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    Written in Cpp. The Disruptor is a concurrent programming framework for exchanging and coordinating work as a continuous series of events. Add a description, image, and links to the lmax-disruptor topic page so that developers can more easily learn about it. Curate this topic. To associate your repository with the lmax-disruptor topic, visit your repo's landing page and select "manage topics.

    Learn more. Skip to content. Here are 21 public repositories matching this topic Language: All Filter by language. Sort options. Star 1. Updated Jun 6, Java. Updated May 14, Java. Star Updated Jun 19, Java. Updated Jun 17, Java. Star 7. Updated Oct 13, C. Star 5. Updated Jun 27, Java.

    The traditional model of sessions and database transactions provides a helpful error handling capability. Should anything go wrong, it's easy to throw away everything that happened so far in the interaction. Session data is transient, and can be discarded, at the cost of some irritation to the user if in the middle of something complicated. If an error occurs on the database side you can rollback the transaction. LMAX's in-memory structures are persistent across input events, so if there is an error it's important to not leave that memory in an inconsistent state.

    However there's no automated rollback facility. As a consequence the LMAX team puts a lot of attention into ensuring the input events are fully valid before doing any mutation of the in-memory persistent state. They have found that testing is a key tool in flushing out these kinds of problems before going into production.

    Although the business logic occurs in a single thread, there are a number tasks to be done before we can invoke a business object method. The original input for processing comes off the wire in the form of a message, this message needs to be unmarshaled into a form convenient for Business Logic Processor to use. Event Sourcing relies on keeping a durable journal of all the input events, so each input message needs to be journaled onto a durable store.

    Finally the architecture relies on a cluster of Business Logic Processors, so we have to replicate the input messages across this cluster. Similarly on the output side, the output events need to be marshaled for transmission over the network. Figure 2: The activities done by the input disruptor using UML activity diagram notation.

    The replicator and journaler involve IO and therefore are relatively slow. Also these three tasks are relatively independent, all of them need to be done before the Business Logic Processor works on a message, but they can done in any order.

    So unlike with the Business Logic Processor, where each trade changes the market for subsequent trades, there is a natural fit for concurrency. To handle this concurrency the LMAX team developed a special concurrency component, which they call a Disruptor [11]. At a crude level you can think of a Disruptor as a multicast graph of queues where producers put objects on it that are sent to all the consumers for parallel consumption through separate downstream queues.

    When you look inside you see that this network of queues is really a single data structure - a ring buffer. Each producer and consumer has a sequence counter to indicate which slot in the buffer it's currently working on. This way the producer can read the consumers' counters to ensure the slot it wants to write in is available without any locks on the counters. Similarly a consumer can ensure it only processes messages once another consumer is done with it by watching the counters.

    Figure 3: The input disruptor coordinates one producer and four consumers. Output disruptors are similar but they only have two sequential consumers for marshaling and output. Each topic has its own disruptor. The disruptors I've described are used in a style with one producer and multiple consumers, but this isn't a limitation of the design of the disruptor.

    The disruptor can work with multiple producers too, in this case it still doesn't need locks. A benefit of the disruptor design is that it makes it easier for consumers to catch up quickly if they run into a problem and fall behind. If the unmarshaler has a problem when processing on slot 15 and returns when the receiver is on slot 31, it can read data from slots in one batch to catch up.

    This batch read of the data from the disruptor makes it easier for lagging consumers to catch up quickly, thus reducing overall latency. I've described things here, with one each of the journaler, replicator, and unmarshaler - this indeed is what LMAX does. But the design would allow multiple of these components to run. If you ran two journalers then one would take the even slots and the other journaler would take the odd slots.

    This allows further concurrency of these IO operations should this become necessary. The ring buffers are large: 20 million slots for input buffer and 4 million slots for each of the output buffers. The sequence counters are 64bit long integers that increase monotonically even as the ring slots wrap.

    Like the rest of the system, the disruptors are bounced overnight. This bounce is mainly done to wipe memory so that there is less chance of an expensive garbage collection event during trading. I also think it's a good habit to regularly restart, so that you rehearse how to do it for emergencies. The journaler's job is to store all the events in a durable form, so that they can be replayed should anything go wrong. LMAX does not use a database for this, just the file system.

    They stream the events onto the disk. In modern terms, mechanical disks are horribly slow for random access, but very fast for streaming - hence the tag-line "disk is the new tape". Earlier on I mentioned that LMAX runs multiple copies of its system in a cluster to support rapid failover. The replicator keeps these nodes in sync. Only the leader node listens directly to input events and runs a replicator.

    The replicator broadcasts the input events to the follower nodes. Should the leader node go down, it's lack of heartbeat will be noticed, another node becomes leader, starts processing input events, and starts its replicator. Each node has its own input disruptor and thus has its own journal and does its own unmarshaling. Even with IP multicasting, replication is still needed because IP messages can arrive in a different order on different nodes.

    The leader node provides a deterministic sequence for the rest of the processing. The unmarshaler turns the event data from the wire into a java object that can be used to invoke behavior on the Business Logic Processor. Therefore, unlike the other consumers, it needs to modify the data in the ring buffer so it can store this unmarshaled object.

    The rule here is that consumers are permitted to write to the ring buffer, but each writable field can only have one parallel consumer that's allowed to write to it. This preserves the principle of only having a single writer. The disruptor is a general purpose component that can be used outside of the LMAX system.

    Usually financial companies are very secretive about their systems, keeping quiet even about items that aren't germane to their business. Not just has LMAX been open about its overall architecture, they have open-sourced the disruptor code - an act that makes me very happy. Not just will this allow other organizations to make use of the disruptor, it will also allow for more testing of its concurrency properties. The LMAX architecture caught people's attention because it's a very different way of approaching a high performance system to what most people are thinking about.

    So far I've talked about how it works, but haven't delved too much into why it was developed this way. This tale is interesting in itself, because this architecture didn't just appear. It took a long time of trying more conventional alternatives, and realizing where they were flawed, before the team settled on this one.

    Most business systems these days have a core architecture that relies on multiple active sessions coordinated through a transactional database. Betfair is a betting site that allows people to bet on sporting events. It handles very high volumes of traffic with a lot of contention - sports bets tend to burst around particular events.

    To make this work they have one of the hottest database installations around and have had to do many unnatural acts in order to make it work. Based on this experience they knew how difficult it was to maintain Betfair's performance and were sure that this kind of architecture would not work for the very low latency that a trading site would require.

    As a result they had to find a different approach. Their initial approach was to follow what so many are saying these days - that to get high performance you need to use explicit concurrency. For this scenario, this means allowing orders to be processed by multiple threads in parallel. However, as is often the case with concurrency, the difficulty comes because these threads have to communicate with each other.

    Processing an order changes market conditions and these conditions need to be communicated. The Actor model relies on independent, active objects with their own thread that communicate with each other via queues. Many people find this kind of concurrency model much easier to deal with than trying to do something based on locking primitives. The team built a prototype exchange using the actor model and did performance tests on it.

    What they found was that the processors spent more time managing queues than doing the real logic of the application. Queue access was a bottleneck. When pushing performance like this, it starts to become important to take account of the way modern hardware is constructed. The phrase Martin Thompson likes to use is "mechanical sympathy".

    The term comes from race car driving and it reflects the driver having an innate feel for the car, so they are able to feel how to get the best out of it. Many programmers, and I confess I fall into this camp, don't have much mechanical sympathy for how programming interacts with hardware. What's worse is that many programmers think they have mechanical sympathy, but it's built on notions of how hardware used to work that are now many years out of date. These days going to main memory is a very slow operation in CPU-terms.

    CPUs have multiple levels of cache, each of which of is significantly faster. So to increase speed you want to get your code and data in those caches. At one level, the actor model helps here. You can think of an actor as its own object that clusters code and data, which is a natural unit for caching. But actors need to communicate, which they do through queues - and the LMAX team observed that it's the queues that interfere with caching. The explanation runs like this: in order to put some data on a queue, you need to write to that queue.

    Similarly, to take data off the queue, you need to write to the queue to perform the removal. This is write contention - more than one client may need to write to the same data structure. To deal with the write contention a queue often uses locks. But if a lock is used, that can cause a context switch to the kernel. When this happens the processor involved is likely to lose the data in its caches.

    The conclusion they came to was that to get the best caching behavior, you need a design that has only one core writing to any memory location [17]. Multiple readers are fine, processors often use special high-speed links between their caches. But queues fail the one-writer principle.

    This analysis led the LMAX team to a couple of conclusions. Firstly it led to the design of the disruptor, which determinedly follows the single-writer constraint. Secondly it led to idea of exploring the single-threaded business logic approach, asking the question of how fast a single thread can go if it's freed of concurrency management.

    The essence of working on a single thread, is to ensure that you have one thread running on one core, the caches warm up, and as much memory access as possible goes to the caches rather than to main memory. This means that both the code and the working set of data needs to be as consistently accessed as possible. Also keeping small objects with code and data together allows them to be swapped between the caches as a unit, simplifying the cache management and again improving performance.

    An essential part of the path to the LMAX architecture was the use of performance testing. The consideration and abandonment of an actor-based approach came from building and performance testing a prototype. Similarly much of the steps in improving the performance of the various components were enabled by performance tests. Mechanical sympathy is very valuable - it helps to form hypotheses about what improvements you can make, and guides you to forward steps rather than backward ones - but in the end it's the testing gives you the convincing evidence.

    Performance testing in this style, however, is not a well-understood topic. Regularly the LMAX team stresses that coming up with meaningful performance tests is often harder than developing the production code. Again mechanical sympathy is important to developing the right tests. Testing a low level concurrency component is meaningless unless you take into account the caching behavior of the CPU.

    One particular lesson is the importance of writing tests against null components to ensure the performance test is fast enough to really measure what real components are doing. Writing fast test code is no easier than writing fast production code and it's too easy to get false results because the test isn't as fast as the component it's trying to measure.

    At first glance, this architecture appears to be for a very small niche. After all the driver that led to it was to be able to run lots of complex transactions with very low latency - most applications don't need to run at 6 million TPS. But the thing that fascinates me about this application, is that they have ended up with a design which removes much of the programming complexity that plagues many software projects. The traditional model of concurrent sessions surrounding a transactional database isn't free of hassles.

    There's usually a non-trivial effort that goes into the relationship with the database. Most performance tuning of enterprise applications involves futzing around with SQL. These days, you can get more main memory into your servers than us old guys could get as disk space. More and more applications are quite capable of putting all their working set in main memory - thus eliminating a source of both complexity and sluggishness.

    Event Sourcing provides a way to solve the durability problem for an in-memory system, running everything in a single thread solves the concurrency issue. There is a considerable overlap here with the growing interest in CQRS. An event sourced, in-memory processor is a natural choice for the command-side of a CQRS system. So what indicates you shouldn't go down this path? This is always a tricky questions for little-known techniques like this, since the profession needs more time to explore its boundaries.

    A starting point, however, is to think of the characteristics that encourage the architecture. One characteristic is that this is a connected domain where processing one transaction always has the potential to change how following ones are processed. With transactions that are more independent of each other, there's less need to coordinate, so using separate processors running in parallel becomes more attractive. LMAX concentrates on figuring the consequences of how events change the world.

    Many sites are more about taking an existing store of information and rendering various combinations of that information to as many eyeballs as they can find - eg think of any media site. Here the architectural challenge often centers on getting your caches right.

    Another characteristic of LMAX is that this is a backend system, so it's reasonable to consider how applicable it would be for something acting in an interactive mode. Increasingly web application are helping us get used to server systems that react to requests, an aspect that does fit in well with this architecture. Where this architecture goes further than most such systems is its absolute use of asynchronous communications, resulting in the changes to the programming model that I outlined earlier.

    These changes will take some getting used to for most teams. Most people tend to think of programming in synchronous terms and are not used to dealing with asynchrony. Yet it's long been true that asynchronous communication is an essential tool for responsiveness. It will be interesting to see if the wider use of asynchronous communication in the javascript world, with AJAX and node.

    The LMAX team found that while it took a bit of time to adjust to asynchronous style, it soon became natural and often easier. In particular error handling was much easier to deal with under this approach.

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