For example, it shows you the thread ID and so-called native ID. It turns out, these IDs are actually known by the operating system. If you know the operating system’s utility called top, which is a built in one, it has a switch -H. With the H switch, it actually shows individual threads rather than processes. After all, why does this top utility that was supposed to be showing which processes are consuming your CPU, why does it have a switch to show you the actual threads? There was also this rather obscure many-to-many model, in which case you had multiple user threads, typically a smaller number of kernel threads, and the JVM was doing mapping between all of these.
It’s absolutely fine to start 10,000 concurrent connections, because you won’t pay the price of 10,000 carrier or kernel threads, because these virtual threads will be hibernated anyway. 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.
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 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. 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.
There’s a list of APIs that do not play well with Project Loom, so it’s easy to shoot yourself in the foot. QCon Plus is a virtual conference for senior software engineers and architects that covers the trends, best practices, and solutions leveraged by the world’s most innovative software organizations. Involved in open-source, DZone’s Most Valuable Blogger, used to be very active on StackOverflow.
You won’t need to make these changes once virtual threads are promoted out of preview. While implementing async/await is easier than full-blown continuations and fibers, that solution falls far too short of addressing the problem. In other words, it does not solve what’s known as the «colored function» problem. The main technical mission in implementing continuations — and indeed, of this entire project — is adding to HotSpot the ability to capture, store and resume callstacks not as part of kernel threads. As we will see, a thread is not an atomic construct, but a composition of two concerns — a scheduler and a continuation. With Project Loom, we simply start 10,000 threads, each thread per each image.
Kotlin & Loom: a developer perspective
Plus, you might need to wrap the low-level API, just as we did using the Loom class. A Loom instance wraps a StructuredTaskScope, which isn’t directly part of JEP 425, but of a structured concurrency API, defined in JEP 428. Both are part of the Loom project and are previewed/incubated in Java 19. Despite being a prime example of a distributed algorithm, there isn’t that much concurrency in Raft. For sure, there are multiple nodes running in parallel and exchanging messages, but this happens on separate nodes. As far as a single node is concerned, especially in our event-driven, actor-like implementation, concurrency is reduced—on purpose—to a minimum.
Using conventional Java threads, when a server was idling on a request, an operating system thread was also idling, which severely limited the scalability of servers. As Nicolai Parlog has explained, «Operating systems can’t increase the efficiency of platform threads, but the JDK will make better use of them by severing the one-to-one relationship between its threads and OS threads.» One of the most far-reaching Java 19 updates is the introduction of virtual threads. Virtual threads are part of Project Loom, and are available in Java 19 as a preview.
Rather, the virtual thread signals that it can’t do anything right now, and the native thread can grab the next virtual thread, without CPU context switching. But how can this be done without using asynchronous I/O APIs? After all, Project Loom is determined to save programmers from “callback hell”. https://globalcloudteam.com/ Is it possible to combine some desirable characteristics of the two worlds? Be as effective as asynchronous or reactive programming, but in a way that one can program in the familiar, sequential command sequence? Oracle’s Project Loom aims to explore exactly this option with a modified JDK.
Project Loom: Virtual Threads
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. Of course, there are some limits here, because we still have a limited amount of memory and CPU. Now it’s easy, every time a new HTTP connection comes in, you just create a new virtual thread, as if nothing happens.
WISP 1 supported objectMonitor and was deployed with core e-commerce services, but did not support workStealing. Therefore, only some short tasks could be converted to coroutines . Netty threads were still threads and required complex and tricky configurations. WISP 2 focuses on performance and compatibility with existing code. In short, the existing multi-thread I/O-intensive performance of Java applications may improve asynchronously simply by adding the JVM parameters of WISP 2. The most basic way to use a virtual thread is with Thread.startVirtualThread.
Understanding Project Loom Concurrency Models
In this case updateInventory() and updateOrder() will leak and continue to run in the background. I’ve not looked into the goroutine implementation, so I couldn’t tell you how it compares to what I’ve read loom is doing. It’s not surprising that a huge number of data processing workloads run on the JVM . Not only that, but Oracle has open sourced previously closed source projects pertaining to the JVM and tooling around it. Java is a language and computing platform from the 90s and it shows.
It’s just that the API finally allows us to build in a much different, much easier way. This would be accomplished via virtual threads, delimited continuations and tail calls. You might think that it’s actually fantastic because you’re handling more load. 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.
Also, it means we can take any piece of code, it could be running a loop, it could be doing some recursive function, whatever, and we can all the time and every time we want, we can suspend it, and then bring it back to life. Continuations are actually useful, even without multi-threading. Structured concurrency aims to simplify multi-threaded and parallel programming. It treats multiple tasks running in different threads as a single unit of work, streamlining error handling and cancellation while improving reliability and observability. This helps to avoid issues like thread leaking and cancellation delays. Being an incubator feature, this might go through further changes during stabilization.
- Threads managed by the operating system are a costly resource, which means you can typically have at most a few thousands of them, but not hundreds of thousands, or even millions.
- JEP 425, Virtual Threads , introduces virtual threads, lightweight threads that dramatically reduce the effort of writing, maintaining, and observing high-throughput concurrent applications, to the Java platform.
- If instead you create 4 virtual threads, you will basically do the same amount of work.
- If fibers are represented by Threads, then some changes would need to be made to such striped data structures.
- Project Loom has made it into the JDK through JEP 425.
A program that is inefficient today, consuming a native thread for each HTTP connection, could run unchanged on the Project Loom JDK and suddenly be efficient and scalable. Thanks to the changed java.net/java.io libraries, which are then using virtual threads. In the context of virtual threads, “channels” are particularly worth mentioning here. Kotlin and Clojure offer these as the preferred communication model for their coroutines. Instead of shared, mutable state, they rely on immutable messages that are written to a channel and received from there by the receiver. Whether channels will become part of Project Loom, however, is still open.
When you’re doing a thread dump, which is probably one of the most valuable things you can get when troubleshooting your application, you won’t see virtual threads which are not running at the moment. Continuations that you see in here are actually quite common in different languages. You have coroutines or goroutines, in languages like Kotlin and Go. 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.
When it comes to concurrency, to the degree that we’ve been using it, there haven’t been significant differences. Manual supervisionOtherRepresenting node role as a data structureAnd finally, summarising Loom vs ZIO—but only in the scope of the Saft implementation! Keep in mind that we do not aim to run a comprehensive comparison here. Raft is a single, specific use-case that doesn’t use both Loom’s and ZIO capabilities to their full extent. In Java, we could reify that requirement in the method’s signature by the lack of checked exceptions. However, checked exceptions have major usability flaws and they didn’t stand the test of time—they’re often considered a weak part of Java’s design.
However, it no longer runs, so it will be woken up by your operating system. A new version that takes advantage of virtual threads, notice that if you’re currently running a virtual thread, a different piece of code is run. If you’ve seen green threads or fibers in other programming languages, virtual threads are similar concepts.
This is contrary to the preceding principle that each thread does not need to be blocked. Therefore, with this method, although the number of threads is reduced and memory is saved, the performance benefits are very limited. WISP has obvious advantages over the asynchronous programming mode. Theoretically, as long as one library encapsulates all JDK blocking methods, it is easy to write asynchronous programs. The rewritten blocking library function itself needs to be widely used in many programs.
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. For instance, in an existing Java application using platform threads, you can easily switch to using virtual threads by replacing new Thread invocations with something like Thread.ofVirtual().
Those are technically very similar and address the same problem. However, there’s at least one small but interesting difference from a developer’s perspective. project loom java For coroutines, there are special keywords in the respective languages (in Clojure a macro for a «go block», in Kotlin the «suspend» keyword).