Spark StreamingはFlume,Mysql(FlumeベースのPullモード)を統合し,Mysqlにリアルタイムでデータを保存する

クラスタの割り当ては次のとおりです.

192.168.58.11	spark01
192.168.58.12	spark02
192.168.58.13	spark03
spark  ：spark-2.1.0-bin-hadoop2.7
flume  ：apache-flume-1.7.0-bin

flumeの構成は次のとおりです.

#flume    
#bin/flume-ng agent -n a1 -f conf/a1.conf -c conf -Dflume.root.logger=INFO,console
a1.channels = c1
a1.sinks = k1
a1.sources = r1

a1.sources.r1.type = spooldir
a1.sources.r1.spoolDir = /opt/kevin/log

a1.channels.c1.type = memory
a1.channels.c1.capacity = 100000
a1.channels.c1.transactionCapacity = 100000

a1.sinks.k1.type = org.apache.spark.streaming.flume.sink.SparkSink
a1.sinks.k1.channel = c1
a1.sinks.k1.hostname = 192.168.58.11(IP  )
a1.sinks.k1.port = 1234(   )

#  source、channel、sink
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1

Spark Streamingプログラム

package com.kk.sparkstreaming.flume

import org.apache.log4j.Level
import org.apache.log4j.Logger
import org.apache.spark.SparkConf
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.flume.FlumeUtils
import org.apache.spark.storage.StorageLevel
import org.apache.spark.api.java.StorageLevels
import java.sql.DriverManager
import java.sql.PreparedStatement
import java.sql.Connection

object FlumePull {
  def main(args: Array[String]): Unit = {
    //       
    // Logger.getLogger("org.apache.spark").setLevel(Level.ERROR)
    //Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)

    //  StreamingContext    StreamingContext(conf: SparkConf, batchDuration: Duration)
    //Duration    
    val sparkConf = new SparkConf().setAppName("FlumePull")
    val sparkStream = new StreamingContext(sparkConf, Seconds(3)); //  3       

    //  pull  
    val flumeDStream = FlumeUtils.createPollingStream(sparkStream, "192.168.58.11",1234, StorageLevels.MEMORY_AND_DISK_SER_2)
    
    // Flume     :
    //e    FLume        Event
    val data = flumeDStream.map { e =>
      {
        new String(e.event.getBody.array());
      }
    }
   data.print()
    val datas = data.map(line => {
      val index: Array[String] = line.split(",");
      val con = index(1);
      (con, 1)
    })

    datas.print()
    datas.foreachRDD(cs => {
      var conn: Connection = null;
      var ps: PreparedStatement = null;
      try {
        Class.forName("com.mysql.jdbc.Driver").newInstance();
        cs.foreachPartition(f => {
          conn = DriverManager.getConnection("jdbc:mysql://192.168.58.14:3306/storm?useUnicode=true&characterEncoding=utf8", "root", "kevin");
          ps = conn.prepareStatement("insert into result values(?,?)");
          f.foreach(s => {
            ps.setString(1, s._1);
            ps.setInt(2, s._2);
            ps.executeUpdate();
          })
        })
      } catch {
        case t: Throwable => t.printStackTrace() // TODO: handle error
      } finally {
        if (ps != null) {
          ps.close()
        }
        if (conn != null) {
          conn.close();
        }
      }
    })
    sparkStream.start()
    sparkStream.awaitTermination();
  }
     
}

pom.xmlファイル

	UTF-8
	2.2.1
	2.11.1




	
		org.scala-lang
		scala-library
		${scala.version}
	
	
	
		org.scala-lang
		scala-compiler
		${scala.version}
	
	
	
		org.scala-lang
		scala-reflect
		${scala.version}
	
	
	
		org.apache.spark
		spark-core_2.11
		${spark.version}
	
	
	
		org.apache.spark
		spark-streaming_2.11
		${spark.version}
	
	
	
		org.apache.spark
		spark-sql_2.11
		${spark.version}
	
	
	
		org.apache.spark
		spark-streaming-flume_2.11
		2.2.2
	

必要なjarパッケージ:spark-streaming-flume-sink_2.10-2.1.0.JArはクラスタで実行するにはspark-streaming-flume-sink_2.10-2.1.0.JArはFlumeのlibディレクトリの下にコピーし、SparkのjarパッケージをFlumeのlibディレクトリの下にコピーする必要がある.またmysqlドライバパッケージが必要で、クラスタの各マシンのSparkのjarsディレクトリの下でテストする:1.まずFlume 2を起動する必要がある.Spark Streamingプログラム3を起動します.ログファイルを/opt/kevin/logにコピー注意:問題をシーケンス化し、起動順序