大数据学习day22------spark05------1. 学科最受欢迎老师解法补充 2. 自定义排序

1. 学科最受欢迎老师解法补充

day21中该案例的解法四还有一个问题，就是当各个老师受欢迎度是一样的时候，其排序规则就处理不了，以下是对其优化的解法

实现方式五

FavoriteTeacher5

package com._51doit.spark04

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

import scala.collection.mutable

object FavoriteTeacher5 {
  def main(args: Array[String]): Unit = {
    val isLocal = args(0).toBoolean
    //创建SparkConf，然后创建SparkContext
    val conf = new SparkConf().setAppName(this.getClass.getSimpleName)
    if (isLocal) {
      conf.setMaster("local[*]")
    }
    val sc = new SparkContext(conf)
    //指定以后从哪里读取数据创建RDD
    val lines: RDD[String] = sc.textFile(args(1))
    //对数据进行切分
    val subjectTeacherAndOne = lines.map(line => {
      val fields = line.split("/")
      val subject = fields(2).split("[.]")(0)
      val teacher = fields(3)
      ((subject, teacher), 1)
    })

    //计算所有的学科，并收集到Driver端
    val subjects: Array[String] = subjectTeacherAndOne.map(_._1._1).distinct().collect()
    //paritioner是在Driver端被new出来的，但是他的方法是在Executor中被调用的
    val partitioner = new SubjectPartitioner3(subjects)
    //根据指定的key和分区器进行聚合(减少一次shuffle)
    val reduced: RDD[((String, String), Int)] = subjectTeacherAndOne.reduceByKey(partitioner, _+_)
    val topN = args(2).toInt
    val result = reduced.mapPartitions(it => {
      //定义一个key排序的集合TreeSet
      val sorter = new mutable.TreeSet[OrderingBean]()
      //遍历出迭代器中的数据
      it.foreach(t => {
        sorter += new OrderingBean(t._1._1, t._1._2, t._2)
        if (sorter.size > topN) {
          val last = sorter.last
          //移除最后一个
          sorter -= last
        }
      })
      sorter.iterator
    })

    val r = result.collect()

    println(r.toBuffer)

    sc.stop()

  }

}

class SubjectPartitioner3(val subjects: Array[String]) extends Partitioner {

  //初始化分器的分区规则
  val rules = new mutable.HashMap[String, Int]()
  var index = 0
  for(sub <- subjects) {
    rules(sub) = index
    index += 1
  }

  override def numPartitions: Int = subjects.length

  //该方法会在Executor中的Task中被调用
  override def getPartition(key: Any): Int = {
    val tuple = key.asInstanceOf[(String, String)]
    val subject = tuple._1
    //到实现初始化的规则中查找这个学科对应的分区编号
    rules(subject)

  }
}

View Code

OrderingBean(重新定义的排序规则)

package com._51doit.spark04

import scala.collection.mutable.ArrayBuffer

class OrderingBean(val subject: String, val name: String, val count: Int) extends Ordered[OrderingBean] with Serializable {

  val equiv = new ArrayBuffer[(String, String, Int)]()

  equiv += ((subject, name, count))

  override def compare(that: OrderingBean): Int = {

    if (this.count == that.count) {
      equiv += ((that.subject, that.name, that.count))
      0
    } else {
      -(this.count - that.count)
    }
  }


  override def toString: String =
    if (equiv.size > 1) {
      equiv.toString()
    } else
      s"($subject, $name, $count)"
}

View Code

实现方式六（使用repartitionAndSortWithinPartitions）

　　repartitionAndSortWithinPartitions按照指定的分区器进行排序并且在每个分区内进行排序

FavoriteTeacher6

object FavoriteTeacher06 {
  def main(args: Array[String]): Unit = {
    val isLocal = args(0).toBoolean
    //创建SparkConf，然后创建SparkContext
    val conf = new SparkConf().setAppName(this.getClass.getSimpleName)
    if (isLocal) {
      conf.setMaster("local[1]")
    }
    val sc = new SparkContext(conf)
    //指定以后从哪里读取数据创建RDD
    val lines: RDD[String] = sc.textFile(args(1))
    //对数据进行切分
    val subjectTeacherAndOne = lines.map(line => {
      val fields = line.split("/")
      val subject = fields(2).split("[.]")(0)
      val teacher = fields(3)
      ((subject, teacher), 1)
    })
    //聚合
    val reduced = subjectTeacherAndOne.reduceByKey(_ + _)
    //计算所有的学科，并收集到Driver端
    val subjects: Array[String] = reduced.map(_._1._1).distinct().collect()
    //paritioner是在Driver端被new出来的，但是他的方法是在Executor中被调用的
    val partitioner = new SubjectPartitionerV2(subjects)
    //对原来的数据进行整理
    val keyByRDD: RDD[((String, String, Int), Null)] = reduced
      .map(t => ((t._1._1, t._1._2, t._2), null))
    //隐式转换
    implicit val orderRules = new Ordering[(String, String, Int)] {
      override def compare(x: (String, String, Int), y: (String, String, Int)): Int = {
        -(x._3 - y._3)
      }
    }
    val topN = args(2).toInt
    //repartitionAndSortWithinPartitions按照指定的分区器进行排序并且在每个分区内进行排序
    val result: RDD[((String, String, Int), Null)] = keyByRDD
      .repartitionAndSortWithinPartitions(partitioner)
    result.foreachPartition(it => {
      var index = 1
      while (it.hasNext && index <= topN) {
        val tuple = it.next()
        println(tuple)
        index += 1
      }
    })
    sc.stop()
  }
}

View Code

SubjectPartitionerV2

class SubjectPartitionerV2(val subjects: Array[String]) extends Partitioner {

  //初始化分器的分区规则
  val rules = new mutable.HashMap[String, Int]()
  var index = 0
  for(sub <- subjects) {
    rules(sub) = index
    index += 1
  }
  override def numPartitions: Int = subjects.length
  //该方法会在Executor中的Task中被调用
  override def getPartition(key: Any): Int = {
    val tuple = key.asInstanceOf[(String, String, Int)]
    val subject = tuple._1
    //到实现初始化的规则中查找这个学科对应的分区编号
    rules(subject)
  }
}

View Code

2. 自定义排序

数据形式：姓名，年龄，颜值

需求：首先按照颜值排序（颜值高的排前面），当颜值相同的情况下，年龄小的人排前面

2.1 第一种形式

思路，定义一个Boy类（case class），用来加载这些属性，利用隐式转换定义一个排序规则，具体如下

CustomSort1

object CustomSort1 {
  def main(args: Array[String]): Unit = {
    import com._51doit.spark04.MyPredef

    val conf: SparkConf = new SparkConf()
      .setAppName(this.getClass.getSimpleName)
      .setMaster("local[*]")

    // 创建SparkContext
    val sc: SparkContext = new SparkContext(conf)
    val lines: RDD[String] = sc.parallelize(List("jack,30,99.99", "sherry,18,9999.99", "Tom,29,99.99"))
    // 处理数据,使用mapPartitions，减少Boy的创建
    val BoyRDD: RDD[Boy] = lines.mapPartitions(it => {
      it.map(t => {
        val split: Array[String] = t.split(",")
        Boy(split(0), split(1).toInt, split(2).toDouble)
      })
    })
    import MyPredef.Boy2OrderingBoy
    val res: RDD[Boy] = BoyRDD.sortBy(t => t)
    print(res.collect().toBuffer)
  }
}

MyPredef

两种写法都行

Ordered的形式

object MyPredef {
  implicit val Boy2OrderingBoy: Boy => Ordered[Boy] = (boy:Boy) => new Ordered[Boy]{
    override def compare(that: Boy): Int = {
      if(boy.fv == that.fv){
        boy.age - that.age
      } else{
        -(boy.fv - that.fv).toInt
      }
    }
  }
}

Ordering的形式

object MyPredef {
    implicit val Boy2OrderingBoy: Ordering[Boy] = new Ordering[Boy] {
      override def compare(x: Boy, y:Boy): Int = {
        if (x.fv == y.fv) {
          x.age - y.age
        } else {
          -(x.fv - y.fv).toInt
        }
      }
    }
}

Boy

case class Boy(name:String, age:Int, fv: Double)

2.2 第二种形式（借助元组）

object CustomSort2 {
  def main(args: Array[String]): Unit = {
    val conf: SparkConf = new SparkConf()
      .setAppName(this.getClass.getSimpleName)
      .setMaster("local[*]")

    // 创建SparkContext
    val sc: SparkContext = new SparkContext(conf)
    val lines: RDD[String] = sc.parallelize(List("jack,30,99.99", "sherry,18,9999.99", "Tom,29,99.99"))
    // 处理数据,使用mapPartitions，减少Boy的创建
    val tpRDD: RDD[(String, Int, Double)] = lines.mapPartitions(it => {
      it.map(t => {
        val split: Array[String] = t.split(",")
        (split(0), split(1).toInt, split(2).toDouble)
      })
    })
    // 使用元组的默认规则进行排序
    val sorted: RDD[(String, Int, Double)] = tpRDD.sortBy(t => (-t._3, t._2))
    println(sorted.collect().toBuffer)
  }
}

大数据学习day22------spark05------1. 学科最受欢迎老师解法补充 2. 自定义排序

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