Spark createDirectStream 维护 Kafka offset(Scala)

Posted 右介

tags:

篇首语:本文由小常识网(cha138.com)小编为大家整理,主要介绍了Spark createDirectStream 维护 Kafka offset(Scala)相关的知识,希望对你有一定的参考价值。

createDirectStream方式需要自己维护offset,使程序可以实现中断后从中断处继续消费数据。

KafkaManager.scala

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}")
      }
    }
  }
}

  主程序中

def initKafkaParams = {
    Map[String, String](
      "metadata.broker.list" -> Constants.KAFKA_BROKERS,
      "group.id " -> Constants.KAFKA_CONSUMER_GROUP,
      "fetch.message.max.bytes" -> "20971520",
      "auto.offset.reset" -> "smallest"
    )
  } 

// kafka参数
val kafkaParams = initKafkaParams
val manager = new KafkaManager(kafkaParams)
val messageDstream = manager.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, Set(topic)) 

// 更新offsets
manager.updateZKOffsets(rdd)

  

以上是关于Spark createDirectStream 维护 Kafka offset(Scala)的主要内容,如果未能解决你的问题,请参考以下文章

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

Spark Kafka 基于Direct自己管理offset

Spark Streaming - 从Kafka读取json并将json写入其他Kafka主题

spark JavaDirectKafkaWordCount 例子分析

spark streaming task 序列化源码

Spark-Streaming DirectKafka count 案例