Code to make backend calls is going to be in one part of the application. Code to process the response from the backend is going to be in a different part of the program. Now, if you want to loop or put some conditional operations for the entire backend call it’s going to be tricker. On the other hand with Java virtual threads, you can create millions of threads (since threads are no longer bottleneck) and implement your entire backend call code sequentially. We now know we can create more virtual threads than platform threads. One could be tempted to use virtual threads to perform long computations (CPU-bound workload).
- Thus, scheduling virtual threads is the responsibility of the JVM.
- Indeed, the current scheduler does not support preempting tasks.
- Even though OS threads are precious and finite resources, their time is extensively wasted in this platform threads’ architecture.
- Thus, virtual threads comparatively occupy much less memory than platform threads.
- This stack chunk object doesn’t occupy much memory like an operating system thread.
- Suppose you run in a constrained environment, such as containers.
This way, multiple threads are involved in handling a single async request. For applications with a smaller load, Threads might outperform virtual ones, simply because the context switching is low when there are few active clients. Loom won’t make your applications faster it will just increase its throughput, and if throughput is not a problem https://www.globalcloudteam.com/ then stick to platform threads. Fortunately, Quarkus provides a massive ecosystem that is ready to be used in virtual threads. Mutiny, the reactive programming library used in Quarkus, and the Vert.x Mutiny bindings provides the ability to write blocking code (so, no fear, no learning curve) which do not pin the carrier thread.
Virtual Threads are Mounted to Platform Threads
On the other hand, when virtual threads are waiting(i.e. step #1, step #2, step #4, step #6 ), they are stored in the java heap memory region just like any other object(aka stack chunk object). This stack chunk object doesn’t occupy much memory like an operating system thread. Only when a virtual thread needs to do real work, it’s tied up to the underlying operating system. Thus, virtual threads comparatively occupy much less memory than platform threads. In most architectures, the number of requests that an application can process is directly proportional to the number of threads that are available in the application server thread pool. Because every single customer request is processed by a single unique thread.
It has considerable potential to improve the application’s availability. Here is a diagram showing Java virtual threads – executed by platform threads – which are again executed by OS threads. This is explained in more detail in later sections of this
Java virtual thread tutorial.
thoughts on “Java Virtual Threads – Easy introduction”
Because virtual threads are threads and have little new API surface of their own, there is relatively little to learn in order to use virtual threads. But there are actually quite a few things we need to unlearn in order to use them effectively. Java developers may recall that in the Java 1.0 days, some JVMs implemented threads using user-mode, or “green”, threads. Virtual threads bear a superficial similarity to green threads in that they are both managed by the JVM rather than the OS, but this is where the similarity ends. The green threads of the 90s still had large, monolithic stacks. They were very much a product of their time, when systems were single-core and OSes didnt have thread support at all.
In fact, the above function allows us to print some helpful information concerning virtual threads that will be very handy in understanding what’s going on. At its core it performs a basic event loop that monitors all of the synchronous networking read, connect, and accept operations that are not immediately ready when invoked in a virtual thread. When the I/O operation becomes ready, the poller will be notified and subsequently unpark the appropriate parked virtual thread. Asynchronous and non-blocking APIs are more challenging to work with (than synchronous APIs), in part because they lead to code constructs that are not natural for a (typical) human. Synchronous APIs are for the most part easier to work with; the code is easier to write, easier to read, and easier to debug (with stack traces that make sense!). One of the compelling value propositions of Project Loom is to avoid having to make this choice – it should be possible for the synchronous code to scale.
Java Virtual Thread Diagram
Virtual threads occupy less space than platform threads in memory. Hence, it becomes possible to use more virtual threads than platform threads simultaneously without blowing up the memory. It has potential to improve an applications availability, throughput and code quality on top of reducing memory consumption. This post intends to introduce java virtual threads in an easily understandable manner. In other words, the platform thread does not switch
between executing multiple virtual threads – except in the case of blocking network calls.
So, continuations execution is implemented using a lot of native calls to the JVM, and it’s less understandable when looking at the JDK code. However, we can still look at some concepts at the roots of virtual threads. When using threads before Java 19 and Project Loom, creating a thread using the constructor was relatively uncommon.
JS uses async/await to do this, we will discuss other ways of achieving concurrency shortly. Parallel means that two or more tasks are executed at the same time. However modern CPUs are always multi-core and single-core CPUs are mostly outdated and no longer widely used since they are greatly outperformed by the multi-core ones. This is because modern applications are designed to utilize multiple cores and they always need a few things to be done simultaneously. To build a container running a Quarkus application using virtual threads compiled into a native executable, you must
make sure you have a Linux/AMD64 executable (or ARM64 if you are targeting ARM machines).
Virtual threads (V-Threads) for a better concurrency handling
They do not block the OS thread while they are waiting or sleeping. Platform threads have always been easy to model, program and debug because they use the platform’s unit of concurrency to represent the application’s unit of concurrency. Virtual Threads (JEP 444) – After a couple of rounds of prereleases, this JEP has become finalized in JDK 21. Read on to discover when and how to use virtual threads in your applications. So, spinning a new thread implies to start off a new high tied OS thread (also known in technical literature as carrier thread) that allocates additional stack and memory space. Because of this, there’s a limit to the number of threads that a program can generate, otherwise it could run out of memory.
This is one of the biggest changes to come to Java in a long time — and at the same time, is an almost imperceptible change. There is almost zero new API surface, and virtual threads behave almost exactly like the threads we already know. Indeed, to use virtual threads effectively, there is more unlearning than learning to be done.
The Scheduler and Cooperative Scheduling
That’s why Golang made its way into the industry (besides Google support). Goroutines are a concept very similar to Java’s virtual threads and they solve the same problem. One big advantage of coroutines (so virtual threads) is that they can generate high levels of concurrency without the drawback of callbacks. To create a new virtual thread, we have to use Thread.startVirtualThread(runnable). Notice how the control is yielded between the two code blocks, first the generator prints the number then the loop prints it.