Java Project Loom: Understand the new Java concurrency model

When we start such a thread here on line two, this thread will run somewhere in the background. The virtual machine will make sure that our current flow of execution can continue, but this separate thread actually runs somewhere. At this point in time, we have two separate execution paths running at the same time, concurrently.

Or managing context-data for so many virtual threads, similar to what we have as thread-local for current threads. So, Java decided to map every thread to a separate native kernel thread. Essentially the JVM threads became a thin wrapper around the operating system threads. This simplified the programming model, and Java could leverage the benefits of parallelism with preemptive scheduling of threads by the kernel across multiple cores. One caveat, if a virtual thread is executing some code inside a synchronized block, it cannot be detached from its platform thread.

Exploring Project Loom

Wrapping up a function in a continuation doesn’t really run that function, it just wraps a Lambda expression, nothing specific to see here. However, if I now run the continuation, so if I call run on that object, I will go into foo function, and it will continue running. It runs the first line, and then goes to bar method, it goes to bar function, it continues running.

The ExecutorService would attempt to create 10,000 platform threads, and thus 10,000 OS threads, and the program might crash, depending on the machine and operating system. A virtual thread is an instance of java.lang.Thread that is not tied to a particular OS thread. A platform thread, by contrast, is an instance of java.lang.Thread implemented in the traditional way, as a thin wrapper around an OS thread. The continuations used in the virtual thread implementation override onPinned so that if a virtual thread attempts to park while its continuation is pinned (see above), it will block the underlying carrier thread.

What does this mean to regular Java developers?

The async Servlet API was introduced to release server threads so the server could continue serving requests while a worker thread continues working on the request. Project Loom has revisited all areas in the Java runtime libraries that can block and updated the code to yield if the code encounters blocking. Java’s concurrency utils (e.g. ReentrantLock, CountDownLatch, CompletableFuture) can be used on Virtual Threads without blocking underlying Platform Threads. This change makes Future’s .get() and .get(Long, TimeUnit) good citizens on Virtual Threads and removes the need for callback-driven usage of Futures. At a high level, a continuation is a representation in code of the execution flow. In other words, a continuation allows the developer to manipulate the execution flow by calling functions.

A million virtual threads require at least a million objects, but so do a million tasks sharing a pool of platform threads. In addition, application code that processes requests typically maintains data across I/O operations. Thread-per-request code can keep that data in local variables, which are stored on virtual thread stacks in the heap, while asynchronous code must keep that same data in heap objects that are passed from one stage of the pipeline to the next.

Stop using Integer ID’s in your Database

So in a thread-per-request model, the throughput will be limited by the number of OS threads available, which depends on the number of physical cores/threads available on the hardware. To work around this, you have to use shared thread pools or asynchronous concurrency, both of which have their drawbacks. Thread pools have many limitations, like thread leaking, deadlocks, resource thrashing, etc.

• Project Looms changes the existing Thread implementation from the mapping of an OS thread, to an abstraction that can either represent such a thread or a virtual thread.
• Recent years have seen the introduction of many asynchronous APIs to the Java ecosystem, from asynchronous NIO in the JDK, asynchronous servlets, and many asynchronous third-party libraries.
• It introduces virtual threads as a solution to improve performance and scalability, particularly when dealing with blocking APIs commonly found in Spring MVC applications.
• Longer term, the biggest benefit of virtual threads looks to be simpler application code.

It essentially means that we are waiting for this background task to finish. It was originally intended to provide job-control operations such as stopping all threads in a group. Modern code is more likely to use the thread pool APIs of the java.util.concurrent package, introduced in Java 5. ThreadGroup supported the isolation of applets in early Java releases, but the Java security architecture evolved significantly in Java 1.2 and thread groups no longer played a significant role.

Fibers

Furthermore, any idle thread does not block, waiting for the task and pulls it from the tail of another thread’s deque instead. This type of scheduling is not optimal for Java applications in particular. Presently, Java relies on OS implementations for both the continuation and the scheduler.

Stepping over a blocking operation behaves as you would expect, and single stepping doesn’t jump from one task to another, or to scheduler code, as happens when debugging asynchronous code. This has been facilitated by changes to support virtual threads at the JVM TI level. We’ve also engaged the IntelliJ IDEA and NetBeans debugger teams to test debugging virtual threads in those IDEs.

Distributed Locks in Spring-boot Microservice Environment

Even more interestingly, from the kernel point of view, there is no such thing as a thread versus process. This is just a basic unit of scheduling in the operating system. The only difference between them is just a single flag, when you’re creating a thread rather than a process. When you’re creating a new thread, it shares the same memory with the parent thread.

So the conformity of the API that heavy-weight or the new light-weight threads support will lead to a better user experience. So, maintaining the abstraction, we don’t really care how the function internally decomposes the program. It’s all fine, so far, as all lines of execution java virtual threads terminate with the function. Alternatively, the scopes of concurrent executions are cleanly nested. It emphasizes that if control splits into concurrent tasks, they must join up again. The operating system kernel supports and manages the kernel-level threads directly.

Revision of Concurrency Utilities

For example, class loading occurs frequently only during startup and only very infrequently afterwards, and, as explained above, the fiber scheduler can easily schedule around such blocking. Many uses of synchronized only protect memory access and block for extremely short durations — so short that the issue can be ignored altogether. Similarly, for the use of Object.wait, which isn’t common in modern code, anyway (or so we believe at this point), which uses j.u.c.