Kafka：无丢失提取kafka的值，详解kafka的消费过程

目录：

1、需求

2、代码步鄹

3、代码展现

4、pom.xml文件

5、结果展现

---

---

1、需求

**前提：将org.apache.spark.streaming.kafka.KafkaCluster这个类抽出来变成KafkaClusterHelper
** 需求：
1、将kafka中的数据无丢失提取，且存到本地
2、详解Kafka读取数据步鄹
3、详解Zookeeper存储TopicAndPartition和对应的Offset

2、代码步鄹

步鄹：
1、将org.apache.spark.streaming.kafka.KafkaCluster这个类抽出来变成KafkaClusterHelper
2、编写ZookeeperHelper类便于将TopicAndPartition和对应的Offset存储到Zookeeper中
3、将类变成对象kafkaHelper
4、通过kafkaHelper.getFromOffsets获取开始的Offset，如果不是第一次则从Zookeeper中获取TopicAndPartition和对应的Offset
5、通过kafkaHelper.getLatestLeaderOffsets获取最后的Offset
6、通过org.apache.spark.streaming.kafka.OffsetRange类将TopicAndPartition和对应的Offset转为对象且放入Array数组中
7、通过org.apache.spark.streaming.kafka.KafkaUtils.createRDD方法创建RDD
8、将RDD存储本地
9、最后ZookeeperHelper类将TopicAndPartition和对应的Offset存储到Zookeeper中

3、代码展现

总计三个类：kafkaConsumer.scala KafkaClusterHelper.scala ZookeeperHelper.scala

###kafkaConsumer.scala

package com.donews.localspark

import com.donews.util.{KafkaClusterHelper,ZookeeperHelper}
import kafka.common.TopicAndPartition
import kafka.serializer.StringDecoder
import org.apache.spark.streaming.kafka.{KafkaUtils, OffsetRange}
import org.apache.spark.{SparkConf, SparkContext}
/**
  * Created by yuhui on 2016/11/17.
  */

object kafkaConsumer extends Serializable{

  val topicsSet = Set("donews_website_nginx_log")

  val filePath = "E:\\web_nginx_log"

  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("App_Name").setMaster("local[4]").set("sp‌​ark.driver.port", "180‌​80");
    val sc = new SparkContext(conf)
    val blockSize = 1024 * 1024 * 128 // 128MB
    sc.hadoopConfiguration.setInt("dfs.blocksize", blockSize)

    val kafkaParams = Map[String, String](
      "metadata.broker.list" -> "tagtic-master:9092,tagtic-slave01:9092,tagtic-slave02:9092,tagtic-slave03:9092",
      "auto.offset.reset" -> "smallest"
    )

    val kafkaHelper = new KafkaClusterHelper(kafkaParams)

    var num: Long = 0

    try {
      //获取Zookeeper中最新的offset,如果第一次则取kafkaParams中的smallest
      val offsets = ZookeeperHelper.loadOffsets(topicsSet, kafkaHelper.getFromOffsets(kafkaParams, topicsSet))

      //获取kafka中最新的offset
      val latestOffsets = KafkaClusterHelper.checkErrors(kafkaHelper.getLatestLeaderOffsets(offsets.keySet))

      val offsetRanges = offsets.keys.map { tp =>
      val fromOffset = offsets(tp)
      val latestOffset = latestOffsets(tp).offset

        println("topicName和partition====>"+tp+ "  fromOffset====>"+fromOffset+"  latestOffset====>"+latestOffset)

        //OffsetRange(tp, 8800000, Math.min(fromOffset + 1024 * 1024, latestOffset)) //限制成大约是500M
        OffsetRange(tp, 170000, 170006) //限制成大约是500M

      }.toArray

      val rdd = KafkaUtils.createRDD[String, String, StringDecoder, StringDecoder](sc, kafkaParams, offsetRanges)
      println("rdd.count()====================》"+rdd.count())

      //rdd存在本地
      rdd.map(line=>{val lenth = line.toString().substring(38,line.toString().length-1)}).coalesce(1,true).saveAsTextFile(filePath)

      val nextOffsets = offsetRanges.map(x => (TopicAndPartition(x.topic, x.partition), x.untilOffset)).toMap
      //将offset存储到zookeeper，zookeeper存储路径可以删除，保证数据不丢失及数据重新读入
      ZookeeperHelper.storeOffsets(nextOffsets)

    }

  }

}

ZookeeperHelper.scala

package com.donews.util

import kafka.common.TopicAndPartition
import org.apache.curator.framework.CuratorFrameworkFactory
import org.apache.curator.retry.ExponentialBackoffRetry
import org.slf4j.LoggerFactory

import scala.collection.JavaConversions._

/**
  * Created by yuhui on 16-6-8.
  */
object ZookeeperHelper {
  val LOG = LoggerFactory.getLogger(ZookeeperHelper.getClass)
  val client = {
    val client = CuratorFrameworkFactory
      .builder
      .connectString(WebConfig.ZOOKEEPER_CONNECT)
      .retryPolicy(new ExponentialBackoffRetry(1000, 3))
      .namespace("statistic")
      .build()
    client.start()
    client
  }

  //zookeeper创建路径
  def ensurePathExists(path: String): Unit = {
    if (client.checkExists().forPath(path) == null) {
      client.create().creatingParentsIfNeeded().forPath(path)
    }
  }

  //zookeeper加载offset的方法
  def loadOffsets(topicSet: Set[String], defaultOffset: Map[TopicAndPartition, Long]): Map[TopicAndPartition, Long] = {
    val kafkaOffsetPath = s"/kafkaOffsets"
    ensurePathExists(kafkaOffsetPath)
    val offsets = for {
    //t就是路径webstatistic/kafkaOffsets下面的子目录遍历
      t <- client.getChildren.forPath(kafkaOffsetPath)
      if topicSet.contains(t)
    //p就是新路径   /webstatistic/kafkaOffsets/donews_website
      p <- client.getChildren.forPath(s"$kafkaOffsetPath/$t")
    } yield {
      //遍历路径下面的partition中的offset
      val data = client.getData.forPath(s"$kafkaOffsetPath/$t/$p")
      //将data变成Long类型
      val offset = java.lang.Long.valueOf(new String(data)).toLong
      (TopicAndPartition(t, Integer.parseInt(p)), offset)
    }
    defaultOffset ++ offsets.toMap
  }

  //zookeeper存储offset的方法
  def storeOffsets(offsets: Map[TopicAndPartition, Long]): Unit = {
    val kafkaOffsetPath = s"/kafkaOffsets"
    if (client.checkExists().forPath(kafkaOffsetPath) == null) {
      client.create().creatingParentsIfNeeded().forPath(kafkaOffsetPath)
    }
    for ((tp, offset) <- offsets) {
      val data = String.valueOf(offset).getBytes
      val path = s"$kafkaOffsetPath/${tp.topic}/${tp.partition}"
      ensurePathExists(path)
      client.setData().forPath(path, data)
    }
  }
}

KafkaClusterHelper.scala

package com.donews.util

/**
  * Created by yuhui on 16-6-29.
  * copy from spark-kafka source
  */


import java.util.Properties

import kafka.api._
import kafka.common.{ErrorMapping, OffsetAndMetadata, OffsetMetadataAndError, TopicAndPartition}
import kafka.consumer.{ConsumerConfig, SimpleConsumer}
import org.apache.spark.SparkException

import scala.collection.mutable.ArrayBuffer
import scala.util.Random
import scala.util.control.NonFatal

/**
  * Convenience methods for interacting with a Kafka cluster.
  *
  * @param kafkaParams Kafka 
  *                    configuration parameters.
  *                    Requires "metadata.broker.list" or "bootstrap.servers" to be set with Kafka broker(s),
  *                    NOT zookeeper servers, specified in host1:port1,host2:port2 form
  */

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

  import KafkaClusterHelper.{Err, LeaderOffset, SimpleConsumerConfig}

  // ConsumerConfig isn't serializable
  @transient private var _config: SimpleConsumerConfig = null

  def config: SimpleConsumerConfig = this.synchronized {
    if (_config == null) {
      _config = SimpleConsumerConfig(kafkaParams)
    }
    _config
  }

  def connect(host: String, port: Int): SimpleConsumer =
    new SimpleConsumer(host, port, config.socketTimeoutMs,
      config.socketReceiveBufferBytes, config.clientId)

  def findLeaders(
                   topicAndPartitions: Set[TopicAndPartition]
                 ): Either[Err, Map[TopicAndPartition, (String, Int)]] = {
    val topics = topicAndPartitions.map(_.topic)
    val response = getPartitionMetadata(topics).right
    val answer = response.flatMap { tms: Set[TopicMetadata] =>
      val leaderMap = tms.flatMap { tm: TopicMetadata =>
        tm.partitionsMetadata.flatMap { pm: PartitionMetadata =>
          val tp = TopicAndPartition(tm.topic, pm.partitionId)
          if (topicAndPartitions(tp)) {
            pm.leader.map { l =>
              tp -> (l.host -> l.port)
            }
          } else {
            None
          }
        }
      }.toMap

      if (leaderMap.keys.size == topicAndPartitions.size) {
        Right(leaderMap)
      } else {
        val missing = topicAndPartitions.diff(leaderMap.keySet)
        val err = new Err
        err.append(new SparkException(s"Couldn't find leaders for ${missing}"))
        Left(err)
      }
    }
    answer
  }

  def getPartitions(topics: Set[String]): Either[Err, Set[TopicAndPartition]] = {
    getPartitionMetadata(topics).right.map { r =>
      r.flatMap { tm: TopicMetadata =>
        tm.partitionsMetadata.map { pm: PartitionMetadata =>
          TopicAndPartition(tm.topic, pm.partitionId)
        }
      }
    }
  }

  def getPartitionMetadata(topics: Set[String]): Either[Err, Set[TopicMetadata]] = {
    val req = TopicMetadataRequest(
      TopicMetadataRequest.CurrentVersion, 0, config.clientId, topics.toSeq)
    val errs = new Err
    withBrokers(Random.shuffle(config.seedBrokers), errs) { consumer =>
      val resp: TopicMetadataResponse = consumer.send(req)
      val respErrs = resp.topicsMetadata.filter(m => m.errorCode != ErrorMapping.NoError)

      if (respErrs.isEmpty) {
        return Right(resp.topicsMetadata.toSet)
      } else {
        respErrs.foreach { m =>
          val cause = ErrorMapping.exceptionFor(m.errorCode)
          val msg = s"Error getting partition metadata for '${m.topic}'. Does the topic exist?"
          errs.append(new SparkException(msg, cause))
        }
      }
    }
    Left(errs)
  }


  //获取kafka最新的offset
  def getLatestLeaderOffsets(
                              topicAndPartitions: Set[TopicAndPartition]
                            ): Either[Err, Map[TopicAndPartition, LeaderOffset]] =
    getLeaderOffsets(topicAndPartitions, OffsetRequest.LatestTime)

  def getEarliestLeaderOffsets(
                                topicAndPartitions: Set[TopicAndPartition]
                              ): Either[Err, Map[TopicAndPartition, LeaderOffset]] =
    getLeaderOffsets(topicAndPartitions, OffsetRequest.EarliestTime)

  def getLeaderOffsets(
                        topicAndPartitions: Set[TopicAndPartition],
                        before: Long
                      ): Either[Err, Map[TopicAndPartition, LeaderOffset]] = {
    getLeaderOffsets(topicAndPartitions, before, 1).right.map { r =>
      r.map { kv =>
        // mapValues isnt serializable, see SI-7005
        kv._1 -> kv._2.head
      }
    }
  }

  private def flip[K, V](m: Map[K, V]): Map[V, Seq[K]] =
    m.groupBy(_._2).map { kv =>
      kv._1 -> kv._2.keys.toSeq
    }

  def getLeaderOffsets(
                        topicAndPartitions: Set[TopicAndPartition],
                        before: Long,
                        maxNumOffsets: Int
                      ): Either[Err, Map[TopicAndPartition, Seq[LeaderOffset]]] = {
    findLeaders(topicAndPartitions).right.flatMap { tpToLeader =>
      val leaderToTp: Map[(String, Int), Seq[TopicAndPartition]] = flip(tpToLeader)
      val leaders = leaderToTp.keys
      var result = Map[TopicAndPartition, Seq[LeaderOffset]]()
      val errs = new Err
      withBrokers(leaders, errs) { consumer =>
        val partitionsToGetOffsets: Seq[TopicAndPartition] =
          leaderToTp((consumer.host, consumer.port))
        val reqMap = partitionsToGetOffsets.map { tp: TopicAndPartition =>
          tp -> PartitionOffsetRequestInfo(before, maxNumOffsets)
        }.toMap
        val req = OffsetRequest(reqMap)
        val resp = consumer.getOffsetsBefore(req)
        val respMap = resp.partitionErrorAndOffsets
        partitionsToGetOffsets.foreach { tp: TopicAndPartition =>
          respMap.get(tp).foreach { por: PartitionOffsetsResponse =>
            if (por.error == ErrorMapping.NoError) {
              if (por.offsets.nonEmpty) {
                result += tp -> por.offsets.map { off =>
                  LeaderOffset(consumer.host, consumer.port, off)
                }
              } else {
                errs.append(new SparkException(
                  s"Empty offsets for ${tp}, is ${before} before log beginning?"))
              }
            } else {
              errs.append(ErrorMapping.exceptionFor(por.error))
            }
          }
        }
        if (result.keys.size == topicAndPartitions.size) {
          return Right(result)
        }
      }
      val missing = topicAndPartitions.diff(result.keySet)
      errs.append(new SparkException(s"Couldn't find leader offsets for ${missing}"))
      Left(errs)
    }
  }

  // Consumer offset api
  // scalastyle:off
  // https://cwiki.apache.org/confluence/display/KAFKA/A+Guide+To+The+Kafka+Protocol#AGuideToTheKafkaProtocol-OffsetCommit/FetchAPI
  // scalastyle:on

  // this 0 here indicates api version, in this case the original ZK backed api.
  private def defaultConsumerApiVersion: Short = 0

  // Try a call against potentially multiple brokers, accumulating errors
  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()
        }
      }
    }
  }

   //获取kafka最开始的offset
    def getFromOffsets(kafkaParams: Map[String, String], topics: Set[String]): Map[TopicAndPartition, Long] = {
      val reset = kafkaParams.get("auto.offset.reset").map(_.toLowerCase)
      val result = for {
        topicPartitions <- getPartitions(topics).right
        leaderOffsets <- (if (reset == Some("smallest")) {
          getEarliestLeaderOffsets(topicPartitions)
        } else {
          getLatestLeaderOffsets(topicPartitions)
        }).right
      } yield {
        leaderOffsets.map { case (tp, lo) =>
          (tp, lo.offset)
        }
      }
    KafkaClusterHelper.checkErrors(result)
  }
}


object KafkaClusterHelper {
  type Err = ArrayBuffer[Throwable]

  /** If the result is right, return it, otherwise throw SparkException */
  def checkErrors[T](result: Either[Err, T]): T = {
    result.fold(
      errs => throw new SparkException(errs.mkString("\n")),
      ok => ok
    )
  }


  case class LeaderOffset(host: String, port: Int, offset: Long)

  /**
    * High-level kafka consumers connect to ZK.  ConsumerConfig assumes this use case.
    * Simple consumers connect directly to brokers, but need many of the same configs.
    * This subclass won't warn about missing ZK params, or presence of broker params.
    */

  class SimpleConsumerConfig private(brokers: String, originalProps: Properties)
    extends ConsumerConfig(originalProps) {
    val seedBrokers: Array[(String, Int)] = brokers.split(",").map { hp =>
      val hpa = hp.split(":")
      if (hpa.size == 1) {
        throw new SparkException(s"Broker not in the correct format of : [$brokers]")
      }
      (hpa(0), hpa(1).toInt)
    }
  }


  object SimpleConsumerConfig {
    /**
      * Make a consumer config without requiring group.id or zookeeper.connect,
      * since communicating with brokers also needs common settings such as timeout
      */
    def apply(kafkaParams: Map[String, String]): SimpleConsumerConfig = {
      // These keys are from other pre-existing kafka configs for specifying brokers, accept either
      val brokers = kafkaParams.get("metadata.broker.list")
        .orElse(kafkaParams.get("bootstrap.servers"))
        .getOrElse(throw new SparkException(
          "Must specify metadata.broker.list or bootstrap.servers"))

      val props = new Properties()
      kafkaParams.foreach { case (key, value) =>
        // prevent warnings on parameters ConsumerConfig doesn't know about
        if (key != "metadata.broker.list" && key != "bootstrap.servers") {
          props.put(key, value)
        }
      }

      Seq("zookeeper.connect", "group.id").foreach { s =>
        if (!props.containsKey(s)) {
          props.setProperty(s, "")
        }
      }

      new SimpleConsumerConfig(brokers, props)
    }
  }
}  

4、pom.xml文件

    
      com.alibaba
      fastjson
      1.2.11
    

    
      org.apache.spark
      spark-sql_2.11
      1.6.1
    

    
      org.apache.spark
      spark-streaming_2.11
      1.6.1
    

    
    org.apache.spark
    spark-streaming-kafka_2.11
    1.6.1
    

  

5、结果展现

总计有40个Partition，每个Partition中只获取6条数据，结果保存到本地的数据只有240条

Zookeeper中存储TopicAndPartition对应的Offset

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