Spark 基於自定義分區的方式 分析點擊流日誌

通過自定義分區的方式，實現對點擊流日誌統計，並取出每個模塊中點擊排行前三的鏈接。

點擊日誌樣本：

20160321101954 http://java.study.163.com/java/course/javaee.shtml
20160321101954 http://java.study.163.com/java/course/android.shtml
20160321101954 http://java.study.163.com/java/video.shtml
20160321101954 http://java.study.163.com/java/teacher.shtml
20160321101954 http://java.study.163.com/java/course/android.shtml
20160321101954 http://php.study.163.com/php/teacher.shtml
20160321101954 http://net.study.163.com/net/teacher.shtml

/**
  * Created by zn on 2017/5/4.
  */
object UrlCountPartition {

  def main(args: Array[String]) {

    val conf = new SparkConf().setAppName("UrlCountPartition").setMaster("local[2]")
    val sc = new SparkContext(conf)

    //rdd1將數據切分，元組中放的是（URL， 1）
    val rdd1 = sc.textFile("/Users/ning/Downloads/wangyiyun.log").map(line => {
      val f = line.split("\t")
      (f(1), 1)
    })
    val rdd2 = rdd1.reduceByKey(_ + _)

    val rdd3 = rdd2.map(t => {
      val url = t._1
      val host = new URL(url).getHost
      (host, (url, t._2))
    })

    val ints = rdd3.map(_._1).distinct().collect()  
    //使用自定義分區
    val hostParitioner = new HostParitioner(ints)
     
//    val rdd4 = rdd3.partitionBy(new HashPartitioner(ints.length))

    val rdd4 = rdd3.partitionBy(hostParitioner).mapPartitions(it => {
      it.toList.sortBy(_._2._2).reverse.take(2).iterator
    })

    rdd4.saveAsTextFile("/Users/ning/Downloads/out.spark")


    //println(rdd4.collect().toBuffer)
    sc.stop()

  }
}

/**
  * 決定了數據到哪個分區裏面
  * @param ins
  */
class HostParitioner(ins: Array[String]) extends Partitioner {

  val parMap = new mutable.HashMap[String, Int]()
  var count = 0
  for(i <- ins){
    parMap += (i -> count)
    count += 1
  }

  override def numPartitions: Int = ins.length

  override def getPartition(key: Any): Int = {
    parMap.getOrElse(key.toString, 0)
  }
}