Question

我是spark scala的新手，想要找到每个部门的最高工资

Dept,Salary
Dept1,1000
Dept2,2000
Dept1,2500
Dept2,1500
Dept1,1700
Dept2,2800

我在下面的代码中实现了

import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.SparkConf


object MaxSalary {
  val sc = new SparkContext(new SparkConf().setAppName("Max Salary").setMaster("local[2]"))

  case class Dept(dept_name : String, Salary : Int)

  val data = sc.textFile("file:///home/user/Documents/dept.txt").map(_.split(","))
  val recs = data.map(r => (r(0), Dept(r(0), r(1).toInt)))
  val a = recs.max()???????
})
}

但坚持如何实现group by和max功能。我正在使用配对RDD。

由于

Answer 1

这可以使用带有以下代码的RDD来完成：

val emp = sc.textFile("file:///home/user/Documents/dept.txt")
            .mapPartitionsWithIndex( (idx, row) => if(idx==0) row.drop(1) else row )
            .map(x => (x.split(",")(0).toString, x.split(",")(1).toInt))

val maxSal = emp.reduceByKey(math.max(_,_))

应该给你：

Array[(String, Int)] = Array((Dept1,2500), (Dept2,2800))

Answer 2

如果您使用的是数据集，那么这就是解决方案

case class Dept(dept_name : String, Salary : Int)


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

  val sq = new SQLContext(sc)

  import sq.implicits._
  val file = "resources/ip.csv"

  val data = sc.textFile(file).map(_.split(","))

  val recs = data.map(r => Dept(r(0), r(1).toInt )).toDS()


  recs.groupBy($"dept_name").agg(max("Salary").alias("max_solution")).show()

输出：

+---------+------------+
|dept_name|max_solution|
+---------+------------+
|    Dept2|        2800|
|    Dept1|        2500|
+---------+------------+

Scala - Groupby和Max on pair RDD

2 个答案: