When blocked, the actual carrier-thread (that was running the run-body of the virtual thread), gets engaged for executing some other virtual-thread’s run. So effectively, the carrier-thread is not sitting idle but executing some other work. And comes back to continue the execution of the original virtual-thread whenever unparked. But here, you have a single carrier-thread in a way executing the body of multiple virtual-threads, switching from one to another when blocked. In comes Project Loom with virtual threads that become the single unit of concurrency.

Again we see that virtual threads are generally more performant, with the difference being most pronounced at low concurrency and when concurrency exceeds the number of processor cores available to the test. Project Loom proposes to solve this through user-mode threads which rely on Java runtime implementation of continuations and schedulers instead of the OS implementation. With just a few changes, you can start using virtual threads in your Spring application and take advantage of its performance improvements. To answer these questions you must understand the thread-per-request model. When a request is handled by the web server it is using a Java Thread and that thread is tied to an operating system thread. When you make a call to a blocking operation like reading or persisting to a database that thread is blocked from doing anything else until the request is fulfilled.

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We’ve put on it garbage collection, code optimization, and now, as the next step, also concurrency. I must admit, that Project Loom is seriously awesome. It seems very viable for end-users, effectively providing lightweight concurrency everywhere, without the need for a library or a framework. A simple, synchronous web server will be able to handle many more requests without requiring more hardware. To understand how it is possible we need to dive into Thread.sleep() method.

• At this point, all you have to do is just creating threads every single time you want to.
• It finds and extracts relevant information within your enterprise, from devices, edge and private cloud, synthesizes them into assessments and conclusions, and proposes solutions.
• Project Loom represents a significant step forward in making Java more efficient, developer-friendly, and scalable in the realm of concurrent programming.
• With “fire and forget”, there is no way of knowing when the recursion is done.
• We’ve put on it garbage collection, code optimization, and now, as the next step, also concurrency.

This article will be more functional than technical, as it covers how this project looms coming into the picture. The only difference in asynchronous mode is that the worker threads steal the task from the head of another deque. Since suspending a continuation would also require it to store the call stack, it’s also a goal of project Loom to add lightweight stack retrieval while resuming the continuation. Now, in order to suspend a continuation, it’s required to store the entire call-stack. And similarly, retrieve the call-stack on resumption.

THREAD LOCALS

Assume we got some time-consuming tasks, which we want to run in the background of our application. However, forget about automagically scaling up to a million of private threads in real-life scenarios without knowing what you are doing. When you want java virtual threads to make an HTTP call or rather send any sort of data to another server, you (or rather the library maintainer in a layer far, far away) will open up a Socket. We can achieve the same functionality with structured concurrency using the code below.

It also may mean that you are overloading your database, or you are overloading another service, and you haven’t changed much. You just changed a single line that changes the way threads are created rather than platform, then you move to the virtual threads. Suddenly, you have to rely on these low level CountDownLatches, semaphores, and so on.

Java Project Loom

Continuations are actually useful, even without multi-threading. 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. No matter how much heap you allocate, you have to factor out the extra memory consumed by your threads.

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Please use the OpenJDK JDK

The virtual machine will make sure that our current flow of execution can continue, but this separate thread actually runs somewhere. At this point in time, we have two separate execution paths running at the same time, concurrently. It essentially means that we are waiting for this background task to finish.

More importantly, you can actually see, what is the amount of CPU consumed by each and every of these threads? Does it mean that Linux has some special support for Java? Because it turns out that not only user threads on your JVM are seen as kernel threads by your operating system. On newer Java versions, even thread names are visible to your Linux operating system.

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Technically, this particular example could easily be implemented with just a scheduled ExecutorService, having a bunch of threads and 1 million tasks submitted to that executor. It’s just that the API finally allows us to build in a much different, much easier way. Project Loom features a lightweight concurrency construct for Java. There are some prototypes already introduced in the form of Java libraries. The project is currently in the final stages of development and is planned to be released as a preview feature with JDK19.

If you have been around for a very long time, you may remember that early versions of Java had “green threads” that were mapped to an OS thread. When a green thread blocked, its carrier thread was also blocked, preventing all other green threads from making progress. In the end, the Loom made almost no significant changes to the JDK API except for the newly structured concurrency API. Instead, it provides a better implementation of existing and familiar abstractions. For example, if we have server handling requests that come through a network, the limit in the number of threads can severely limit your application to well below the capacity of the hardware or the network.

Future of Project Loom

I barely remember how they work, and I will either have to relearn them or use some higher level mechanisms. This is probably where reactive programming or some higher level abstractions still come into play. From that perspective, I don’t believe Project Loom will revolutionize the way we develop software, or at least I hope it won’t.