内容简介:本套系列博客从真实商业环境抽取案例进行总结和分享,并给出Spark源码解读及商业实战指导,请持续关注本套博客。版权声明:本套Spark源码解读及商业实战归作者(秦凯新)所有,禁止转载,欢迎学习。TransportContext 内部握有创建TransPortClient和TransPortServer的方法实现,但却属于最底层的RPC通讯设施。为什么呢?因为成员变量RPCHandler是抽象的,并没有具体的消息处理,而且TransportContext功能也在于创建TransPortClient客户端和T
本套系列博客从真实商业环境抽取案例进行总结和分享,并给出Spark源码解读及商业实战指导,请持续关注本套博客。版权声明:本套Spark源码解读及商业实战归作者(秦凯新)所有,禁止转载,欢迎学习。
Spark商业环境实战及调优进阶系列
- Spark商业环境实战-Spark内置框架rpc通讯机制及RpcEnv基础设施
- Spark商业环境实战-Spark事件监听总线流程分析
- Spark商业环境实战-Spark存储体系底层架构剖析
- Spark商业环境实战-Spark底层多个MessageLoop循环线程执行流程分析
- Spark商业环境实战-Spark二级调度系统Stage划分算法和最佳任务调度细节剖析
1. Spark 内置框架rpc通讯机制
TransportContext 内部握有创建TransPortClient和TransPortServer的方法实现,但却属于最底层的RPC通讯设施。为什么呢?
因为成员变量RPCHandler是抽象的,并没有具体的消息处理,而且TransportContext功能也在于创建TransPortClient客户端和TransPortServer服务端。具体解释如下:
Contains the context to create a {@link TransportServer}, {@link TransportClientFactory}, and to setup Netty Channel pipelines with a {@link org.apache.spark.network.server.TransportChannelHandler}. 复制代码
所以TransportContext只能为最底层的通讯基础。上层为NettyRPCEnv高层封装,并持有TransportContext引用,在TransportContext中传入NettyRpcHandler实体,来实现netty通讯回调Handler处理。TransportContext代码片段如下:
/* The TransportServer and TransportClientFactory both create a TransportChannelHandler for each * channel. As each TransportChannelHandler contains a TransportClient, this enables server * processes to send messages back to the client on an existing channel. */ public class TransportContext { private final Logger logger = LoggerFactory.getLogger(TransportContext.class); private final TransportConf conf; private final RpcHandler rpcHandler; private final boolean closeIdleConnections; private final MessageEncoder encoder; private final MessageDecoder decoder; public TransportContext(TransportConf conf, RpcHandler rpcHandler) { this(conf, rpcHandler, false); } 复制代码
1.1 客户端和服务端统一的消息接收处理器 TransportChannelHandlerer
TransportClient 和TransportServer 在配置Netty的pipeLine的handler处理器时,均采用TransportChannelHandler, 来做统一的消息receive处理。为什么呢?在于统一消息处理入口,TransportChannelHandlerer根据消息类型执行不同的处理,代码片段如下:
public void channelRead(ChannelHandlerContext ctx, Object request) throws Exception { if (request instanceof RequestMessage) { requestHandler.handle((RequestMessage) request); } else if (request instanceof ResponseMessage) { responseHandler.handle((ResponseMessage) request); } else { ctx.fireChannelRead(request); } 复制代码
}
TransportContext初始化Pipeline的代码片段:
public TransportChannelHandler initializePipeline( SocketChannel channel, RpcHandler channelRpcHandler) { try { TransportChannelHandler channelHandler = createChannelHandler(channel, channelRpcHandler); channel.pipeline() .addLast("encoder", ENCODER) .addLast(TransportFrameDecoder.HANDLER_NAME, NettyUtils.createFrameDecoder()) .addLast("decoder", DECODER) .addLast("idleStateHandler", new IdleStateHandler(0, 0, conf.connectionTimeoutMs() / 1000)) .addLast("handler", channelHandler); return channelHandler; } catch (RuntimeException e) { logger.error("Error while initializing Netty pipeline", e); throw e; } 复制代码
客户端和服务端统一的消息接收处理器 TransportChannelHandlerer 是这个函数:createChannelHandler(channel, channelRpcHandler)实现的,也即统一了这个netty的消息接受处理,代码片段如下:
/** * Creates the server- and client-side handler which is used to handle both RequestMessages and * ResponseMessages. The channel is expected to have been successfully created, though certain * properties (such as the remoteAddress()) may not be available yet. */ private TransportChannelHandler createChannelHandler(Channel channel, RpcHandler rpcHandler) { TransportResponseHandler responseHandler = new TransportResponseHandler(channel); TransportClient client = new TransportClient(channel, responseHandler); TransportRequestHandler requestHandler = new TransportRequestHandler(channel, client, rpcHandler, conf.maxChunksBeingTransferred()); return new TransportChannelHandler(client, responseHandler, requestHandler, conf.connectionTimeoutMs(), closeIdleConnections); } 复制代码
不过transportClient对应的是TransportResponseHander,TransportServer对应的的是TransportRequestHander。 在进行消息处理时,首先会经过TransportChannelHandler根据消息类型进行处理器选择,分别进行netty的消息生命周期管理:
- exceptionCaught
- channelActive
- channelInactive
- channelRead
- userEventTriggered
1.2 transportClient对应的是ResponseMessage
客户端一旦发送消息(均为Request消息),就会在
private final Map<Long, RpcResponseCallback> outstandingRpcs;
private final Map<StreamChunkId, ChunkReceivedCallback> outstandingFetches
中缓存,用于回调处理。
1.3 transportServer对应的是RequestMessage
服务端接收消息类型(均为Request消息)
- ChunkFetchRequest
- RpcRequest
- OneWayMessage
- StremRequest
服务端响应类型(均为Response消息):
- ChunkFetchSucess
- ChunkFetchFailure
- RpcResponse
- RpcFailure
2. Spark RpcEnv基础设施
2.1 上层建筑NettyRPCEnv
上层建筑NettyRPCEnv,持有TransportContext引用,在TransportContext中传入NettyRpcHandler实体,来实现netty通讯回调Handler处理
- Dispatcher
- TransportContext
- TransPortClientFactroy
- TransportServer
- TransportConf
2.2 RpcEndPoint 与 RPCEndPointRef 端点
- RpcEndPoint 为服务端
- RPCEndPointRef 为客户端
2.2 Dispacher 与 Inbox 与 Outbox
- 一个端点对应一个Dispacher,一个Inbox , 多个OutBox
- RpcEndpoint:RPC端点 ,Spark针对于每个节点(Client/Master/Worker)都称之一个Rpc端点 ,且都实现RpcEndpoint接口,内部根据不同端点的需求,设计不同的消息和不同的业务处理,如果需要发送(询问)则调用Dispatcher
- RpcEnv:RPC上下文环境,每个Rpc端点运行时依赖的上下文环境称之为RpcEnv
- Dispatcher:消息分发器,针对于RPC端点需要发送消息或者从远程RPC接收到的消息,分发至对应的指令收件箱/发件箱。如果指令接收方是自己存入收件箱,如果指令接收方为非自身端点,则放入发件箱
- Inbox:指令消息收件箱,一个本地端点对应一个收件箱,Dispatcher在每次向Inbox存入消息时,都将对应EndpointData加入内部待Receiver Queue中,另外Dispatcher创建时会启动一个单独线程进行轮询Receiver Queue,进行收件箱消息消费
- OutBox:指令消息发件箱,一个远程端点对应一个发件箱,当消息放入Outbox后,紧接着将消息通过TransportClient发送出去。消息放入发件箱以及发送过程是在同一个线程中进行,这样做的主要原因是远程消息分为RpcOutboxMessage, OneWayOutboxMessage两种消息,而针对于需要应答的消息直接发送且需要得到结果进行处理
- TransportClient:Netty通信客户端,根据OutBox消息的receiver信息,请求对应远程TransportServer
- TransportServer:Netty通信服务端,一个RPC端点一个TransportServer,接受远程消息后调用Dispatcher分发消息至对应收发件箱
Spark在Endpoint的设计上核心设计即为Inbox与Outbox,其中Inbox核心要点为:
- 内部的处理流程拆分为多个消息指令(InboxMessage)存放入Inbox
- 当Dispatcher启动最后,会启动一个名为【dispatcher-event-loop】的线程扫描Inbox待处理InboxMessage,并调用Endpoint根据InboxMessage类型做相应处理
- 当Dispatcher启动最后,默认会向Inbox存入OnStart类型的InboxMessage,Endpoint在根据OnStart指令做相关的额外启动工作,端点启动后所有的工作都是对OnStart指令处理衍生出来的,因此可以说OnStart指令是相互通信的源头。
-
注意: 一个端点对应一个Dispacher,一个Inbox , 多个OutBox,可以看到 inbox在Dispacher 中且在EndPointData内部:
private final RpcHandler rpcHandler; /** * A message dispatcher, responsible for routing RPC messages to the appropriate endpoint(s). */ private[netty] class Dispatcher(nettyEnv: NettyRpcEnv) extends Logging { private class EndpointData( val name: String, val endpoint: RpcEndpoint, val ref: NettyRpcEndpointRef) { val inbox = new Inbox(ref, endpoint) } private val endpoints = new ConcurrentHashMap[String, EndpointData] private val endpointRefs = new ConcurrentHashMap[RpcEndpoint, RpcEndpointRef] // Track the receivers whose inboxes may contain messages. private val receivers = new LinkedBlockingQueue[EndpointData] 复制代码
-
注意: 一个端点对应一个Dispacher,一个Inbox , 多个OutBox,可以看到 OutBox在NettyRpcEnv内部:
private[netty] class NettyRpcEnv( val conf: SparkConf, javaSerializerInstance: JavaSerializerInstance, host: String, securityManager: SecurityManager) extends RpcEnv(conf) with Logging { private val dispatcher: Dispatcher = new Dispatcher(this) private val streamManager = new NettyStreamManager(this) private val transportContext = new TransportContext(transportConf, new NettyRpcHandler(dispatcher, this, streamManager)) /** * A map for [[RpcAddress]] and [[Outbox]]. When we are connecting to a remote [[RpcAddress]], * we just put messages to its [[Outbox]] to implement a non-blocking `send` method. */ private val outboxes = new ConcurrentHashMap[RpcAddress, Outbox]() 复制代码
2.3 Dispacher 与 Inbox 与 Outbox
Dispatcher的代码片段中,包含了核心的消息发送代码逻辑,意思是:向服务端发送一条消息,也即同时放进Dispatcher中的receiverrs中,也放进inbox的messages中。这个高层封装,如Master和Worker端点发送消息都是通过NettyRpcEnv中的 Dispatcher来实现的。在Dispatcher中有一个线程,叫做MessageLoop,实现消息的及时处理。
/** * Posts a message to a specific endpoint. * * @param endpointName name of the endpoint. * @param message the message to post * @param callbackIfStopped callback function if the endpoint is stopped. */ private def postMessage( endpointName: String, message: InboxMessage, callbackIfStopped: (Exception) => Unit): Unit = { val error = synchronized { val data = endpoints.get(endpointName) if (stopped) { Some(new RpcEnvStoppedException()) } else if (data == null) { Some(new SparkException(s"Could not find $endpointName.")) } else { data.inbox.post(message) receivers.offer(data) None } } 复制代码
注意:默认第一条消息为onstart,为什么呢?看这里:
看到下面的 new EndpointData(name, endpoint, endpointRef) 了吗?
def registerRpcEndpoint(name: String, endpoint: RpcEndpoint): NettyRpcEndpointRef = { val addr = RpcEndpointAddress(nettyEnv.address, name) val endpointRef = new NettyRpcEndpointRef(nettyEnv.conf, addr, nettyEnv) synchronized { if (stopped) { throw new IllegalStateException("RpcEnv has been stopped") } if (endpoints.putIfAbsent(name, new EndpointData(name, endpoint, endpointRef)) != null) { throw new IllegalArgumentException(s"There is already an RpcEndpoint called $name") } val data = endpoints.get(name) endpointRefs.put(data.endpoint, data.ref) receivers.offer(data) // for the OnStart message } endpointRef 复制代码
}
注意EndpointData里面包含了inbox,因此Inbox初始化的时候,放进了onstart
private class EndpointData( val name: String, val endpoint: RpcEndpoint, val ref: NettyRpcEndpointRef) { val inbox = new Inbox(ref, endpoint) 复制代码
}
onstart在Inbox初始化时出现了,注意每一个端点只有一个inbox,比如:master 节点。
2.4 发送消息流程为分为两种,一种端点(Master)自己把消息发送到本地Inbox,一种端点(Master)接收到消息后,通过TransPortRequestHander接收后处理,扔进Inbox
2.4.1 端点(Master)自己把消息发送到本地Inbox
- endpoint(Master) -> NettyRpcEnv-> Dispatcher -> postMessage -> MessageLoop(Dispatcher) -> inbox -> process -> endpoint.receiveAndReply 复制代码
解释如下:端点通过自己的RPCEnv环境,向自己的Inbox中发送消息,然后交由Dispatch来进行消息的处理,调用了端点自己的receiveAndReply方法
-
这里着重讲一下MessageLoop是什么时候启动的,参照Dispatcher的代码段如下,一旦初始化就会启动,因为是成员变量:
private val threadpool: ThreadPoolExecutor = { val numThreads = nettyEnv.conf.getInt("spark.rpc.netty.dispatcher.numThreads", math.max(2, Runtime.getRuntime.availableProcessors())) val pool = ThreadUtils.newDaemonFixedThreadPool(numThreads, "dispatcher-event-loop") for (i <- 0 until numThreads) { pool.execute(new MessageLoop) } pool } 复制代码
-
接着讲nettyRpcEnv是何时初始化的,Dispatcher是何时初始化的?
master初始化RpcEnv环境时,调用NettyRpcEnvFactory().create(config)进行初始化nettyRpcEnv,然后其成员变量Dispatcher开始初始化,然后Dispatcher内部成员变量threadpool开始启动messageLoop,然后开始处理消息,可谓是一环套一环啊。如下是Master端点初始化RPCEnv。
在NettyRpcEnv中,NettyRpcEnvFactory的create方法如下:
其中nettyRpcEnv.startServer,代码段如下,然后调用底层 transportContext.createServer来创建Server,并初始化netty 的 pipeline:
server = transportContext.createServer(host, port, bootstraps) dispatcher.registerRpcEndpoint( RpcEndpointVerifier.NAME, new RpcEndpointVerifier(this, dispatcher)) 复制代码
最终端点开始不断向自己的Inboxz中发送消息即可,代码段如下:
private def postMessage( endpointName: String, message: InboxMessage, callbackIfStopped: (Exception) => Unit): Unit = { error = synchronized { val data = endpoints.get(endpointName) if (stopped) { Some(new RpcEnvStoppedException()) } else if (data == null) { Some(new SparkException(s"Could not find $endpointName.")) } else { data.inbox.post(message) receivers.offer(data) None } } 复制代码
2.4.2 端点(Master)接收到消息后,通过TransPortRequestHander接收后处理,扔进Inbox
- endpointRef(Worker) ->TransportChannelHandler -> channelRead0 -> TransPortRequestHander -> handle -> processRpcRequest ->NettyRpcHandler(在NettyRpcEnv中) -> receive -> internalReceive -> dispatcher.postToAll(RemoteProcessConnected(remoteEnvAddress)) (响应)-> dispatcher.postRemoteMessage(messageToDispatch, callback) (发送远端来的消息放进inbox)-> postMessage -> inbox -> process 复制代码
如下图展示了整个消息接收到inbox的流程:
下图展示了 TransportChannelHandler接收消息:
@Override public void channelRead0(ChannelHandlerContext ctx, Message request) throws Exception { if (request instanceof RequestMessage) { requestHandler.handle((RequestMessage) request); } else { responseHandler.handle((ResponseMessage) request); } } 复制代码
然后TransPortRequestHander来进行消息匹配处理:
最终交给inbox的process方法,实际上由端点 endpoint.receiveAndReply(context)方法处理:
/** * Process stored messages. */ def process(dispatcher: Dispatcher): Unit = { var message: InboxMessage = null inbox.synchronized { if (!enableConcurrent && numActiveThreads != 0) { return } message = messages.poll() if (message != null) { numActiveThreads += 1 } else { return } } while (true) { safelyCall(endpoint) { message match { case RpcMessage(_sender, content, context) => try { endpoint.receiveAndReply(context).applyOrElse[Any, Unit](content, { msg => throw new SparkException(s"Unsupported message $message from ${_sender}") }) } catch { case NonFatal(e) => context.sendFailure(e) // Throw the exception -- this exception will be caught by the safelyCall function. // The endpoint's onError function will be called. throw e } case OneWayMessage(_sender, content) => endpoint.receive.applyOrElse[Any, Unit](content, { msg => throw new SparkException(s"Unsupported message $message from ${_sender}") }) case OnStart => endpoint.onStart() if (!endpoint.isInstanceOf[ThreadSafeRpcEndpoint]) { inbox.synchronized { if (!stopped) { enableConcurrent = true } } } case OnStop => val activeThreads = inbox.synchronized { inbox.numActiveThreads } assert(activeThreads == 1, s"There should be only a single active thread but found $activeThreads threads.") dispatcher.removeRpcEndpointRef(endpoint) endpoint.onStop() assert(isEmpty, "OnStop should be the last message") case RemoteProcessConnected(remoteAddress) => endpoint.onConnected(remoteAddress) case RemoteProcessDisconnected(remoteAddress) => endpoint.onDisconnected(remoteAddress) case RemoteProcessConnectionError(cause, remoteAddress) => endpoint.onNetworkError(cause, remoteAddress) } } inbox.synchronized { // "enableConcurrent" will be set to false after `onStop` is called, so we should check it // every time. if (!enableConcurrent && numActiveThreads != 1) { // If we are not the only one worker, exit numActiveThreads -= 1 return } message = messages.poll() if (message == null) { numActiveThreads -= 1 return } } } 复制代码
}
以上所述就是小编给大家介绍的《Spark内置框架rpc通讯机制及RpcEnv基础设施-Spark商业环境实战》,希望对大家有所帮助,如果大家有任何疑问请给我留言,小编会及时回复大家的。在此也非常感谢大家对 码农网 的支持!
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