In the original code snippet I see named (with a counter) VThreads, so, be aware of https://bugs.openjdk.org/browse/JDK-8372410
Il giorno ven 23 gen 2026 alle ore 15:52 Jianbin Chen <[email protected]> ha scritto: > I'm sorry — I forgot to mention the machine I used for the load test. My > server is 2 cores and 4 GB RAM, and the JVM heap was set to 2880m. Under my > test load (about 20,000 QPS), with non‑pooled virtual threads you generate > at least 20,000 × 8 KB = ~156 MB of byte[] allocations per second just from > that 8 KB buffer; that doesn't include other object allocations. With a > 2880 MB heap this allocation rate already forces very frequent GC, and > frequent GC raises CPU usage, which in turn significantly increases average > response time and p99 / p999 latency. > > Pooling is usually introduced to solve performance issues — object pools > and connection pools exist to quickly reuse cached resources and improve > performance. So pooling virtual threads also yields obvious benefits, > especially for memory‑constrained, I/O‑bound applications (gateways, > proxies, etc.) that are sensitive to latency. > > Best Regards. > Jianbin Chen, github-id: funky-eyes > > Robert Engels <[email protected]> 于 2026年1月23日周五 22:20写道： > >> I understand. I was trying explain how you can not use thread locals and >> maintain the performance. It’s unlikely allocating a 8k buffer is a >> performance bottleneck in a real program if the task is not cpu bound >> (depending on the granularity you make your tasks) - but 2M tasks running >> simultaneously would require 16gb of memory not including the stack. >> >> You cannot simply use the thread per task model without an understanding >> of the cpu, IO, and memory footprints of your tasks and then configure >> appropriately. >> >> On Jan 23, 2026, at 8:10 AM, Jianbin Chen <[email protected]> wrote: >> >> >> I'm sorry, Robert—perhaps I didn't explain my example clearly enough. >> Here's the code in question: >> >> ```java >> Executor executor2 = new ThreadPoolExecutor( >> 200, >> Integer.MAX_VALUE, >> 0L, >> java.util.concurrent.TimeUnit.SECONDS, >> new SynchronousQueue<>(), >> Thread.ofVirtual().name("test-threadpool-", 1).factory() >> ); >> ``` >> >> In this example, the pooled virtual threads don't implement any >> backpressure mechanism; they simply maintain a core pool of 200 virtual >> threads. Given that the queue is a `SynchronousQueue` and the maximum pool >> size is set to `Integer.MAX_VALUE`, once the concurrent tasks exceed 200, >> its behavior becomes identical to that of non-pooled virtual threads. >> >> From my perspective, this example demonstrates that the benefits of >> pooling virtual threads outweigh those of creating a new virtual thread for >> every single task. In IO-bound scenarios, the virtual threads are directly >> reused rather than being recreated each time, and the memory footprint of >> virtual threads is far smaller than that of platform threads (which are >> controlled by the `-Xss` flag). Additionally, with pooled virtual threads, >> the 8KB `byte[]` cache I mentioned earlier (stored in `ThreadLocal`) can >> also be reused, which further reduces overall memory usage—wouldn't you >> agree? >> >> Best Regards. >> Jianbin Chen, github-id: funky-eyes >> >> Robert Engels <[email protected]> 于 2026年1月23日周五 21:52写道： >> >>> Because VT are so efficient to create, without any back pressure they >>> will all be created and running at essentially the same time (dramatically >>> raising the amount of memory in use) - versus with a pool of size N you >>> will have at most N running at once. In a REAL WORLD application there are >>> often external limiters (like number of tcp connections) that provide a >>> limit. >>> >>> If your tasks are purely cpu bound you should probably be using a capped >>> thread pool of platform threads as it makes no sense to have more threads >>> than available cores. >>> >>> >>> >>> On Jan 23, 2026, at 7:42 AM, Jianbin Chen <[email protected]> wrote: >>> >>> >>> The question is why I need to use a semaphore to control the number of >>> concurrently running tasks. In my particular example, the goal is simply to >>> keep the concurrency level the same across different thread pool >>> implementations so I can fairly compare which one completes all the tasks >>> faster. This isn't solely about memory consumption—purely from a >>> **performance** perspective (e.g., total throughput or wall-clock time to >>> finish the workload), the same number of concurrent tasks completes >>> noticeably faster when using pooled virtual threads. >>> >>> My email probably didn't explain this clearly enough. In reality, I have >>> two main questions: >>> >>> 1. When a third-party library uses `ThreadLocal` as a cache/pool (e.g., >>> to hold expensive reusable objects like connections, formatters, or >>> parsers), is switching to a **pooled virtual thread executor** the only >>> viable solution—assuming we cannot modify the third-party library code? >>> >>> 2. When running the exact same number of concurrent tasks, pooled >>> virtual threads deliver better performance. >>> >>> Both questions point toward the same conclusion: for an application >>> originally built around a traditional platform thread pool, after upgrading >>> to JDK 21/25, moving to a **pooled virtual threads** approach is generally >>> superior to simply using non-pooled (unbounded) virtual threads. >>> >>> If any part of this reasoning or conclusion is mistaken, I would really >>> appreciate being corrected — thank you very much in advance for any >>> feedback or different experiences you can share! >>> >>> Best Regards. >>> Jianbin Chen, github-id: funky-eyes >>> >>> robert engels <[email protected]> 于 2026年1月23日周五 20:58写道： >>> >>>> Exactly, this is your problem. The total number of tasks will all be >>>> running at once in the thread per task model. >>>> >>>> On Jan 23, 2026, at 6:49 AM, Jianbin Chen <[email protected]> wrote: >>>> >>>> >>>> Hi Robert, >>>> >>>> Thanks you, but I'm a bit confused. In the example above, I only set >>>> the core pool size to 200 virtual threads, but for the specific test case >>>> we’re talking about, the concurrency isn’t actually being limited by the >>>> pool size at all. Since the maximum thread count is Integer.MAX_VALUE and >>>> it’s using a SynchronousQueue, tasks are handed off immediately and a new >>>> thread gets created to run them right away anyway. >>>> >>>> Best Regards. >>>> Jianbin Chen, github-id: funky-eyes >>>> >>>> robert engels <[email protected]> 于 2026年1月23日周五 20:28写道： >>>> >>>>> Try using a semaphore to limit the maximum number of tasks in progress >>>>> at anyone time - that is what is causing your memory spike. Think of it >>>>> this way since VT threads are so cheap to create - you are essentially >>>>> creating them all at once - making the working set size equally to the >>>>> maximum. So you have N * WSS, where as in the other you have POOLSIZE * >>>>> WSS. >>>>> >>>>> On Jan 23, 2026, at 4:14 AM, Jianbin Chen <[email protected]> wrote: >>>>> >>>>> >>>>> Hi Alan, >>>>> >>>>> Thanks for your reply and for mentioning JEP 444. >>>>> I’ve gone through the guidance in JEP 444 and have some understanding >>>>> of it — which is exactly why I’m feeling a bit puzzled in practice and >>>>> would really like to hear your thoughts. >>>>> >>>>> Background — ThreadLocal example (Aerospike) >>>>> ```java >>>>> private static final ThreadLocal<byte[]> BufferThreadLocal = new >>>>> ThreadLocal<byte[]>() { >>>>> @Override >>>>> protected byte[] initialValue() { >>>>> return new byte[DefaultBufferSize]; >>>>> } >>>>> }; >>>>> ``` >>>>> This Aerospike code allocates a default 8KB byte[] whenever a new >>>>> thread is created and stores it in a ThreadLocal for per-thread caching. >>>>> >>>>> My concern >>>>> - With a traditional platform-thread pool, those ThreadLocal byte[] >>>>> instances are effectively reused because threads are long-lived and >>>>> pooled. >>>>> - If we switch to creating a brand-new virtual thread per task (no >>>>> pooling), each virtual thread gets its own fresh ThreadLocal byte[], which >>>>> leads to many short-lived 8KB allocations. >>>>> - That raises allocation rate and GC pressure (despite collectors like >>>>> ZGC), because ThreadLocal caches aren’t reused when threads are ephemeral. >>>>> >>>>> So my question is: for applications originally designed around >>>>> platform-thread pools, wouldn’t partially pooling virtual threads be >>>>> beneficial? For example, Tomcat’s default max threads is 200 — if I keep a >>>>> pool of 200 virtual threads, then when load exceeds that core size, a >>>>> SynchronousQueue will naturally cause new virtual threads to be created on >>>>> demand. This seems to preserve the behavior that ThreadLocal-based >>>>> libraries expect, without losing the ability to expand under spikes. Since >>>>> virtual threads are very lightweight, pooling a reasonable number (e.g., >>>>> 200) seems to have negligible memory downside while retaining ThreadLocal >>>>> cache effectiveness. >>>>> >>>>> Empirical test I ran >>>>> (I ran a microbenchmark comparing an unpooled per-task virtual-thread >>>>> executor and a ThreadPoolExecutor that keeps 200 core virtual threads.) >>>>> >>>>> ```java >>>>> public static void main(String[] args) throws InterruptedException { >>>>> Executor executor = >>>>> Executors.newThreadPerTaskExecutor(Thread.ofVirtual().name("test-", >>>>> 1).factory()); >>>>> Executor executor2 = new ThreadPoolExecutor( >>>>> 200, >>>>> Integer.MAX_VALUE, >>>>> 0L, >>>>> java.util.concurrent.TimeUnit.SECONDS, >>>>> new SynchronousQueue<>(), >>>>> Thread.ofVirtual().name("test-threadpool-", 1).factory() >>>>> ); >>>>> >>>>> // Warm-up >>>>> for (int i = 0; i < 10100; i++) { >>>>> executor.execute(() -> { >>>>> // simulate I/O wait >>>>> try { Thread.sleep(100); } catch (InterruptedException e) >>>>> { throw new RuntimeException(e); } >>>>> }); >>>>> executor2.execute(() -> { >>>>> // simulate I/O wait >>>>> try { Thread.sleep(100); } catch (InterruptedException e) >>>>> { throw new RuntimeException(e); } >>>>> }); >>>>> } >>>>> >>>>> // Ensure JIT + warm-up complete >>>>> Thread.sleep(5000); >>>>> >>>>> long start = System.currentTimeMillis(); >>>>> CountDownLatch countDownLatch = new CountDownLatch(50000); >>>>> for (int i = 0; i < 50000; i++) { >>>>> executor.execute(() -> { >>>>> try { Thread.sleep(100); countDownLatch.countDown(); } >>>>> catch (InterruptedException e) { throw new RuntimeException(e); } >>>>> }); >>>>> } >>>>> countDownLatch.await(); >>>>> System.out.println("thread time: " + (System.currentTimeMillis() - >>>>> start) + " ms"); >>>>> >>>>> start = System.currentTimeMillis(); >>>>> CountDownLatch countDownLatch2 = new CountDownLatch(50000); >>>>> for (int i = 0; i < 50000; i++) { >>>>> executor2.execute(() -> { >>>>> try { Thread.sleep(100); countDownLatch2.countDown(); } >>>>> catch (InterruptedException e) { throw new RuntimeException(e); } >>>>> }); >>>>> } >>>>> countDownLatch.await(); >>>>> System.out.println("thread pool time: " + >>>>> (System.currentTimeMillis() - start) + " ms"); >>>>> } >>>>> ``` >>>>> >>>>> Result summary >>>>> - In my runs, the pooled virtual-thread executor (executor2) performed >>>>> better than the unpooled per-task virtual-thread executor. >>>>> - Even when I increased load by 10x or 100x, the pooled virtual-thread >>>>> executor still showed better performance. >>>>> - In realistic workloads, it seems pooling some virtual threads >>>>> reduces allocation/GC overhead and improves throughput compared to >>>>> strictly >>>>> unpooled virtual threads. >>>>> >>>>> Final thought / request for feedback >>>>> - From my perspective, for systems originally tuned for >>>>> platform-thread pools, partially pooling virtual threads seems to have no >>>>> obvious downside and can restore ThreadLocal cache effectiveness used by >>>>> many third-party libraries. >>>>> - If I’ve misunderstood JEP 444 recommendations, virtual-thread >>>>> semantics, or ThreadLocal behavior, please point out what I’m missing. I’d >>>>> appreciate your guidance. >>>>> >>>>> Best Regards. >>>>> Jianbin Chen, github-id: funky-eyes >>>>> >>>>> Alan Bateman <[email protected]> 于 2026年1月23日周五 17:27写道： >>>>> >>>>>> On 23/01/2026 07:30, Jianbin Chen wrote: >>>>>> > : >>>>>> > >>>>>> > So my question is: >>>>>> > >>>>>> > **In scenarios where third-party libraries heavily rely on >>>>>> ThreadLocal >>>>>> > for caching / buffering (and we cannot change those libraries to >>>>>> use >>>>>> > object pools instead), is explicitly pooling virtual threads (using >>>>>> a >>>>>> > ThreadPoolExecutor with virtual thread factory) considered a >>>>>> > recommended / acceptable workaround?** >>>>>> > >>>>>> > Or are there better / more idiomatic ways to handle this kind of >>>>>> > compatibility issue with legacy ThreadLocal-based libraries when >>>>>> > migrating to virtual threads? >>>>>> > >>>>>> > I have already opened a related discussion in the Dubbo project >>>>>> (since >>>>>> > Dubbo is one of the libraries affected in our stack): >>>>>> > >>>>>> > https://github.com/apache/dubbo/issues/16042 >>>>>> > >>>>>> > Would love to hear your thoughts — especially from people who have >>>>>> > experience running large-scale virtual-thread-based services with >>>>>> > mixed third-party dependencies. >>>>>> > >>>>>> >>>>>> The guidelines that we put in JEP 444 [1] is to not pool virtual >>>>>> threads >>>>>> and to avoid caching costing resources in thread locals. Virtual >>>>>> threads >>>>>> support thread locals of course but that is not useful when some >>>>>> library >>>>>> is looking to share a costly resource between tasks that run on the >>>>>> same >>>>>> thread in a thread pool. >>>>>> >>>>>> I don't know anything about Aerospike but working with the >>>>>> maintainers >>>>>> of that library to re-work its buffer management seems like the right >>>>>> course of action here. Your mail says "byte buffers". If this is >>>>>> ByteBuffer it might be that they are caching direct buffers as they >>>>>> are >>>>>> expensive to create (and managed by the GC). Maybe they could look at >>>>>> using MemorySegment (it's easy to get a ByteBuffer view of a memory >>>>>> segment) and allocate from an arena that better matches the lifecycle. >>>>>> >>>>>> Hopefully others will share their experiences with migration as it is >>>>>> indeed challenging to migrate code developed for thread pools to work >>>>>> efficiently on virtual threads where there is 1-1 relationship >>>>>> between >>>>>> the task to execute and the thread. >>>>>> >>>>>> -Alan >>>>>> >>>>>> [1] https://openjdk.org/jeps/444#Thread-local-variables >>>>>> >>>>>
