Spark Streaming 快速入门系列(3) | DStream中如何创建数据源

不温卜火

发布于 2020-10-28 17:39:45

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发布于 2020-10-28 17:39:45

文章被收录于专栏：不温卜火

Spark Streaming 原生支持一些不同的数据源。

一. RDD 队列(测试用)

1. 用法及说明

测试过程中，可以通过使用ssc.queueStream(queueOfRDDs)来创建DStream，每一个推送到这个队列中的RDD，都会作为一个DStream处理。

2. 案例实操需求：循环创建几个 RDD，将 RDD 放入队列。通过 Spark Streaming创建 Dstream，计算 WordCount

package com.buwenbuhuo.spark.streaming.day01

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming.dstream.{DStream, InputDStream}
import org.apache.spark.streaming.{Seconds, StreamingContext}

import scala.collection.mutable

/**
 *
 * @author 不温卜火
 * @create 2020-08-07 13:08
 *  MyCSDN ：  https://buwenbuhuo.blog.csdn.net/
 *
 */
object WordCount1 {
  def main(args: Array[String]): Unit = {
    // 从RDD队列中读取数据,仅仅用于压力测试
    val conf: SparkConf = new SparkConf().setMaster("local[2]").setAppName("WordCount1")
    val ssc: StreamingContext = new StreamingContext(conf,Seconds(3))
    val rdds: mutable.Queue[RDD[Int]] = mutable.Queue[RDD[Int]]()
    val sourceStream: InputDStream[Int] = ssc.queueStream(rdds,false)
    val result: DStream[Int] = sourceStream.reduce(_+_)
    result.print()
    ssc.start()

    val sc: SparkContext = ssc.sparkContext
    while (true) {
      rdds.enqueue(sc.parallelize(1 to 100))
     Thread.sleep(10)
    }


    ssc.awaitTermination()
  }
}

3. 运行结果

二. 自定义数据源

1. 使用及说明

其实就是自定义接收器

需要继承Receiver，并实现onStart、onStop方法来自定义数据源采集。

2. 需求:

自定义数据源，实现监控某个端口号，获取该端口号内容。

3. 源码

package com.buwenbuhuo.spark.streaming.day01


import java.io.{BufferedReader, InputStreamReader}
import java.net.Socket

import org.apache.spark.SparkConf
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.dstream.ReceiverInputDStream
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.receiver.Receiver

/**
 *
 * @author 不温卜火
 * @create 2020-08-07 17:03
 *  MyCSDN ：  https://buwenbuhuo.blog.csdn.net/
 *
 */
object MyReceiverDemo {
  def main(args: Array[String]): Unit = {
    val conf: SparkConf = new SparkConf().setMaster("local[*]").setAppName("MyReceiverDemo")
    val ssc: StreamingContext = new StreamingContext(conf,Seconds(3))

    val sourceStream: ReceiverInputDStream[String] = ssc.socketTextStream("hadoop002",9999)

    sourceStream
        .flatMap(_.split(" "))
        .map((_,1))
        .reduceByKey(_ + _)
        .print()

    ssc.start()
    ssc.awaitTermination()
  }
}
// 接收器从socket接受数据
class MyReceiver(host:String,port:Int) extends Receiver[String](storageLevel = StorageLevel.MEMORY_ONLY) {

  var socket:Socket = _
  var reader:BufferedReader = _
  override def onStart(): Unit = {
    runInThread{
      try {
        socket = new Socket(host, port)
        reader = new BufferedReader(new InputStreamReader(socket.getInputStream, "utf-8"))
        var line: String = reader.readLine()
        // 当对方发送一个流结束标志的时候，会受到null
        while (line != null && socket.isConnected) {
          store(line)
          line = reader.readLine()  // 如果流中没有数据，这将会一直阻塞
        }
      }catch {
        case e => e.printStackTrace()
      }finally {
        restart("重启服务器")
        // 自动立即调用onStop，然后再调用onStart
      }
    }
  }

  // 在一个子线程中去执行传入的代码
  def runInThread(op: => Unit) ={
      new Thread(){
        override def run():Unit = op
      }.start()
  }

  // 释放资源
  override def onStop(): Unit = {
    if(socket != null) socket.close()
    if(reader != null) reader.close()
  }
}

4. 测试结果

nc -lk 9999

三. Kafka 数据源

1. 准备工作

1. 用法及说明

在工程中需要引入 Maven 依赖 spark-streaming-kafka_2.11来使用它。

包内提供的 KafkaUtils 对象可以在 StreamingContext和JavaStreamingContext中以你的 Kafka 消息创建出 DStream。

两个核心类：KafkaUtils、KafkaCluster

2. 导入依赖

<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-streaming-kafka-0-8_2.11</artifactId>
    <version>2.1.1</version>
</dependency>

3. 启动zookeeper和kafka集群

[bigdata@hadoop002 zookeeper-3.4.10]$ bin/start-allzk.sh 
[bigdata@hadoop002 kafka]$ start-kafkaall.sh

4. 开启一个生产者和消费端

// 生产者
[bigdata@hadoop002 kafka]$ bin/kafka-console-producer.sh --broker-list hadoop002:9092 --topic first0810

// 消费者
[bigdata@hadoop002 kafka]$ bin/kafka-console-consumer.sh --bootstrap-server hadoop002:9092 --topic first0810

5. 通过IDEA接收数据

package com.buwenbuhuo.spark.streaming.day01.kafka
import kafka.serializer.StringDecoder
import org.apache.kafka.clients.consumer.ConsumerConfig
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}

/**
 **
   @author 不温卜火
 * @create 2020-08-10 9:34
 * MyCSDN ：  https://buwenbuhuo.blog.csdn.net/
 *
 */
object WorldCount1 {
  def main(args: Array[String]): Unit = {
    val conf: SparkConf = new SparkConf().setMaster("local[*]").setAppName("WorldCount1")
    val ssc = new StreamingContext(conf, Seconds(3))

    val params: Map[String, String] = Map[String,String](
      "bootstrap.servers" -> "hadoop002:9092,hadoop003:9092,hadoop004:9092",
      "group.id" -> "0810"
    )

    val srouceStream: InputDStream[(String, String)] = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](
      ssc,
      params,
      Set("first0810")
    )
    srouceStream.print
    ssc.start()
    ssc.awaitTermination()

  }
}
/*
kafkaUtils
 */

2. 正式运行

2.1 直接消费

没有checkpoint，数据有可能丢失

1. 源码

package com.buwenbuhuo.spark.streaming.day01.kafka
import kafka.serializer.StringDecoder
import org.apache.kafka.clients.consumer.ConsumerConfig
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.{DStream, InputDStream}
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}

/**
 **
   @author 不温卜火
 * @create 2020-08-10 9:34
 * MyCSDN ：  https://buwenbuhuo.blog.csdn.net/
 *
 */
object WorldCount1 {
  def main(args: Array[String]): Unit = {
    val conf: SparkConf = new SparkConf().setMaster("local[*]").setAppName("WorldCount1")
    val ssc = new StreamingContext(conf, Seconds(3))

    val params: Map[String, String] = Map[String,String](
      "bootstrap.servers" -> "hadoop002:9092,hadoop003:9092,hadoop004:9092",
      "group.id" -> "0810"
    )

    KafkaUtils
      .createDirectStream[String, String, StringDecoder, StringDecoder](
          ssc,
          params,
          Set("first0810")
    ).flatMap {
      case (_, v) =>
        v.split("\\W+")
    }.map((_,1))
        .reduceByKey(_ + _)
        .print()

    ssc.start()
    ssc.awaitTermination()

  }
}
/*
kafkaUtils
 */

2. 运行结果

2.2 with checkpoint(解决数据丢失问题)

缺点：小文件过多

1. 源码

package com.buwenbuhuo.spark.streaming.day01.kafka

import kafka.serializer.StringDecoder
import org.apache.spark.SparkConf
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}

/**
 **
 *
 * @author 不温卜火
 * @create 2020-08-10 11:24
 * MyCSDN ：  https://buwenbuhuo.blog.csdn.net/
 *
 */
object WordCount2 {
  def creatSSC(): StreamingContext ={
    val conf: SparkConf = new SparkConf().setMaster("local[*]").setAppName("WorldCount2")
    val ssc = new StreamingContext(conf, Seconds(3))
    // 把offset的跟踪在checkpoint中
    ssc.checkpoint("ck1")

    val params: Map[String, String] = Map[String,String](
      "bootstrap.servers" -> "hadoop002:9092,hadoop003:9092,hadoop004:9092",
      "group.id" -> "0810"
    )

    KafkaUtils
      .createDirectStream[String, String, StringDecoder, StringDecoder](
        ssc,
        params,
        Set("first0810")
      ).flatMap {
      case (_, v) =>
        v.split("\\W+")
    }.map((_,1))
      .reduceByKey(_ + _)
      .print()

    // 返回一个ssc
    ssc
  }
  def main(args: Array[String]): Unit = {
    /*
    从ckeckpoint中恢复一个StreamingContext，
    如果ckeckpoint不存在，则调用后面的函数去创建一个StreamingContext
     */
    val ssc: StreamingContext = StreamingContext.getActiveOrCreate("ck1",creatSSC)


    ssc.start()
    ssc.awaitTermination()

  }
}

2. 运行结果

2.3 with checkpoint的改良版本(常用)

1. 源码

package com.buwenbuhuo.spark.streaming.day01.kafka

import kafka.common.TopicAndPartition
import kafka.message.MessageAndMetadata
import kafka.serializer.StringDecoder
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka.KafkaCluster.Err
import org.apache.spark.streaming.kafka.{HasOffsetRanges, KafkaCluster, KafkaUtils, OffsetRange}
import org.apache.spark.streaming.{Seconds, StreamingContext}

/**
 **
 *
 * @author 不温卜火
 * @create 2020-08-10 12:29
 * MyCSDN ：  https://buwenbuhuo.blog.csdn.net/
 *
 */
object WorldCount3 {
  val groupId = "0810"
  val params: Map[String, String] = Map[String, String](
    "bootstrap.servers" -> "hadoop002:9092,hadoop003:9092,hadoop004:9092",
    "group.id" -> groupId)
  val topics: Set[String] = Set("first0810")
  // KafkaUtils   KafkaCluster
  val cluster: KafkaCluster = new KafkaCluster(params)
  /*
  读取开始的offsets
   */
  def readOffsets() = {
    var resultMap = Map[TopicAndPartition, Long]()
    // 1.获取这些topic的所有分区
    val topicAndPartitionSetEither: Either[Err, Set[TopicAndPartition]] = cluster.getPartitions(topics)
    topicAndPartitionSetEither match {
      // 2. 获取topic和分区的信息
      case Right(topicAndPartitionSet: Set[TopicAndPartition]) =>
        // 3. 获取到分区信息和他的offset
        val topicAndPartitionToLongEither: Either[Err, Map[TopicAndPartition, Long]] =
          cluster.getConsumerOffsets(groupId, topicAndPartitionSet)
        topicAndPartitionToLongEither match {
            // 没有每个topic的每个分区都已经存储过偏移量，表示曾经消费过，而且也维护过这个偏移量
          case Right(map) =>
              resultMap ++= map
            // 表示这个topic的这个分区是第一次消费
          case _ =>
            topicAndPartitionSet.foreach(topicAndPartition => {
              resultMap += topicAndPartition -> 0L
            })
        }

      case  _=>  // 表示不存在任何topic
    }

    resultMap
  }
  def saveOffsets(stream: InputDStream[String]): Unit ={
    // 保存offset一定从kafka消费到的直接的那个Stream保存
    // 每个批次执行一次传递过去的函数
    stream.foreachRDD(rdd =>{
      var map: Map[TopicAndPartition, Long] = Map[TopicAndPartition,Long]()
      // 如果这个rdd是直接来自与Kafka，则可以强转成 HasOffsetRanges
      val hasOffsetRanges: HasOffsetRanges = rdd.asInstanceOf[HasOffsetRanges]
      // 所有的分区的偏移量
      val ranges: Array[OffsetRange] = hasOffsetRanges.offsetRanges
      ranges.foreach(OffsetRange => {
        val key: TopicAndPartition = OffsetRange.topicAndPartition()
        val value: Long = OffsetRange.untilOffset
        map += key -> value
      })
      cluster.setConsumerOffsets(groupId,map)
    })
  }

  def main(args: Array[String]): Unit = {
    val conf: SparkConf = new SparkConf().setMaster("local[*]").setAppName("WorldCount3")
    val ssc = new StreamingContext(conf, Seconds(3))


    val sourceStream: InputDStream[String] = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder, String](
      ssc,
      params,
      readOffsets(),
      (handler: MessageAndMetadata[String, String]) => handler.message()
    )
    sourceStream
        .flatMap(_.split("\\W+"))
        .map((_,1))
        .reduceByKey(_+_)
        .print(1000)  //  如果不写数字具体为10行


    saveOffsets(sourceStream)
    ssc.start()
    ssc.awaitTermination()

  }
}