一、简介
- 版本：1.1.1
- API层，是一个Facade模式，封装了Kafka所有功能对外提供服务，通过请求中的ApiKeys，进行请求分发，调用对应的API进行处理
- API层，创建了个线程用于进行逻辑处理、IO操作，所有的API行为均线程中完成
二、整体架构
2.1 核心逻辑
相对于网络层，API层的实现相当简单，只是对底层实现的封装，得益于此，从API层几乎就能了解到Kafka broker所能提供的全部功能
- 启动num.io.threads个RequestHandler
- 每个RequestHandler都从全局requestQueue中poll Request
- 根据Request的ApiKeys调用对应的handle*Api进行实际的业务逻辑处理
2.2 核心类、方法介绍
KafkaRequestHandler
|-- run()
KafkaApis
|-- handle() // 所有请求的入口，根据ApiKeys分发请求
三、核心流程分析
3.1 启动流程
启动流程前半部分和网络层一致，这里不再赘述
// KafkaServer.scala
def startup() {
// 初始化api层
apis = new KafkaApis(socketServer.requestChannel, replicaManager, adminManager, groupCoordinator, transactionCoordinator,
kafkaController, zkClient, config.brokerId, config, metadataCache, metrics, authorizer, quotaManagers,
fetchManager, brokerTopicStats, clusterId, time, tokenManager)
// 初始化IO线程池
requestHandlerPool = new KafkaRequestHandlerPool(config.brokerId, socketServer.requestChannel, apis, time,
config.numIoThreads)
}
// KafkaRequestHandler.scala
class KafkaRequestHandlerPool(val brokerId: Int,
val requestChannel: RequestChannel,
val apis: KafkaApis,
time: Time,
numThreads: Int) extends Logging with KafkaMetricsGroup {
val runnables = new mutable.ArrayBuffer[KafkaRequestHandler](numThreads)
for (i <- 0 until numThreads) {
createHandler(i)
}
def createHandler(id: Int): Unit = synchronized {
runnables += new KafkaRequestHandler(id, brokerId, aggregateIdleMeter, threadPoolSize, requestChannel, apis, time)
KafkaThread.daemon("kafka-request-handler-" + id, runnables(id)).start()
}
}
实际就是实例化API层的入口，再启动num.io.threads个RequestHandler线程，用于实际处理请求，值得注意的是这里的线程池，其实不是真正意义上的线程池，线程数目是固定的，只有通过动态参数改变线程池大小时，才会重新调整线程数目
def resizeThreadPool(newSize: Int): Unit = synchronized {
val currentSize = threadPoolSize.get
info(s"Resizing request handler thread pool size from $currentSize to $newSize")
if (newSize > currentSize) {
// 补上差额的线程
for (i <- currentSize until newSize) {
createHandler(i)
}
} else if (newSize < currentSize) {
for (i <- 1 to (currentSize - newSize)) {
// 关闭部分线程直到达到目标值
runnables.remove(currentSize - i).stop()
}
}
threadPoolSize.set(newSize)
}
3.2 请求分发流程
RequestHandler线程启动后，每个线程都一直从网络层获取Request再交给KafkaApis进行请求分发
// KafkaRequestHandler.scala
def run() {
while (!stopped) {
// 从全局的阻塞队列里面拉取请求（网络层处理完并反序列化后转换成request）
val req = requestChannel.receiveRequest(300)
req match {
// 关闭命令
case RequestChannel.ShutdownRequest =>
shutdownComplete.countDown()
return
// 正常请求
case request: RequestChannel.Request =>
// 请求路由分发
apis.handle(request)
case null => // continue
}
}
shutdownComplete.countDown()
}
// RequestChannel.scala
private val requestQueue = new ArrayBlockingQueue[BaseRequest](queueSize)
def receiveRequest(timeout: Long): RequestChannel.BaseRequest =
requestQueue.poll(timeout, TimeUnit.MILLISECONDS)
// KafkaApis.scala
def handle(request: RequestChannel.Request) {
// 只是根据request中的apiKey分发请求，实际处理逻辑由对应的handle*方法实现
request.header.apiKey match {
case ApiKeys.PRODUCE => handleProduceRequest(request)
case ApiKeys.FETCH => handleFetchRequest(request)
case ApiKeys.LIST_OFFSETS => handleListOffsetRequest(request)
case ApiKeys.METADATA => handleTopicMetadataRequest(request)
case ApiKeys.LEADER_AND_ISR => handleLeaderAndIsrRequest(request)
case ApiKeys.STOP_REPLICA => handleStopReplicaRequest(request)
case ApiKeys.UPDATE_METADATA => handleUpdateMetadataRequest(request)
case ApiKeys.CONTROLLED_SHUTDOWN => handleControlledShutdownRequest(request)
case ApiKeys.OFFSET_COMMIT => handleOffsetCommitRequest(request)
case ApiKeys.OFFSET_FETCH => handleOffsetFetchRequest(request)
case ApiKeys.FIND_COORDINATOR => handleFindCoordinatorRequest(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 ApiKeys.DELETE_RECORDS => handleDeleteRecordsRequest(request)
case ApiKeys.INIT_PRODUCER_ID => handleInitProducerIdRequest(request)
case ApiKeys.OFFSET_FOR_LEADER_EPOCH => handleOffsetForLeaderEpochRequest(request)
case ApiKeys.ADD_PARTITIONS_TO_TXN => handleAddPartitionToTxnRequest(request)
case ApiKeys.ADD_OFFSETS_TO_TXN => handleAddOffsetsToTxnRequest(request)
case ApiKeys.END_TXN => handleEndTxnRequest(request)
case ApiKeys.WRITE_TXN_MARKERS => handleWriteTxnMarkersRequest(request)
case ApiKeys.TXN_OFFSET_COMMIT => handleTxnOffsetCommitRequest(request)
case ApiKeys.DESCRIBE_ACLS => handleDescribeAcls(request)
case ApiKeys.CREATE_ACLS => handleCreateAcls(request)
case ApiKeys.DELETE_ACLS => handleDeleteAcls(request)
case ApiKeys.ALTER_CONFIGS => handleAlterConfigsRequest(request)
case ApiKeys.DESCRIBE_CONFIGS => handleDescribeConfigsRequest(request)
case ApiKeys.ALTER_REPLICA_LOG_DIRS => handleAlterReplicaLogDirsRequest(request)
case ApiKeys.DESCRIBE_LOG_DIRS => handleDescribeLogDirsRequest(request)
case ApiKeys.SASL_AUTHENTICATE => handleSaslAuthenticateRequest(request)
case ApiKeys.CREATE_PARTITIONS => handleCreatePartitionsRequest(request)
case ApiKeys.CREATE_DELEGATION_TOKEN => handleCreateTokenRequest(request)
case ApiKeys.RENEW_DELEGATION_TOKEN => handleRenewTokenRequest(request)
case ApiKeys.EXPIRE_DELEGATION_TOKEN => handleExpireTokenRequest(request)
case ApiKeys.DESCRIBE_DELEGATION_TOKEN => handleDescribeTokensRequest(request)
case ApiKeys.DELETE_GROUPS => handleDeleteGroupsRequest(request)
}
}
- RequestHandler从全局阻塞队列获取网络层组装完的Request，并调用KafkaApi的handle方法
- handle方法类似web开发中的dispatch，根据ApiKeys调用对应的handle*执行实际业务逻辑
可以看到API层只是对执行线程、根据ApiKeys进行请求路由的封装。实际逻辑由KafkaApis类中的handle*方法实现，而其中最核心的方法为PRODUCE与FETCH请求，分别对应消息的生产与消费，下面的文章将专门针对这两种API进行源码分析