Performance Issues with Virtual Threads + ThreadLocal Caching in Third-Party Libraries (JDK 25)
Jianbin Chen
jianbin at apache.org
Fri Jan 23 14:10:13 UTC 2026
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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