大数据Flink的SQL在API中进行操作

使用Flink SQL/TABLE API完成下面功能。

有如下数据

字段分别为：姓名，年龄，性别，班级，考试成绩

zhangsan,18,man,1707e,81.5

lisi,22,woman,1707e,77.5

wangwu,28,woman,1707e,82.0

zhaoliu,24,man,1707e,73.5

qianqi,18,woman,1707e,91.0

maba,22,man,1707e,84.0

sunjiiu,27,woman,1707e,88.0

xiaoming,20,man,1710e,73.5

xiaohong,21,woman,1710e,80.0

xiaozhang,22,man,1710e,73.5

xiaoli,19,woman,1710e,92.0

xiaowang,26,man,1710e,86.5

使用flinktable读取以上数据，进行统计分析。

1.计算各班平均成绩；

2.计算总分最高的班级名称和总分；

3.计算各班最高成绩的学生姓名；

4.计算1707e成绩最高的女生姓名；

5.计算各班男生平均年龄，女生平均年龄；

6.计算1710e最小年龄学生姓名；

7.计算各班男生个数和女生个数。

 

import org.apache.flink.api.scala._
import org.apache.flink.table.api.TableEnvironment
import org.apache.flink.table.api.{Table, TableEnvironment}
import org.apache.flink.types.Row
object Job629 {

语言
方法


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2012/12/14 06:49:08

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

    //批处理环境
    val env = ExecutionEnvironment.getExecutionEnvironment
    env.setParallelism(1)

    //表环境
    val tableEnvironment = TableEnvironment.getTableEnvironment(env)

    //获取数据
    val dataDS: DataSet[String] = env.readTextFile("src/main/resources/job629.txt")
    //封装样例类
    val stuDS = dataDS.map(x => {
      val arr: Array[String] = x.split(",")
      Student(arr(0).trim, arr(1).trim.toInt, arr(2).trim, arr(3).trim, arr(4).trim.toDouble)
    })

    //注册表
    tableEnvironment.registerDataSet("stu",stuDS)

    //    1.计算各班平均成绩；
    val clazzAvg: Table = tableEnvironment.sqlQuery("select clazz,avg(score) from stu group by clazz")
    val sql1: DataSet[Row] = tableEnvironment.toDataSet[Row](clazzAvg)
   // sql1.print()

    //    2.计算总分最高的班级名称和总分；
    val scoreMaxClazz: Table = tableEnvironment.sqlQuery(
      """
         select c2.clazz,c2.sumScore from
        (select sum(score) sumScore from stu group by clazz) c1 join
        (select clazz,sum(score) sumScore from stu group by clazz order by sumScore desc limit 1) c2
        on c1.sumScore = c2.sumScore
      """)
    val sql2: DataSet[Row] = tableEnvironment.toDataSet[Row](scoreMaxClazz)
   // sql2.print()

    //    3.计算各班最高成绩的学生姓名；
    val scoreMaxName: Table = tableEnvironment.sqlQuery(
      """
        select s2.clazz,s1.name,s2.maxScore from stu s1 join
         (select clazz,max(score) maxScore from stu group by clazz) s2
          on s1.score = s2.maxScore
      """)
    val sql3: DataSet[Row] = tableEnvironment.toDataSet[Row](scoreMaxName)
    //sql3.print()

    //    4.计算1707e成绩最高的女生姓名；
    val scoreMaxF: Table = tableEnvironment.sqlQuery(
      """
        select s1.name,s2.maxScore from stu s1 join
        (select max(score) maxScore from stu where clazz = '1707e' and sex = 'woman') s2
        on s1.score = s2.maxScore
      """)
    val sql4: DataSet[Row] = tableEnvironment.toDataSet[Row](scoreMaxF)
    //sql4.print()

    //    5.计算各班男生平均年龄，女生平均年龄；
    val avgAge: Table = tableEnvironment.sqlQuery(
      """
       select clazz,sex,avg(age) from stu group by clazz,sex
      """)
    val sql5: DataSet[Row] = tableEnvironment.toDataSet[Row](avgAge)
    //sql5.print()

    //    6.计算1710e最小年龄学生姓名；
    val minAgeName: Table = tableEnvironment.sqlQuery(
      """
      select s1.name,s2.minAge from stu s1 join
       (select min(age) minAge from stu where clazz = '1710e') s2
       on s1.age = s2.minAge
      """)
    val sql6: DataSet[Row] = tableEnvironment.toDataSet[Row](minAgeName)
    //sql6.print()

    //    7.计算各班男生个数和女生个数。
    val count: Table = tableEnvironment.sqlQuery(
      """
     select clazz,sex,count(sex) from stu group by clazz,sex
      """)
    val sql7: DataSet[Row] = tableEnvironment.toDataSet[Row](count)
   //sql7.print()



  }
}

case class Student(name:String,age:Int,sex:String,clazz:String,score:Double)