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spark streaming整合kafka-直連的方式

import kafka.common.TopicAndPartition
import kafka.message.MessageAndMetadata
import kafka.serializer.StringDecoder
import kafka.utils.{ZKGroupTopicDirs, ZkUtils}
import org.I0Itec.zkclient.ZkClient
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming.dstream.{DStream, InputDStream}
import org.apache.spark.streaming.kafka.{HasOffsetRanges, KafkaUtils, OffsetRange}
import org.apache.spark.streaming.{Duration, StreamingContext}

/**
  * Created by zx on 2017/7/31.
  */
object KafkaDirectWordCountV2 {

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

    //指定組名
    val group = "g001"
    //建立SparkConf
    val conf = new SparkConf().setAppName("KafkaDirectWordCount").setMaster("local[2]")
    //建立SparkStreaming,並設定間隔時間
    val ssc = new StreamingContext(conf, Duration(5000))
    //指定消費的 topic 名字
    val topic = "wwcc"
    //指定kafka的broker地址(sparkStream的Task直連到kafka的分割槽上,用更加底層的API消費,效率更高)
    val brokerList = "node-4:9092,node-5:9092,node-6:9092"

    //指定zk的地址,後期更新消費的偏移量時使用(以後可以使用Redis、MySQL來記錄偏移量)
    val zkQuorum = "node-1:2181,node-2:2181,node-3:2181"
    //建立 stream 時使用的 topic 名字集合,SparkStreaming可同時消費多個topic
    val topics: Set[String] = Set(topic)

    //建立一個 ZKGroupTopicDirs 物件,其實是指定往zk中寫入資料的目錄,用於儲存偏移量
    val topicDirs = new ZKGroupTopicDirs(group, topic)
    //獲取 zookeeper 中的路徑 "/g001/offsets/wordcount/"
    val zkTopicPath = s"${topicDirs.consumerOffsetDir}"

    //準備kafka的引數
    val kafkaParams = Map(
      "metadata.broker.list" -> brokerList,
      "group.id" -> group,
      //從頭開始讀取資料
      "auto.offset.reset" -> kafka.api.OffsetRequest.SmallestTimeString
    )

    //zookeeper 的host 和 ip,建立一個 client,用於跟新偏移量量的
    //是zookeeper的客戶端,可以從zk中讀取偏移量資料,並更新偏移量
    val zkClient = new ZkClient(zkQuorum)

    //查詢該路徑下是否位元組點(預設有位元組點為我們自己儲存不同 partition 時生成的)
    // /g001/offsets/wordcount/0/10001"
    // /g001/offsets/wordcount/1/30001"
    // /g001/offsets/wordcount/2/10001"
    //zkTopicPath  -> /g001/offsets/wordcount/
    val children = zkClient.countChildren(zkTopicPath)

    var kafkaStream: InputDStream[(String, String)] = null

    //如果 zookeeper 中有儲存 offset,我們會利用這個 offset 作為 kafkaStream 的起始位置
    var fromOffsets: Map[TopicAndPartition, Long] = Map()

    //如果儲存過 offset
    if (children > 0) {
      for (i <- 0 until children) {
        // /g001/offsets/wordcount/0/10001

        // /g001/offsets/wordcount/0
        val partitionOffset = zkClient.readData[String](s"$zkTopicPath/${i}")
        // wordcount/0
        val tp = TopicAndPartition(topic, i)
        //將不同 partition 對應的 offset 增加到 fromOffsets 中
        // wordcount/0 -> 10001
        fromOffsets += (tp -> partitionOffset.toLong)
      }
      //Key: kafka的key   values: "hello tom hello jerry"
      //這個會將 kafka 的訊息進行 transform,最終 kafak 的資料都會變成 (kafka的key, message) 這樣的 tuple
      val messageHandler = (mmd: MessageAndMetadata[String, String]) => (mmd.key(), mmd.message())
	  
      //通過KafkaUtils建立直連的DStream(fromOffsets引數的作用是:按照前面計算好了的偏移量繼續消費資料)
	  //[String, String, StringDecoder, StringDecoder,     (String, String)]
	  //  key    value    key的解碼方式   value的解碼方式 
      kafkaStream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder, (String, String)](ssc, kafkaParams, fromOffsets, messageHandler)
    } else {
      //如果未儲存,根據 kafkaParam 的配置使用最新(largest)或者最舊的(smallest) offset
      kafkaStream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topics)
    }

    //偏移量的範圍
    var offsetRanges = Array[OffsetRange]()

    //直連方式只有在KafkaDStream的RDD中才能獲取偏移量,那麼就不能到呼叫DStream的Transformation
    //所以只能子在kafkaStream呼叫foreachRDD,獲取RDD的偏移量,然後就是對RDD進行操作了
    //依次迭代KafkaDStream中的KafkaRDD
    kafkaStream.foreachRDD { kafkaRDD =>
      //只有KafkaRDD可以強轉成HasOffsetRanges,並獲取到偏移量
      offsetRanges = kafkaRDD.asInstanceOf[HasOffsetRanges].offsetRanges
      val lines: RDD[String] = kafkaRDD.map(_._2)

      //對RDD進行操作,觸發Action
      lines.foreachPartition(partition =>
        partition.foreach(x => {
          println(x)
        })
      )

      for (o <- offsetRanges) {
        //  /g001/offsets/wordcount/0
        val zkPath = s"${topicDirs.consumerOffsetDir}/${o.partition}"
        //將該 partition 的 offset 儲存到 zookeeper
        //  /g001/offsets/wordcount/0/20000
        ZkUtils.updatePersistentPath(zkClient, zkPath, o.untilOffset.toString)
      }
    }

    ssc.start()
    ssc.awaitTermination()

  }






}