一零一九、岗位数据分析（Spark）

目录

建表语句

原始数据

数据分析

完整代码

分析岗位数据如下要求：

分析不同学历的平均薪资（每个学历的平均估值（最高薪资平均值、最低薪资平均值求平均）

分析不同岗位的平均薪资（求每个学历的平均估值（最高薪资平均值、最低薪资平均值求平均）

分析各公司提供的岗位

建表语句

DROP TABLE IF EXISTS `job`;
CREATE TABLE `job` (
  `address` varchar(255) DEFAULT NULL,
  `company` varchar(255) DEFAULT NULL,
  `edu` varchar(255) DEFAULT NULL,
  `jobName` varchar(255) DEFAULT NULL,
  `salary` varchar(255) DEFAULT NULL,
  `size` varchar(255) DEFAULT NULL
) ENGINE=InnoDB DEFAULT CHARSET=utf8;

原始数据

数据分析

1、获取原始数据

val source: RDD[Row] = sprak.read
      .format("jdbc")
      .option("url", "jdbc:mysql://localhost:3306/crawler")
      .option("dbtable", "job")
      .option("user", "root")
      .option("password", "123456")
      .load()
      .rdd
    source.foreach(println)

 可见，题目要求为最低工资平均值 与 最高工资平均值，先对数据格式进行处理一下

 2、格式化数据

val data: RDD[(String, String, String, String, Float, Float, String)] = source.map(item => {
      val salary: Any = item(4)
      val min: String = salary.toString.split("-")(0)
      val minsalary: String =
        if (min.contains("面议")) min.substring(4)
        else min
      val max: Int = salary.toString.split("-")(1).length - 1
      val maxsalary: String = salary.toString.split("-")(1).substring(0, max)
      (item(0).toString, item(1).toString, item(2).toString, item(3).toString, minsalary.toFloat, maxsalary.toFloat, item(5).toString)
    })
 
    data.foreach(println)

 处理完数据后如图，并将其转为小数，方便后续运算

3、转为DF，创建表

import sprak.implicits._
    val dataDF: DataFrame = data.toDF("address", "company", "edu", "jobName", "minsalary", "maxsalary", "size")
 
    dataDF.createOrReplaceTempView("salary")

4、分析不同学历的平均薪资（每个学历的平均估值（最高薪资平均值、最低薪资平均值求平均））

val sql1 =
      """
        |select s.edu,(s.avg_minsalary + s.avg_maxsalary)/2 as avgSalary from
        |(select edu,avg(minsalary) as avg_minsalary,avg(maxsalary) as avg_maxsalary from salary group by edu) s
        |""".stripMargin 
 
    sprak.sql(sql1).show()

5、分析不同岗位的平均薪资（求每个学历的平均估值（最高薪资平均值、最低薪资平均值求平均））

val sql2 =
      """
        |select s.jobName,(s.avg_minsalary+s.avg_maxsalary)/2 as avgSalary from
        |(select jobName,avg(minsalary) as avg_minsalary,avg(maxsalary) as avg_maxsalary from salary group by jobName) s
        |""".stripMargin 
 
    sprak.sql(sql2).show()

 6、分析各公司提供的岗位

val sql3 =
      """
        |select company,count(jobName) as jobNum from salary group by company
        |""".stripMargin //分析不同岗位的平均薪资
 
    sprak.sql(sql3).show()

完整代码

package example.spark.test
 
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Row, SparkSession}
 
 
object WordCount {
  def main(args: Array[String]): Unit = {
    val sprak: SparkSession = SparkSession.builder().master("local[6]").appName("job")
      .config("spark.sql.warehouse.dir", "E:/")
      .getOrCreate()
 
    sprak.sparkContext.setLogLevel("ERROR")
 
    val source: RDD[Row] = sprak.read
      .format("jdbc")
      .option("url", "jdbc:mysql://localhost:3306/crawler")
      .option("dbtable", "job")
      .option("user", "root")
      .option("password", "123456")
      .load()
      .rdd
    source.foreach(println)
    println("------------------------------------------------------------------------------------")
    //    val data: RDD[(String, String, String, String, Float, Float, String)] = source.map(item => {
    //      var salary = item(4)
    //      val minsalary: String = if (salary.toString.split("-")(0).contains("面议"))
    //        salary.toString.split("-")(0).substring(4)
    //      else salary.toString.split("-")(0)
    //      val maxsalary: String = salary.toString.split("-")(1).substring(0, salary.toString.split("-")(1).length - 1)
    //      (item(0).toString, item(1).toString, item(2).toString, item(3).toString, minsalary.toFloat, maxsalary.toFloat, item(5).toString)
    //    })
    val data: RDD[(String, String, String, String, Float, Float, String)] = source.map(item => {
      val salary: Any = item(4)
      val min: String = salary.toString.split("-")(0)
      val minsalary: String =
        if (min.contains("面议")) min.substring(4)
        else min
      val max: Int = salary.toString.split("-")(1).length - 1
      val maxsalary: String = salary.toString.split("-")(1).substring(0, max)
      (item(0).toString, item(1).toString, item(2).toString, item(3).toString, minsalary.toFloat, maxsalary.toFloat, item(5).toString)
    })
 
    data.foreach(println)
    println("------------------------------------------------------------------------------------")
 
    import sprak.implicits._
    val dataDF: DataFrame = data.toDF("address", "company", "edu", "jobName", "minsalary", "maxsalary", "size")
 
    dataDF.createOrReplaceTempView("salary")
 
    val sql1 =
      """
        |select s.edu,(s.avg_minsalary + s.avg_maxsalary)/2 as avgSalary from
        |(select edu,avg(minsalary) as avg_minsalary,avg(maxsalary) as avg_maxsalary from salary group by edu) s
        |""".stripMargin
 
    sprak.sql(sql1).show()
    println("------------------------------------------------------------------------------------")
    val sql2 =
      """
        |select s.jobName,(s.avg_minsalary+s.avg_maxsalary)/2 as avgSalary from
        |(select jobName,avg(minsalary) as avg_minsalary,avg(maxsalary) as avg_maxsalary from salary group by jobName) s
        |""".stripMargin
 
    sprak.sql(sql2).show()
    println("------------------------------------------------------------------------------------")
    val sql3 =
      """
        |select company,count(jobName) as jobNum from salary group by company
        |""".stripMargin //分析不同岗位的平均薪资
 
    sprak.sql(sql3).show()
 
 
  }
}