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Revolution in Java Concurrency or Obscure Implementation Detail?




Nurkiewicz: I’d like to talk about Project Loom, a very new and exciting initiative that will land eventually in the Java Virtual Machine. Most importantly, I would like to briefly explain whether it’s going to be a revolution in the way we write concurrent software, or maybe it’s just some implementation detail that’s going to be important for framework or library developers, but we won’t really see it in real life. The first question is, what is Project Loom? The question I give you in the subtitle is whether it’s going to be a revolution or just an obscure implementation detail. My name is Tomasz Nurkiewicz.


First of all, we would like to understand how we can create millions of threads using Project Loom. This is an overstatement. In general, this will be possible with Project Loom. As you probably know, these days, it’s only possible to create hundreds, maybe thousands of threads, definitely not millions. This is what Project Loom unlocks in the Java Virtual Machine. This is mainly possible by allowing you to block and sleep everywhere, without paying too much attention to it. Blocking, sleeping, or any other locking mechanisms were typically quite expensive, in terms of the number of threads we could create. These days, it’s probably going to be very safe and easy. The last but the most important question is, how is it going to impact us developers? Is it actually so worthwhile, or maybe it’s just something that is buried deeply in the virtual machine, and it’s not really that much needed?

User Threads and Kernel Threads

Before we actually explain, what is Project Loom, we must understand what is a thread in Java? I know it sounds really basic, but it turns out there’s much more into it. First of all, a thread in Java is called a user thread. Essentially, what we do is that we just create an object of type thread, we parse in a piece of code. 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. The last line is joining. It essentially means that we are waiting for this background task to finish. This is not typically what we do. Typically, we want two things to run concurrently.

This is a user thread, but there’s also the concept of a kernel thread. A kernel thread is something that is actually scheduled by your operating system. I will stick to Linux, because that’s probably what you use in production. With the Linux operating system, when you start a kernel thread, it is actually the operating system’s responsibility to make sure all kernel threads can run concurrently, and that they are nicely sharing system resources like memory and CPU. For example, when a kernel thread runs for too long, it will be preempted so that other threads can take over. It more or less voluntarily can give up the CPU and other threads may use that CPU. It’s much easier when you have multiple CPUs, but most of the time, this is almost always the case, you will never have as many CPUs as many kernel threads are running. There has to be some coordination mechanism. This mechanism happens in the operating system level.

Genius Dog 336 x 280 - Animated

User threads and kernel threads aren’t actually the same thing. User threads are created by the JVM every time you say newthread.start. Kernel threads are created and managed by the kernel. That’s obvious. This is not the same thing. In the very prehistoric days, in the very beginning of the Java platform, there used to be this mechanism called the many-to-one model. In the many-to-one model. The JVM was actually creating user threads, so every time you set newthread.start, a JVM was creating a new user thread. However, these threads, all of them were actually mapped to a single kernel thread, meaning that the JVM was only utilizing a single thread in your operating system. It was doing all the scheduling, so making sure your user threads are effectively using the CPU. All of this was done inside the JVM. The JVM from the outside was only using a single kernel thread, which means only a single CPU. Internally, it was doing all this back and forth switching between threads, also known as context switching, it was doing it for ourselves.

There was also this rather obscure many-to-many model, in which case you had multiple user threads, typically a smaller number of kernel threads, and the JVM was doing mapping between all of these. However, luckily, the Java Virtual Machine engineers realized that there’s not much point in duplicating the scheduling mechanism, because the operating system like Linux already has all the facilities to share CPUs and threads with each other. They came up with a one-to-one model. With that model, every single time you create a user thread in your JVM, it actually creates a kernel thread. There is one-to-one mapping, which means effectively, if you create 100 threads, in the JVM you create 100 kernel resources, 100 kernel threads that are managed by the kernel itself. This has some other interesting side effects. For example, thread priorities in the JVM are effectively ignored, because the priorities are actually handled by the operating system, and you cannot do much about them.

It turns out that user threads are actually kernel threads these days. To prove that that’s the case, just check, for example, jstack utility that shows you the stack trace of your JVM. Besides the actual stack, it actually shows quite a few interesting properties of your threads. For example, it shows you the thread ID and so-called native ID. It turns out, these IDs are actually known by the operating system. If you know the operating system’s utility called top, which is a built in one, it has a switch -H. With the H switch, it actually shows individual threads rather than processes. This might be a little bit surprising. After all, why does this top utility that was supposed to be showing which processes are consuming your CPU, why does it have a switch to show you the actual threads? It doesn’t seem to make much sense.

However, it turns out, first of all, it’s very easy with that tool to show you the actual Java threads. Rather than showing a single Java process, you see all Java threads in the output. More importantly, you can actually see, what is the amount of CPU consumed by each and every of these threads? This is useful. Why is that the case? Does it mean that Linux has some special support for Java? Definitely not. Because it turns out that not only user threads on your JVM are seen as kernel threads by your operating system. On newer Java versions, even thread names are visible to your Linux operating system. Even more interestingly, from the kernel point of view, there is no such thing as a thread versus process. Actually, all of these are called tasks. 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. When you’re creating a new process, it does not. It’s just a matter of a single bit when choosing between them. From the operating system’s perspective, every time you create a Java thread, you are creating a kernel thread, which is, in some sense you’re actually creating a new process. This may actually give you some overview like how heavyweight Java threads actually are.

First of all, they are Kernel resources. 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.

Project Loom – Goal

This is where Project Loom shines. This is still work in progress, so everything can change. I’m just giving you a brief overview of how this project looks like. Essentially, the goal of the project is to allow creating millions of threads. This is an advertising talk, because you probably won’t create as many. Technically, it is possible, and I can run millions of threads on this particular laptop. How is it achieved? First of all, there’s this concept of a virtual thread. A virtual thread is very lightweight, it’s cheap, and it’s a user thread. By lightweight, I mean you can really allocate millions of them without using too much memory. There’s a virtual thread. Secondly, there’s also a carrier thread. 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. In some sense, it’s like an implementation of an actor system where we have millions of actors using a small pool of threads. All of this can be achieved using a so-called continuation. Continuation is a programming construct that was put into the JVM, at the very heart of the JVM. There are actually similar concepts in different languages. Continuation, the software construct is the thing that allows multiple virtual threads to seamlessly run on very few carrier threads, the ones that are actually operated by your Linux system.

Virtual Threads

I will not go into the API too much because it’s subject to change. As you can see, it’s actually fairly simple. You essentially say Thread.startVirtualThread, as opposed to new thread or starting a platform thread. A platform thread is your old typical user threads, that’s actually a kernel thread, but we’re talking about virtual threads here. We can create a thread from scratch. 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. It’s not really a thread pool, per se. You can also create a ThreadFactory if you need it in some API, but this ThreadFactory just creates virtual threads. That’s very simple API.

The API is not the important part, I would like you to actually understand what happens underneath, and what impact may it have on your code bases. A virtual thread is essentially a continuation plus scheduler. A scheduler is a pool of physical called carrier threads that are running your virtual threads. Typically, a scheduler is just a fork join pool with a handful of threads. You don’t need more than one to four, maybe eight carrier threads, because they use the CPU very effectively. Every time a virtual thread no longer needs a CPU, it will just give up the scheduler, it will no longer use a thread from that scheduler, and another virtual thread will kick in. That’s the first mechanism. How does the virtual thread and the scheduler know that the virtual thread no longer needs a scheduler?

This is where continuations come into play. This is a fairly convoluted explanation. Essentially, a continuation is a piece of code that can suspend itself at any moment in time and then it can be resumed later on, typically on a different thread. You can freeze your piece of code, and then you can unlock it, or you can unhibernate it, you can wake it up on a different moment in time, and preferably even on a different thread. This is a software construct that’s built into the JVM, or that will be built into the JVM.


Let’s look into a very simple pseudo-code here. This is a main function that calls foo, then foo calls bar. There’s nothing really exciting here, except from the fact that the foo function is wrapped in a continuation. 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. Then on line 16, something really exciting and interesting happens. The function bar voluntarily says it would like to suspend itself. The code says that it no longer wishes to run for some bizarre reason, it no longer wishes to use the CPU, the carrier thread. What happens now is that we jump directly back to line four, as if it was an exception of some kind. We jump to line four, we continue running. The continuation is suspended. Then we move on, and in line five, we run the continuation once again. Will it run the foo function once more? Not really, it will jump straight to line 17, which essentially means we are continuing from the place we left off. This is really surprising. 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. This is the foundation of Project Loom. Continuations are actually useful, even without multi-threading.

Thread Sleep

Continuations that you see in here are actually quite common in different languages. You have coroutines or goroutines, in languages like Kotlin and Go. You have async/await in JavaScript. You have generators in Python, or fibers in Ruby. All of these are actually very similar concepts, which are finally brought into the JVM. What difference does it make? Let’s see how thread sleep is implemented. It used to be simply a function that just blocks your current thread so that it still exists on your operating system. However, it no longer runs, so it will be woken up by your operating system. A new version that takes advantage of virtual threads, notice that if you’re currently running a virtual thread, a different piece of code is run.

This piece of code is quite interesting, because what it does is it calls yield function. It suspends itself. It voluntarily says that it no longer wishes to run because we asked that thread to sleep. That’s interesting. Why is that? Before we actually yield, we schedule unparking. Unparking or waking up means basically, that we would like ourselves to be woken up after a certain period of time. Before we put ourselves to sleep, we are scheduling an alarm clock. This scheduling will wake us up. It will continue running our thread, it will continue running our continuation after a certain time passes by. In between calling the sleep function and actually being woken up, our virtual thread no longer consumes the CPU. At this point, the carrier thread is free to run another virtual thread. Technically, you can have millions of virtual threads that are sleeping without really paying that much in terms of the memory consumption.

Hello, world!

This is our Hello World. This is overblown, because everyone says millions of threads and I keep saying that as well. That’s the piece of code that you can run even right now. You can download Project Loom with Java 18 or Java 19, if you’re cutting edge at the moment, and just see how it works. There is a count variable. If you put 1 million, it will actually start 1 million threads, and your laptop will not melt and your system will not hang, it will simply just create these millions of threads. As you already know, there is no magic here. Because what actually happens is that we created 1 million virtual threads, which are not kernel threads, so we are not spamming our operating system with millions of kernel threads. The only thing these kernel threads are doing is actually just scheduling, or going to sleep, but before they do it, they schedule themselves to be woken up after a certain time. Technically, this particular example could easily be implemented with just a scheduled ExecutorService, having a bunch of threads and 1 million tasks submitted to that executor. There is not much difference. As you can see, there is no magic here. It’s just that the API finally allows us to build in a much different, much easier way.

Carrier Thread

Here’s another code snippet of the carrier threads. The API may change, but the thing I wanted to show you is that every time you create a virtual thread, you’re actually allowed to define a carrierExecutor. In our case, I just create an executor with just one thread. Even with just a single thread, single carriers, or single kernel thread, you can run millions of threads as long as they don’t consume the CPU all the time. Because, after all, Project Loom will not magically scale your CPU so that it can perform more work. It’s just a different API, it’s just a different way of defining tasks that for most of the time are not doing much. They are sleeping blocked on a synchronization mechanism, or waiting on I/O. There’s no magic here. It’s just a different way of performing or developing software.

Structured Concurrency

There’s also a different algorithm or a different initiative coming as part of Project Loom called structured concurrency. It’s actually fairly simple. There’s not much to say here. Essentially, it allows us to create an ExecutorService that waits for all tasks that were submitted to it in a try with resources block. This is just a minor addition to the API, and it may change.

Tasks, Not Threads

The reason I’m so excited about Project Loom is that finally, we do not have to think about threads. When you’re building a server, when you’re building a web application, when you’re building an IoT device, whatever, you no longer have to think about pooling threads, about queues in front of a thread pool. At this point, all you have to do is just creating threads every single time you want to. It works as long as these threads are not doing too much work. Because otherwise, you just need more hardware. There’s nothing special here. If you have a ton of threads that are not doing much, they’re just waiting for data to arrive, or they are just locked on a synchronization mechanism waiting for a semaphore or CountDownLatch, whatever, then Project Loom works really well. We no longer have to think about this low level abstraction of a thread, we can now simply create a thread every time for every time we have a business use case for that. There is no leaky abstraction of expensive threads because they are no longer expensive. As you can probably tell, it’s fairly easy to implement an actor system like Akka using virtual threads, because essentially what you do is you create a new actor, which is backed by a virtual thread. There is no extra level of complexity that arises from the fact that a large number of actors has to share a small number of threads.

Use Cases

A few use cases that are actually insane these days, but they will be maybe useful to some people when Project Loom arrives. For example, let’s say you want to run something after eight hours, so you need a very simple scheduling mechanism. Doing it this way without Project Loom is actually just crazy. Creating a thread and then sleeping for eight hours, because for eight hours, you are consuming system resources, essentially for nothing. With Project Loom, this may be even a reasonable approach, because a virtual thread that sleeps consumes very little resources. You don’t pay this huge price of scheduling operating system resources and consuming operating system’s memory.

Another use case, let’s say you’re building a massive multiplayer game, or a very highly concurrent server, or a chat application like WhatsApp that needs to handle millions of connections, there is actually nothing wrong with creating a new thread per each player, per each connection, per each message even. Of course, there are some limits here, because we still have a limited amount of memory and CPU. Anyways, confront that with the typical way of building software where you had a limited worker pool in a servlet container like Tomcat, and you had to do all these fancy algorithms that are sharing this thread pool, and making sure it’s not exhausted, making sure you’re monitoring the queue. Now it’s easy, every time a new HTTP connection comes in, you just create a new virtual thread, as if nothing happens. This is how we were taught Java 20 years ago, then we realized it’s a poor practice. These days, it may actually be a valuable approach again.

Another example. Let’s say we want to download 10,000 images. With Project Loom, we simply start 10,000 threads, each thread per each image. That’s just it. Using the structured concurrency, it’s actually fairly simple. Once we reach the last line, it will wait for all images to download. This is really simple. Once again, confront that with your typical code, where you would have to create a thread pool, make sure it’s fine-tuned. There’s a caveat here. Notice that with a traditional thread pool, all you had to do was essentially just make sure that your thread pool is not too big, like 100 threads, 200 threads, 500, whatever. This was the natural limit of concurrency. You cannot download more than 100 images at once, if you have just 100 threads in your standard thread pool.

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 all they do is just like being blocked on I/O. In Project Loom, every blocking operation, so I/O like network typically, so waiting on a synchronization mechanism like semaphores, or sleeping, all these blocking operations are actually yielding, which means that they are voluntarily giving up a carrier thread. 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. Only when the data arrives, the JVM will wake up your virtual thread. In the meantime, you don’t pay the price. This is pretty cool. However, you just have to be aware of the fact that the kernel threads of your thread pools were actually just natural like limit to concurrency. Just blindly switching from platform threads, the old ones, to virtual threads will change the semantics of your application.

To make matters even worse, if you would like to use Project Loom directly, you will have to relearn all these low level structures like CountDownLatch or semaphore to actually do some synchronization or to actually do some throttling. This is not the path I would like to take. I would definitely like to see some high level frameworks that are actually taking advantage of Project Loom.

Problems and Limitations – Deep Stack

Do we have such frameworks and what problems and limitations can we reach here? Before we move on to some high level constructs, so first of all, if your threads, either platform or virtual ones have a very deep stack. This is your typical Spring Boot application, or any other framework like Quarkus, or whatever, if you put a lot of different technologies like adding security, aspect oriented programming, your stack trace will be very deep. With platform threads, the size of the stack trace is actually fixed. It’s like half a megabyte, 1 megabyte, and so on. With virtual threads, the stack trace can actually shrink and grow, and that’s why virtual threads are so inexpensive, especially in Hello World examples, where all what they do is just like sleeping most of the time, or incrementing a counter, or whatever. In real life, what you will get normally is actually, for example, a very deep stack with a lot of data. If you suspend such a virtual thread, you do have to keep that memory that holds all these stack lines somewhere. The cost of the virtual thread will actually approach the cost of the platform thread. Because after all, you do have to store the stack trace somewhere. Most of the time it’s going to be less expensive, you will use less memory, but it doesn’t mean that you can create millions of very complex threads that are doing a lot of work. It’s just an advertising gimmick. It doesn’t hold true for normal workloads. Keep that in mind. There’s no magic here.

Problems and Limitations – Preemption

Another thing that’s not yet handled is preemption, when you have a very CPU intensive task. Let’s say you have 4 CPU cores, and you create 4 platform threads, or 4 kernel threads that are doing very CPU intensive work, like crunching numbers, cryptography, hashing, compression, encoding, whatever. If you have 4 physical threads, or platform threads doing that, you’re essentially just maxing your CPU. If instead you create 4 virtual threads, you will basically do the same amount of work. It doesn’t mean that if you replace 4 virtual threads with 400 virtual threads, you will actually make your application faster, because after all, you do use the CPU. There’s not much hardware to do the actual work, but it gets worse. Because if you have a virtual thread that just keeps using the CPU, it will never voluntarily suspend itself, because it never reaches a blocking operation like sleeping, locking, waiting for I/O, and so on. In that case, it’s actually possible that you will only have a handful of virtual threads that never allow any other virtual threads to run, because they just keep using the CPU. That’s the problem that’s already handled by platform threads or kernel threads because they do support preemption, so stopping a thread in some arbitrary moment in time. It’s not yet supported with Project Loom. It may be one day, but it’s not yet the case.

Problems and Limitations – Unsupported APIs

There’s also a whole list of unsupported APIs. 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 are sleep, which we already saw. All of these APIs need to be rewritten so that they play well with Project Loom. However, there’s a whole bunch of APIs, most importantly, the file API. I just learned that there’s some work happening. There’s a list of APIs that do not play well with Project Loom, so it’s easy to shoot yourself in the foot.

Problems and Limitations – Stack vs. Heap Memory

One more thing. With Project Loom, you no longer consume the so-called stack space. The virtual threads that are not running at the moment, which is technically called pinned, so they are not pinned to a carrier thread, but they are suspended. These virtual threads actually reside on heap, which means they are subject to garbage collection. In that case, it’s actually fairly easy to get into a situation where your garbage collector will have to do a lot of work, because you have a ton of virtual threads. You don’t pay the price of platform threads running and consuming memory, but you do get the extra price when it comes to garbage collection. The garbage collection may take significantly more time. This was actually an experiment done by the team behind Jetty. After switching to Project Loom as an experiment, they realized that the garbage collection was doing way more work. The stack traces were actually so deep under normal load, that it didn’t really bring that much value. That’s an important takeaway.

The Need for Reactive Programming

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. Because RestTemplate underneath uses HTTP client from Apache, which uses sockets, and sockets are rewritten so that every time you block, or wait for reading or writing data, you are actually suspending your virtual thread. It seems like RestTemplate or any other blocking API is exciting again. At least that’s what we might think, you no longer need reactive programming and all these like WebFluxes, RxJavas, Reactors, and so on.

What Loom Addresses

Project Loom addresses just a tiny fraction of the problem, it addresses asynchronous programming. It makes asynchronous programming much easier. However, it doesn’t address quite a few other features that are supported by reactive programming, namely backpressure, change propagation, composability. These are all features or frameworks like Reactor, or Akka, or Akka streams, whatever, which are not addressed by Loom because Loom is actually quite low level. After all, it’s just a different way of creating threads.

When to Install New Java Versions

Should you just blindly install the new version of Java whenever it comes out and just switch to virtual threads? I think the answer is no, for quite a few reasons. First of all, the semantics of your application change. You no longer have this natural way of throttling because you have a limited number of threads. Also, the profile of your garbage collection will be much different. We have to take that into account.

When Project Loom Will be Available

When will Project Loom be available? It was supposed to be available in Java 17, we just got Java 18 and it’s still not there. Hopefully, it will be ready when it’s ready. Hopefully, we will live into that moment. I’m experimenting with Project Loom for quite some time already. It works. It sometimes crashes. It’s not vaporware, it actually exists.


I leave you with a few materials which I collected, more presentations and more articles that you might find interesting. Quite a few blog posts that explain the API a little bit more thoroughly. A few more critical or skeptic points of view, mainly around the fact that Project Loom won’t really change that much. It’s especially for the people who believe that we will no longer need reactive programming because we will all just write our code using plain Project Loom. Also, my personal opinion, that’s not going to be the case, we will still need some higher level abstraction.

Questions and Answers

Cummins: How do you debug it? Does it make it harder to debug? Does it make it easier to debug? What tooling support is there? Is there more tooling support coming?

Nurkiewicz: The answer is actually twofold. On one hand, it’s easier, because you no longer have to hop between threads so much, in reactive programming or asynchronous programming in general. What you typically do is that you have a limited number of threads, but you jump between threads very often, which means that stack traces are cut in between, so you don’t see the full picture. It gets a little bit convoluted, and frameworks like Reactor try to somehow reassemble the stack trace, taking into account that you are jumping between thread pools, or some asynchronous Netty threads. In that case, Loom makes it easier, because you can survive, you can make a whole request just in a single thread, because logically, you’re still on the same thread, this thread is being paused. It’s being unpinned, and pinned back to a carrier thread. When the exception arises, this exception will show the whole stack trace because you’re not jumping between threads. What you typically do is that when you want to do something asynchronous, you put it into a thread pool. Once you’re in a thread pool, you lose the original stack trace, you lose the original thread.

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. In some sense, it’s going to be easier. However, you will still be probably using multiple threads to handle a single request. That problem doesn’t really go away. In some cases, it will be easier but it’s not like an entirely better experience. On the other hand, you now have 10 times or 100 times more threads, which are all doing something. These aren’t really like Java threads. You won’t, for example, see them on a thread dump. This may change but that’s the case right now. You have to take that into account. When you’re doing a thread dump, which is probably one of the most valuable things you can get when troubleshooting your application, you won’t see virtual threads which are not running at the moment.

If you are doing the actual debugging, so you want to step over your code, you want to see, what are the variables? What is being called? What is sleeping or whatever? You can still do that. Because when your virtual thread runs, it’s a normal Java thread. It’s a normal platform thread because it uses carrier thread underneath. You don’t really need any special tools. However, you just have to remember on the back of your head, that there is something special happening there, that there is a whole variety of threads that you don’t see, because they are suspended. As far as JVM is concerned, they do not exist, because they are suspended. They’re just objects on heap, which is surprising.

Cummins: It’s hard to know which is worse, you have a million threads, and they don’t turn up in your heap thread dump, or you have a million threads and they do turn up in your heap dump.

Nurkiewicz: Actually, reactive is probably the worst here because you have million ongoing requests, for example, HTTP requests, and you don’t see them anywhere. Because with reactive, with truly asynchronous APIs, HTTP database, whatever, what happens is that you have a thread that makes a request, and then absolutely forgets about that request until it gets a response. A single thread handles hundreds of thousands of requests concurrently or truly concurrently. In that case, if you make a thread dump, it’s actually the worst of both worlds, because what you see is just a very few reactive threads like Netty, for example, which is typically used. These native threads are not actually doing any business logic, because most of the time, they are just waiting for data to be sent or received. Troubleshooting a reactive application using a thread dump is actually very counterproductive. In that case, virtual threads are actually helping a little bit, because at least you will see the running threads.

Cummins: It’s probably like a lot of things where when the implementation moves closer to our mental model, because nobody has a mental model of thread pools, they have a mental model of threads, and so then when you get those two closer together, it means that debugging is easier.

Nurkiewicz: I really love the quote by Cay Horstmann, that you’re no longer thinking about this low level abstraction of a thread pool, which is convoluted. You have a bunch of threads that are reused. There’s a queue, you’re submitting a task. It stands in a queue, it waits in that queue. You no longer have to think about it. You have a bunch of tasks that you need to run concurrently. You just run them, you just create a thread and get over it. That was the promise of actor systems like Akka, that when you have 100,000 connections, you create 100,000 actors, but actors reuse threads underneath, because that’s how JVM works at the moment. With virtual threads, you just create a new virtual thread per connection, per player, per message, whatever. It’s closer, surprisingly, to an Erlang model, where you were just starting new processes. Of course, it’s really far away from Erlang still, but it’s a little bit closer to that.

Cummins: Do you think we’re going to see a new world of problem reproduction ickiness, where some of us are on Java 19 and taking advantage of threads, and some of us are not. At the top level, it looks similar, but then once you go underneath the behavior is really fundamentally different. Then we get these non-reproducible things where it’s the timing dependency plus a different implementation means that we just spend all our time chasing weird threading variations.

Nurkiewicz: I can give you even a simpler example of when it can blow up. We used to rely on the fact that thread pool is the natural way of throttling tasks. When you have a thread pool of 20 threads, it means you will not run more than 20 tasks at the same time. If you just blindly replace ExecutorService with this virtual thread, ExecutorService, the one that doesn’t really pull any threads, it just starts them like crazy, you no longer have this throttling mechanism. If you naively refactor from Java 18 to Java 19, because Project Loom was already merged to project 19, to the master branch. If you just switch to Project Loom, you will be surprised, because suddenly, the level of concurrency that you achieve on your machine is way greater than you expected.

You might think that it’s actually fantastic because you’re handling more load. It also may mean that you are overloading your database, or you are overloading another service, and you haven’t changed much. You just changed a single line that changes the way threads are created rather than platform, then you move to the virtual threads. Suddenly, you have to rely on these low level CountDownLatches, semaphores, and so on. 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. It will significantly change the way libraries or frameworks can be written so that we can take advantage of them.

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Corning redefines tough with Corning Gorilla Glass Victus 2 – PR Newswire



The newest Gorilla Glass better survives drops on rougher surfaces, like concrete

MUMBAI, India, Dec. 1, 2022 /PRNewswire/ — Corning Incorporated (NYSE: GLW) today unveiled its newest glass innovation, Corning® Gorilla® Glass Victus® 2. Corning continues to push the boundaries of glass by expanding its Corning® Gorilla® Glass portfolio. With a new glass composition, Gorilla Glass Victus 2 delivers improved drop performance on rough surfaces like concrete, the world’s most abundantly engineered material, while preserving the scratch resistance of Gorilla Glass Victus.

“Smartphones are the center of our digital lives, and the requirement for exceptional scratch and drop resistance has only increased with our growing reliance on clear, damage-free displays,” said David Velasquez, vice president and general manager, Gorilla Glass. “Surfaces matter, and rough surfaces like concrete are everywhere.”

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Corning’s extensive research has shown that 84% of consumers across three of the largest smartphone markets – China, India, and the United States – cite durability as the number one purchasing consideration behind brand itself.

“We challenged our scientists not only to create a glass composition that was durable enough to better survive drops from waist height onto rougher surfaces than asphalt, but to improve cover-glass performance for larger and heavier devices,” said Velasquez. “With more sophisticated and varied designs, today’s smartphones are nearly 15% heavier, and screen sizes are up to 10% larger, than they were four years ago – increasing both the stress on the cover glass and the probability of damage. Gorilla Glass Victus 2 redefines tough for consumers and OEMs.”

In lab tests, Gorilla Glass Victus 2 survived drops of up to one meter on a surface replicating concrete. Competitive aluminosilicate glasses from other manufacturers typically failed when dropped from half a meter or less. In addition, Gorilla Glass Victus 2 continued to survive drops up to two meters on a surface replicating asphalt and maintained scratch resistance up to four times better than competitive aluminosilicate.

For more than a decade, Gorilla Glass has helped revolutionize communication and transform the mobile consumer electronics industry. With nearly half a billion global consumers yet to transition to smartphones, Gorilla Glass will continue to solve tough consumer challenges and meet the needs of current and future smartphone users. 

Gorilla Glass Victus 2 is currently being evaluated by multiple customers and is expected to reach the market within the next few months.

Gorilla Glass has been designed into more than 8 billion devices by more than 45 major brands. Throughout the company’s Mobile Consumer Electronics (MCE) market access platform, Corning continues its legacy of innovation with its market-leading cover glasses as well as glass and optics for semiconductor products that enhance performance, deliver new connectivity features, enable new designs, and support immersive user experiences with augmented reality and 3D sensing.

About Corning Incorporated

Corning ( is one of the world’s leading innovators in materials science, with a 170-year track record of life-changing inventions. Corning applies its unparalleled expertise in glass science, ceramic science, and optical physics along with its deep manufacturing and engineering capabilities to develop category-defining products that transform industries and enhance people’s lives. Corning succeeds through sustained investment in RD&E, a unique combination of material and process innovation, and deep, trust-based relationships with customers who are global leaders in their industries. Corning’s capabilities are versatile and synergistic, which allows the company to evolve to meet changing market needs, while also helping its customers capture new opportunities in dynamic industries. Today, Corning’s markets include optical communications, mobile consumer electronics, display, automotive, solar, semiconductors, and life sciences.

Media relations contacts

Abhishek Vora
[email protected]

Erica D’souza
[email protected]

Follow Corning: RSS Feeds | Facebook | Twitter | YouTube


SOURCE Corning Incorporated

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Overwatch 2: Sojourn the Main Target of Nerfs in Season 2 Update – IGN



Blizzard Entertainment has revealed what we can expect from Season 2 of Overwatch 2, including balancing updates, limited-time events, skins, and more ahead of its December 6 launch date.

Alongside the introduction of the new Omnic revolutionary tank hero Ramattra and the winding Shambali Monastery Escort map, Season 2 is also bringing some important balancing changes, according to Blizzard Entertainment’s official blog post.

Most significantly, Blizzard Entertainment has moved to nerf the damage hero Sojourn, who according to a blog post on the company’s website has been dominating the high-skill competitive scene in recent weeks, while “remaining a challenge for players without the same mechanical skills”.

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Overwatch 2: All 35 Heroes at Launch

To address the issue, the developers announced that they would be “focussing on the lethality of her Rail Gun at distance for Season 2”. This has been done to encourage players of all skills to use Sojurn’s mobility and her power slide to “close the distance for more devastating right-clicks”.

Doomfist — another tank — will also receive significant balancing tweaks to better reflect “his role as the team’s front line”. Ana, Bastion, Junker Queen, Kiriko, Mercy, and Symmetra will also be tweaked when the new season goes live on December 6, though Blizzard Entertainment has yet to reveal exactly how.

Next month’s launch will also bring a new map pool to Overwatch 2. Rialto and Blizzard World will be entering rotation alongside the new map Shambali Monastery. Oasis and Nepal will also be appearing “at different times of day”, while Hollywood and Watchpoint: Gibraltar will be rotated out.

Blizzard revealed details for the Greek mythology themed Season 2 Battle Pass. Progression rewards for the premium pass will include a Poseidon skin for Ramattra and a Hades costume for Pharah. The “pinnacle reward” for the pass is a Mythic Zeus skin for Junker Queen, which comes with “new customizations, weapon models, voice lines, and special effects”.

Season 2 is also set to bring three special events to the game. Winter Wonderland will run from December 13 – January 4, while the Lunar New Year event will start on January 17 and end on February 1.

Special skins will be up for grabs during each event, including “Epic Ice Queen Brigitte, Legendary Winged Victory Mercy, and Legendary Kkachi Echo.”

Sandwiched between these seasonal celebrations is the Battle for Olympus event, which will be around for a limited time from January 5 – 19. During this period players will be able to earn skins as they engage in a new game mode which will see certain heroes “imbued with awesome god-like powers”.

Finally, Season 2 will feature catch up challenges that will allow players to unlock heroes, including Kiriko from Season 1. To stay up to date and find out how to get the most out of your hero, be sure to check out more of IGN’s coverage of Overwatch 2.

Anthony is a freelance contributor covering science and video gaming news for IGN. He has over eight years experience of covering breaking developments in multiple scientific fields and absolutely no time for your shenanigans. Follow him on Twitter @BeardConGamer

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Xiaomi 13 Pro camera detailed with a 1-inch main sensor, floating telephoto lens




The Xiaomi 13 series is arriving tomorrow, and today the company revealed some key features of the camera on the Pro member. According to the released teasers, the main shooter will have a 50 MP 1” Sony IMX989 sensor, while the telephoto camera will have 75mm equivalent, which is 3x magnification.

Xiaomi revealed how the new “floating” telephoto lens would work in a 20-second video – it is moving the elements closer to achieve infinite focus and take pictures of far objects; when it needs to take close-ups (up to 10 cm), the elements are moving away. The camera is developed with know-how from Leica, which once again is having its branding on the back of the phone.

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The Xiaomi 13 Pro also appeared in a leaked hands-on video, revealing the camera design and some specs like MIUI 14 on top of Android 13, 8/128 GB memory variant and a Qualcomm chipset with 3.19 GHz CPU, which is unsurprisingly the Snapdragon 8 Gen 2.

We also got some punchy camera samples, but since they were posted on social media, they have been heavily compressed.

Camera samples at 3x zoom
Camera samples at 3x zoom
Camera samples at 3x zoom

Camera samples at 3x zoom

Camera samples from up close
Camera samples from up close
Camera samples from up close
Camera samples from up close

Camera samples from up close

Camera samples of main 50 MP shooter
Camera samples of main 50 MP shooter
Camera samples of main 50 MP shooter
Camera samples of main 50 MP shooter

Camera samples of main 50 MP shooter

The Xiaomi 13 series is bringing LPDDR5X RAM and UFS 4.0 storage for extra snappy performance. We expect a Xiaomi 13 and a Xiaomi 13 Pro at the December 1 event, as well as announcements of a Xiaomi Watch S2 wearable and the MIUI 14 interface.

Source (in Chinese) | Via

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