spark streaming kafka1.4.1中的低阶api createDirectStream使用总结(转)

版权声明：本文为博主原创文章，转载请写明出处。

转自：http://blog.csdn.net/ligt0610/article/details/47311771

       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}

 import scala.reflect.ClassTag

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

   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(s"get kafka partition failed: ${partitionsE.left.get}")
       val partitions = partitionsE.right.get
       val consumerOffsetsE = kc.getConsumerOffsets(groupId, partitions)
       if (consumerOffsetsE.isLeft)
         throw new SparkException(s"get kafka consumer offsets failed: ${consumerOffsetsE.left.get}")
       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(s"get kafka partition failed: ${partitionsE.left.get}")
       val partitions = partitionsE.right.get
       val consumerOffsetsE = kc.getConsumerOffsets(groupId, partitions)
       if (consumerOffsetsE.isLeft) hasConsumed = false
       if (hasConsumed) {// 消费过
         /**
          * 如果streaming程序执行的时候出现kafka.common.OffsetOutOfRangeException，
          * 说明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 earliestLeaderOffsetsE = kc.getEarliestLeaderOffsets(partitions)
         if (earliestLeaderOffsetsE.isLeft)
           throw new SparkException(s"get earliest leader offsets failed: ${earliestLeaderOffsetsE.left.get}")
         val earliestLeaderOffsets = earliestLeaderOffsetsE.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")) {
           val leaderOffsetsE = kc.getEarliestLeaderOffsets(partitions)
           if (leaderOffsetsE.isLeft)
             throw new SparkException(s"get earliest leader offsets failed: ${leaderOffsetsE.left.get}")
           leaderOffsets = leaderOffsetsE.right.get
         } else {
           val leaderOffsetsE = kc.getLatestLeaderOffsets(partitions)
           if (leaderOffsetsE.isLeft)
             throw new SparkException(s"get latest leader offsets failed: ${leaderOffsetsE.left.get}")
           leaderOffsets = leaderOffsetsE.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()
   }
 }