Understanding Project Loom Concurrency Models

Each platform thread had to process ten tasks sequentially, each lasting about one second. The attempt in listing 1 to start 10,000 threads will bring most computers to their knees . Attention – possibly the program reaches the thread limit of your operating system, and your computer might actually “freeze”. Or, more likely, the program will crash with an error message like the one below.

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. Also, the kotlin support of vert.x has already encapsulated all JDK blocking methods. Similar to traditional threads, a virtual thread is also an instance of java.lang.Thread that runs its code on an underlying OS thread, but it does not block the OS thread for the code’s entire lifetime. Keeping the OS threads free means that many virtual threads can run their Java code on the same OS thread, effectively sharing it.

He is a regular speaker at all the major Java conferences and is the mastermind behind The Java Specialists’ Newsletter. In this talk, we will show a brief introduction to Project Loom. We then look at how we can prepare our code bases so that the migration to Loom is easier. We show how long-running tasks impact the liveness of our system. We look at what type of code we will need to refactor so that it is ready when Loom lands. Unfortunately, the chemical stability of CFCs turned out to be a problem that threatened the whole world, as scientists discovered in the 1980s.

Native threads are kicked off the CPU by the operating system, regardless of what they’re doing . Even an infinite loop will not block the CPU core this way, others will still get their turn. On the virtual thread level, however, there’s no such scheduler – the virtual thread itself must return control to the native thread.

Comparing Performance Of Platform Threads And Virtual Threads

The beauty of the model is that developers can stick to the familiar thread-per-request programming model without running into scaling issues due to a limited number of available threads. I highly recommend you to read the JEP of Project Loom, which is very well written and provides much more details and context. If application code encounters a blocking method, Loom will offload https://globalcloudteam.com/ the virtual thread from the current carrier to make room for other virtual threads. Virtual threads are cheap and managed by the JVM, meaning that you can have many, if not millions. The beauty of this model is that developers can stick to the familiar per-request thread programming model without running into scaling problems due to the limited number of threads available.

The response to the client is always ultimately written back by eventLoop at the entry. Constantly extract the “lower part” (continuation part.) of the program. This requires some thinking and hence, let’s introduce the kotlin coroutine to simplify the program.

Understanding Project Loom Concurrency Models

WISP supports switching when a native function is installed on a stack, but Project Loom does not. Project Loom serializes the context and then save it, which saves memory but reduces the switching efficiency. Project Loom is a standard coroutine implementation on OpenJDK. In WISP 1, the parameters of connected applications and the implementation of WISP are deeply adapted. However, coroutine still has an advantage as WISP correctly switches the scheduling of ubiquitous synchronized blocks in JDK.

The first idea for how to make calls non-blocking is offloading JDBC calls to an Executor . While this approach somewhat works, it comes with several drawbacks that neglect the benefits of a reactive programming model. WISP 2 is mainly designed for I/O-intensive server scenarios, where most companies use online services . WISP 2 is a benchmark for Java coroutine functionality and is now an ideal product in terms of product format, performance, and stability. To date, hundreds of applications, and tens of thousands of containers have already been deployed on WISP 1 or WISP 2.

Virtual Threads In Java Project Loom

Oracle’s Project Loom aims to explore exactly this option with a modified JDK. It brings a new lightweight construct for concurrency, named virtual threads. Now, for CPU-constrained code (which also doesn’t use virtual threads to begin with), it doesn’t make much sense to overuse more threads than are physically supported by a given CPU.

Assume that all the libraries we depend on, such as Dubbo, support callbacks. This helps to run the programs written in the synchronous mode in the asynchronous mode. Next, we will replace the Executors.newFixedThreadPool with Executors.newVirtualThreadPerTaskExecutor(). This will execute all the tasks in virtual threads instead of platform threads.

Misunderstanding 2: The Overhead Of Context Switching Is High

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. For coroutines, there are special keywords in the respective languages (in Clojure a macro for a “go block”, in Kotlin the “suspend” keyword). The same method can be executed unmodified by a virtual thread, or directly by a native thread. In this example, suspendCoroutine is introduced to obtain a reference to the current Continuation, run a segment of code, and ultimately suspend the current coroutine.

• But “the more, the merrier” doesn’t apply for native threads – you can definitely overdo it.
• But in any case it’s worth pointing out that CPU-bound code may behavior differently with virtual threads than with classic OS-level threads.
• While providing users with the greatest convenience, this ensures compatibility with the existing code.
• With releases already happening, there’s no need to guess about Project Loom nor wait potentially three years to test drive an API.
• Virtual threads, also referred to as green threads or user threads, moves the responsibility of scheduling from the OS to the application, in this case the JVM.
• WISP 2 not only allows users to enjoy the rich resources of the Java ecosystem but also supports asynchronous programs, keeping the Java platform up to date.
• I highly recommend you to read the JEP of Project Loom, which is very well written and provides much more details and context.

Such synchronized block does not make the application incorrect, but it limits the scalability of the application similar to platform threads. There are two specific scenarios in which a virtual thread can block the platform thread . Virtual threads do not support the stop(), suspend(), or resume() methods. These methods throw an UnsupportedOperationException when invoked on a virtual thread.

2 Avoid Using Thread

They do not block the OS thread while they are waiting or sleeping. Developing using virtual threads are near identical to developing using traditional threads. This removes the scalability issues of blocking I/O, but without the added code complexity of using asynchronous I/O, since we are back to a single thread only overseeing a single connection.

The chemical industry has been developing newer alternatives intended to be safer for both people and climate, but as we saw with CFCs and HFCs, inert chemicals can have unintended consequence. Several industry leaders have supported efforts to phase out HFCs. Ammonia and hydrocarbons like butane evaporate at room temperature and have been used as refrigerants since the early 20th century. Their greater reactivity means their compressors and plumbing have to be more corrosion-resistant and leak-proof to be safe.

Besides, the lock-free scheduling implementation greatly reduces the scheduling overhead compared to kernel implementation. The earliest version, WISP 1, was deeply customized for these scenarios. For example, requests received by HSF are automatically processed in a coroutine instead of a thread pool.

Relational Databases And Reactive

Each thread counts 100M and then prints out the time it took from scheduling the thread to completing it. For example, if we scale a million virtual threads in the application, there will be a million ThreadLocal instances along with the data they refer to. Such a large number of instances can put enough burden on the physical memory and it should be avoided. In the following example, we are submitting 10,000 tasks and waiting for all of them to complete. The code will create 10,000 virtual threads to complete these 10,000 tasks. In this way, Executor will be able to run 100 tasks at a time and other tasks will need to wait.

Seeing these results, the big question of course is whether this unfair scheduling of CPU-bound threads in Loom poses a problem in practice or not. Ron and Tim had an expanded debate on that point, which I recommend you to check out to form an opinion yourself. As per Ron, support for yielding at points in program execution other than blocking methods has been implemented in Loom already, but this hasn’t been merged into the mainline with the initial drop of Loom. It should be easy enough though to bring this back if the current behavior turns out to be problematic. Having been in the workings for several years, Loom got merged into the mainline of OpenJDK just recently and is available as a preview feature in the latest Java 19 early access builds.

The test results show that, under high pressure, QPS and RT are improved by 10% to 20%. Therefore, the two preceding misunderstandings have a certain causal relationship with multithreading overhead, but the actual overhead comes from thread blocking and wake-up scheduling. Since kernel switching and context switching are fast, project loom it’s crucial to understand what produces multithreading overhead. According to the table, the context switching and sys CPU usage are significantly reduced, the response time is reduced by 11.45%, and queries per second is increased by 18.13%. Have created a short and practical intro into what project loom is all about.

As shown in the above figure, the hotspot produces a large amount of scheduling overhead. The following figure shows the top-H of the DRDS stress test on Elastic Compute Service . According to the figure, hundreds of application threads are hosted by eight Carrier threads and distributed evenly on several CPU core threads to be run. 2) Some Java EE standards are used for thread-level blocking modes (such as Java Database Connectivity ). LibHunt tracks mentions of software libraries on relevant social networks.

So the underlying Fiber attempts to continue on a previous flow that was using a blocking API. Oracle announced ADBA, which is an initiative to provide a standardized API for asynchronous database access in Java by using futures. Everything in ADBA is a work in progress, and the team behind ADBA is happy to get feedback. A bunch of Postgres folks is working on a Postgres ADBA driver that can be used for first experiments. WISP 2 supports workStealing and therefore converts all threads into coroutines. This idea is taken from a recent speech by Ron Pressler, the developer of Quasar and Project Loom.

But this pattern limits the throughput of the server because the number of concurrent requests becomes directly proportional to the server’s hardware performance. So, the number of available threads has to be limited even in multi-core processors. Let’s start with the challenge that led to the development of virtual threads.