【LMAX Disruptor 介绍】

LMAX Disruptor ----A High Performance Inter-Thread Messaging Library

Disruptor是一个用于在线程间通信的高效低延时的消息组件，它像个增强的队列，并且它是让LMAX Exchange跑的如此之快的一个关键创新。

The best way to understand what the Disruptor is, is to compare it to something well understood and quite similar in purpose. In the case of the Disruptor this would be Java's BlockingQueue. Like a queue the purpose of the Disruptor is to move data (e.g. messages or events) between threads within the same process. However there are some key features that the Disruptor provides that distinguish it from a queue. They are:

Multicast events to consumers, with consumer dependency graph.

Pre-allocate memory for events.

Optionally lock-free.

Disruptor 是一个 Java 的并发编程框架，大大的简化了并发程序开发的难度，在性能上也比 Java 本身提供的一些并发包要好。

 

Core Concepts

Before we can understand how the Disruptor works, it is worthwhile defining a number of terms that will be used throughout the documentation and the code. For those with a DDD bent, think of this as the ubiquitous language of the Disruptor domain.

Ring Buffer: The Ring Buffer is often considered the main aspect of the Disruptor, however from 3.0 onwards the Ring Buffer is only responsible for the storing and updating of the data (Events) that move through the Disruptor. And for some advanced use cases can be completely replaced by the user.

Sequence: The Disruptor uses Sequences as a means to identify where a particular component is up to. Each consumer (EventProcessor) maintains a Sequence as does the Disruptor itself. The majority of the concurrent code relies on the the movement of these Sequence values, hence the Sequence supports many of the current features of an AtomicLong. In fact the only real difference between the 2 is that the Sequence contains additional functionality to prevent false sharing between Sequences and other values.

Sequencer: The Sequencer is the real core of the Disruptor. The 2 implementations (single producer, multi producer) of this interface implement all of the concurrent algorithms use for fast, correct passing of data between producers and consumers.

Sequence Barrier: The Sequence Barrier is produced by the Sequencer and contains references to the main published Sequence from the Sequencer and the Sequences of any dependent consumer. It contains the logic to determine if there are any events available for the consumer to process.

Wait Strategy: The Wait Strategy determines how a consumer will wait for events to be placed into the Disruptor by a producer. More details are available in the section about being optionally lock-free.

Event: The unit of data passed from producer to consumer. There is no specific code representation of the Event as it defined entirely by the user.

EventProcessor: The main event loop for handling events from the Disruptor and has ownership of consumer's Sequence. There is a single representation called BatchEventProcessor that contains an efficient implementation of the event loop and will call back onto a used supplied implementation of the EventHandler interface.

EventHandler: An interface that is implemented by the user and represents a consumer for the Disruptor.

Producer: This is the user code that calls the Disruptor to enqueue Events. This concept also has no representation in the code.

To put these elements into context, below is an example of how LMAX uses the Disruptor within its high performance core services, e.g. the exchange.

 

 

Disruptor 的核心概念

先从了解 Disruptor 的核心概念开始，来了解它是如何运作的。下面介绍的概念模型，既是领域对象，也是映射到代码实现上的核心对象。

Ring Buffer

如其名，环形的缓冲区。曾经 RingBuffer 是 Disruptor 中的最主要的对象，但从3.0版本开始，其职责被简化为仅仅负责对通过 Disruptor 进行交换的数据（事件）进行存储和更新。在一些更高级的应用场景中，Ring Buffer 可以由用户的自定义实现来完全替代。

Sequence  Disruptor

通过顺序递增的序号来编号管理通过其进行交换的数据（事件），对数据(事件)的处理过程总是沿着序号逐个递增处理。一个 Sequence 用于跟踪标识某个特定的事件处理者( RingBuffer/Consumer )的处理进度。虽然一个 AtomicLong 也可以用于标识进度，但定义 Sequence 来负责该问题还有另一个目的，那就是防止不同的 Sequence 之间的CPU缓存伪共享(Flase Sharing)问题。

（注：这是 Disruptor 实现高性能的关键点之一，网上关于伪共享问题的介绍已经汗牛充栋，在此不再赘述）。

Sequencer 

Sequencer 是 Disruptor 的真正核心。此接口有两个实现类 SingleProducerSequencer、MultiProducerSequencer ，它们定义在生产者和消费者之间快速、正确地传递数据的并发算法。

Sequence Barrier

用于保持对RingBuffer的 main published Sequence 和Consumer依赖的其它Consumer的 Sequence 的引用。 Sequence Barrier 还定义了决定 Consumer 是否还有可处理的事件的逻辑。

Wait Strategy

定义 Consumer 如何进行等待下一个事件的策略。 （注：Disruptor 定义了多种不同的策略，针对不同的场景，提供了不一样的性能表现）

Event

在 Disruptor 的语义中，生产者和消费者之间进行交换的数据被称为事件(Event)。它不是一个被 Disruptor 定义的特定类型，而是由 Disruptor 的使用者定义并指定。

EventProcessor

EventProcessor 持有特定消费者(Consumer)的 Sequence，并提供用于调用事件处理实现的事件循环(Event Loop)。

EventHandler

Disruptor 定义的事件处理接口，由用户实现，用于处理事件，是 Consumer 的真正实现。

Producer

即生产者，只是泛指调用 Disruptor 发布事件的用户代码，Disruptor 没有定义特定接口或类型。

 

 

 

Multicast Events

This is the biggest behavioural difference between queues and the Disruptor. When you have multiple consumers listening on the same Disruptor all events are published to all consumers in contrast to a queue where a single event will only be sent to a single consumer. The behaviour of the Disruptor is intended to be used in cases where you need to independent multiple parallel operations on the same data. The canonical example from LMAX is where we have three operations, journalling (writing the input data to a persistent journal file), replication (sending the input data to another machine to ensure that there is a remote copy of the data), and business logic (the real processing work). The Executor-style event processing, where scale is found by processing different events in parallel at the same is also possible using the WorkerPool. Note that is bolted on top of the existing Disruptor classes and is not treated with the same first class support, hence it may not be the most efficient way to achieve that particular goal.

 

Looking at Figure 1. is possible to see that there are 3 Event Handlers listening (JournalConsumer, ReplicationConsumer and ApplicationConsumer) to the Disruptor, each of these Event Handlers will receive all of the messages available in the Disruptor (in the same order). This allow for work for each of these consumers to operate in parallel.

 

Consumer Dependency Graph

To support real world applications of the parallel processing behaviour it was necessary to support co-ordination between the consumers. Referring back to the example described above, it necessary to prevent the business logic consumer from making progress until the journalling and replication consumers have completed their tasks. We call this concept gating, or more correctly the feature that is a super-set of this behaviour is called gating. Gating happens in two places. Firstly we need to ensure that the producers do not overrun consumers. This is handled by adding the relevant consumers to the Disruptor by calling RingBuffer.addGatingConsumers(). Secondly, the case referred to previously is implemented by constructing a SequenceBarrier containing Sequences from the components that must complete their processing first.

 

Referring to Figure 1. there are 3 consumers listening for Events from the Ring Buffer. There is a dependency graph in this example. The ApplicationConsumer depends on the JournalConsumer and ReplicationConsumer. This means that the JournalConsumer and ReplicationConsumer can run freely in parallel with each other. The dependency relationship can be seen by the connection from the ApplicationConsumer's SequenceBarrier to the Sequences of the JournalConsumer and ReplicationConsumer. It is also worth noting the relationship that the Sequencer has with the downstream consumers. One of its roles is to ensure that publication does not wrap the Ring Buffer. To do this none of the downstream consumer may have a Sequence that is lower than the Ring Buffer's Sequence less the size of the Ring Buffer. However using the graph of dependencies an interesting optimisation can be made. Because the ApplicationConsumers Sequence is guaranteed to be less than or equal to JournalConsumer and ReplicationConsumer (that is what that dependency relationship ensures) the Sequencer need only look at the Sequence of the ApplicationConsumer. In a more general sense the Sequencer only needs to be aware of the Sequences of the consumers that are the leaf nodes in the dependency tree.

 

Event Preallocation

One of the goals of the Disruptor was to enable use within a low latency environment. Within low-latency systems it is necessary to reduce or remove memory allocations. In Java-based system the purpose is to reduce the number stalls due to garbage collection (in low-latency C/C++ systems, heavy memory allocation is also problematic due to the contention that be placed on the memory allocator).

To support this the user is able to preallocate the storage required for the events within the Disruptor. During construction and EventFactory is supplied by the user and will be called for each entry in the Disruptor's Ring Buffer. When publishing new data to the Disruptor the API will allow the user to get hold of the constructed object so that they can call methods or update fields on that store object. The Disruptor provides guarantees that these operations will be concurrency-safe as long as they are implemented correctly.

 

Optionally Lock-free

Another key implementation detail pushed by the desire for low-latency is the extensive use of lock-free algorithms to implement the Disruptor. All of the memory visibility and correctness guarantees are implemented using memory barriers and/or compare-and-swap operations. There is only one use-case where a actual lock is required and that is within the BlockingWaitStrategy. This is done solely for the purpose of using a Condition so that a consuming thread can be parked while waiting for new events to arrive. Many low-latency systems will use a busy-wait to avoid the jitter that can be incurred by using a Condition, however in number of system busy-wait operations can lead to significant degradation in performance, especially where the CPU resources are heavily constrained. E.g. web servers in virtualised-environments.