【Kafka源码】处理请求

目录

• 一、KafkaRequestHandlerPool
• 二、KafkaApis.handle
  • 2.1 ApiKeys枚举类
• 三、Request数据结构
  • 3.1 requestId
  • 3.2 header
  • 3.3 body

---

在KafkaServer中的入口在：

apis = new KafkaApis(socketServer.requestChannel, replicaManager, groupCoordinator,
        kafkaController, zkUtils, config.brokerId, config, metadataCache, metrics, authorizer)
requestHandlerPool = new KafkaRequestHandlerPool(config.brokerId, socketServer.requestChannel, apis, config.numIoThreads)

首先根据相关参数，实例化KafkaApis，然后实例化KafkaRequestHandlerPool。下面我们首先看下KafkaRequestHandlerPool。

一、KafkaRequestHandlerPool

class KafkaRequestHandlerPool(val brokerId: Int,
                              val requestChannel: RequestChannel,
                              val apis: KafkaApis,
                              numThreads: Int) extends Logging with KafkaMetricsGroup {

  /* a meter to track the average free capacity of the request handlers */
  private val aggregateIdleMeter = newMeter("RequestHandlerAvgIdlePercent", "percent", TimeUnit.NANOSECONDS)

  this.logIdent = "[Kafka Request Handler on Broker " + brokerId + "], "
  val threads = new Array[Thread](numThreads)
  val runnables = new Array[KafkaRequestHandler](numThreads)
  for(i <- 0 until numThreads) {
    runnables(i) = new KafkaRequestHandler(i, brokerId, aggregateIdleMeter, numThreads, requestChannel, apis)
    threads(i) = Utils.daemonThread("kafka-request-handler-" + i, runnables(i))
    threads(i).start()
  }
//...
}

主要是启动了numThreads个数的线程，然后线程中执行的内容是KafkaRequestHandler。

/**
 * 响应kafka请求的线程
 */
class KafkaRequestHandler(id: Int,
                          brokerId: Int,
                          val aggregateIdleMeter: Meter,
                          val totalHandlerThreads: Int,
                          val requestChannel: RequestChannel,
                          apis: KafkaApis) extends Runnable with Logging {
  this.logIdent = "[Kafka Request Handler " + id + " on Broker " + brokerId + "], "

  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)//这边是如何处理请求的重点
      } catch {
        case e: Throwable => error("Exception when handling request", e)
      }
    }
  }
    //shutdown。。
}

在run方法中，我们可以看到，主要处理消息的地方是api.handle(req)。下面我们主要看下这块的内容。

二、KafkaApis.handle

直接看代码：

/**
 * Top-level method that handles all requests and multiplexes to the right api
 */
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))
      ApiKeys.forId(request.requestId) match {//根据requestId，调用不同的方法，处理不同的请求
        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 requestId => throw new KafkaException("Unknown api code " + requestId)
      }
    } catch {
      case e: Throwable =>
        if (request.requestObj != null) {
          request.requestObj.handleError(e, requestChannel, request)
          error("Error when handling request %s".format(request.requestObj), e)
        } else {
          val response = request.body.getErrorResponse(request.header.apiVersion, e)
          val respHeader = new ResponseHeader(request.header.correlationId)

          /* If request doesn't have a default error response, we just close the connection.
             For example, when produce request has acks set to 0 */
          if (response == null)
            requestChannel.closeConnection(request.processor, request)
          else
            requestChannel.sendResponse(new Response(request, new ResponseSend(request.connectionId, respHeader, response)))

          error("Error when handling request %s".format(request.body), e)
     }
  } finally
    request.apiLocalCompleteTimeMs = SystemTime.milliseconds
}

2.1 ApiKeys枚举类

PRODUCE(0, "Produce"),//生产者消息
FETCH(1, "Fetch"),//消费者获取消息
LIST_OFFSETS(2, "Offsets"),//获取偏移量
METADATA(3, "Metadata"),//获取topic源数据
LEADER_AND_ISR(4, "LeaderAndIsr"),
STOP_REPLICA(5, "StopReplica"),//停止副本复制
UPDATE_METADATA_KEY(6, "UpdateMetadata"),//更新源数据
CONTROLLED_SHUTDOWN_KEY(7, "ControlledShutdown"),//controller停止
OFFSET_COMMIT(8, "OffsetCommit"),//提交offset
OFFSET_FETCH(9, "OffsetFetch"),//获取offset
GROUP_COORDINATOR(10, "GroupCoordinator"),//组协调
JOIN_GROUP(11, "JoinGroup"),//加入组
HEARTBEAT(12, "Heartbeat"),//心跳
LEAVE_GROUP(13, "LeaveGroup"),//离开组
SYNC_GROUP(14, "SyncGroup"),//同步组
DESCRIBE_GROUPS(15, "DescribeGroups"),//描述组
LIST_GROUPS(16, "ListGroups"),//列出组
SASL_HANDSHAKE(17, "SaslHandshake"),//加密握手
API_VERSIONS(18, "ApiVersions");//版本

这块比较简单，主要的是Request的数据结构，还有后续的处理方法。下面我们逐步来分析。

三、Request数据结构

所有的请求，最终都会变成这个RequestChannel.Request。所以我们先看下这个Request。

case class Request(processor: Int, connectionId: String, session: Session, private var buffer: ByteBuffer, startTimeMs: Long, securityProtocol: SecurityProtocol) {
    //...
    val requestId = buffer.getShort()

    private val keyToNameAndDeserializerMap: Map[Short, (ByteBuffer) => RequestOrResponse]=
      Map(ApiKeys.FETCH.id -> FetchRequest.readFrom,
        ApiKeys.CONTROLLED_SHUTDOWN_KEY.id -> ControlledShutdownRequest.readFrom
      )

    val requestObj =
      keyToNameAndDeserializerMap.get(requestId).map(readFrom => readFrom(buffer)).orNull

    val header: RequestHeader =
      if (requestObj == null) {
        buffer.rewind
        try RequestHeader.parse(buffer)
        catch {
          case ex: Throwable =>
            throw new InvalidRequestException(s"Error parsing request header. Our best guess of the apiKey is: $requestId", ex)
        }
      } else
        null
    val body: AbstractRequest =
      if (requestObj == null)
        try {
          // For unsupported version of ApiVersionsRequest, create a dummy request to enable an error response to be returned later
          if (header.apiKey == ApiKeys.API_VERSIONS.id && !Protocol.apiVersionSupported(header.apiKey, header.apiVersion))
            new ApiVersionsRequest
          else
            AbstractRequest.getRequest(header.apiKey, header.apiVersion, buffer)
        } catch {
          case ex: Throwable =>
            throw new InvalidRequestException(s"Error getting request for apiKey: ${header.apiKey} and apiVersion: ${header.apiVersion}", ex)
        }
      else
        null

    buffer = null
    private val requestLogger = Logger.getLogger("kafka.request.logger")

    def requestDesc(details: Boolean): String = {
      if (requestObj != null)
        requestObj.describe(details)
      else
        header.toString + " -- " + body.toString
    }
    //...
}

主要有几个部分，

• 首先是requestId，是一个short类型的值。
• 然后是header，即消息头，是一个RequestHeader
• 最后是body，是消息的内容，类型为AbstractRequest

3.1 requestId

这个requestId表示的是api的类型，KafkaApis需要根据这个requestId，来判断调用哪个方法处理消息。

3.2 header

我们看下RequestHeader的结构。

private final short apiKey;
private final short apiVersion;
private final String clientId;
private final int correlationId;

主要是四个变量，apiKey，APIVersion，clientId，correlationId。

3.3 body

消息体，对应的类为AbstractRequest。主要的内容是根据版本号和apiKey来解析出消息的具体内容。

public static AbstractRequest getRequest(int requestId, int versionId, ByteBuffer buffer) {
    ApiKeys apiKey = ApiKeys.forId(requestId);
    switch (apiKey) {
        case PRODUCE:
            return ProduceRequest.parse(buffer, versionId);
        case FETCH:
            return FetchRequest.parse(buffer, versionId);
        case LIST_OFFSETS:
            return ListOffsetRequest.parse(buffer, versionId);
        case METADATA:
            return MetadataRequest.parse(buffer, versionId);
        case OFFSET_COMMIT:
            return OffsetCommitRequest.parse(buffer, versionId);
        case OFFSET_FETCH:
            return OffsetFetchRequest.parse(buffer, versionId);
        case GROUP_COORDINATOR:
            return GroupCoordinatorRequest.parse(buffer, versionId);
        case JOIN_GROUP:
            return JoinGroupRequest.parse(buffer, versionId);
        case HEARTBEAT:
            return HeartbeatRequest.parse(buffer, versionId);
        case LEAVE_GROUP:
            return LeaveGroupRequest.parse(buffer, versionId);
        case SYNC_GROUP:
            return SyncGroupRequest.parse(buffer, versionId);
        case STOP_REPLICA:
            return StopReplicaRequest.parse(buffer, versionId);
        case CONTROLLED_SHUTDOWN_KEY:
            return ControlledShutdownRequest.parse(buffer, versionId);
        case UPDATE_METADATA_KEY:
            return UpdateMetadataRequest.parse(buffer, versionId);
        case LEADER_AND_ISR:
            return LeaderAndIsrRequest.parse(buffer, versionId);
        case DESCRIBE_GROUPS:
                return DescribeGroupsRequest.parse(buffer, versionId);
        case LIST_GROUPS:
            return ListGroupsRequest.parse(buffer, versionId);
        case SASL_HANDSHAKE:
            return SaslHandshakeRequest.parse(buffer, versionId);
        case API_VERSIONS:
            return ApiVersionsRequest.parse(buffer, versionId);
        default:
            throw new AssertionError(String.format("ApiKey %s is not currently handled in `getRequest`, the " +
                    "code should be updated to do so.", apiKey));
    }
}

这块的请求类型很多，想要了解具体结构的，可以到每个类中具体看。