三、使用sparkSql将student.txt转换成DateFrame进行相关操作（40分） 字段如下： s_id,s_name,s_sex,s_birthday,s_class,s_specialty,s_school,s_register_time 创建样例类Stusent 1.创建SparkSession 2.获取sparkContext对象 设置日志打印级别 sc.setLogLevel(WARN) 3.加载数据 4.切分每一行封装数据到Student中 5.加载数据到Row对象中 7.利用personRDD与Schema创建DataFrame 8.DSL操作显示DataFrame的数据结果 9.将DataFrame注册成表 10.sql语句操作 11.关闭资源

以下是对应的代码：

import org.apache.spark.sql.{DataFrame, Row, SparkSession}
import org.apache.spark.{SparkConf, SparkContext}

// 创建样例类Student
case class Student(s_id: String, s_name: String, s_sex: String, s_birthday: String, s_class: String, s_specialty: String, s_school: String, s_register_time: String)

object StudentDemo {
  def main(args: Array[String]): Unit = {
    // 创建SparkConf对象
    val conf = new SparkConf().setAppName("StudentDemo").setMaster("local[*]")

    // 创建SparkContext对象
    val sc = new SparkContext(conf)
    sc.setLogLevel("WARN")

    // 创建SparkSession对象
    val spark = SparkSession.builder().config(conf).getOrCreate()

    // 加载数据
    val data = sc.textFile("student.txt")

    // 切分每一行封装数据到Student中
    val studentRDD = data.map(line => {
      val fields = line.split(",")
      Student(fields(0), fields(1), fields(2), fields(3), fields(4), fields(5), fields(6), fields(7))
    })

    // 加载数据到Row对象中
    val rowRDD = studentRDD.map(student => Row(student.s_id, student.s_name, student.s_sex, student.s_birthday, student.s_class, student.s_specialty, student.s_school, student.s_register_time))

    // 利用personRDD与Schema创建DataFrame
    val schema = spark.sqlContext.createDataFrame(rowRDD, Student.getClass)

    // DSL操作显示DataFrame的数据结果
    schema.show()

    // 将DataFrame注册成表
    schema.createOrReplaceTempView("student")

    // sql语句操作
    val resultDF: DataFrame = spark.sql("select * from student where s_sex = '女'")

    // DSL操作显示DataFrame的数据结果
    resultDF.show()

    // 关闭资源
    spark.stop()
    sc.stop()
  }
}