【转】跟我学Kafka之NIO通信机制

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跟我学Kafka之NIO通信机制

 

main 2016-03-31 16:54:06 浏览166 评论0

摘要： 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...

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.ProduceKey	producer请求	ProducerRequest
RequestKeys.FetchKey	consumer请求	FetchRequest
RequestKeys.OffsetsKey	topic的offset请求	OffsetRequest
RequestKeys.MetadataKey	topic元数据请求	TopicMetadataRequest
RequestKeys.LeaderAndIsrKey	leader和isr信息更新请求	LeaderAndIsrRequest
RequestKeys.StopReplicaKey	停止replica请求	StopReplicaRequest
RequestKeys.UpdateMetadataKey	更新元数据请求	UpdateMetadataRequest
RequestKeys.ControlledShutdownKey	controlledShutdown请求	ControlledShutdownRequest
RequestKeys.OffsetCommitKey	commitOffset请求	OffsetCommitRequest
RequestKeys.OffsetFetchKey	consumer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。