM. It used to be simply a function that just blocks your current thread so that it still exists on your operating system. However, it no longer runs, so it will be woken up by your operating system. A new version that project loom java takes advantage of virtual threads, notice that if you’re currently running a virtual thread, a different piece of code is run. Before we actually explain, what is Project Loom, we must understand what is a thread in Java? I know it sounds really basic, but it turns out there’s much more into it.
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The benefits of switching to a virtual thread executor are marginal in terms of container overhead. Still, while code changes to use virtual threads are minimal, Garcia-Ribeyro said, there are a few that some developers may have to make — especially to older applications. « Before Loom, we had two options, neither of which was really good, » said Aurelio Garcia-Ribeyro, senior director of project management at Oracle, in a presentation at the Oracle DevLive conference this week. The Loom project started in 2017 and has undergone many changes and proposals. Virtual threads were initially called fibers, but later on they were renamed to avoid confusion. Today with Java 19 getting closer to release, the project has delivered the two features discussed above.
- « Before Loom, we had two options, neither of which was really good, » said Aurelio Garcia-Ribeyro, senior director of project management at Oracle, in a presentation at the Oracle DevLive conference this week.
- This is exactly what Java’s Project Loom is offering.
- More importantly, every thread you create in your Java Virtual Machine consumes more or less around 1 megabyte of memory, and it’s outside of heap.
- First, let’s see how many platform threads vs. virtual threads we can create on a machine.
- Typically, ExecutorService has a pool of threads that can be reused in case of new VirtualThreadExecutor, it creates a new virtual thread every time you submit a task.
- The test web application was also designed to minimise the common overhead and highlight the differences between the tests.
This is an advertising talk, because you probably won’t create as many. Technically, it is possible, and I can run millions of threads on this particular laptop. First of all, there’s this concept of a virtual thread. A virtual thread is very lightweight, it’s cheap, and it’s a user thread.
b. Internal user-mode continuation
In vast majority of use cases you will set the data once and only read that data throughout the request context, but that cannot be enforced easily, meaning data.set(« data ») can be called anytime, anyplace and any number of times. This is the first design difference that Scoped Values made — they make the data immutable, set only once and are read-only afterwards. The only difference in asynchronous mode is that the current working threads steal the task from the head of another deque. ForkJoinPool adds a task scheduled by another running task to the local queue. In the Scala implementations, there are rich APIs for describing concurrently running processes and their interactions (and which can use fibers as one of the options).
This is just a minor addition to the API, and it may change. The future is looking brighter with the ongoing development of Project Loom, an initiative that aims to revolutionize concurrency in Java by introducing lightweight threads, or fibers. Well, as in any other benchmark it’s impossible to tell without having something to baseline of. So lets do the same processing using platform threads and see the comparison. Very simple benchmarking on a Intel CPU (i5–6200U) shows half a second (0.5s) for creating 9000 threads and only five seconds (5s) for launching and executing one million virtual threads.
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Hence it only matters, in what order the computations are composed. Numerous projects have shown that working directly with thread synchronization primitives (such as mutexes and locks) usually leads to deadlocks, thread starvation or other bugs. By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct. Spring Runtime offers support and binaries for OpenJDK™, Spring, and Apache Tomcat® in one simple subscription.
When doing a simple service, small application or a quick script, I don’t want to deal with any kind of wrappers, be it Future or IO. Fibers and their “codes like sync, works like async” model will make my life much easier. An interesting research area, trying to combine the composability of the asynchronous style with the simplicity of the blocking style, is the monadic-reflection project for Scala 3. It utilises Loom under the hood, as well as Scala 3’s context functions, and allows seamlessly converting between the two representations. Both Scala and Kotlin fibers enqueue work units to a queue; a scheduler then runs them on a thread pool, thus allowing to run many fibers on a much smaller thread pool. First, we can once again use familiar constructs to express our business logic.
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For example, we can decouple creation and execution orders of our computations. It doesn’t matter, in what order Callable instances are created. These representations are lazy, and their actual effects only take place once the computation is evaluated.
