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Tag: Asynchronous

  • If Virtual Threads are the solution, what is the problem?

    Java’s Virtual Threads (aka Project Loom or JEP 444) have arrived as a full platform feature in Java 21, which has generated considerable interest and many projects (including Eclipse Jetty) are adding support.

    I have previously been somewhat skeptical about how significant any advantages Virtual Threads actually have over Platform Threads (aka Native Threads). I’ve also pointed out that cheap Threads can do expensive things, so that using Virtual Threads may not be a universal panacea for concurrent programming.

    However, even with those doubts, it is clear that Virtual Threads do have advantages in memory utilization and speed of startup. In this blog we look at what kinds of applications may benefit from those advantages.

    In short we investigate what scalability problems are Virtual Threads the solution for.

    Axioms

    Firstly let’s agree on what is accepted about Virtual Thread usage:

    • Writing asynchronous code is extraordinary difficult. “Yeah I know” you say… yeah but no, it is harder than that! Avoiding the need to write application logic in asynchronous style is key to improving the quality and stability of an application. This blog is not generally advocating you write your applications in an asynchronous style.
    • Virtual Threads are very cheap to create. From a performance perspective there is no reason to pool already started Virtual Threads and such pools are considered an anti pattern. If a Virtual Thread is needed, then just create a new one.
    • Virtual Threads use less memory. This is accepted, but with some significant caveats. Specifically the memory saving is achieved because Virtual Threads only allocate stack memory as needed, whilst Platform Threads provision stack size based on a worst case maximal usage. This is not exactly an apples vs oranges comparison.

    If some are good, are more even better?

    Consider a blocking style application is running on a traditional application server that is not scaling sufficiently. On inspection you see that all the Threads in the pool (default size 200) are allocated and that there are no Threads available to do more work!

    Would making more Threads available be the solution to this scalability problem? Perhaps 2000 Platform Threads will help? Still slow? Let’s try 10,000 Platform Threads! Running out of memory? Then perhaps unlimited Virtual Threads will solve the scalability problems?

    What if on further inspection it is found that the pool Threads are mostly blocked waiting for a JDBC Database connection from the JDBC Connection Pool (default size 8) and that as a result the Thread pool is exhausted.

    If every request needs the database, then any additional Threads will all just block on the same JDBC pool, thus more Threads will not make a more Scalable solution.

    Alternatively, if only some requests need to use the database, then having more Threads would allow request that do not need the database to proceed to completion. However, a fraction of requests would still end up blocked on the JBDC pool. Thus any limited Platform Thread pool could still become exhausted.

    With unlimited Virtual Threads there is no effective limit on the number of Threads, so non database requests could always continue, but the queue of Threads waiting on JBDC would also be unlimited as would the total of any resources held by those Threads whilst waiting. Thus the application would only scale for some types of request, whilst giving JDBC dependent requests the same poor Quality of Service as before.

    Finite Resources

    If an application’s scalability is constrained by access to a finite resource, then it is unlikely that “more Threads” is the solution to any scalability problems. Just like you can’t solve traffic by adding cars to a congested road, adding Threads to an already busy server may make things worse.

    Some common examples of finite resources that applications can encounter are:

    • CPU: If the server CPU is near 100% utilization, then the existing number of Threads are sufficient to keep it fully loaded. More and/or faster CPUs are needed before any increase in Threads could be beneficial.
    • Database: Many database technologies cannot handle many concurrent requests, so parallelism is restricted. If the bottleneck is the database, then it needs to be re-engineered rather than laid siege to by more concurrent Threads.
    • Local Network: An application may block reading or writing data because it has reached the limit on the local network. In such cases, more Threads will not increase throughput, but they might improve latency if some threads can progress reading new requests and have responses ready to write once network becomes less congested. However there is a cost in waiting (see below).
    • Locks: Parallel applications often use some form of lock or mutual exclusion to serialize access to common data structures. Contention on those locks can limit parallelism and require redesign rather than just more Threads.
    • Caches: CPU, memory, file system and object caches are key tools in speeding up execution. However, if too many different tasks are executed concurrently, the capacity of these caches to hold relevant data may be exceeded and execution with a cold cache can be very slow. Sometimes it is better to do less things concurrently and serialize the excess so that caches can be more effective that trying to do everything at once.

    If an application’s lack of scalability is due to Threads waiting for finite resources, then any additional Threads (Platform or Virtual) are unlikely to help and may make your application less stable. At best, careful redesign is needed before Thread counts can be increased in any advantageous way.

    Infinite (OK Scalable) Resources

    Not all resources are finite and some can be considered infinite, at least for some purposes. But let’s call them “Scalable” rather than infinite. Examples of scalable resources that an application may block on include:

    • Database: Not all databases are created equal and some types of database have scalability in excess of the request rates experienced by a server. However, such scalability often comes at a latency cost as the database may be remote and/or distributed, thus applications may block waiting for the database, even if it has capacity to handle more requests in parallel.
    • Micro services: A scalable database is really just a specific example of a micro service that may be provided by a remote and/or distributed system that has effectively infinite capacity at the cost of some latency. Applications can often find themselves waiting on one or more such services.
    • Remote Networks: Local data center networks are often very VERY fast and in many situations they can outstrip even the combined capacity of many client systems. An application sending/receiving larger content may may block writing/reading them due to a slow client, but still have enough local network capacity to communicate with many other clients in parallel.
    • Local Filesystems: Typically file systems are faster than networks, but slower than CPU. They also may have significant latency vs throughput tradeoffs (less so now that drives seldom need to spin physical disks). Thus Threads may block on local IO even though there is additional capacity available.

    Applications that lack scalability due to Threads waiting for such scalable resources may benefit from more Threads. Whilst some Threads are waiting for the a database, micro service, slow client network or file system, it is likely that other Threads can progress even if they need to access the same types of resources.

    Platform Threads pools can easily be increased to many 1000’s or more before typical servers will have memory issues. If scalability is needed beyond that, then Virtual Threads can offer practically unlimited additional Thread, but see the caveats below.

    Furthermore, the fast starting Virtual Threads can be of significant benefit in situations where small jobs with long latency can be carried out in parallel. Consider an application that processes request using data from several micro services, each with some access latency. If these are done serially, then the total request latency is the summation of all. Sometimes asynchronous code is used to execute micro service request in parallel, but spinning up a couple of Virtual Threads in this situation is simpler, less error prone and applicable to more APIs.

    Too Much of a Good Thing?

    There is also some concern with low latency scalable resources that seldom block with Virtual Threads. Since Virtual Threads are not preempted, there can be starvation and/or fairness problems if they are not blocked by slow resources. This is probably a good problem to have, but will need some management on extreme scales for some applications.

    The Cost of Waiting

    We have identified that there are indeed scalable resources on which an application may wait with many Threads. However, there is no such thing as a free lunch and waiting Threads may have a significant cost, even if they are Virtual. Specifically how/where an application waits can greatly affect resource usage.

    Consider a traditional application server with a limited Thread pool that is running near capacity, but with additional demand. While the 200 odd Threads are busy handling 200 concurrent request, there are additional request waiting to be handled. However, in an asynchronous server like Jetty, those additional requests can be cheaply parked and may be represented just be a single set bit in a selector or perhaps a tiny entry in a queue that holds only a reference to a connection that is ready to be read.

    Now consider if requests were serviced by Virtual Threads instead of waiting for a pooled Platform Thread to become available. Pending requests would be allowed to proceed to some blocking point in the application. Waiting like this within the application can have additional expenses including:

    • An input buffer will be allocated to read the request and any content it has.
    • A read is performed into the input buffer, thus removing network back pressure so a client is enabled to send more request/data even if the server is unable to handle them.
    • An object representation of the request will be built, containing at least the meta data and frequently some application data if there is an XML or JSON payload
    • Sessions may be activated and brought into memory from caches or passivation stores.
    • The allocated Thread runs deep inside the application code, potentially reaching near maximal stack depth.
    • Application objects created on the heap are held in memory with references from the stack.
    • An output buffer may be allocated, along with additional character conversion resources.

    When request handling blocks within the application, all these additional resources may be allocated and held during that wait. Worse still, because of the lack of back pressure, a client may send more request/data resulting in more Threads and associated resources being allocated and also being held whilst the application waits for some resource.

    Provisioning for the Worst Case

    We have seen that there are indeed applications that may benefit from having additional Threads available to service requests. But we have also seen that such additional Threads may incur additional costs beyond just the stack size. Waiting/Blocking within an application will typically be done with a deep stack and other resources allocated. Whilst Virtual Threads might be effectively infinite, it is unlikely that these other required resources are equally scalable.

    When an application experiences a worst case peak in load, then ultimately some resource will run out. To provide good Quality of Service, it is vital that such resource exhaustion is handled gracefully, allowing some request handling to continue rather than suffering catastrophic failure.

    With traditional Platform Thread based pools, stack memory is already provisioned for worst case stacks for all Threads and the thread pool sized limit is also an indirect limit on the number of concurrent resources used. Threads have sufficient resources available to complete there handling whilst any excess requests suffer latency whilst waiting cheaply for an available Thread. Furthermore, the back pressure resulting from not reading all offered requests can prevent additional load from sent by the clients. Thread limits are imperfect resource limits, but at least they are some kind of limit that can provide some graceful degradation under load.

    Alternatively, an application using Virtual Threads that has no explicit resource management will be likely to exhaust some of the resources used by those Threads. This can result in an OutOfMemoryException or similar, as the unlimited Virtual Threads each allocate deep stacks and other resources needed for request handling. The cost of average memory savings may be insufficient provisioning for the worst case resulting in catastrophic failure rather than graceful degradation. An analogy is that building more roads can actually make traffic worse if the added cars overwhelm other infrastructure.

    Many applications are written without explicit resource limitations/management. Instead they rely on the imperfect Thread pool for at least some minimal protection. If that is removed, then some form of explicit resource limitation/management is likely to be needed in its place. Stable servers need to be provisioned for the worst case, not the average one.

    Conclusion

    There are applications that can scale better if more Threads are available, but it is not all applications (at least not without significant redesign). Consideration needs to be given to what will limit the worst case load for a server/application if it is not to be Threads. Specifically, the costs of waiting within the application may be such that scalability is likely to have a limit that will not be enforced by practically infinite Virtual Threads.

    It may be that resources have limitations well within the capacity of large but limited Platform Thread pools, which are perfectly capable of scaling to many thousands of threads. So experiments with scaling a Platform Thread pool should first be used to see what limits do apply to an application.

    If no upper limit is found before Platform Threads exhaust kernel memory, then Virtual Threads will allow scaling beyond that limit until some other limit is found. Thus the ultimate resource limit will need to be explicitly managed if catastrophic failure is to be avoided (but, to be fair, applications using Thread pools should also do some explicit resource limit management rather than rely just on the course limits of a Thread pool).

    Recommendation

    If Virtual Threads are not the general solution to scalability then what is? There is no one-size-fits-all solution, but I believe many applications that are limited by blocking on the network would benefit from being deployed in a server like Eclipse Jetty, that can do much of the handling for them asynchronously. Let Jetty read your requests asynchronously and prepare the content as parsed JSON, XML, or form data. Only then allocate a Thread (Virtual or Platform) with a large output buffer so the application can be written in blocking style, but will not block on either reading the request or writing the response. Finally, once the response is prepared, then let Jetty flush it to the network asynchronously. Jetty has always somewhat supported this model (e.g. by delaying dispatch to a Servlet until the first packet of data arrives), but with Jetty-12 we are adding more mechanisms to asynchronously prepare requests and flush responses, whilst leaving the application written in blocking style. More to come on this in future blogs!

  • Do Looms Claims Stack Up? Part 2: Thread Pools?

    “Project Loom aims to drastically reduce the effort of writing, maintaining, and observing high-throughput concurrent applications that make the best use of available hardware. … The problem is that the thread, the software unit of concurrency, cannot match the scale of the application domain’s natural units of concurrency — a session, an HTTP request, or a single database operation. …  Whereas the OS can support up to a few thousand active threads, the Java runtime can support millions of virtual threads. Every unit of concurrency in the application domain can be represented by its own thread, making programming concurrent applications easier. Forget about thread-pools, just spawn a new thread, one per task.” – Ron Pressler, State of Loom, May 2020

    In this series of blogs, we are examining the new Loom virtual thread features now available in OpenJDK 16 early access releases. In part 1 we saw that Loom’s claim of 1,000,000 virtual threads was true, but perhaps a little misleading, as that only applies to threads with near-empty stacks.  If threads actually have deep stacks, then the achieved number of virtual threads is bound by memory and is back to being the same order of magnitude as kernel threads.  In this part, we will further examine the claims and ramifications of Project Loom, specifically if we can now forget about Thread Pools. Spoiler: Cheap threads can do expensive things!
    All the code from this blog is available in our loom-trial project and has been run on my dev machine (Intel® Core™ i7-6820HK CPU @ 2.70GHz × 8, 32GB memory,  Ubuntu 20.04.1 LTS 64-bit, OpenJDK Runtime Environment (build 16-loom+9-316)) with no specific tuning and default settings unless noted otherwise. 

    Matching the scale?

    Project Loom makes the claim that applications need threads because kernel threads “cannot match the scale of the application domain’s natural units of concurrency”!
    Really???  We’ve seen that without tuning, we can achieve 32k of either type of thread on my laptop.  We think it would be fair to assume that with careful tuning, that could be stretched to beyond 100k for either technology.  Is this really below the natural scale of most applications?  How many applications have a natural scale of more than 32k simultaneous parallel tasks?  Don’t get me wrong, there are many apps that do exceed those scales and Jetty has users that put an extra 0 on that, but they are the minority and in reality very few applications are ever going to see that demand for concurrency.
    So if the vast majority of applications would be covered by blocking code with a concurrency of 32k, then what’s the big deal? Why do those apps need Loom? Or, by the same argument, why would they need to be written in asynchronous style?
    The answer is that you rarely see any application deployed with 10,000s of threads; instead, threads are limited by a thread pool, typically to 100s or 1000s of threads.  The default thread pool size in jetty is 200, which we sometimes see increased to 1000s, but we have never seen a 32k thread pool even though my un-tuned laptop could supposedly support it!
    So what’s going on? Why are thread pools typically so limited and what about the claim that Loom means we can “Forget about thread pools”?

    Why Thread Pools?

    One reason we are told that thread pools are used is because kernel threads are slow to start, thus having a bunch of them pre-started, waiting for a task in a pool improves latency.  Loom claims their virtual threads are much faster to start, so let’s test that with StartThreads, which reports:

    kStart(ns) ave:137,903 from:1,000 min:47,466 max:6,048,540
vStart(ns) ave: 10,881 from:1,000 min: 4,648 max:  486,078

    So that claim checks out. Virtual threads start an order of magnitude faster than kernel threads.  If start time was the only reason for thread pools, then Loom’s claim of forgetting about thread pools would hold.
    But start time only explains why we have thread pools, but it doesn’t explain why thread pools are frequently sized far below the systems capacity for threads: 100s instead of 10,000s?  What is the reason that thread pools are sized as they are?

    Why Small Thread Pools?

    Giving a thread a task to do is a resource commitment. It is saying that a flow of control may proceed to consume CPU, memory and other resources that will be needed to run to completion or at least until a blocking point, where it can wait for those resources.  Most of those resources are not on the stack,  thus limiting the number of available threads is a way to limit a wide range of resource consumption and give quality of service:

    • If your back-end services can only handle 100s of simultaneous requests, then a thread pool with 100s of threads will avoid swamping them with too much load. If your JDBC driver only has 100 pooled connections, then 1,000,000 threads hammering on those connections or other locks are going to have a lot of contention.
    • For many applications a late response is a wrong response, thus it may well be better to handle 1000 tasks in a timely way with the 1001st task delayed, rather than to try to run all 1001 tasks together and have them all risk being late.
    • Graceful degradation under excess load.  Processing a task will need to use heap memory and if too much memory is demanded an OutOfMemeoryException is fatal for all java applications.  Limiting the number of threads is a coarse grained way of limiting a class of heap usage.  Indeed in part 1, we saw that it was heap memory that limited the number of virtual threads.

    Having a limited thread pool allows an application to be tested to that limit so that it can be proved that an application has the memory and other resources necessary to service all of those threads.  Traditional thinking has been that if the configured number of threads is insufficient for the load presented, then either the excess load must wait, or the application should start using asynchronous techniques to more efficiently use those threads (rather than increase the number of threads beyond the resource capacity of the machine).
    A limited thread pool is a coarse grained limit on all resources, not only threads.  Limiting the number of threads puts a limit on concurrent lock contention, memory consumption and CPU usage.

    Virtual Threads vs Thread Pool

    Having established that there might be some good reasons to use thread pools, let’s see if Loom gives us any good reasons not to use them?   So we have created a FakeDataBase class which simulates a JDBC connection pool of 100 connections with a semaphore and then in ManyTasks we run 100,000 tasks that do 2 selects and 1 insert to the database, with a small amount of CPU consumed both with and without the semaphore acquired.   The core of the thread pool test is:

     for (int i = 0; i < tasks; i++)
   pool.execute(newTask(latch));

    and this is compared against the Loom virtual thread code of:

     for (int i = 0 ; i < tasks; i++)
   Thread.builder().virtual().task(newTask(latch)).start();

    And the results are…. drum roll… pretty much the same for both types of thread:

    Pooled  K Threads 33,729ms
Spawned V Threads 34,482ms

    The pooled kernel thread does appear to be consistently a little bit better, but this test is not that rigorous so let’s call it the same, which is kind of expected as the total duration is pretty much going to be primarily constrained by the concurrency of the database.
    So were there any difference at all?  Here is the system monitor graph during both runs: kernel threads with a pool are the left hand first period (60-30s) and then virtual threads after a change over peak (30s – 0s):

    Kernel threads with thread pool do not stress the CPU at all, but virtual threads alone use almost twice as much CPU! There is also a hint of more memory being used.
    The thread pool has 100k tasks in the thread pool queue, 100 kernel threads that take tasks, 100 at a time, and each task takes one of 100 semaphores permits 3 times, with little or no contention.
    The Loom approach has 100k independent virtual threads that each contend 3 times for the 100 semaphore permits, with up to 99,900 threads needing to be added then removed 3 times from the semaphore’s wake up queue.  The extra queuing for virtual threads could easily explain the excess CPU needed, but more investigation is needed to be definitive.
    However, tasks limited by a resource like JDBC are not really the highly concurrent tasks that Loom is targeted at.  To truly test Loom (and async), we need to look at a type of task that just won’t scale with blocking threads dispatched from a thread pool.

    Virtual Threads vs Async APIs

    One highly concurrent work load that we often see on Jetty is chat room style interaction (or games) written on CometD and/or WebSocket.  Such applications often have many 10,000s or even 100,000s of connections to the server that are mostly idle, waiting for a message to receive or an event to send. Currently we achieve these scales only by asynchronous threadless waiting, with all its ramifications of complex async APIs into the application needing async callbacks.  Luckily, CometD was originally written when there was only async servlets and not async IO, thus it still has the option to be deployed using blocking I/O reads and writes.  This gives it good potential to be a like for like comparison between async pooled kernel threads vs blocking virtual threads.
    However, we still have a concern that this style of application/load will not be suitable for Loom because each message to a chat room will fan out to the 10s, 100s or even 1000s of other users waiting in that room.  Thus a single read could result in many blocking write operations, which are typically done with deep stacks (parsing, framework, handling, marshalling, then writing) and other resources (buffers, locks etc). You can see in the following flame graph from a CometD load test using Loom virtual threads, that even with a fast client the biggest block of time is spent in the blue peak on the left, that is writing with deep stacks. It is this part of the graph that needs to scale if we have either more and/or slower clients:

    Jetty with CometD chat on Loom

    To fairly test Loom, it is not sufficient to just replace the limited pool of kernel threads with infinite virtual threads.  Jetty goes to lots of effort with its eat what you kill scheduling using reserved threads to ensure that whenever a selector thread calls a potentially blocking task, another selector thread has been executed.  We can’t just put Loom virtual threads on top of this, else it will be paying the cost and complexity of core Jetty plus the overheads of Loom.  Moreover, we have also learnt the risk of Thread Starvation that can result in highly concurrent applications if you defer important tasks (e.g. HTTP/2 flow control).  Since virtual threads can be postponed (potentially indefinitely) by CPU bound applications or the use of non-Loom-aware locks (such as the synchronized keyword), they are not suitable for all tasks within Jetty.
    Thus we think a better approach is to keep the core of Jetty running on kernel threads, but to spawn a virtual thread to do the actual work of reading, parsing, and calling the application and writing the response.  If we flag those tasks with InvocationType.NON_BLOCKING, then they will be called directly by the selector thread, with no executor overhead. These tasks can then spawn a new virtual thread to proceed with the reading, parsing,  handling, marshalling, writing and blocking.  Thus we have created the jetty-10.0.x-loom branch, to use this approach and hopefully give a good basis for fair comparisons.
    Our initial runs with our CometD benchmark with just 20 clients resulted in long GCs followed by out of memory failures! This is due to the usage of ThreadLocal for gathering latency statistics and each virtual thread was creating a latency capture data structure, only to use it once and then throw it away!  While this problem is solvable by changing the CometD benchmark code, it reaffirms that threads use resources other than stack and that Loom virtual threads are not a drop in replacement for kernel threads.
    We are aware that the handling of ThreadLocal is a well known problem in Loom, but until solved it may be a surprisingly hard problem to cope with, since you don’t typically know if a library your application depends on uses ThreadLocal or not.
    With the CometD benchmark modified to not use ThreadLocal, we can now take Loom/Jetty/CometD to a moderate number of clients (1000 which generated the flame graph above) with the following results:

    CLIENT: Async Jetty/CometD server
========================================
Testing 1000 clients in 100 rooms, 10 rooms/client
Sending 1000 batches of 10x50 bytes messages every 10000 µs
Elapsed = 10015 ms
- - - - - - - - - - - - - - - - - - - -
Outgoing: Rate = 990 messages/s - 99 batches/s - 12.014 MiB/s
Incoming: Rate = 99829 messages/s - 35833 batches/s(35.89%) - 26.352 MiB/s
                @     _  3,898 µs (112993, 11.30%)
                   @  _  7,797 µs (141274, 14.13%)
                   @  _  11,696 µs (136440, 13.65%)
                   @  _  15,595 µs (139590, 13.96%) ^50%
                   @  _  19,493 µs (142883, 14.29%)
                  @   _  23,392 µs (130493, 13.05%)
                @     _  27,291 µs (112283, 11.23%) ^85%
        @             _  31,190 µs (59810, 5.98%) ^95%
  @                   _  35,088 µs (12968, 1.30%)
 @                    _  38,987 µs (4266, 0.43%) ^99%
@                     _  42,886 µs (2150, 0.22%)
@                     _  46,785 µs (1259, 0.13%)
@                     _  50,683 µs (910, 0.09%)
@                     _  54,582 µs (752, 0.08%)
@                     _  58,481 µs (567, 0.06%)
@                     _  62,380 µs (460, 0.05%) ^99.9%
@                     _  66,278 µs (365, 0.04%)
@                     _  70,177 µs (232, 0.02%)
@                     _  74,076 µs (82, 0.01%)
@                     _  77,975 µs (13, 0.00%)
@                     _  81,873 µs (2, 0.00%)
Messages - Latency: 999792 samples
Messages - min/avg/50th%/99th%/max = 209/15,095/14,778/35,815/78,184 µs
Messages - Network Latency Min/Ave/Max = 0/14/78 ms
SERVER: Async Jetty/CometD server
========================================
Operative System: Linux 5.8.0-33-generic amd64
JVM: Oracle Corporation OpenJDK 64-Bit Server VM 16-ea+25-1633 16-ea+25-1633
Processors: 12
System Memory: 89.26419% used of 31.164349 GiB
Used Heap Size: 73.283676 MiB
Max Heap Size: 2048.0 MiB
- - - - - - - - - - - - - - - - - - - -
Elapsed Time: 10568 ms
   Time in Young GC: 5 ms (2 collections)
   Time in Old GC: 0 ms (0 collections)
Garbage Generated in Eden Space: 3330.0 MiB
Garbage Generated in Survivor Space: 4.227936 MiB
Average CPU Load: 397.78314/1200
========================================
Jetty Thread Pool:
    threads:                174
    tasks:                  302146
    max concurrent threads: 34
    max queue size:         152
    queue latency avg/max:  0/11 ms
    task time avg/max:      1/3316 ms

     

    CLIENT: Loom Jetty/CometD server
========================================
Testing 1000 clients in 100 rooms, 10 rooms/client
Sending 1000 batches of 10x50 bytes messages every 10000 µs
Elapsed = 10009 ms
- - - - - - - - - - - - - - - - - - - -
Outgoing: Rate = 990 messages/s - 99 batches/s - 13.774 MiB/s
Incoming: Rate = 99832 messages/s - 41201 batches/s(41.27%) - 27.462 MiB/s
                 @    _  2,718 µs (99690, 9.98%)
                   @  _  5,436 µs (116281, 11.64%)
                   @  _  8,155 µs (115202, 11.53%)
                   @  _  10,873 µs (108572, 10.87%)
                  @   _  13,591 µs (106951, 10.70%) ^50%
                   @  _  16,310 µs (117139, 11.72%)
                   @  _  19,028 µs (114531, 11.46%)
                @     _  21,746 µs (94080, 9.42%) ^85%
            @         _  24,465 µs (71479, 7.15%)
      @               _  27,183 µs (34358, 3.44%) ^95%
  @                   _  29,901 µs (11526, 1.15%) ^99%
 @                    _  32,620 µs (4513, 0.45%)
@                     _  35,338 µs (2123, 0.21%)
@                     _  38,056 µs (988, 0.10%)
@                     _  40,775 µs (562, 0.06%)
@                     _  43,493 µs (578, 0.06%) ^99.9%
@                     _  46,211 µs (435, 0.04%)
@                     _  48,930 µs (187, 0.02%)
@                     _  51,648 µs (31, 0.00%)
@                     _  54,366 µs (27, 0.00%)
@                     _  57,085 µs (1, 0.00%)
Messages - Latency: 999254 samples
Messages - min/avg/50th%/99th%/max = 192/12,630/12,476/29,704/54,558 µs
Messages - Network Latency Min/Ave/Max = 0/12/54 ms
SERVER: Loom Jetty/CometD server
========================================
Operative System: Linux 5.8.0-33-generic amd64
JVM: Oracle Corporation OpenJDK 64-Bit Server VM 16-loom+9-316 16-loom+9-316
Processors: 12
System Memory: 88.79622% used of 31.164349 GiB
Used Heap Size: 61.733116 MiB
Max Heap Size: 2048.0 MiB
- - - - - - - - - - - - - - - - - - - -
Elapsed Time: 10560 ms
   Time in Young GC: 23 ms (8 collections)
   Time in Old GC: 0 ms (0 collections)
Garbage Generated in Eden Space: 8068.0 MiB
Garbage Generated in Survivor Space: 3.6905975 MiB
Average CPU Load: 413.33084/1200
========================================
Jetty Thread Pool:
    threads:                14
    tasks:                  0
    max concurrent threads: 0
    max queue size:         0
    queue latency avg/max:  0/0 ms
    task time avg/max:      0/0 ms

    The results here are a bit mixed, but there are some positives for Loom:

    • Both approaches easily achieved the 1000 msg/s sent to the server and 99.8k msg/s received from the server (messages have an average fan-out of a factor 100).
    • The Loom version broke up those messages into 41k responses/s whilst the async version used bigger batches at 35k responses/s, which each response carrying more messages. We need to investigate why, but we think Loom is faster at starting to run the task (no time in the thread pool queue, no time to “wake up” an idle thread).
    • Loom had better latency, both average (~12.5 ms vs ~14.8 ms) and max (~54.6 ms vs ~78.2 ms)
    • Loom used more CPU: 413/1200 vs 398/1200 (4% more)
    • Loom generated more garbage: ~8068.0 MiB vs ~3330.0 MiB and less objects made it to survivor space.

    This is an interesting but inconclusive result.  It is at a low scale on a fast loopback network with a client unlikely to cause blocking, so not really testing either approach.  We now need to scale this test to many 10,000s of clients on a real network, which will require multiple load generation machines and careful measurement.  This will be the subject of part 3 (probably some weeks away).

    Conclusion (part 2) – Cheap threads can do expensive things

    It is good that Project Loom adds inexpensive and fast spawning/blocking virtual threads to the JVM.  But cheap threads can do expensive things!
    Having 1,000,000 concurrent application entities is going to take memory, CPU and other resources, no matter if they block or use async callbacks. It may be that entirely different programming styles are needed for Loom, as is suggested by Loom Structured Concurrency, however we have not yet seen anything that provides limitations on resources that can be used by unlimited spawning of virtual threads. There are also indications that Loom’s flexible stack management comes with a CPU cost.   However, it has been moderately simple to update Jetty to experiment with using Loom to call a blocking application and we’d very much encourage others to load test their application on the jetty-10.0.x-loom branch.
    Many of Loom’s claims have stacked up: blocking code is much easier to write, virtual threads are very fast to start and cheap to block. However, other key claims either do not hold up or have yet to be substantiated: we do not think virtual threads give natural scaling as threads themselves are not the limiting factor, rather it is the resources that are used that determines the scaling.  The suggestion to “Forget about thread-pools, just spawn a new thread…” feels like an invitation to create unstable applications unless other substantive resource management strategies are put into place.
    Given that Duke’s “new clothes” woven by Loom are not one-size-fits-all, it would be a mistake to stop developing asynchronous APIs for things such as DNS and JDBC on the unsubstantiated suggestion that Loom virtual threads will make them unnecessary.

  • Jetty-9.3 Features!

    Jetty 9.3.0 is almost ready and Release Candidate 1 is available for download and testing!  So this is just a quick blog to introduce you to what is new and encourage you to try it out!

    HTTP2

    The headline feature in Jetty-9.3 is HTTP/2 support. This protocol is now a proposed standard from the IETF and described in RFC7540. The Jetty team has been closely involved with the development of this standard, and while we have some concerns about the result, we believe that there are significant quality of service gains to be had by deploying HTTP/2.   The protocol has features that can greatly reduce the time to render a web page, which is good for clients; plus it has some good economies in using a fewer connections, which is good for servers.

    Jetty has comprehensive support for HTTP/2: Client, Server with negotiated, upgraded and direct connections and the protocol is already supported by the majority of current browsers. Since HTTP2 is substantially based on the SPDY protocol, we have dropped SPDY support from Jetty-9.3.

    Deploying HTTP/2 in the server is just the same as configuring a https connector : java -jar $JETTY_HOME/start.jar --add-to-startd=http2 will get you going (more blogs and doco coming)!

    Webtide is actively seeking users interested in deploying HTTP2 and collaborating on analysis of load, latency, configuration and optimisations.

    ALPN

    To support standard based negotiation of protocols over new connections (eg. to select HTTP2 or HTTPS),  Jetty-9.3 supports the Application Layer Protocol Negotiation mechanism which replaces our previous support for NPN.

    ALPN will automatically be enabled when HTTP2 is enable with start.jar, which downloads a non-eclipse jar containing our own extension to Open JVM and is not covered by the eclipse licenses.

    SNI

    Jetty-9.3 also supports Server Name Indications during TLS/SSL negotiation.  This allows the key store to contain multiple server certificates that have a specific or wild card domain(s) encoded in their distinguished name or by the Subject Alternate Name X.509 extension.     This allows a server with many virtual hosts/contexts to pick the appropriate TLS/SSL certificate for a connection.

    Enabling SNI support is a simple as adding the multiple certificates to your keystore file!

    Java 8

    Jetty-9.3 is built and targeted for Java 8.  This change was prompted by the SNI extension reliance on a Java 8 API and the HTTP2 specification need for TLS ciphers that are only available in Java 8.  It is possible to build Jetty-9.3 for Java 7 and we were considering releasing it as such with a few configuration tricks to enable the few classes that require java 8, however we decided that since java 7 is end-of-life is was not worth the complication to support it directly in the release.   If you really need java 7, then please speak to Webtide about a build of 9.3 for 7.

    Eat What You Kill

    It is impossible to change the protocol as server speaks without dramatic changes on how it is optimized to scale to high loads and low through puts.  The support of HTTP2 requires some fundamental changes to the core scheduling strategies, specifically with regards to the challenge of handling multiplexed requests from a single connection.   Jetty 9.3 contains a new scheduling strategy nicked named Eat What You Kill that makes 9.3 faster out of the box and gives us the opportunity to continue to improve throughput and latency as we tune the algorithm.

    Reactive Asynchronous IO Flows?

    Jetty 9.2 already supports the Servlet Asynchronous IO API and Asynchronous Servlets.  However, in Jetty 9.3 that support has been made even more fundamental and all IO in Jetty is now fundamentally asynchronous from the connector to the servlet streams and robust under arbitrary access from non container managed threads.

    So Jetty-9.3 is a good basis on which to develop with the servlet asynchronous APIs, however as we have some concerns with the complexity of those APIs, we are actively experimenting with better APIs based on Reactive Programming and specifically on the Flow abstraction developed by Doug Lea as a candidate class for Java 9.   We have a working prototype that runs on Jetty-9.3 which we hope to release soon.  Please contact us if you are interested in  participating in this development, as real use-cases are required to test these abstractions!

  • HTTP/2 Support for HttpClient

    Jetty’s HttpClient is a fast, scalable, asynchronous implementation of a HTTP client.
    But it is even more.
    Jetty’s HttpClient provides a high level API with HTTP semantic. This means that your applications will be able to perform HTTP requests and receive HTTP responses with a rich API. For example, you can use HttpClient to perform REST requests from the client or from within your web application to third party REST services.
    Jetty’s HttpClient provides also pluggable transports. This means that the concept of a HTTP request and response is translated by HttpClient to SPDY, FastCGI, HTTP/1.1 or other protocols and transported over the network in SPDY, FastCGI and HTTP/1.1 formats, in a way that is totally transparent for the application, which will only see a high level HTTP request and a response.
    Applications will get improved performance when using more performant transports.
    The new addition in Jetty 9.3 is a HTTP/2 transport for HttpClient, replacing the SPDY transport.
    This means that now HttpClient can talk to a regular HTTP/1.1 server, or to a FastCGI server that serves PHP pages, or to a HTTP/2 server transparently.
    The HTTP/2 specification is in its final phases, so the HTTP/2 protocol is now stable and well supported: Firefox, Chrome, Internet Explorer 11 already supports HTTP/2, and as the time passes they will be enabling HTTP/2 by default (some already have).
    And it’s not only browsers and servers such as Google, Twitter, etc.: also tools and libraries such as curl and nghttp2, among many others.
    The Jetty project implemented HTTP/2 since June 2014 and this very website has been served using Jetty’s HTTP/2 implementation for now over 6 months, helping to finalize the interoperability among different implementations.
    You are probably already reading this blog entry served via HTTP/2, if you are using a recent browser.
    Contact us if you are interested in deploying HTTP/2 in your infrastructure and benefit from the performance improvements that it brings.

  • JavaOne 2014 Servlet 3.1 Async I/O Session

    Greg Wilkins gave the following session at JavaOne 2014 about Servlet 3.1 Async I/O.
    It’s a great talk in many ways.
    You get to know from an insider of the Servlet Expert Group about the design of the Servlet 3.1 Async I/O APIs.
    You get to know from the person the created, developed Jetty and implemented Servlet specifications for 19 years what are the gotchas of these new APIs.
    You get to know many great insights on how to write correct asynchronous code, and believe it – it’s not as straightforward as you think !
    You get to know that Jetty 9 supports Servlet 3.1 and you should start using it today 🙂
    Enjoy !