Structured concurrency: will Java Loom beat Kotlin’s coroutines?

Reactive is a set of principles to build robust, efficient, and concurrent applications and systems. These principles let you handle more load than traditional approaches while using the resources more efficiently while also reacting to failures gracefully. One of the challenges of any new approach is how compatible it will be with existing code.

Depending on the benchmark, you might even conclude that Loom’s direct interface to OS threads is not faster than Kotlin. Besides, once Loom is final, also Kotlin coroutines will directly use the same interface. As consequence the toolkits will, in the near future, rely on the same concurrency primitives. With this new version, the threads look much better .

Java makes it so easy to create new threads, and almost all the time the program ends-up creating more threads than the CPU can schedule in parallel. Let’s say that we have a two-lane road , and 10 cars want to use the road at the same time. Naturally, this is not possible, but think about how this situation is currently handled. Traffic lights allow a controlled number of cars onto the road and make the traffic use the road in an orderly fashion. That would be a very naive implementation of this concept.

Threads can do a variety of tasks, such as read from a file, write to a database, take input from a user, and so on. I like the programming model of Reactor, but it fights against all the tools in the JVM ecosystem. Using virtual threads would give us the stream programming model, but keep it aligned with the underlying tools and ecosystems (AMP/Profilers/Debuggers/Logging/etc… Consider the case of a web-framework, where there is a separate thread-pool to handle i/o and the other for execution of http requests. For simple HTTP requests, one might serve the request from the http-pool thread itself. But if there are any blocking high CPU operations, we let this activity happen on a separate thread asynchronously.

And now you can perform a single task on a single virtual thread. Adding Loom to Java will definitively open up a new domain of problems, bugs and best practices. Which means that in the short term, probably nothing will change for Java or Kotlin development. This will be the crux of the matter, we predict. In particular in so called “business domains”, such as e-commerce, insurances and banks, Kotlin provides additional safety, whereas Java does not.

Why OpenShift is essential for containerized applications

With the rise of powerful and multicore CPUs, more raw power is available for applications to consume. In Java, threads are used to make the application project loom java work on multiple tasks concurrently. A developer starts a Java thread in the program, and tasks are assigned to this thread to get processed.

Loom has a list with virtual thread-friendly functions, indicating that you have to know in advance if a function can be used or not. In contrast, Kotlin has the suspend keyword, which marks a function for consumption in a coroutine context. It’s a straightforward entity with a single field . Note that it uses io.quarkus.hibernate.reactive.panache.PanacheEntity, the reactive variant of PanacheEntity. So, behind the scenes, Hibernate uses the execution model we described above. It interacts with the database without blocking the thread.

Project Loom and Reactive Streams

It is still early to commit to anything, but as @OlegDokuka said it is not going to be an either-or choice. Otherwise, if you use all the power of Project Reactor and build a complete stream processing solution – think twice if you need Loom and what it offers to you that Reactor does not. Do the same another way around – think if the Loom ever offers you the same number of operators that enable you to manipulate over your async executions easily. Be prepared to use .block() operator to transform your functional Mono into imperative value T and that is basically it.

This resulted in hitting the green spot that we aimed for in the graph shown earlier. So, our echo server will change as follows. Note that the part that changed is only the thread scheduling part; the logic inside the thread remains the same. The wiki says Project Loom supports “easy-to-use, high-throughput lightweight concurrency and new programming models on the Java platform.”

Structured concurrency: will Java Loom beat Kotlin’s coroutines?

It also improves the concurrency as it removes the constraint on the number of threads. Finally, it also improves response time as it reduces the number of thread switches. So, with the reactive execution https://globalcloudteam.com/ model, the requests are processed using I/O threads. An I/O thread can handle multiple concurrent requests. Here is the trick and one of the most significant differences between reactive and imperative.

• When a request comes in, a thread carries the task up until it reaches the DB, wherein the task has to wait for the response from DB.
• Red Hat OpenShift Open, hybrid-cloud Kubernetes platform to build, run, and scale container-based applications — now with developer tools, CI/CD, and release management.
• What we potentially will get is performance similar to asynchronous, but with synchronous code.
• The Quarkus architecture is ready to support Loom as soon as it’s become globally available.
• Depending on the benchmark, you might even conclude that Loom’s direct interface to OS threads is not faster than Kotlin.

It’s often easier to write synchronous code because you don’t have to keep writing code to put things down and pick them back up every time you can’t make forward progress. Straightforward “do this, then do that, if this happens do this other thing” code is easier to write than a state machine updating explicit state. Virtual threads can give you most of the benefits of asynchronous code while your coding experience is much closer to that of writing synchronous code. In this guide, we will get you started with some reactive features of Quarkus. We are going to implement a simple CRUD application. Yet, unlike in the Hibernate with Panache guide, it uses the reactive features of Quarkus.

Checking if the site connection is secure

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. With Loom, we write synchronous code, and let someone else decide what to do when blocked.

The last extension is the reactive database driver for PostgreSQL. Hibernate Reactive uses that driver to interact with the database without blocking the caller thread. To better understand the contrast, we need to explain the difference between the reactive and imperative execution models. It’s essential to comprehend that Reactive is not just a different execution model, but that distinction is necessary to understand this guide. As mentioned above, in this guide, we are going to implement a reactive CRUD application.

Benchmarking improved conntrack performance in OvS 3.0.0

Although the application computer is waiting for the database, many resources are being used on the application computer. With the rise of web-scale applications, this threading model can become the major bottleneck for the application. 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.

The problem solvers who create careers with code. The scheduler allocates the thread to a CPU core to get it executed. In the modern software world, the operating system fulfills this role of scheduling tasks to the CPU. In this article, we’ll explain more about threads and introduce Project Loom, which supports high-throughput and lightweight concurrency in Java to help simplify writing scalable software.

Reactive Resource

Locking is easy — you just make one big lock around your transactions and you are good to go. That doesn’t scale; but fine-grained locking is hard. Hard to get working, hard to choose the fineness of the grain. When to use are obvious in textbook examples; a little less so in deeply nested logic. Lock avoidance makes that, for the most part, go away, and be limited to contended leaf components like malloc(). Having said it, I think future’s are unavoidable for several other scenarios….

Ready to start developing apps?

Because the interaction with the database is non-blocking and asynchronous, we need to use asynchronous constructs to implement our HTTP resource. Quarkus uses Mutiny as its central reactive programming model. So, it supports returning Mutiny types from HTTP endpoints.

OpenShift developer sandbox (free)

So, the whole processing of the request runs on this worker thread. Indeed, to handle multiple concurrent requests, you need multiple threads; and so your application concurrency is constrained by the number of threads. In addition, these threads are blocked as soon as your code interacts with remote services. So, it leads to inefficient usage of the resources, as you may need more threads, and each thread, as they are mapped to OS threads, has a cost in terms of memory and CPU. One solution is making use of reactive programming. So, if a CPU has four cores, there may be multiple event loops but not exceeding to the number of CPU cores.

If you want to make it better, fork the website and show us what you’ve got. To run the application, don’t forget to start a database and provide the configuration to your application. In this case, we use Fruit.findById to retrieve the fruit. It returns a Uni, which will complete when the database has retrieved the row.

There are other ways to configure the application – please check the configuration guide to have an overview of the possibilities (such as env variable, .env files and so on). In FruitsEndpointTest.java you can see how the test for the fruit application can be implemented. Since the beginning, Reactive has been an essential tenet of the Quarkus architecture. It includes many reactive features and offers a broad ecosystem. When I run this program and hit the program with, say, 100 calls, the JVM thread graph shows a spike as seen below . The command I executed to generate the calls is very primitive, and it adds 100 JVM threads.

We don’t get benefit over asynchronous API. What we potentially will get is performance similar to asynchronous, but with synchronous code. I want to use Reactor to simplify asynchronous programming. This guide is a brief introduction to some reactive features offered by Quarkus. Quarkus is a reactive framework, and so offers a lot of reactive features. The parameters passed to the application are described in the datasource guide.