spark streaming读kafka写elasticsearch（真实生产环境）

spark streaming作为当下依然火热的流计算引擎，对于kafka和elasticsearch都有很好的api支持，以下是笔者在实际生产环境中的一个数据从kafka到es案例。

首先说一下几个重点：

• kafka消费offset使用mysql保存，一开始使用了ScalikeJDBC这个包来实现对数据库的访问，后来感觉用起来不太灵活，所以就写了原生的mysql JDBC工具类，也有不少好处，比如事务控制更加灵活，不再需要导入ScalikeJDBC的那好几个jar包
• 数据库获取offset后，会与kafka最早的offset做一个校验，因为机器资源紧张，kafka里数据只保存一天，如果spark streaming任务因为故障或是其他原因停了一段时间，这时再开启任务从mysql获取的offset有可能会超出kafka里offset的范围，这时就要把当前的offset进行替换。如果对数据处理速度有要求的话这一步可以注释掉，等需要的时候再开启。

第一步 Maven配置

这里相关工具的版本：
scala:2.11.8
spark:2.3.4
kafka:0.10.1.0
elasticsearch:7.0.0
maven配置如下：

	<properties>
        <scala.version>2.11.8</scala.version>
        <spark.version>2.3.4</spark.version>
    </properties>

    <dependencies>
        <!-- 导入scala的依赖 -->
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency>

        <!-- 导入spark的依赖 -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.elasticsearch</groupId>
            <artifactId>elasticsearch-spark-20_2.11</artifactId>
            <version>7.0.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka-0-10_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
            <version>1.2.49</version>
        </dependency>
        <!-- mysql的连接驱动依赖 -->
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.38</version>
        </dependency>
    </dependencies>

第二步 Mysql建表

CREATE TABLE `kafka_offset` (
  `topic` varchar(255) NOT NULL,
  `groupid` varchar(128) NOT NULL,
  `partition` int(11) NOT NULL,
  `fromoffset` bigint(20) DEFAULT NULL,
  `untiloffset` bigint(20) DEFAULT NULL
  PRIMARY KEY (`topic`,`groupid`,`partition`) USING BTREE
) ENGINE=InnoDB DEFAULT CHARSET=utf8

第二步 JDBC工具类

JDBCUtil.scala

package com.test

import java.math.BigDecimal
import java.sql._

import org.apache.kafka.common.TopicPartition
import org.apache.spark.sql.Row
import org.apache.spark.streaming.kafka010.OffsetRange

import scala.language.postfixOps

object JDBCUtil {

  /*
  1.jdbc 地址
  2.用户名
  3.密码
   */
  val url="jdbc:mysql://localhost:3306/test?characterEncoding=utf-8&autoReconnect=true&failOverReadOnly=false&useSSL=false&rewriteBatchedStatements=true"
  val username="user"
  val password="12345"

  /*
  获取连接
   */
  def getConnection:Connection={
    Class.forName("com.mysql.jdbc.Driver")
    DriverManager.getConnection(url,username,password)
  }
  /*
  关闭连接
   */
  def closeConnection(conn:Connection): Unit ={
    if(conn!=null)
      conn.close()
  }
  /*
  关闭查询通道
   */
  def closePreparedStatement(prepareStatement:PreparedStatement): Unit ={
    if(prepareStatement!=null)
      prepareStatement.close()
  }

  def closeResultSet(rs:ResultSet):Unit={
    if(rs!=null)
      rs.close()
  }

  /**
    * 查询kafka的offset
    * @param groupid
    * @return
    */
  def selectKafkaOffset(groupid:String):Map[TopicPartition,Long]={
    val conn:Connection=getConnection
    val sql="select * from `kafka_offset` where `groupid`='"+groupid+"' "
    val pstmt:PreparedStatement=conn.prepareStatement(sql)
    val rs:ResultSet=pstmt.executeQuery()
    var map=Map[TopicPartition,Long]()
    while(rs.next()){
      map+=(new TopicPartition(rs.getString("topic"),rs.getInt("partition")) -> rs.getLong("untiloffset"))
    }
    closePreparedStatement(pstmt)
    closeResultSet(rs)
    closeConnection(conn)
    map
  }

  /**
    * 插入kafka的offset
    * @param offsetRanges
    * @param groupid
    */
  def replaceKafkaOffset(offsetRanges:scala.Array[OffsetRange],groupid:String):Unit={
    val conn:Connection=getConnection
    val sql="replace into `kafka_offset`(`topic`,`groupid`,`partition`,`fromoffset`,`untiloffset`) values (?,?,?,?,?)"
    val preparedStatement:PreparedStatement=conn.prepareStatement(sql)
    for(or <- offsetRanges){
      preparedStatement.setString(1,or.topic)
      preparedStatement.setString(2,groupid)
      preparedStatement.setInt(3,or.partition)
      preparedStatement.setLong(4,or.fromOffset)
      preparedStatement.setLong(5,or.untilOffset)
      preparedStatement.addBatch()
    }
    preparedStatement.executeBatch()
    closePreparedStatement(preparedStatement)
    closeConnection(conn)
  }

}

第三步 主程序

package com.test

import com.alibaba.fastjson.JSON
import org.apache.kafka.clients.consumer.{ConsumerConfig, ConsumerRecord, KafkaConsumer}
import org.apache.kafka.common.TopicPartition
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.{SQLContext, SparkSession}
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka010._
import org.apache.spark.streaming.{Minutes, Seconds, StreamingContext}
import org.elasticsearch.spark.rdd.EsSpark
import scala.collection.JavaConverters._

object KafkaToES {

  //	检查mysql里offset是否超出范围
  def getCheckedOffset(topics:Set[String],kafkaParams:Map[String, Object],fromdbOffset:Map[TopicPartition,Long]):Map[TopicPartition,Long]={
    val kc=new KafkaConsumer[String,String](kafkaParams.asJava)
    val beginOffsetMap=scala.collection.mutable.Map[TopicPartition,Long]()
    for (topic <- topics) {
      kc.partitionsFor(topic).asScala.foreach(partitionInfo => {
        val topicPartition=new TopicPartition(topic,partitionInfo.partition())
        kc.assign(Seq(topicPartition).asJava)
        kc.seekToBeginning(Seq(topicPartition).asJava)
        beginOffsetMap+=(topicPartition -> kc.position(topicPartition))
      })
    }
    kc.close()
    fromdbOffset.map(f => {
      val beginOffset=beginOffsetMap.getOrElse(f._1,0).toString.toLong
      if(beginOffset > f._2){
        (f._1,beginOffset)
      }else{
        f
      }
    })
  }

  def run():Unit={
		
	  val conf=new SparkConf().setAppName("KafkaToES")
	  //	是否自动创建es index
	  conf.set("es.index.auto.create", "true")
	  //	es节点地址
      conf.set("es.nodes","ip1,ip2")
	  //	es的账号密码,没有的话不用配
      conf.set("es.net.http.auth.user", "user")
      conf.set("es.net.http.auth.pass", "12345")
	  //	配置spark序列化
	  conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
	  //	设置executor的堆外内存,数据量大的话建议调大一点
	  conf.set("spark.executor.memoryOverhead","2048")
	  //   避免spark每次任务获取过多的kafka条数  公式为 kafka分片数*调度周期(秒)*maxRatePerPartition = 总条数
	  conf.set("spark.streaming.backpressure.enabled","true")
	  conf.set("spark.streaming.kafka.maxRatePerPartition","1000")
	  
	  val sc=new SparkContext(conf)
	  val streamContext=new StreamingContext(sc,Seconds(10))
	  
	  //	设置groupid和要读取的topic
	  val groupid="test"
	  val topics=Set("topic1","topic2")
	  
	  //	配置kafka相关参数
      val kafkaParams = Map[String, Object](
        "bootstrap.servers" -> "ip1:9092,ip2:9092",
        "key.deserializer" -> classOf[StringDeserializer],
        "value.deserializer" -> classOf[StringDeserializer],
        "group.id" -> groupid,
        "auto.offset.reset" -> "latest",
        "enable.auto.commit" -> (false: java.lang.Boolean),
		//	分区获取的最大数据量,根据实际内存调整
        "max.partition.fetch.bytes" -> "2097152"
      )
		
	  //	从mysql获取offset
      val fromdbOffset:Map[TopicPartition,Long]=JDBCUtil.selectKafkaOffset(groupid)

      var kafkaDStream:InputDStream[ConsumerRecord[String, String]]=null

      if(fromdbOffset.isEmpty){
	    //	第一次启动mysql里没有offset信息的情况
        kafkaDStream=KafkaUtils.createDirectStream[String, String](
          streamContext,
          LocationStrategies.PreferConsistent,
          ConsumerStrategies.Subscribe[String, String](topics, kafkaParams)
        )
      }else{
	    //	mysql里有offset数据,校验并消费
        val checkedOffset=getCheckedOffset(topics,kafkaParams,fromdbOffset)
        kafkaDStream=KafkaUtils.createDirectStream[String,String](
          streamContext,
          LocationStrategies.PreferConsistent,
          ConsumerStrategies.Assign[String,String](checkedOffset.keys,kafkaParams,checkedOffset)
        )
      }

	  //	开始处理消费到的数据
      kafkaDStream.foreachRDD(rdd => {
	    //	获取新的offset信息
        val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges

		val jsonRDD=rdd.map(_.value()).map(l =>{
		  //	业务处理部分
          val jsonObject=JSON.parseObject(l)
		  
		  //	拿到kafka里的message,需要的话进行字段解析,解析结果放入jsonObject
          val s=jsonObject.get("message").toString
		  
          jsonObject.toJSONString
        })

		//	配置插入es的相关参数
        val mapConf = Map(
		  //	要插入es的index
          ("es.resource" , "spark-test"),
		  //	插入过程中的一些配置,根据实际情况调整
          ("es.batch.size.bytes" , "10mb"),
          ("es.batch.size.entries" , "50000"),
          ("es.batch.write.retry.count", "10"),
          ("es.batch.write.retry.wait", "3000")
        )

		//	执行插入
        EsSpark.saveJsonToEs(jsonRDD,mapConf)
		
        //	提交offset到mysql
        JDBCUtil.replaceKafkaOffset(offsetRanges,groupid)
      })
	  
	 //	启动
    streamContext.start()
    streamContext.awaitTermination()
  }

  def main(args: Array[String]): Unit = {
    run()
  }
}

第四步 报错解决

1、org.apache.kafka.clients.consumer.OffsetOutOfRangeException: Offsets out of range with no configured reset policy for partitions
解决：使用offset校验方法，或者直接换个groupid，这样会丢一部分数据
2、Container killed by YARN for exceeding memory limits. 16.9 GB of 16 GB physical memory used. Consider boosting spark.yarn.executor.memoryOverhead
解决：一开始按它说的调大了spark.yarn.executor.memoryOverhead这个参数，但是看日志的时候发现应该用spark.executor.memoryOverhead这个参数，可能是spark版本问题

来源：CSDN

作者：极伪

链接：https://blog.csdn.net/weixin_42473019/article/details/104742668