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

以下是代码实现：

import org.apache.spark.sql.{Row, SparkSession}
import org.apache.spark.sql.types.{StringType, StructField, StructType}

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 StudentDF {
  def main(args: Array[String]): Unit = {
    // 创建SparkSession
    val spark = SparkSession.builder()
      .appName("StudentDF")
      .master("local[*]")
      .getOrCreate()

    // 获取sparkContext对象，设置日志打印级别
    val sc = spark.sparkContext
    sc.setLogLevel("WARN")

    // 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 studentRowRDD = 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 = StructType(List(
      StructField("s_id", StringType, true),
      StructField("s_name", StringType, true),
      StructField("s_sex", StringType, true),
      StructField("s_birthday", StringType, true),
      StructField("s_class", StringType, true),
      StructField("s_specialty", StringType, true),
      StructField("s_school", StringType, true),
      StructField("s_register_time", StringType, true)
    ))

    val studentDF = spark.createDataFrame(studentRowRDD, schema)

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

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

    // sql语句操作
    val resultDF = spark.sql("select s_name,s_sex,s_class from student where s_specialty='计算机科学与技术'")
    resultDF.show()

    // 关闭资源
    spark.stop()
  }
}

注意：需要将student.txt放在项目根目录下。