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In Multiverse I pool everything, the goal is to have zero waste. We use the cache coherency model as a very efficient means of propagating changes between cores. The only time we need to go to journals is if we have a complete system failure and we need to recover. So with the boxing version you have 1 object per transaction and with the primitive version you are creating zero waste. Do you warm up your application after you restart? Stuff like dirty reads, non repeatable reads, phantom reads and writes skew still are possible. We believe the Disruptor is a much better fit.


Should a Customer class extend a LinkedList just as an optimisation? Perhaps I can help you here. Retiring Mainframe Programmers: Should I Care? TPS and therefore you are now able to cope with a larger volume of customers. We can warm up the application before making it available again. So that is why I decided to write my own. Within a component we have a clean single threaded OO model with all communication between components via message passing. If by barrier you mean memory barrier or fence then we use volatile, or the private sun. EventProcessors maintain their own high water mark of what event they have processed up to. If the design is taken back to the original principles of OO then all communication is via message passing.


This is just a free market and drives natural price efficiency. Could you elaborate some more on how you do this? All services are replicated to multiple backup nodes. EventProcessor when the ack arrives. As you can see the scalability of the primitive version is almost linearly and with the boxing version it is a mess. On contention you keep retrying which can be very costly on GC even for the identity of the nodes. We have other strategies dependent on use whereby they either spin with a yield to release the quantum and at the other extreme use condition variables to signal when latency is not so important or CPU core are restricted.


It depends on the class of failure. Could you elaborate a bit how you are determining that your code is machine friendly even though you are using a virtual machine? It is however very difficult to get exact behaviour and I often end up running a series of experiments and measuring the result so I can confirm the theories I want to test. None of this effects our matching engine. For example, the polymorphic call you pointed out depends on the number of implementations for an interface seen during class loading. Q2: When a thread is waiting for work, I assume it spins? Each come in over their own channels.


If the Bus logic is stalling on 101. Some of the tips include: understand the platform, model the domain, create a clear separation of concerns, choose data structures wisely, and run business logic on a single thread. Each EventProcesssor has a top level exception handler that can decide on the appropriate course of action to take which could be anything from removing the node from the cluster to simply marking the event as dead and not for further processing. There is no connection to any kind of rendering framework, although I have included JFreeChart in the Benchy artifacts for ease of use. Whereas the business logic depends on the RingBuffer and the journaller, replicator and unmarshaller before it can progress a given sequence. Only if you want to introduce blocking next to locking, things get more complicated. Stuff like dirty reads, non repeatable reads, phantom reads and writeskew still are possible. Btw which OS are you using?


JIT more area to do its work. And with a nesting of method calls, you are still going to end up with some parts being inlined, and some parts not, since at some point the resulting method body exceeds some limit. The whole point to the talk is that it is best not to use locks on highly contended data. Good insights in that you need to know what is happening on a lower level instead of believing that the JVM is smart enough to make everything perfect. Once past our firewalls everyone traverses the same infrastructure. QCon is a conference that is organized by the community, for the community.


It can also happen when different data gets written. Open Source proven application servers, messaging platforms, protocols, frameworks, etc. But I agree that STM is not the holy grail for concurrent problems. It also is great that it can combine the Javacode and the generated assembler code in just a single view. Intel VTune really is the only tool I know about that helps. In fact there is our system there is only 1 contended CAS per service. There also seem to be quite some interest in your architecture and your philosophy regarding this.


If not, where might you post it? Apart from it being difficult to add, it is even more difficult to get fast. All this happens without locks. Wholesale market makers can use the financial extranets and retail customers can use the Internet. We have others that range from parsers to network packet capture devices. And finally, how do you handle failover? We hope to have a tech blog up soon and it will probably be linked from the community site or our home page.


However to keep InfoQ free we need your support. Thanks for the interest. It seems like the business logic and the marshaller can just be on the same ring buffer as the other tasks. Equity exchanges are closed for many hours. All results are stored in a database so you can track performance over time. Outside our firewalls we have no control over the latency we can offer. The nice thing about this approach is everyone can hold on to a read consistent copy. You say that array can perform better than linked lists since with a linked list you keep on jumping all over the place in the heap. The big performance hit comes from cores contending on trying to write the same cache line.


We have other slaves nodes in our disaster recovery location running less than one second behind the master. And there are some command line tools for listing, displaying, deleting benchmarks. It seems that there are not many of such test frameworks. All our messaging, reliable delivery, consistency models, resource management, and HA are our own intellectual property. Good input validation, deterministic logic and lots of tests. ThresholdBarrier represents the concerns of maintaining the dependencies. Logical pipelines are a very useful model for this type of problem. Macro level benchmarks on realistic data have much more value in my experience.


The Disruptor pattern is at the heart of this. That is good credentials and impressive volume. If you mean our higher level ThresholdBarrier class then this is proprietary concept as part of the Disruptor. However the dependent business logic will not progress until it gets the ack. The spreads are tight because we have multiple market makers competing with each other to be top of book and in addition retail customers can place their own orders inside the spread to become top of book themselves, thus further reducing the spread. PrintCompilation can be useful to see what is being compiled and why. But the containers for the actual values are not interesting in your business code, so these can be reused. The replicator has two threads internally.


It effectively pipelines requests so it can progress with replicating 102 before it receives the ack from 101. Not only does a monitor cause the changes on a thread to be made visible it also prevents instruction reordering and has the potential to context switch to the kernel for arbitration. We have spent a lot of time getting this to perform very fast by developing our own algorithms. If the model is a tree that is not too deep you can CAS the root to swap in the changes after a copy. These changes can be effectively propagated via the L3 cache and socket interlinks to other cores reading the data. If the replicator is stalling on 101. For the web clients we take HTTP traffic from Servlets running in the Caucho Resin web container.


Still a lot of the concepts from the old days there show up in your presentation. Later this year we may write in more detail about this or even open source some of the code. The result is a high quality conference experience where a tremendous amount of attention and investment has gone into having the best content on the most important topics presented by the leaders in our community. My personal view is that STM is a great approach for extremely large data sets where the potential for contention is very low. Java via the synchronised keyword. The actual available sequence is what is returned which may be ahead of the requested sequence allowing batch progress. You mentioned that JEE was not a valid option here but still all the talk is within Java context. The main proposition for our exchange is for wholesale market makers to compete offering the best prices to the retail customers. Martin Thompson and Michael Barker talk about building a HPC financial system handling over 100K tps at less than 1ms latency by having a new approach to infrastructure and software.


We only work with advertisers relevant to our readers. This is running in a component performance test case in isolation. EventProcessor is waiting for. Groovy so you have a full blown programming language available for building it. This is why we use a single thread to own the data writes with other threads only reading this data as it is consumed. JIT has less area to do its magic. With object arrays you can only access the reference addresses very cheap, but the content of the objects still can be very expensive to obtain.


The point we are trying to make is that queues as an implementation for synchronising activities are not well suited to modern hardware. As your own slides indicate, a single read L3 takes 42 cycles. What is very important for us is we offer predictable latency to the wholesale market makers so they can offer wholesale prices. However most people design pipelines without parallel running because trying to synchronise multiple parallel stages is very difficult with queues between the stages in an efficient and none contended manner. No IP addresses need to migrate because we use IP Multicast for all communications. Flushing registers and marking cache lines as modified does not mean they are evicted from the cache of the core that is the sole writer. During this period a lot of things are reset such as FIX sequence numbers. The initial version of our system used that model, however we struggled to get the code to look right.


The down side is the garbage it creates which is also often promoted incurring further cost and requiring later compaction. So each thread that needs to modify data adds another ring buffer. The CAS needs a memory barrier for others to see the changes. If you are using a database, setting the correct isolation level, the database is going to take care of it. Financial exchanges have an end of trading day period. What are you using to integrate the whole performance aspect in the build process? We understand why you use ad blockers. How do you handle that? The ring buffer contains a strictly increasing sequence which it uses to index every message. Other transactions still can continue and depending on the stm implementation they can have a stable view of reality even though an update is in progress.


Hotspot can do both call site and called method inlining which if make your methods small and not complex it has more options. But if GnuPlot or any other kind of rendering framework is needed, you can do so. And that replication system should be in the closest possible state to the single threaded business logic system as otherwise in a failover you will depend on how fast you can recover from the journaling system and pray that this journaling system is up to date. You need to replace the modified node, and all nodes up to the root node, copying references on the way, aka path copy. The Ring Buffers provide a logical separation between the inputs and outputs of the system. It is perfectly possible for a request to fail at any stage. The buffers in our RingBuffer of the Disruptor can be heap or direct byte buffers depending on how we want to use them. Each event processor contains a sequence number indicating the latest message processed. Another problem with stm is what happens with long running transactions; if there is some contention, a long running transaction could be obstructed by shorter ones. One is sending messages to its cluster pair without blocking.


The business logic does not do IO or contend on a locks. It sounds like what you are doing on STM is very interesting and a great approach for some classes of problem. However be aware that micro benchmarks can be very misleading. Delphi Object Pascal language from Embarcadero. If the failure is more benign such as the marshaller parsing a corrupt message then it would be marked so and ignored by the business logic. The whole pipeline cannot run as this speed and is greatly affected by the network or storage employed by the journaller and replicator.


For small segments of code in most cases it is doable to reason about it, but for larger systems it is quite hard. Contention management is one subject that needs a lot of research. Have you tried direct byte buffers to reduce GC cost? Other problem domains where contention is high requires an approach where the concurrency management cost does not exceed the cost of doing the work. That is quite interesting. Often it is not just the JVM which surprises us. The cost at 42 cycles from the L3 to the executing core is the same for each thread.


This allows lots of work to progress in parallel. We use this opportunity to bounce our application because we cannot guarantee that a full GC will compact the old generation with CMS. ThresholdBarrier will busy spin on a volatile variable. If 1 or 2 it can still be optimised away but for 3 or more implementations it usually ends up as a vtable lookup. Some have no dependent stages and all happen in parallel. IPC is not a constant given the mix of instructions executed. The 6M TPS relates to purely the business logic thread running our matching engine logic. If the replicator is stalling onn 101.


If the JMM is applied correctly, you will only get sequential consistency which is a very low form of read consistency. You then to CAS the root node and retry if someone else beats you to it. Or do you restart at a quiet time and let it warm itself? The business logic will not progress to 102 before 101 is finished. The JIT will always do better on simpler code. The disruptor pattern is a whole system pattern that tries to avoid contention wherever possible. This is an area I feel deserves some debate because the current approaches are running out of steam. Getting a return of 200 when 190 is requested means that the events from 190 to 200 in the RingBuffer are now available to be processed by that EventProcessor. Java architecture does not support cache coherency in hardware.


Normally profilers only are able to show information on the method level and it this is called extremely frequent, you will only see information about parent calls. In the infrastructure where all the concurrent code is, we rely on the Java Memory Model to provide visibility of memory between threads. We chose this one example of a pipeline to demonstrate use of the Disruptor. For some context we call all consumers of events an EventProcessor. We can go for over 5 days without needing this but do it each day for good measure. The default for this is 35 bytes of bytecode which is pretty small. In this case the node should be marked for removal from the cluster. The point I was making is you do not need to replace the whole tree as this is the benefit of a tree were you always navigate from the root.


We had to develop our own micro performance testing framework. Works like a charm. If stages run in parallel then as you say you can focus on improving the slowest stage. You are correct that you do have 3 billion cycles per second on a 3Ghz chip per thread. This is a very nice architecture. If this is the master node and the replicator is not getting acks from the lockstep slave then the cluster is informed and the master can run at risk until a new lockstep slave is promoted. NET environment now, I can confirm most of your statements. If they cannot run in parallel then the overall cost of the pipeline can dominate. And as you mention in the talk the smaller the latency the smaller the spread which is much better for the customer.


For example if the journaller failed to write a log entry this would likely be because the storage device has failed or become full. Please consider whitelisting us. Methods, spoke to Josh Evans, former engineering manager at Netflix on how Netflix does DevOps and the freedom and responsibility culture that undermines their way of working. RingBuffer for both incoming and outgoing messages. You made a valid point saying that a retail trading exchange has a bigger volume than a corporate trading exchange. If retail customers locate their servers geographically close to ours then they can get low latency. We have our own custom application server that is launched as a Java process.


Even a volatile write can limit performance considerably. For me this comes down to the mechanical sympathy for how caches work regardless of language. The single thread only needs a memory barrier to make the changes visible when the updates are complete. Retail customers and the wholesale market makers are putting orders into the same exchange. However i am still not sure how you are handling your error conditions. If only one writer then only a memory barrier without CAS would suffice.


What kind of barriers are you using to synchronize the read threads and the write thread? JIT has a very hard time inlining it. What is does is share the cost of fetching the same shared data from main memory to the L3 in some cases. It was a really nice presenation. They demonstrate these features, as well as the tools to deploy these models at large scale. The journaller, replicator and unmarshaller only depend on the RingBuffer progressing. For example the difference between 10GigE with kernel bypass and standard 1GigE is nearly an order of magnitude on both throughput and latency. If the model is a more complex graph the state machine for retries quickly becomes very complex. However the thread that creates the data that the others are reading will likely just push the data to the other cores via the L3 so main memory is not involved. The business logic, jounaller, replicator and marshaller are all examples of EventProcessors.


The point we were trying to make was less about short methods specifically, but more generally about simple code. Open Source contributor to projects like PostgreSQL, JBoss, GNU Classpath and most recently Mono. So these containers are reused. That would be a very cool feature to have. My experience with high performance is that feels like a juggling act where each feature or performance improvement, is another ball you need to keep in the air? Assuming LMAX is written in Java, is this presentation a true reflection of its implementation? It gives feedback on all kinds of behavior like branch mis predications or cache failures. The MIN: on the presentation is a means of expressing that ThresholdBarrier on which the business logic is waiting will return the minimum sequence of all the EventProcessors and RingBuffer it is waiting on. Are there flags in the ring buffer entries that indicate what work has been done to the entry? Yes this can also be achieved with bytecode weaving and other techniques but is it really the right approach as general design principles?


We have swappable strategies for this. If the entities are indirect from the identity nodes then the indirection can cause a cache miss. They have to contend on claiming ownership. As soon as you have more than one thread trying to write the same data at a minimum you need CAS and a state machine with retries, or locks at worst. My experience is that in most cases performance is not the most important in a lot of applications. PrintOptoAssembly with a debug build can be useful for seeing what assembly code is being produced when Hotspot complies a method. With these improvements one is able to lift om some of the benefits from the STM in a more traditional concurrent application.


JVM in less time than it takes to do a GC.

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