Project Loom is certainly a game-changing feature from Java so far. This new lightweight concurrency model supports high throughput and aims to make it easier for Java coders to write, debug, and maintain concurrent Java applications. Indeed, some languages and language runtimes successfully provide a lightweight thread implementation, most famous are Erlang and Go, and the feature is both very useful and popular. In case of Project Loom, you don’t offload your work into a separate thread pool, because whenever you’re blocked your virtual thread has very little cost.
Amid the emergence of many new asynchronous frameworks and languages, this article throws light on how WISP 2 brings the coroutine capability of Go into Java to keep it up to date. The terminally deprecated suspend(), resume(), and stop() methods always throw an exception. The API defines suspend(), java enhancement proposals pursue virtual threads resume(), and stop() methods that are inherently deadlock prone and unsafe. The API defines enumerate() methods that are inherently racy. The stream decoders and encoders used by InputStreamReader and OutputStreamWriter now use the same lock as the enclosing InputStreamReader or OutputStreamWriter.
Problems and Limitations – Unsupported APIs
In WISP 2, all threads are converted into coroutines, and no adaption is needed. The subsequent logic is continued in the callback thread pool or the original service thread pool. Constantly extract the “lower part” (continuation part.) of the program.
Again, threads — at least in this context — are a fundamental abstraction, and do not imply any programming paradigm. In particular, they refer only to the abstraction allowing programmers to write sequences of code that can run and pause, and not to any mechanism of sharing information among threads, such as shared memory or passing messages. Many applications written for the Java Virtual Machine https://globalcloudteam.com/ are concurrent — meaning, programs like servers and databases, that are required to serve many requests, occurring concurrently and competing for computational resources. Project Loom is intended to significantly reduce the difficulty of writing efficient concurrent applications, or, more precisely, to eliminate the tradeoff between simplicity and efficiency in writing concurrent programs.
JDK 8 brought asynchronous programming support and more concurrency improvements. While things have continued to improve over multiple versions, there has been nothing groundbreaking in Java for the last three decades, apart from support for concurrency and multi-threading using OS threads. More importantly, every thread you create in your Java Virtual Machine consumes more or less around 1 megabyte of memory, and it’s outside of heap. No matter how much heap you allocate, you have to factor out the extra memory consumed by your threads. This is actually a significant cost, every time you create a thread, that’s why we have thread pools. That’s why we were taught not to create too many threads on your JVM, because the context switching and memory consumption will kill us.
Usage in a real database (Raft)
This makes lightweight Virtual Threads an exciting approach for application developers and the Spring Framework. Past years indicated a trend towards applications that communicate over the network with each other. Many applications make use of data stores, message brokers, and remote services.
From that perspective, I don’t believe Project Loom will revolutionize the way we develop software, or at least I hope it won’t. It will significantly change the way libraries or frameworks can be written so that we can take advantage of them. Another question is whether we still need reactive programming. If you think about it, we do have a very old class like RestTemplate, which is like this old school blocking HTTP client. With Project Loom, technically, you can start using RestTemplate again, and you can use it to, very efficiently, run multiple concurrent connections.
Project Loom: The new Java Concurrency Model
Web servers like Jetty have long been using NIO connectors, where you have just a few threads able to keep open hundreds of thousand or even a million connections. From this point of view, there is only a need for a mechanism to save or resume the execution context and requirement to use the non-blocking thread + callback method in the blocking library function to release or resume the coroutine. This helps to run the programs written in the synchronous mode in the asynchronous mode. New methods in com.sun.jdi.request.ThreadStartRequest and com.sun.jdi.request.ThreadDeathRequest limit the events generated for the request to platform threads. A new capability, can_support_virtual_threads, gives agents finer control over thread start and end events for virtual threads. Typically, a virtual thread will unmount when it blocks on I/O or some other blocking operation in the JDK, such as BlockingQueue.take().
- It runs the first line, and then goes to bar method, it goes to bar function, it continues running.
- For example, suppose that a task needs to wait for a second.
- If we introduced a new class to represent user-mode threads then currentThread() would have to return some sort of wrapper object that looks like a Thread but delegates to the user-mode thread object.
- 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.
- Loom offers the same simulation advantages of FoundationDB’s Flow language but with the advantage that it works well with almost the entire Java runtime.
- User threads are created by the JVM every time you say newthread.start.
Fibry Generators don’t have these limitations, and can let you customize how many elements to keep in memory ; more elements usually mean better performance, but Fibry has also other ways to tune speed. Please note that every generator is back by a thread / fiber, and while it can process millions of elements per second, it might still be slower than other solutions. Some languages like Python have the possibility to yield a value, meaning that the function returns an iterator / generator. Java does not have such a feature, but now Fibry implements several mechanisms for that, offering you a choice between simplicity and speed. This is a bit less elegant and more complex than having a yield keyword, but at least it can be customized based on your needs.
Misunderstanding 1: Context Switching is Caused by Accessing the Kernel
However, coroutine still has an advantage as WISP correctly switches the scheduling of ubiquitous synchronized blocks in JDK. For example, binding the connections of the proxy client and backend connections to the same Netty thread and localizing all thread operations. To meet these two conditions, an ideal method is eventloop + asynchronous callback. 1) The number of active threads is approximately equal to the number of CPUs. When the preceding program is tested on the ECS Bare Metal Instance server, each pipe operation only takes about 334 ns.
I think it’s all a moot point though, as it basically just demonstrates the next iteration of Paul Graham’s Blub Paradox. Loom threads have the same basic semantics as normal threads. There’s nothing special about them that relates to immutability. So, I’m not sure it has affected the speed or ease of shipping improvements, rather than enabled a class of work that would have previously been impossible to do safely . We are a group of ninja software architects, product managers, software engineers, and data scientists. We solve complex problems, take pride in what we deliver, and work hard to deliver value to our customers and partners.
It’s easy to see how massively increasing thread efficiency, and dramatically reducing the resource requirements for handling multiple competing needs, will result in greater throughput for servers. Better handling of requests and responses is a bottom-line win for a whole universe of existing and to-be-built Java applications. Before looking more closely at Loom’s solution, it should be mentioned that a variety of approaches have been proposed for concurrency handling. In general, these amount to asynchronous programming models. Some, like CompletableFutures and Non-Blocking IO, work around the edges of things by improving the efficiency of thread usage.
The handleOrder() task will be blocked on inventory.get() even though updateOrder() threw an error. Ideally, we would like the handleOrder() task to cancel updateInventory() when a failure occurs in updateOrder() so that we are not wasting time. One of the main goals of Project Loom is to actually rewrite all the standard APIs. For example, socket API, or file API, or lock APIs, so lock support, semaphores, CountDownLatches. All of these APIs need to be rewritten so that they play well with Project Loom.
Discovering remote actors
Project Loom’s mission is to make it easier to write, debug, profile and maintain concurrent applications meeting today’s requirements. Project Loom will introduce fibers as lightweight, efficient threads managed by the Java Virtual Machine, that let developers use the same simple abstraction but with better performance and lower footprint. A fiber is made of two components — a continuation and a scheduler. As Java already has an excellent scheduler in the form of ForkJoinPool, fibers will be implemented by adding continuations to the JVM.
Listing 3. Continuation example
In my free time, I enjoy contributing to open-source projects and staying up-to-date with the latest developments in the tech industry. Here is a quick comparison between platform threads and virtual threads. The Fiber class allows developers to create and manage fibers, which are lightweight threads that are managed by the Java Virtual Machine rather than the operating system. The Fiber class provides methods for creating, scheduling, and synchronizing fibers, as well as for suspending, resuming, canceling, and completing them. In the context of Project Loom, fiber is a type of lightweight thread that is managed by the Java Virtual Machine rather than an operating system. Fibers are similar to threads in that they allow a program to execute multiple tasks concurrently, but they are more efficient and easier to use because they are managed by the JVM.
By lightweight, I mean you can really allocate millions of them without using too much memory. A carrier thread is the real one, it’s the kernel one that’s actually running your virtual threads. Of course, the bottom line is that you can run a lot of virtual threads sharing the same carrier thread.
Embracing Virtual Threads
The implications of this for Java server scalability are breathtaking, as standard request processing is married to thread count. Project Loom is still in the early stages of development and is not yet available in a production release of the JVM. However, it has the potential to greatly improve the performance and scalability of Java applications that rely on concurrency. A continuation can be thought of as a “snapshot” of the fiber’s execution, including the current call stack, local variables, and program counter. When a fiber is resumed, it picks up from where it left off by restoring the state from the continuation.