Spark SQL 2.4.8 操作 Dataframe的两种方式_Sql Server

一、测试数据

7369,SMITH,CLERK,7902,1980/12/17,800,20
7499,ALLEN,SALESMAN,7698,1981/2/20,1600,300,30
7521,WARD,SALESMAN,7698,1981/2/22,1250,500,30
7566,JONES,MANAGER,7839,1981/4/2,2975,20
7654,MARTIN,SALESMAN,7698,1981/9/28,1250,1400,30
7698,BLAKE,MANAGER,7839,1981/5/1,2850,30
7782,CLARK,MANAGER,7839,1981/6/9,2450,10
7788,SCOTT,ANALYST,7566,1987/4/19,3000,20
7839,KING,PRESIDENT,1981/11/17,5000,10
7844,TURNER,SALESMAN,7698,1981/9/8,1500,0,30
7876,ADAMS,CLERK,7788,1987/5/23,1100,20
7900,JAMES,CLERK,7698,1981/12/3,9500,30
7902,FORD,ANALYST,7566,1981/12/3,3000,20
7934,MILLER,CLERK,7782,1982/1/23,1300,10

二、创建DataFrame

方式一：DSL方式操作

实例化SparkContext和SparkSession对象
利用StructType类型构建schema，用于定义数据的结构信息
通过SparkContext对象读取文件，生成RDD
将RDD[String]转换成RDD[Row]
通过SparkSession对象创建dataframe
完整代码如下：

				?

									package com.scala.demo.sql

									import org.apache.spark.{SparkConf, SparkContext}

									import org.apache.spark.sql.{Row, SparkSession}

									import org.apache.spark.sql.types.{DataType, DataTypes, StructField, StructType}

									object Demo01 {

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

									    // 1.创建SparkContext和SparkSession对象

									    val sc = new SparkContext(new SparkConf().setAppName("Demo01").setMaster("local[2]"))

									    val sparkSession = SparkSession.builder().getOrCreate()

									    // 2. 使用StructType来定义Schema

									    val mySchema = StructType(List(

									      StructField("empno", DataTypes.IntegerType, false),

									      StructField("ename", DataTypes.StringType, false),

									      StructField("job", DataTypes.StringType, false),

									      StructField("mgr", DataTypes.StringType, false),

									      StructField("hiredate", DataTypes.StringType, false),

									      StructField("sal", DataTypes.IntegerType, false),

									      StructField("comm", DataTypes.StringType, false),

									      StructField("deptno", DataTypes.IntegerType, false)

									    ))

									    // 3. 读取数据

									    val empRDD = sc.textFile("file:///D:\\TestDatas\\emp.csv")

									    // 4. 将其映射成ROW对象

									    val rowRDD = empRDD.map(line => {

									      val strings = line.split(",")

									      Row(strings(0).toInt, strings(1), strings(2), strings(3), strings(4), strings(5).toInt,strings(6), strings(7).toInt)

									    })

									    // 5. 创建DataFrame

									    val dataFrame = sparkSession.createDataFrame(rowRDD, mySchema)

									    // 6. 展示内容 DSL

									    dataFrame.groupBy("deptno").sum("sal").as("result").sort("sum(sal)").show()

									  }

									}

结果如下：

Spark SQL 2.4.8 操作 Dataframe的两种方式

方式二：SQL方式操作

实例化SparkContext和SparkSession对象
创建case class Emp样例类，用于定义数据的结构信息
通过SparkContext对象读取文件，生成RDD[String]
将RDD[String]转换成RDD[Emp]
引入spark隐式转换函数（必须引入）
将RDD[Emp]转换成DataFrame
将DataFrame注册成一张视图或者临时表
通过调用SparkSession对象的sql函数，编写sql语句
停止资源
具体代码如下：

				?

									package com.scala.demo.sql

									import org.apache.spark.rdd.RDD

									import org.apache.spark.sql.{Row, SparkSession}

									import org.apache.spark.{SparkConf, SparkContext}

									import org.apache.spark.sql.types.{DataType, DataTypes, StructField, StructType}

									// 0. 数据分析

									// 7499,ALLEN,SALESMAN,7698,1981/2/20,1600,300,30

									// 1. 定义Emp样例类

									case class Emp(empNo:Int,empName:String,job:String,mgr:String,hiredate:String,sal:Int,comm:String,deptNo:Int)

									object Demo02 {

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

									    // 2. 读取数据将其映射成Row对象

									    val sc = new SparkContext(new SparkConf().setMaster("local[2]").setAppName("Demo02"))

									    val mapRdd = sc.textFile("file:///D:\\TestDatas\\emp.csv")

									      .map(_.split(","))

									    val rowRDD:RDD[Emp] = mapRdd.map(line => Emp(line(0).toInt, line(1), line(2), line(3), line(4), line(5).toInt, line(6), line(7).toInt))

									    // 3。创建dataframe

									    val spark = SparkSession.builder().getOrCreate()

									    // 引入spark隐式转换函数

									    import spark.implicits._

									    // 将RDD转成Dataframe

									    val dataFrame = rowRDD.toDF

									    // 4.2 sql语句操作

									    // 1、将dataframe注册成一张临时表

									    dataFrame.createOrReplaceTempView("emp")

									    // 2. 编写sql语句进行操作

									    spark.sql("select deptNo,sum(sal) as total from emp group by deptNo order by total desc").show()

									    // 关闭资源

									    spark.stop()

									    sc.stop()

									  }

									}