Each of those can succeed or fail independently. Ideally, the handleOrder() method should fail if any subtask fails. However, if a failure occurs in one subtask, things get messy. This is not I/O bound, it is purely compute bound. Parallel Streams work exceptionally well in this case, and it’s hard (impossible?) to do better.
With this approach with Project Loom, notice that I’m actually starting as many concurrent connections, as many concurrent virtual threads, as many images there are. I personally don’t pay that much price for starting these threads because https://www.globalcloudteam.com/ all they do is just like being blocked on I/O. 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.
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Project Loom team has done a great job on this front, and Fiber can take the Runnable interface. To be complete, note that Continuation also implements Runnable. When I run this program and hit the program with, say, 100 calls, the JVM thread graph java project loom shows a spike as seen below (output from jconsole). The command I executed to generate the calls is very primitive, and it adds 100 JVM threads. You can learn more about reactive programming here and in this free e-book by Clement Escoffier.
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.
INFOQ EVENTS
Virtual threads are wrapped upon platform threads, so you may consider them an illusion that JVM provides, the whole idea is to make lifecycle of threads to CPU bound operations. For example, if you want to serialize one task after another, we would use an executor service backed by a single thread. Currently reactive programming paradigms are often used to solve performance problems, not because they fit the problem.
You can create it using a builder method, whatever. You can also create a very weird ExecutorService. This ExecutorService doesn’t actually pull threads. 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.
Structured Concurrency
For example, data store drivers can be more easily transitioned to the new model. 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. Virtual threads have a very different behavior and performance profile than platform threads.
- If it gets the expected response, the preview status of the virtual threads will then be removed by the time of the release of JDK21.
- The implementation becomes even more fragile and puts a lot more responsibility on the developer to ensure there are no issues like thread leaks and cancellation delays.
- When you’re creating a new thread, it shares the same memory with the parent thread.
- Continuations are actually useful, even without multi-threading.
- We will be discussing the prominent parts of the model such as the virtual threads, Scheduler, Fiber class and Continuations.
Discover new ideas and insights from senior practitioners driving change in software. Learn what’s next in software from world-class leaders pushing the boundaries. Attend in-person or get video-only pass to recordings. Fibers are designed to allow for something like the synchronous-appearing code flow of JavaScript’s async/await, while hiding away much of the performance-wringing middleware in the JVM. The problem solvers who create careers with code. Trying to get up to speed with Java 19’s Project Loom, I watched Nicolai Parlog’s talk and read several blog posts.
The InfoQ Newsletter
Go’s language with goroutines was a solution, now they can write Sync code and also handle C10K+. So now Java comes up with Loom, which essentially copies the Go’s solution, soon we will have Fibers and Continuations and will be able to write Sync code again. I may be wrong, but as far as I understand, the whole Reactive/Event Loop thing, and Netty in particular, was invented as an answer to the C10K+ problem.
Not really, it will jump straight to line 17, which essentially means we are continuing from the place we left off. 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.
Learn more about Java, multi-threading, and Project Loom
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 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. Check out these additional resources to learn more about Java, multi-threading, and Project Loom. We want updateInventory() and updateOrder() subtasks to be executed concurrently.
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.
Problems and Limitations – Deep Stack
OS threads are at the core of Java’s concurrency model and have a very mature ecosystem around them, but they also come with some drawbacks and are expensive computationally. Let’s look at the two most common use cases for concurrency and the drawbacks of the current Java concurrency model in these cases. One core reason is to use the resources effectively. And hence we chain with thenApply etc so that no thread is blocked on any activity, and we do more with less number of threads. Loom is more about a native concurrency abstraction, which additionally helps one write asynchronous code.