大数据中Spark任务和集群启动流程是什么样的

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大数据分享Spark任务和集群启动流程

大数据分享Spark任务和集群启动流程，Spark集群启动流程

1.调用start-all.sh脚本,开始启动Master

2.Master启动以后,preStart方法调用了一个定时器,定时检查超时的Worker后删除

3.启动脚本会解析slaves配置文件,找到启动Worker的相应节点.开始启动Worker

4.Worker服务启动后开始调用preStart方法开始向所有的Master进行注册

5.Master接收到Worker发送过来的注册信息,Master开始保存注册信息并把自己的URL响应给Worker

6.Worker接收到Master的URL后并更新,开始调用一个定时器,定时的向Master发送心跳信息

任务提交流程

1.Driver端会通过spark-submit脚本启动SaparkSubmit进程,此时创建了一个非常重要的对象(SparkContext),开始向Master发送消息

2.Master接收到发送过来的信息后开始生成任务信息,并把任务信息放到一个对列里

3.Master把所有有效的Worker过滤出来,按照空闲的资源进行排序

4.Master开始向有效的Worker通知拿取任务信息并启动相应的Executor

5.Worker启动Executor并向Driver反向注册

6.Driver开始把生成的task发送给相应的Executor,Executor开始执行任务

集群启动流程

1.首先创建Master类

import akka.actor.{Actor, ActorSystem, Props}

import com.typesafe.config.{Config, ConfigFactory}

import scala.collection.mutable

import scala.concurrent.duration._

class Master(val masterHost: String, val masterPort: Int) extends Actor{

// 用来存储Worker的注册信息

val idToWorker = new mutable.HashMap[String, WorkerInfo]()

// 用来存储Worker的信息

val workers = new mutable.HashSet[WorkerInfo]()

// Worker的超时时间间隔

val checkInterval: Long = 15000

// 生命周期方法，在构造器之后，receive方法之前只调用一次

override def preStart(): Unit = {

// 启动一个定时器，用来定时检查超时的Worker

import context.dispatcher

context.system.scheduler.schedule(0 millis, checkInterval millis, self, CheckTimeOutWorker)

}

// 在preStart方法之后，不断的重复调用

override def receive: Receive = {

// Worker -> Master

case RegisterWorker(id, host, port, memory, cores) => {

if (!idToWorker.contains(id)){

val workerInfo = new WorkerInfo(id, host, port, memory, cores)

idToWorker += (id -> workerInfo)

workers += workerInfo

println("a worker registered")

sender ! RegisteredWorker(s"akka.tcp://${Master.MASTER_SYSTEM}" +

s"@${masterHost}:${masterPort}/user/${Master.MASTER_ACTOR}")

}

}

case HeartBeat(workerId) => {

// 通过传过来的workerId获取对应的WorkerInfo

val workerInfo: WorkerInfo = idToWorker(workerId)

// 获取当前时间

val currentTime = System.currentTimeMillis()

// 更新最后一次心跳时间

workerInfo.lastHeartbeatTime = currentTime

}

case CheckTimeOutWorker => {

val currentTime = System.currentTimeMillis()

val toRemove: mutable.HashSet[WorkerInfo] =

workers.filter(w => currentTime – w.lastHeartbeatTime > checkInterval)

// 将超时的Worker从idToWorker和workers中移除

toRemove.foreach(deadWorker => {

idToWorker -= deadWorker.id

workers -= deadWorker

})

println(s"num of workers: ${workers.size}")

}

}

}

object Master{

val MASTER_SYSTEM = "MasterSystem"

val MASTER_ACTOR = "Master"

def main(args: Array[String]): Unit = {

val host = args(0)

val port = args(1).toInt

val configStr =

s"""

|akka.actor.provider = "akka.remote.RemoteActorRefProvider"

|akka.remote.netty.tcp.hostname = "$host"

|akka.remote.netty.tcp.port = "$port"

""".stripMargin

// 配置创建Actor需要的配置信息

val config: Config = ConfigFactory.parseString(configStr)

// 创建ActorSystem

val actorSystem: ActorSystem = ActorSystem(MASTER_SYSTEM, config)

// 用actorSystem实例创建Actor

actorSystem.actorOf(Props(new Master(host, port)), MASTER_ACTOR)

actorSystem.awaitTermination()

}

}

2.创建RemoteMsg特质

trait RemoteMsg extends Serializable{

}

// Master -> self(Master)

case object CheckTimeOutWorker

// Worker -> Master

case class RegisterWorker(id: String, host: String,

port: Int, memory: Int, cores: Int) extends RemoteMsg

// Master -> Worker

case class RegisteredWorker(masterUrl: String) extends RemoteMsg

// Worker -> self

case object SendHeartBeat

// Worker -> Master(HeartBeat)

case class HeartBeat(workerId: String) extends RemoteMsg

3.创建Worker类

import java.util.UUID

import akka.actor.{Actor, ActorRef, ActorSelection, ActorSystem, Props}

import com.typesafe.config.{Config, ConfigFactory}

import scala.concurrent.duration._

class Worker(val host: String, val port: Int, val masterHost: String,

val masterPort: Int, val memory: Int, val cores: Int) extends Actor{

// 生成一个Worker ID

val workerId = UUID.randomUUID().toString

// 用来存储MasterURL

var masterUrl: String = _

// 心跳时间间隔

val heartBeat_interval: Long = 10000

// master的Actor

var master: ActorSelection = _

override def preStart(){

// 获取Master的Actor

master = context.actorSelection(s"akka.tcp://${Master.MASTER_SYSTEM}" +

s"@${masterHost}:${masterPort}/user/${Master.MASTER_ACTOR}")

master ! RegisterWorker(workerId, host, port, memory, cores)

}

override def receive: Receive = {

// Worker接收到Master发送过来的注册成功的信息（masterUrl）

case RegisteredWorker(masterUrl) => {

this.masterUrl = masterUrl

// 启动一个定时器，定时给Master发送心跳

import context.dispatcher

context.system.scheduler.schedule(0 millis, heartBeat_interval millis, self, SendHeartBeat)

}

case SendHeartBeat => {

// 向Master发送心跳

master ! HeartBeat(workerId)

}

}

}

object Worker{

val WORKER_SYSTEM = "WorkerSystem"

val WORKER_ACTOR = "Worker"

def main(args: Array[String]): Unit = {

val host = args(0)

val port = args(1).toInt

val masterHost = args(2)

val masterPort = args(3).toInt

val memory = args(4).toInt

val cores = args(5).toInt

val configStr =

s"""

|akka.actor.provider = "akka.remote.RemoteActorRefProvider"

|akka.remote.netty.tcp.hostname = "$host"

|akka.remote.netty.tcp.port = "$port"

""".stripMargin

// 配置创建Actor需要的配置信息

val config: Config = ConfigFactory.parseString(configStr)

// 创建ActorSystem

val actorSystem: ActorSystem = ActorSystem(WORKER_SYSTEM, config)

// 用actorSystem实例创建Actor

val worker: ActorRef = actorSystem.actorOf(

Props(new Worker(host, port, masterHost, masterPort, memory, cores)), WORKER_ACTOR)

actorSystem.awaitTermination()

}

}

4.创建初始化类

class WorkerInfo(val id: String, val host: String, val port: Int,

val memory: Int, val cores: Int) {

// 初始化最后一次心跳的时间

var lastHeartbeatTime: Long = _

}

5.本地测试需要传入参数:

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