本章，我将对网络层中的最后两个组件 RequestChannel 和 RequestHandler 进行讲解。严格来说， RequestChannel 和 RequestHandler 并不属于网络层（Network Layer），当然，它们也不属于API层（API Layer）。
我将RequestChannel和RequestHandler定位为Kafka Server端 网络层与API层之间交互的中间件 。本章，我会分析RequestChannel和RequestHandler的整体架构，以及它们对请求/响应的底层处理细节。
一、RequestChannel
首先，我们来看RequestChannel。RequestChannel会缓存Processor线程发送过来的Request请求，同时也接受API层的Response响应。它的内部有两个重要的队列：
- 一个请求缓存队列requestQueue；
- N个响应缓存队列responseQueues。
requestQueue本质是一个ArrayBlockingQueue， 所有Processor线程共享一个 ，每个Processor线程都会将请求封装成Request对象并入到这个队列里面。
responseQueue本质是一个LinkedBlockingQueue， 每个Processor线程独立拥有一个 ，API层会将响应封装成Response对象并入队。
1.1 内部结构
整体结构大致如下图：
接受Processor线程发送过来的Request请求：
// RequestChannel.scala
def sendRequest(request: RequestChannel.Request) {
requestQueue.put(request)
}
获取Response对象，返回给Processor线程处理：
// RequestChannel.scala
def receiveResponse(processor: Int): RequestChannel.Response = {
val response = responseQueues(processor).poll()
if (response != null)
response.request.responseDequeueTimeMs = Time.SYSTEM.milliseconds
response
}
接受API层的响应结果，并添加到响应队列中：
// RequestChannel.scala
def sendResponse(response: RequestChannel.Response) {
responseQueues(response.processor).put(response)
for(onResponse <- responseListeners)
onResponse(response.processor)
}
二、RequestHandler
我们再来看另一个组件—— RequestHandler ，它是在Kafka Server启动时创建的，内部委托了对API层的调用：
// KafkaServer.scala
var apis: KafkaApis = null
var requestHandlerPool: KafkaRequestHandlerPool = null
def startup() {
try {
//...
if (canStartup) {
// 14.启动请求处理线程池
apis = new KafkaApis(socketServer.requestChannel, replicaManager, adminManager,
groupCoordinator,kafkaController, zkUtils, config.brokerId,
config, metadataCache, metrics, authorizer, quotaManagers,
clusterId, time)
requestHandlerPool = new KafkaRequestHandlerPool(config.brokerId,
socketServer.requestChannel,
apis, time, config.numIoThreads)
}
}
catch {
case e: Throwable =>
fatal("Fatal error during KafkaServer startup. Prepare to shutdown", e)
isStartingUp.set(false)
shutdown()
throw e
}
}
整个处理流程可以用下面这张图表述：
2.1 KafkaRequestHandlerPool
我们来看下KafkaRequestHandlerPool的内部，它封装了一堆线程，可以看成是一个线程池，这些线程启动后，就不断执行 KafkaRequestHandler 任务：
// KafkaRequestHandlerPool.scala
class KafkaRequestHandlerPool(val brokerId: Int,
val requestChannel: RequestChannel,
val apis: KafkaApis,
time: Time,
numThreads: Int) extends Logging with KafkaMetricsGroup {
// 工作线程，默认8个
val threads = new Array[Thread](numThreads)
// KafkaRequestHandler任务
val runnables = new Array[KafkaRequestHandler](numThreads)
for(i <- 0 until numThreads) {
runnables(i) = new KafkaRequestHandler(i, brokerId, aggregateIdleMeter, numThreads, requestChannel, apis, time)
threads(i) = Utils.daemonThread("kafka-request-handler-" + i, runnables(i))
// 启动任务
threads(i).start()
}
}
2.2 KafkaRequestHandler
KafkaRequestHandler本质是一个Runnable任务，它会不断从RequestChannel的请求队列中获取Request对象，然后交给Kafak API层进行处理：
// KafkaRequestHandler.scala
def run() {
while(true) {
try {
var req : RequestChannel.Request = null
while (req == null) {
val startSelectTime = time.nanoseconds
// 从RequestChannel的请求队列中获取Request
req = requestChannel.receiveRequest(300)
}
//...
// 将请求交给API层处理
apis.handle(req)
} catch {
case e: Throwable => error("Exception when handling request", e)
}
}
}
这里我额外展开下，我们看下Kafka API层的内部，其实就是根据Request请求的不同类型进行处理：
// KafkaApis.scala
def handle(request: RequestChannel.Request) {
try {
trace("Handling request:%s from connection %s;securityProtocol:%s,principal:%s".
format(request.requestDesc(true), request.connectionId, request.securityProtocol, request.session.principal))
// 根据Request请求的不同类型进行处理
ApiKeys.forId(request.requestId) match {
case ApiKeys.PRODUCE => handleProducerRequest(request)
case ApiKeys.FETCH => handleFetchRequest(request)
case ApiKeys.LIST_OFFSETS => handleOffsetRequest(request)
case ApiKeys.METADATA => handleTopicMetadataRequest(request)
case ApiKeys.LEADER_AND_ISR => handleLeaderAndIsrRequest(request)
case ApiKeys.STOP_REPLICA => handleStopReplicaRequest(request)
case ApiKeys.UPDATE_METADATA_KEY => handleUpdateMetadataRequest(request)
case ApiKeys.CONTROLLED_SHUTDOWN_KEY => handleControlledShutdownRequest(request)
case ApiKeys.OFFSET_COMMIT => handleOffsetCommitRequest(request)
case ApiKeys.OFFSET_FETCH => handleOffsetFetchRequest(request)
case ApiKeys.GROUP_COORDINATOR => handleGroupCoordinatorRequest(request)
case ApiKeys.JOIN_GROUP => handleJoinGroupRequest(request)
case ApiKeys.HEARTBEAT => handleHeartbeatRequest(request)
case ApiKeys.LEAVE_GROUP => handleLeaveGroupRequest(request)
case ApiKeys.SYNC_GROUP => handleSyncGroupRequest(request)
case ApiKeys.DESCRIBE_GROUPS => handleDescribeGroupRequest(request)
case ApiKeys.LIST_GROUPS => handleListGroupsRequest(request)
case ApiKeys.SASL_HANDSHAKE => handleSaslHandshakeRequest(request)
case ApiKeys.API_VERSIONS => handleApiVersionsRequest(request)
case ApiKeys.CREATE_TOPICS => handleCreateTopicsRequest(request)
case ApiKeys.DELETE_TOPICS => handleDeleteTopicsRequest(request)
case requestId => throw new KafkaException("Unknown api code " + requestId)
}
} catch {
//...
} finally
request.apiLocalCompleteTimeMs = time.milliseconds
}
我们重点看下handleProducerRequest，它对普通Producer生产者的请求进行处理，处理完成后会触发以下回调函数。可以看到， 最终处理完的响应结果会被封装程Response对象，交给RequestChannel处理：
def produceResponseCallback(delayTimeMs: Int) {
// 如果该请求不需要ACK确认
if (produceRequest.acks == 0) {
//...
}
// 如果该请求需要ACK确认
else {
val respBody = request.header.apiVersion match {
case 0 => new ProduceResponse(mergedResponseStatus.asJava)
case version@(1 | 2) => new ProduceResponse(mergedResponseStatus.asJava, delayTimeMs, version)
case version => throw new IllegalArgumentException(s"Version `$version` of ProduceRequest is not handled. Code must be updated.")
}
// 将响应入队到RequestChannel
requestChannel.sendResponse(new RequestChannel.Response(request, respBody))
}
}
三、总结
本章，我对网络层的最后两个组件RequestChannel和RequestHandler进行了讲解。它们的整体设计思路还是比较清晰简单的，本质就是利用不用的内存队列和线程池对请求/响应进行处理，提升整体吞吐量，这种思路在实际的生产应用中运用非常多，我在《分布式系统从理论到实战系列》专栏的实战篇中也讲解过这类运用，感兴趣的读者可以去看一看。
