Spark RDD案例（一）分组TopN

Spark RDD案例（一）分组TopN

1. 背景

1. 作为分布式数据处理引擎，Spark抽象出了很多算子，使得编程对比mapreduce更加遍历，实现需求时，也可以更加灵活，但也更容易出错。
2. 本文是大数据常见场景分组TopN的简化案例，实际企业生产中也会相对频繁遇到类似需求

2. 案例

1. 需求
   以下数据是类似网站日志的记录，需要求出每个科目老师访问次数最多的那2个。
2. 数据

http://bigdata.doit.cn/laozhang
http://bigdata.doit.cn/laozhang
http://bigdata.doit.cn/laozhao
http://bigdata.doit.cn/laozhao
http://bigdata.doit.cn/laozhao
http://bigdata.doit.cn/laozhao
http://bigdata.doit.cn/laozhao
http://bigdata.doit.cn/laoduan
http://bigdata.doit.cn/laoduan
http://javaee.doit.cn/xiaozhang
http://javaee.doit.cn/xiaozhang
http://javaee.doit.cn/laowang
http://javaee.doit.cn/laowang
http://javaee.doit.cn/laowang

2.1 代码一

package com.doit.practice

import com.doit.foreach.ForeachTest
import org.apache.spark.rdd.RDD
import org.apache.spark.{
   
   Partitioner, SparkConf, SparkContext}

/*
* 数据形式如下，http://bigdata.doit.cn/laozhang
* 需求是得出每个科目最受欢迎老师
* 以下是所有数据

* */
object TopNTest1 {
   
   
  def main(args: Array[String]): Unit = {
   
   

    val conf: SparkConf = new SparkConf().setAppName(TopNTest1.getClass.getSimpleName).setMaster("local[*]")

    val sc = new SparkContext(conf)

    val rdd1: RDD[String] = sc.textFile("E:\\DOITLearning\\12.Spark\\teacherlog.txt")

    // 先把数据匹配出来
    val mapedRdd: RDD[((String, String), Int)] = rdd1.map(line => {
   
   
      val strings: Array[String] = line.split("/+")
      val domian: String = strings(1)
      val domainSplits: Array[String] = domian.split("\\.")
      val subject: String = domainSplits(0)
      val name: String = strings(2)

      ((subject, name), 1)
    })
    /*
    * mapedRdd: ArrayBuffer(((bigdata,laozhang),1), ((bigdata,laozhang),1), 
    * ((bigdata,laozhao),1), ((bigdata,laozhao),1), ((bigdata,laozhao),1),
    *  ((bigdata,laozhao),1), ((bigdata,laozhao),1), ((bigdata,laoduan),1), 
    * ((bigdata,laoduan),1), ((javaee,xiaozhang),1), ((javaee,xiaozhang),1), 
    * ((javaee,laowang),1), ((javaee,laowang),1), ((javaee,laowang),1))
    * */
    println("mapedRdd: " + mapedRdd.collect().toBuffer)

    // 使用key进行聚合，把观看次数聚合起来
    val reduceRDD: RDD[((String, String), Int)] = mapedRdd.reduceByKey(_ + _)
    /*
    * reduceRDD: ArrayBuffer(((javaee,xiaozhang),2), ((bigdata,laozhang),2), 
    * ((javaee,laowang),3), ((bigdata,laozhao),5), ((bigdata,laoduan),2))
    * */
    println("reduceRDD: " + reduceRDD.collect().toBuffer)

    // 降序排列
    val sortedRDD: RDD[((String, String), Int)] = reduceRDD.sortBy(_._2, false)
    /*
    * sortedRDD: ArrayBuffer(((bigdata,laozhao),5), ((javaee,laowang),3), 
    * ((javaee,xiaozhang),2), ((bigdata,laozhang),2), ((bigdata,laoduan),2))
    * */
    println("sortedRDD: " + sortedRDD.collect().toBuffer)

    // 按照学科分类
    val subjectReducedRDD: RDD[(String, Iterable[((String, String), Int)])] = reduceRDD.groupBy(_._1._1)
    /*
    * subjectReducedRDD: ArrayBuffer((javaee,CompactBuffer(((javaee,xiaozhang),2), ((javaee,laowang),3))), 
    * (bigdata,CompactBuffer(((bigdata,laozhang),2), ((bigdata,laozhao),5), ((bigdata,laoduan),2))))
    * */
    println("subjectReducedRDD: " + subjectReducedRDD.collect().toBuffer)

    // 将学科分类后数据进行区粒度处理
    val res: RDD[(String, List[((String, String), Int)])] = subjectReducedRDD.mapValues(iter => {
   
   
      iter.toList.sortBy(-_._2).take(2)
    })
    /*
    * res: ArrayBuffer((javaee,List(((javaee,laowang),3), ((javaee,xiaozhang),2))), 
    * (bigdata,List(((bigdata,laozhao),5), ((bigdata,laozhang),2))))
    * */
    println("res: " + res.collect().toBuffer)

    sc.stop()
  }
}

运行结果

res: ArrayBuffer((javaee,List(((javaee,laowang),3), ((javaee,xiaozhang),2))), (bigdata,List(((bigdata,laozhao),5), ((bigdata,laozhang),2))))

上述代码，有用到toList再排序，需要注意内存消耗以及数据量大小，过大则不适合这么处理。

2.2 代码二

val conf: SparkConf = new SparkConf().setAppName(TopNTest1.getClass.getSimpleName).setMaster("local[*]")

    val sc = new SparkContext(conf)

    // http://javaee.doit.cn/laowang
    val rdd1: RDD[String] = sc.textFile("E:\\DOITLearning\\12.Spark\\teacherlog.txt")

    val reducedRDD: RDD[((String, String), Int)] = rdd1.map(line => {
   
   
      val strings: Array[String] = line.split("/+")
      val domain: String = strings(1)
      val name: String = strings(2)
      val domainSplits: Array[String] = domain.split("\\.")
      val subject: String = domainSplits(0)

      ((subject, name), 1)
    }).reduceByKey(_ + _)
    println("reducedRDD: " + reducedRDD.collect().toBuffer)
    /**
     * reducedRDD: ArrayBuffer(((javaee,xiaozhang),2), ((bigdata,laozhang),2),
     * ((javaee,laowang),3), ((bigdata,laozhao),5), ((bigdata,laoduan),2))
     **/

    // 获取科目数组，注意需要去重
    val subjects: Array[String] = reducedRDD.map(e => e._1._1).distinct().collect()
    
    /**
     * ArrayBuffer((javaee,CompactBuffer(((javaee,xiaozhang),2), ((javaee,laowang),3))),
     * (bigdata,CompactBuffer(((bigdata,laozhang),2), ((bigdata,laozhao),5), ((bigdata,laoduan),2))))
     **/

    for (elem <- subjects) {
   
   
      // 过滤出当前科目的所有数据
      val filteredSubjectRDD: RDD[((String, String), Int)] = reducedRDD.filter(e => e._1._1