大数据中Spark任务和集群启动流程是什么样的-创新互联
这篇文章将为大家详细讲解有关大数据中Spark任务和集群启动流程是什么样的，文章内容质量较高，因此小编分享给大家做个参考，希望大家阅读完这篇文章后对相关知识有一定的了解。
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:
关于大数据中Spark任务和集群启动流程是什么样的就分享到这里了，希望以上内容可以对大家有一定的帮助，可以学到更多知识。如果觉得文章不错，可以把它分享出去让更多的人看到。
网站题目：大数据中Spark任务和集群启动流程是什么样的-创新互联
分享网址：http://myzitong.com/article/djjsdg.html