Spark+Kafka的Direct方式将偏移量发送到Zookeeper实现

早过忘川 提交于 2019-12-03 23:35:38

 Apache Spark 1.3.0引入了Direct API,利用Kafka的低层次API从Kafka集群中读取数据,并且在SparkStreaming系统里面维护偏移量相关的信息,并且通过这种方式去实现零数据丢失(zero data loss)相比使用基于Receiver的方法要高效。但是因为是Spark Streaming系统自己维护Kafka的读偏移量,而Spark Streaming系统并没有将这个消费的偏移量发送到Zookeeper中,这将导致那些基于偏移量的Kafka集群监控软件(比如:Apache Kafka监控之Kafka Web ConsoleApache Kafka监控之KafkaOffsetMonitor等)失效。本文就是基于为了解决这个问题,使得我们编写的Spark Streaming程序能够在每次接收到数据之后自动地更新Zookeeper中Kafka的偏移量。

  我们从Spark的官方文档可以知道,维护Spark内部维护Kafka便宜了信息是存储在HasOffsetRanges类的offsetRanges中,我们可以在Spark Streaming程序里面获取这些信息:

val offsetsList = rdd.asInstanceOf[HasOffsetRanges].offsetRanges

这样我们就可以获取所以分区消费信息,只需要遍历offsetsList,然后将这些信息发送到Zookeeper即可更新Kafka消费的偏移量。完整的代码片段如下:

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

      messages.foreachRDD(rdd => {

        val offsetsList = rdd.asInstanceOf[HasOffsetRanges].offsetRanges

        val kc = new KafkaCluster(kafkaParams)

        for (offsets < - offsetsList) {

          val topicAndPartition = TopicAndPartition("iteblog", offsets.partition)

          val o = kc.setConsumerOffsets(args(0), Map((topicAndPartition, offsets.untilOffset)))

          if (o.isLeft) {

            println(s"Error updating the offset to Kafka cluster: ${o.left.get}")

          }

        }

})

  KafkaCluster类用于建立和Kafka集群的链接相关的操作工具类,我们可以对Kafka中Topic的每个分区设置其相应的偏移量Map((topicAndPartition, offsets.untilOffset)),然后调用KafkaCluster类的setConsumerOffsets方法去更新Zookeeper里面的信息,这样我们就可以更新Kafka的偏移量,最后我们就可以通过KafkaOffsetMonitor之类软件去监控Kafka中相应Topic的消费信息,下图是KafkaOffsetMonitor的监控情况:



如果想及时了解Spark、Hadoop或者Hbase相关的文章,欢迎关注微信公共帐号:iteblog_hadoop

  从图中我们可以看到KafkaOffsetMonitor监控软件已经可以监控到Kafka相关分区的消费情况,这对监控我们整个Spark Streaming程序来非常重要,因为我们可以任意时刻了解Spark读取速度。另外,KafkaCluster工具类的完整代码如下:

package org.apache.spark.streaming.kafka

 

import kafka.api.OffsetCommitRequest

import kafka.common.{ErrorMapping, OffsetMetadataAndError, TopicAndPartition}

import kafka.consumer.SimpleConsumer

import org.apache.spark.SparkException

import org.apache.spark.streaming.kafka.KafkaCluster.SimpleConsumerConfig

 

import scala.collection.mutable.ArrayBuffer

import scala.util.Random

import scala.util.control.NonFatal

 

/**

 * User: 过往记忆

 * Date: 2015-06-02

 * Time: 下午23:46

 * bolg: https://www.iteblog.com

 * 本文地址:https://www.iteblog.com/archives/1381

 * 过往记忆博客,专注于hadoop、hive、spark、shark、flume的技术博客,大量的干货

 * 过往记忆博客微信公共帐号:iteblog_hadoop

 */

 

class KafkaCluster(val kafkaParams: Map[String, String]) extends Serializable {

  type Err = ArrayBuffer[Throwable]

 

  @transient private var _config: SimpleConsumerConfig = null

 

  def config: SimpleConsumerConfig = this.synchronized {

    if (_config == null) {

      _config = SimpleConsumerConfig(kafkaParams)

    }

    _config

  }

 

  def setConsumerOffsets(groupId: String,

                         offsets: Map[TopicAndPartition, Long]

                          ): Either[Err, Map[TopicAndPartition, Short]] = {

    setConsumerOffsetMetadata(groupId, offsets.map { kv =>

      kv._1 -> OffsetMetadataAndError(kv._2)

    })

  }

 

  def setConsumerOffsetMetadata(groupId: String,

                                metadata: Map[TopicAndPartition, OffsetMetadataAndError]

                                 ): Either[Err, Map[TopicAndPartition, Short]] = {

    var result = Map[TopicAndPartition, Short]()

    val req = OffsetCommitRequest(groupId, metadata)

    val errs = new Err

    val topicAndPartitions = metadata.keySet

    withBrokers(Random.shuffle(config.seedBrokers), errs) { consumer =>

      val resp = consumer.commitOffsets(req)

      val respMap = resp.requestInfo

      val needed = topicAndPartitions.diff(result.keySet)

      needed.foreach { tp: TopicAndPartition =>

        respMap.get(tp).foreach { err: Short =>

          if (err == ErrorMapping.NoError) {

            result += tp -> err

          } else {

            errs.append(ErrorMapping.exceptionFor(err))

          }

        }

      }

      if (result.keys.size == topicAndPartitions.size) {

        return Right(result)

      }

    }

    val missing = topicAndPartitions.diff(result.keySet)

    errs.append(new SparkException(s"Couldn't set offsets for ${missing}"))

    Left(errs)

  }

 

  private def withBrokers(brokers: Iterable[(String, Int)], errs: Err)

                         (fn: SimpleConsumer => Any): Unit = {

    brokers.foreach { hp =>

      var consumer: SimpleConsumer = null

      try {

        consumer = connect(hp._1, hp._2)

        fn(consumer)

      } catch {

        case NonFatal(e) =>

          errs.append(e)

      } finally {

        if (consumer != null) {

          consumer.close()

        }

      }

    }

  }

 

  def connect(host: String, port: Int): SimpleConsumer =

    new SimpleConsumer(host, port, config.socketTimeoutMs,

      config.socketReceiveBufferBytes, config.clientId)

}

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