1.概述

最近有同学在学习Kafka的网络通信这块内容时遇到一些疑问,关于网络模型和通信流程的相关内容,这里笔者将通过这篇博客为大家来剖析一下这部分内容。

2.内容

Kafka系统作为一个Message Queue,涉及到的网络通信主要包含以下两个方面:

  • Pull:Consumer从消息队列中拉取消息数据;
  • Push:Producer往消息队列中推送消息数据。

要实现高性能的网络通信,可以使用更加底层的TCP协议或者UDP协议来实现。Kafka在Producer、Broker、Consumer之间设计了一套基于TCP层的通信协议,这套协议完全是为了Kafka系统自身需求而定制实现的。

提示:
这里需要注意的是,由于UDP协议是一种不可靠的传输协议,所以Kafka系统采用TCP协议作为服务间的通信协议。

2.1 基本数据类型

通信协议中的基本数据类型分为以下几种:

  • 定长数据类型:例如,int8、int16、int32和、int64,对应到Java语言中,分别是byte、short、int和long
  • 可变数据类型:例如,Java语言中Map、List等
  • 数组:例如,Java语言中的int[]、String[]等

2.2 通信模型

Kafka系统采用的是Reactor多线程模型,即通过一个Acceptor线程处理所有的新连接,通过多个Processor线程对请求进行处理(比如解析协议、封装请求、、转发等)。

提示:
Reactor是一种事件模型,可以将请求提交到一个或者多个服务程序中进行处理。
当收到Client的请求后,Server处理程序使用多路分发策略,由一个非阻塞的线程来接收所有的请求,然后将这些请求转发到对应的工作线程中进行处理。

之后,在Kafka的版本迭代中,新增了一个Handler模块,它通过指定的线程数对请求进行处理。Handler和Processor之间通过一个Block Queue进行连接。如下图所示:

这里 Acceptor是一个继承于AbstractServerThread的线程类,Acceptor的主要目的是监听并且接收Client的请求,同时,建立数据传输通道(SocketChannel),然后通过轮询的方式交给一个Processor处理。其核心代码在Acceptor的run方法中,代码如下:

def run() {
serverChannel.register(nioSelector, SelectionKey.OP_ACCEPT)
startupComplete()
try {
var currentProcessor = 0
while (isRunning) {
try {
val ready = nioSelector.select(500)
if (ready > 0) {
val keys = nioSelector.selectedKeys()
val iter = keys.iterator()
while (iter.hasNext && isRunning) {
try {
val 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)
}
}
}
}
catch {
// We catch all the throwables to prevent the acceptor thread from exiting on exceptions due
// to a select operation on a specific channel or a bad request. We don't want
// the broker to stop responding to requests from other clients in these scenarios.
case e: ControlThrowable => throw e
case e: Throwable => error("Error occurred", e)
}
}
} finally {
debug("Closing server socket and selector.")
swallowError(serverChannel.close())
swallowError(nioSelector.close())
shutdownComplete()
}
}

这里还有一个块通道(BlockingChannel),用于连接Processor和Handler,其代码如下所示:

class BlockingChannel( val host: String,
val port: Int,
val readBufferSize: Int,
val writeBufferSize: Int,
val readTimeoutMs: Int ) extends Logging {
private var connected = false
private var channel: SocketChannel = null
private var readChannel: ReadableByteChannel = null
private var writeChannel: GatheringByteChannel = null
private val lock = new Object()
private val connectTimeoutMs = readTimeoutMs
private var connectionId: String = "" def connect() = lock synchronized {
if(!connected) {
try {
channel = SocketChannel.open()
if(readBufferSize > 0)
channel.socket.setReceiveBufferSize(readBufferSize)
if(writeBufferSize > 0)
channel.socket.setSendBufferSize(writeBufferSize)
channel.configureBlocking(true)
channel.socket.setSoTimeout(readTimeoutMs)
channel.socket.setKeepAlive(true)
channel.socket.setTcpNoDelay(true)
channel.socket.connect(new InetSocketAddress(host, port), connectTimeoutMs) writeChannel = channel
// Need to create a new ReadableByteChannel from input stream because SocketChannel doesn't implement read with timeout
// See: http://stackoverflow.com/questions/2866557/timeout-for-socketchannel-doesnt-work
readChannel = Channels.newChannel(channel.socket().getInputStream)
connected = true
val localHost = channel.socket.getLocalAddress.getHostAddress
val localPort = channel.socket.getLocalPort
val remoteHost = channel.socket.getInetAddress.getHostAddress
val remotePort = channel.socket.getPort
connectionId = localHost + ":" + localPort + "-" + remoteHost + ":" + remotePort
// settings may not match what we requested above
val msg = "Created socket with SO_TIMEOUT = %d (requested %d), SO_RCVBUF = %d (requested %d), SO_SNDBUF = %d (requested %d), connectTimeoutMs = %d."
debug(msg.format(channel.socket.getSoTimeout,
readTimeoutMs,
channel.socket.getReceiveBufferSize,
readBufferSize,
channel.socket.getSendBufferSize,
writeBufferSize,
connectTimeoutMs)) } catch {
case _: Throwable => disconnect()
}
}
} def disconnect() = lock synchronized {
if(channel != null) {
swallow(channel.close())
swallow(channel.socket.close())
channel = null
writeChannel = null
}
// closing the main socket channel *should* close the read channel
// but let's do it to be sure.
if(readChannel != null) {
swallow(readChannel.close())
readChannel = null
}
connected = false
} def isConnected = connected def send(request: RequestOrResponse): Long = {
if(!connected)
throw new ClosedChannelException() val send = new RequestOrResponseSend(connectionId, request)
send.writeCompletely(writeChannel)
} def receive(): NetworkReceive = {
if(!connected)
throw new ClosedChannelException() val response = readCompletely(readChannel)
response.payload().rewind() response
} private def readCompletely(channel: ReadableByteChannel): NetworkReceive = {
val response = new NetworkReceive
while (!response.complete())
response.readFromReadableChannel(channel)
response
} }

3.通信过程

Kafka系统的通信框架也是经过了不同的版本迭代的。例如,在Kafka老的版本中,以NIO作为网络通信的基础,通过将多个Socket连接注册到一个Selector上进行监听,只用一个线程就能管理多个连接,这极大的节省了多线程的资源开销。

在Kafka之后的新版本中,依然以NIO作为网络通信的基础,也使用了Reactor多线程模型,不同的是,新版本将具体的业务处理模块(Handler模块)独立出去了,并用单独的线程池进行控制。如下图所示:

通过上图,我们可以总结一下Kafka的通信流程:

  • Client向Server发送请求时,Acceptor负责接收TCP请求,连接成功后传递给Processor线程;
  • Processor线程接收到新的连接后,将其注册到自身的Selector中,并监听READ事件
  • 当Client在当前连接对象上写入数据时,会触发READ事件,根据TCP协议调用Handler进行处理
  • Handler处理完成后,可能会有返回值给Client,并将Handler返回的结果绑定Response端进行发送

通过总结和分析,我们可以知道Kafka新版中独立Handler模块,用这样以下几点优势:

  • 能够单独指定Handler的线程数,便于调优和管理
  • 防止一个过大的请求阻塞一个Processor线程
  • Request、Handler、Response之间都是通过队列来进行连接的,这样它们彼此之间不存在耦合现象,对提升Kafka系统的性能很有帮助

这里需要注意的是,在Kafka的网络通信中,RequestChannel为Processor线程与Handler线程之间数据交换提供了一个缓冲区,是通信中Request和Response缓存的地方。因此,其作用就是在通信中起到了一个数据缓冲队列的作用。Processor线程将读取到的请求添加至RequestChannel的全局队列(requestQueue)中,Handler线程从请求队列中获取并处理,处理完成后将Response添加至RequestChannel的响应队列(responseQueues)中,通过responseListeners唤醒对应的Processor线程,最后Processor线程从响应队列中取出后发送到Client。实现代码如下:

class RequestChannel(val numProcessors: Int, val queueSize: Int) extends KafkaMetricsGroup {
private var responseListeners: List[(Int) => Unit] = Nil
private val requestQueue = new ArrayBlockingQueue[RequestChannel.Request](queueSize)
private val responseQueues = new Array[BlockingQueue[RequestChannel.Response]](numProcessors)
for(i <- 0 until numProcessors)
responseQueues(i) = new LinkedBlockingQueue[RequestChannel.Response]() newGauge(
"RequestQueueSize",
new Gauge[Int] {
def value = requestQueue.size
}
) newGauge("ResponseQueueSize", new Gauge[Int]{
def value = responseQueues.foldLeft(0) {(total, q) => total + q.size()}
}) for (i <- 0 until numProcessors) {
newGauge("ResponseQueueSize",
new Gauge[Int] {
def value = responseQueues(i).size()
},
Map("processor" -> i.toString)
)
} /** Send a request to be handled, potentially blocking until there is room in the queue for the request */
def sendRequest(request: RequestChannel.Request) {
requestQueue.put(request)
} /** Send a response back to the socket server to be sent over the network */
def sendResponse(response: RequestChannel.Response) {
responseQueues(response.processor).put(response)
for(onResponse <- responseListeners)
onResponse(response.processor)
} /** No operation to take for the request, need to read more over the network */
def noOperation(processor: Int, request: RequestChannel.Request) {
responseQueues(processor).put(RequestChannel.Response(processor, request, null, RequestChannel.NoOpAction))
for(onResponse <- responseListeners)
onResponse(processor)
} /** Close the connection for the request */
def closeConnection(processor: Int, request: RequestChannel.Request) {
responseQueues(processor).put(RequestChannel.Response(processor, request, null, RequestChannel.CloseConnectionAction))
for(onResponse <- responseListeners)
onResponse(processor)
} /** Get the next request or block until specified time has elapsed */
def receiveRequest(timeout: Long): RequestChannel.Request =
requestQueue.poll(timeout, TimeUnit.MILLISECONDS) /** Get the next request or block until there is one */
def receiveRequest(): RequestChannel.Request =
requestQueue.take() /** Get a response for the given processor if there is one */
def receiveResponse(processor: Int): RequestChannel.Response = {
val response = responseQueues(processor).poll()
if (response != null)
response.request.responseDequeueTimeMs = Time.SYSTEM.milliseconds
response
} def addResponseListener(onResponse: Int => Unit) {
responseListeners ::= onResponse
} def shutdown() {
requestQueue.clear()
}
}

4.总结

通过认真阅读和分析Kafka的网络通信层代码,可以收获不少关于NIO的网络通信知识。通过对Kafka的源代码进行阅读和学习,这对大规模Kafka集群性能的调优和问题定位排查是很有帮助的。

5.结束语

这篇博客就和大家分享到这里,如果大家在研究学习的过程当中有什么问题,可以加群进行讨论或发送邮件给我,我会尽我所能为您解答,与君共勉!

另外,博主出书了《Kafka并不难学》和《Hadoop大数据挖掘从入门到进阶实战》,喜欢的朋友或同学, 可以在公告栏那里点击购买链接购买博主的书进行学习,在此感谢大家的支持。关注下面公众号,根据提示,可免费获取书籍的教学视频。

最新文章

  1. iOS 保持界面流畅的技巧 (转载)
  2. VIM 代码折叠
  3. 删除数据库数据,自增id清理
  4. ios 检测应用程序升级问题
  5. Objective-C中的封装、继承、多态、分类
  6. [Django 1.5] jQuery/Ajax 在Django使用 ,如何更新模板里里变量
  7. .NET中使用Redis(二)
  8. 网络通信 --&gt; select()用法
  9. sql自建用户
  10. ng2 配置端口号
  11. tomcat优化之安装并配置apr库
  12. 顺序栈的基本操作中Push压入后的- S.top = S.base + S.stacksize; 作用
  13. C#中的Action和Func和Predicate
  14. Caused by: java.io.FileNotFoundException: class path resource [spring/springmvc.xml] cannot be opene
  15. java线程实现的四种方式
  16. 关于HSTS
  17. SqlDataSource.FilterExpression Property
  18. delphi连接mysql不用添加DSN(mysql connector odbc 5.1版)
  19. ML(4.1): R C4.5
  20. Maven 使用 Nexus 内部库 代理

热门文章

  1. 「学习笔记」 FHQ Treap
  2. redis 为什么是单线程,为什么速度快。
  3. java反射-- Field 用法实践
  4. 【官方文档翻译】SpringBootActuator生产特性
  5. beetlex网关之聚合和url请求过虑
  6. APICloud发布低代码开发平台
  7. P1828 香甜的黄油 Sweet Butter 最短路 寻找一个点使得所有点到它的距离之和最小
  8. 《C# 爬虫 破境之道》:第二境 爬虫应用 — 第三节:处理压缩数据
  9. 【JavaScript 基础知识】一篇关于 JavaScript 一些知识点的总结
  10. Android Linker简介