Performance Issues with Virtual Threads + ThreadLocal Caching in Third-Party Libraries (JDK 25)
Francesco Nigro
nigro.fra at gmail.com
Fri Jan 23 15:11:57 UTC 2026
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 <jianbin at apache.org>
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 <rengels at ix.netcom.com> 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 <jianbin at apache.org> 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 <rengels at ix.netcom.com> 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 <jianbin at apache.org> 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 <robaho at me.com> 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 <jianbin at apache.org> 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 <robaho at me.com> 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 <jianbin at apache.org> 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 <alan.bateman at oracle.com> 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
>>>>>>
>>>>>
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