spark streaming kafka1.4.1中的低阶api createDirectStream使用总结，directstream


       由于目前每天需要从kafka中消费20亿条左右的消息，集群压力有点大，会导致job不同程度的异常退出。原来使用spark1.1.0版本中的createStream函数，但是在数据处理速度跟不上数据消费速度且job异常退出的情况下，可能造成大量的数据丢失。幸好，spark后续版本对这一情况有了很大的改进，1.2版本加入WAL特性，但是性能应该会受到一些影响（本人未测试），1.3版本可以直接通过低阶API从kafka的topic消费消息，并且不再向zookeeper中更新consumer offsets，使得基于zookeeper的consumer offsets的监控工具都会失效。

       官方只是非常简单的描述了可以用以下方法修改zookeeper中的consumer offsets（可以查看http://spark.apache.org/docs/1.4.1/streaming-kafka-integration.html）：

// Hold a reference to the current offset ranges, so it can be used downstream
 var offsetRanges = Array[OffsetRange]()
	
 directKafkaStream.transform { rdd =>
   offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
   rdd
 }.map {
           ...
 }.foreachRDD { rdd =>
   for (o <- offsetRanges) {
     println(s"${o.topic} ${o.partition} ${o.fromOffset} ${o.untilOffset}")
   }
   ...
 }

       所以更新zookeeper中的consumer offsets还需要自己去实现，并且官方提供的两个createDirectStream重载并不能很好的满足我的需求，需要进一步封装。具体看以下KafkaManager类的代码：

package org.apache.spark.streaming.kafka

import kafka.common.TopicAndPartition
import kafka.message.MessageAndMetadata
import kafka.serializer.Decoder
import org.apache.spark.SparkException
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka.KafkaCluster.{LeaderOffset, Err}

import scala.reflect.ClassTag

/**
 * Created by knowpigxia on 15-8-5.
 */
class KafkaManager(val kafkaParams: Map[String, String]) {

  private val kc = new KafkaCluster(kafkaParams)

  /**
   * 创建数据流
   * @param ssc
   * @param kafkaParams
   * @param topics
   * @tparam K
   * @tparam V
   * @tparam KD
   * @tparam VD
   * @return
   */
  def createDirectStream[K: ClassTag, V: ClassTag, KD <: Decoder[K]: ClassTag, VD <: Decoder[V]: ClassTag](
    ssc: StreamingContext, kafkaParams: Map[String, String], topics: Set[String]): InputDStream[(K, V)] =  {
    val groupId = kafkaParams.get("group.id").get
    // 在zookeeper上读取offsets前先根据实际情况更新offsets
    setOrUpdateOffsets(topics, groupId)

    //从zookeeper上读取offset开始消费message
    val messages = {
      val partitionsE = kc.getPartitions(topics)
      if (partitionsE.isLeft) throw new SparkException("get kafka partition failed:")
      val partitions = partitionsE.right.get
      val consumerOffsetsE = kc.getConsumerOffsets(groupId, partitions)
      if (consumerOffsetsE.isLeft) throw new SparkException("get kafka consumer offsets failed:")
      val consumerOffsets = consumerOffsetsE.right.get
      KafkaUtils.createDirectStream[K, V, KD, VD, (K, V)](
        ssc, kafkaParams, consumerOffsets, (mmd: MessageAndMetadata[K, V]) => (mmd.key, mmd.message))
    }
    messages
  }

  /**
   * 创建数据流前，根据实际消费情况更新消费offsets
   * @param topics
   * @param groupId
   */
  private def setOrUpdateOffsets(topics: Set[String], groupId: String): Unit = {
    topics.foreach(topic => {
      var hasConsumed = true
      val partitionsE = kc.getPartitions(Set(topic))
      if (partitionsE.isLeft) throw new SparkException("get kafka partition failed:")
      val partitions = partitionsE.right.get
      val consumerOffsetsE = kc.getConsumerOffsets(groupId, partitions)
      if (consumerOffsetsE.isLeft) hasConsumed = false
      if (hasConsumed) {// 消费过
        /**
         * 如果zk上保存的offsets已经过时了，即kafka的定时清理策略已经将包含该offsets的文件删除。
         * 针对这种情况，只要判断一下zk上的consumerOffsets和earliestLeaderOffsets的大小，
         * 如果consumerOffsets比earliestLeaderOffsets还小的话，说明consumerOffsets已过时,
         * 这时把consumerOffsets更新为earliestLeaderOffsets
         */
        val earliestLeaderOffsets = kc.getEarliestLeaderOffsets(partitions).right.get
        val consumerOffsets = consumerOffsetsE.right.get

        // 可能只是存在部分分区consumerOffsets过时，所以只更新过时分区的consumerOffsets为earliestLeaderOffsets
        var offsets: Map[TopicAndPartition, Long] = Map()
        consumerOffsets.foreach({ case(tp, n) =>
          val earliestLeaderOffset = earliestLeaderOffsets(tp).offset
          if (n < earliestLeaderOffset) {
            println("consumer group:" + groupId + ",topic:" + tp.topic + ",partition:" + tp.partition +
              " offsets已经过时，更新为" + earliestLeaderOffset)
            offsets += (tp -> earliestLeaderOffset)
          }
        })
        if (!offsets.isEmpty) {
          kc.setConsumerOffsets(groupId, offsets)
        }
      } else {// 没有消费过
        val reset = kafkaParams.get("auto.offset.reset").map(_.toLowerCase)
        var leaderOffsets: Map[TopicAndPartition, LeaderOffset] = null
        if (reset == Some("smallest")) {
          leaderOffsets = kc.getEarliestLeaderOffsets(partitions).right.get
        } else {
          leaderOffsets = kc.getLatestLeaderOffsets(partitions).right.get
        }
        val offsets = leaderOffsets.map {
          case (tp, offset) => (tp, offset.offset)
        }
        kc.setConsumerOffsets(groupId, offsets)
      }
    })
  }

  /**
   * 更新zookeeper上的消费offsets
   * @param rdd
   */
  def updateZKOffsets(rdd: RDD[(String, String)]) : Unit = {
    val groupId = kafkaParams.get("group.id").get
    val offsetsList = rdd.asInstanceOf[HasOffsetRanges].offsetRanges

    for (offsets <- offsetsList) {
      val topicAndPartition = TopicAndPartition(offsets.topic, offsets.partition)
      val o = kc.setConsumerOffsets(groupId, Map((topicAndPartition, offsets.untilOffset)))
      if (o.isLeft) {
        println(s"Error updating the offset to Kafka cluster: ${o.left.get}")
      }
    }
  }
}

       接下来再给一个简单的例子：

import kafka.serializer.StringDecoder
import org.apache.log4j.{Level, Logger}
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming.kafka._
import org.apache.spark.streaming.{Seconds, StreamingContext}

/**
 * Created by knowpigxia on 15-8-4.
 */
object DirectKafkaWordCount {

  def dealLine(line: String): String = {
    val list = AnalysisUtil.dealString(line, ',', '"')// 把dealString函数当做split即可
    list.get(0).substring(0, 10) + "-" + list.get(26)
  }

  def processRdd(rdd: RDD[(String, String)]): Unit = {
    val lines = rdd.map(_._2)
    val words = lines.map(dealLine(_))
    val wordCounts = words.map(x => (x, 1L)).reduceByKey(_ + _)
    wordCounts.foreach(println)
  }

  def main(args: Array[String]) {
    if (args.length < 3) {
      System.err.println( s"""
        |Usage: DirectKafkaWordCount <brokers> <topics> <groupid>
        |  <brokers> is a list of one or more Kafka brokers
        |  <topics> is a list of one or more kafka topics to consume from
        |  <groupid> is a consume group
        |
        """.stripMargin)
      System.exit(1)
    }

    Logger.getLogger("org").setLevel(Level.WARN)

    val Array(brokers, topics, groupId) = args

    // Create context with 2 second batch interval
    val sparkConf = new SparkConf().setAppName("DirectKafkaWordCount")
    sparkConf.setMaster("local[*]")
    sparkConf.set("spark.streaming.kafka.maxRatePerPartition", "5")
    sparkConf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")

    val ssc = new StreamingContext(sparkConf, Seconds(2))

    // Create direct kafka stream with brokers and topics
    val topicsSet = topics.split(",").toSet
    val kafkaParams = Map[String, String](
      "metadata.broker.list" -> brokers,
      "group.id" -> groupId,
      "auto.offset.reset" -> "smallest"
    )

    val km = new KafkaManager(kafkaParams)

    val messages = km.createDirectStream[String, String, StringDecoder, StringDecoder](
      ssc, kafkaParams, topicsSet)

    messages.foreachRDD(rdd => {
      if (!rdd.isEmpty()) {
        // 先处理消息
        processRdd(rdd)
        // 再更新offsets
        km.updateZKOffsets(rdd)
      }
    })

    ssc.start()
    ssc.awaitTermination()
  }
}

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