spark中广播变量的使用

import java.sql.{Connection, DriverManager, PreparedStatement}

import org.apache.spark.broadcast.Broadcast
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

/**
  * Created by zx on 2017/10/9.
  */
object IpLoaction2 {

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

    val conf = new SparkConf().setAppName("IpLoaction1").setMaster("local[4]")

    val sc = new SparkContext(conf)

    //取到HDFS中的ip规则
    val rulesLines:RDD[String] = sc.textFile(args(0))
    //整理ip规则数据
    val ipRulesRDD: RDD[(Long, Long, String)] = rulesLines.map(line => {
      val fields = line.split("[|]")
      val startNum = fields(2).toLong
      val endNum = fields(3).toLong
      val province = fields(6)
      (startNum, endNum, province)
    })

    //将分散在多个Executor中的部分IP规则收集到Driver端
    val rulesInDriver: Array[(Long, Long, String)] = ipRulesRDD.collect()

    //将Driver端的数据广播到Executor
    //广播变量的引用（还在Driver端）
    val broadcastRef: Broadcast[Array[(Long, Long, String)]] = sc.broadcast(rulesInDriver)

    //创建RDD，读取访问日志
    val accessLines: RDD[String] = sc.textFile(args(1))

    //整理数据
    val proviceAndOne: RDD[(String, Int)] = accessLines.map(log => {
      //将log日志的每一行进行切分
      val fields = log.split("[|]")
      val ip = fields(1)
      //将ip转换成十进制
      val ipNum = MyUtils.ip2Long(ip)
      //进行二分法查找，通过Driver端的引用或取到Executor中的广播变量
      //（该函数中的代码是在Executor中别调用执行的，通过广播变量的引用，就可以拿到当前Executor中的广播的规则了）
      //Driver端广播变量的引用是怎样跑到Executor中的呢？
      //Task是在Driver端生成的，广播变量的引用是伴随着Task被发送到Executor中的
      val rulesInExecutor: Array[(Long, Long, String)] = broadcastRef.value
      //查找
      var province = "未知"
      val index = MyUtils.binarySearch(rulesInExecutor, ipNum)
      if (index != -1) {
        province = rulesInExecutor(index)._3
      }
      (province, 1)
    })

    //聚合
    //val sum = (x: Int, y: Int) => x + y
    val reduced: RDD[(String, Int)] = proviceAndOne.reduceByKey(_+_)

    //将结果打印
    //val r = reduced.collect()
    //println(r.toBuffer)


    /**
    reduced.foreach(tp => {
      //将数据写入到MySQL中
      //问？在哪一端获取到MySQL的链接的？
      //是在Executor中的Task获取的JDBC连接
      val conn: Connection = DriverManager.getConnection("jdbc:mysql://localhost:3306/bigdata?charatorEncoding=utf-8", "root", "123568")
      //写入大量数据的时候，有没有问题？
      val pstm = conn.prepareStatement("...")
      pstm.setString(1, tp._1)
      pstm.setInt(2, tp._2)
      pstm.executeUpdate()
      pstm.close()
      conn.close()
    })
      */

    //一次拿出一个分区(一个分区用一个连接，可以将一个分区中的多条数据写完在释放jdbc连接，这样更节省资源)
//    reduced.foreachPartition(it => {
//      val conn: Connection = DriverManager.getConnection("jdbc:mysql://localhost:3306/bigdata?characterEncoding=UTF-8", "root", "123568")
//      //将数据通过Connection写入到数据库
//      val pstm: PreparedStatement = conn.prepareStatement("INSERT INTO access_log VALUES (?, ?)")
//      //将一个分区中的每一条数据拿出来
//      it.foreach(tp => {
//        pstm.setString(1, tp._1)
//        pstm.setInt(2, tp._2)
//        pstm.executeUpdate()
//      })
//      pstm.close()
//      conn.close()
//    })

    reduced.foreachPartition(it => MyUtils.data2MySQL(it))


    sc.stop()



  }
}