跟我学Kafka之NIO通信机制

很久没有做技术方面的分享了，今天闲来有空写一篇关于Kafka通信方面的文章与大家共同学习。

一、Kafka通信机制的整体结构

这个图采用的就是我们之前提到的SEDA多线程模型，链接如下： http://www.jianshu.com/p/e184fdc0ade4 1、对于broker来说，客户端连接数量有限，不会频繁新建大量连接。因此一个Acceptor thread线程处理新建连接绰绰有余。 2、Kafka高吐吞量，则要求broker接收和发送数据必须快速，因此用proccssor thread线程池处理，并把读取客户端数据转交给缓冲区，不会导致客户端请求大量堆积。 3、Kafka磁盘操作比较频繁会且有io阻塞或等待，IO Thread线程数量一般设置为proccssor thread num两倍，可以根据运行环境需要进行调节。

二、SocketServer整体设计时序图

Kafka 通信时序图.jpg

说明:

Kafka SocketServer是基于Java NIO来开发的，采用了Reactor的模式，其中包含了1个Acceptor负责接受客户端请求，N个Processor线程负责读写数据，M个Handler来处理业务逻辑。在Acceptor和Processor，Processor和Handler之间都有队列来缓冲请求。

下面我们就针对以上整体设计思路分开讲解各个不同部分的源代码。

2.1 启动初始化工作

def startup() { val quotas = new ConnectionQuotas(maxConnectionsPerIp, maxConnectionsPerIpOverrides) for(i <- 0 until numProcessorThreads) { processors(i) = new Processor(i, time, maxRequestSize, aggregateIdleMeter, newMeter("IdlePercent", "percent", TimeUnit.NANOSECONDS, Map("networkProcessor" -> i.toString)), numProcessorThreads, requestChannel, quotas, connectionsMaxIdleMs) Utils.newThread("kafka-network-thread-%d-%d".format(port, i), processors(i), false).start() } newGauge("ResponsesBeingSent", new Gauge[Int] { def value = processors.foldLeft(0) { (total, p) => total + p.countInterestOps(SelectionKey.OP_WRITE) } }) // register the processor threads for notification of responses requestChannel.addResponseListener((id:Int) => processors(id).wakeup()) // start accepting connections this.acceptor = new Acceptor(host, port, processors, sendBufferSize, recvBufferSize, quotas) Utils.newThread("kafka-socket-acceptor", acceptor, false).start() acceptor.awaitStartup info("Started") }

说明：

ConnectionQuotas对象负责管理连接数/IP， 创建一个Acceptor侦听者线程，初始化N个Processor线程，processors是一个线程数组，可以作为线程池使用，默认是三个，Acceptor线程和N个Processor线程中每个线程都独立创建Selector.open()多路复用器，相关代码在下面：

val numNetworkThreads = props.getIntInRange("num.network.threads", 3, (1, Int.MaxValue))； val serverChannel = openServerSocket(host, port)；

范围可以设定从1到Int的最大值。

2.2 Acceptor线程

def run() { serverChannel.register(selector, SelectionKey.OP_ACCEPT); startupComplete() var currentProcessor = 0 while(isRunning) { val ready = selector.select(500) if(ready > 0) { val keys = selector.selectedKeys() val iter = keys.iterator() while(iter.hasNext && isRunning) { var key: SelectionKey = null try { key = iter.next iter.remove() if(key.isAcceptable) accept(key, processors(currentProcessor)) else throw new IllegalStateException("Unrecognized key state for acceptor thread.") // round robin to the next processor thread currentProcessor = (currentProcessor + 1) % processors.length } catch { case e: Throwable => error("Error while accepting connection", e) } } } } debug("Closing server socket and selector.") swallowError(serverChannel.close()) swallowError(selector.close()) shutdownComplete() }

2.1.1 注册OP_ACCEPT事件

serverChannel.register(selector, SelectionKey.OP_ACCEPT);

2.1.2 内部逻辑

此处采用的是同步非阻塞逻辑，每隔500MS轮询一次，关于同步非阻塞的知识点在http://www.jianshu.com/p/e9c6690c0737 当有请求到来的时候采用轮询的方式获取一个Processor线程处理请求，代码如下：

currentProcessor = (currentProcessor + 1) % processors.length

之后将代码添加到newConnections队列之后返回，代码如下：

def accept(socketChannel: SocketChannel) { newConnections.add(socketChannel) wakeup()} //newConnections是一个线程安全的队列，存放SocketChannel通道 private val newConnections = new ConcurrentLinkedQueue[SocketChannel]()

2.3 kafka.net.Processor

override def run() { startupComplete() while(isRunning) { // setup any new connections that have been queued up configureNewConnections() // register any new responses for writing processNewResponses() val startSelectTime = SystemTime.nanoseconds val ready = selector.select(300) currentTimeNanos = SystemTime.nanoseconds val idleTime = currentTimeNanos - startSelectTime idleMeter.mark(idleTime) // We use a single meter for aggregate idle percentage for the thread pool. // Since meter is calculated as total_recorded_value / time_window and // time_window is independent of the number of threads, each recorded idle // time should be discounted by # threads. aggregateIdleMeter.mark(idleTime / totalProcessorThreads) trace("Processor id " + id + " selection time = " + idleTime + " ns") if(ready > 0) { val keys = selector.selectedKeys() val iter = keys.iterator() while(iter.hasNext && isRunning) { var key: SelectionKey = null try { key = iter.next iter.remove() if(key.isReadable) read(key) else if(key.isWritable) write(key) else if(!key.isValid) close(key) else throw new IllegalStateException("Unrecognized key state for processor thread.") } catch { case e: EOFException => { info("Closing socket connection to %s.".format(channelFor(key).socket.getInetAddress)) close(key) } case e: InvalidRequestException => { info("Closing socket connection to %s due to invalid request: %s".format(channelFor(key).socket.getInetAddress, e.getMessage)) close(key) } case e: Throwable => { error("Closing socket for " + channelFor(key).socket.getInetAddress + " because of error", e) close(key) } } } } maybeCloseOldestConnection } debug("Closing selector.") closeAll() swallowError(selector.close()) shutdownComplete() }

先来重点看一下configureNewConnections这个方法：

private def configureNewConnections() { while(newConnections.size() > 0) { val channel = newConnections.poll() debug("Processor " + id + " listening to new connection from " + channel.socket.getRemoteSocketAddress) channel.register(selector, SelectionKey.OP_READ) } }

循环判断NewConnections的大小，如果有值则弹出，并且注册为OP_READ读事件。 再回到主逻辑看一下read方法。

def read(key: SelectionKey) { lruConnections.put(key, currentTimeNanos) val socketChannel = channelFor(key) var receive = key.attachment.asInstanceOf[Receive] if(key.attachment == null) { receive = new BoundedByteBufferReceive(maxRequestSize) key.attach(receive) } val read = receive.readFrom(socketChannel) val address = socketChannel.socket.getRemoteSocketAddress(); trace(read + " bytes read from " + address) if(read < 0) { close(key) } else if(receive.complete) { val req = RequestChannel.Request(processor = id, requestKey = key, buffer = receive.buffer, startTimeMs = time.milliseconds, remoteAddress = address) requestChannel.sendRequest(req) key.attach(null) // explicitly reset interest ops to not READ, no need to wake up the selector just yet key.interestOps(key.interestOps & (~SelectionKey.OP_READ)) } else { // more reading to be done trace("Did not finish reading, registering for read again on connection " + socketChannel.socket.getRemoteSocketAddress()) key.interestOps(SelectionKey.OP_READ) wakeup() } }

说明

1、把当前SelectionKey和事件循环时间放入LRU映射表中，将来检查时回收连接资源。 2、建立BoundedByteBufferReceive对象，具体读取操作由这个对象的readFrom方法负责进行，返回读取的字节大小。

如果读取完成，则修改状态为receive.complete，并通过requestChannel.sendRequest(req)将封装好的Request对象放到RequestQueue队列中。如果没有读取完成，则让selector继续侦听OP_READ事件。

2.4 kafka.server.KafkaRequestHandler

def run() { while(true) { try { var req : RequestChannel.Request = null while (req == null) { // We use a single meter for aggregate idle percentage for the thread pool. // Since meter is calculated as total_recorded_value / time_window and // time_window is independent of the number of threads, each recorded idle // time should be discounted by # threads. val startSelectTime = SystemTime.nanoseconds req = requestChannel.receiveRequest(300) val idleTime = SystemTime.nanoseconds - startSelectTime aggregateIdleMeter.mark(idleTime / totalHandlerThreads) } if(req eq RequestChannel.AllDone) { debug("Kafka request handler %d on broker %d received shut down command".format( id, brokerId)) return } req.requestDequeueTimeMs = SystemTime.milliseconds trace("Kafka request handler %d on broker %d handling request %s".format(id, brokerId, req)) apis.handle(req) } catch { case e: Throwable => error("Exception when handling request", e) } } }

说明

KafkaRequestHandler也是一个事件处理线程，不断的循环读取requestQueue队列中的Request请求数据，其中超时时间设置为300MS，并将请求发送到apis.handle方法中处理，并将请求响应结果放到responseQueue队列中去。 代码如下：

try{ trace("Handling request: " + request.requestObj + " from client: " + request.remoteAddress) request.requestId match { case RequestKeys.ProduceKey => handleProducerOrOffsetCommitRequest(request) case RequestKeys.FetchKey => handleFetchRequest(request) case RequestKeys.OffsetsKey => handleOffsetRequest(request) case RequestKeys.MetadataKey => handleTopicMetadataRequest(request) case RequestKeys.LeaderAndIsrKey => handleLeaderAndIsrRequest(request) case RequestKeys.StopReplicaKey => handleStopReplicaRequest(request) case RequestKeys.UpdateMetadataKey => handleUpdateMetadataRequest(request) case RequestKeys.ControlledShutdownKey => handleControlledShutdownRequest(request) case RequestKeys.OffsetCommitKey => handleOffsetCommitRequest(request) case RequestKeys.OffsetFetchKey => handleOffsetFetchRequest(request) case RequestKeys.ConsumerMetadataKey => handleConsumerMetadataRequest(request) case requestId => throw new KafkaException("Unknown api code " + requestId) } } catch { case e: Throwable => request.requestObj.handleError(e, requestChannel, request) error("error when handling request %s".format(request.requestObj), e) } finally request.apiLocalCompleteTimeMs = SystemTime.milliseconds }

说明如下：

参数说明对应方法RequestKeys.ProduceKeyproducer请求ProducerRequestRequestKeys.FetchKeyconsumer请求FetchRequestRequestKeys.OffsetsKeytopic的offset请求OffsetRequestRequestKeys.MetadataKeytopic元数据请求TopicMetadataRequestRequestKeys.LeaderAndIsrKeyleader和isr信息更新请求LeaderAndIsrRequestRequestKeys.StopReplicaKey停止replica请求StopReplicaRequestRequestKeys.UpdateMetadataKey更新元数据请求UpdateMetadataRequestRequestKeys.ControlledShutdownKeycontrolledShutdown请求ControlledShutdownRequestRequestKeys.OffsetCommitKeycommitOffset请求OffsetCommitRequestRequestKeys.OffsetFetchKeyconsumer的offset请求OffsetFetchRequest

2.5 Processor响应数据处理

private def processNewResponses() { var curr = requestChannel.receiveResponse(id) while(curr != null) { val key = curr.request.requestKey.asInstanceOf[SelectionKey] curr.responseAction match { case RequestChannel.SendAction => { key.interestOps(SelectionKey.OP_WRITE) key.attach(curr) } } curr = requestChannel.receiveResponse(id) } }

我们回到Processor线程类中，processNewRequest()方法是发送请求，那么会调用processNewResponses()来处理Handler提供给客户端的Response，把requestChannel中responseQueue的Response取出来，注册OP_WRITE事件，将数据返回给客户端。

转载自 并发编程网 - ifeve.com 相关资源：敏捷开发V1.0.pptx