scala spark2.0 sparksql 连接mysql8.0 操作多表 使用 dataframe 及RDD进行数据处理

1、配置文件

package config
import org.apache.spark.sql.SparkSession
import org.apache.spark.{SparkConf, SparkContext}
case object conf {
   private val master = "local[*]"
   val confs: SparkConf = new SparkConf().setMaster(master).setAppName("jobs")
//   val confs: SparkConf = new SparkConf().setMaster("http://laptop-2up1s8pr:4040/").setAppName("jobs")
   val sc = new SparkContext(confs)
   sc.setLogLevel("ERROR")
   val spark_session: SparkSession = SparkSession.builder()
    .appName("jobs").config(confs).getOrCreate()

//   设置支持笛卡尔积 对于spark2.0来说
   spark_session.conf.set("spark.sql.crossJoin.enabled",true)
}

2、连接mysql8.0 操作多表

package operationMysql
import config.conf.{sc, spark_session}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Row}
import config.conf.spark_session.implicits._
object readingMysqlOperation {
  def main(args: Array[String]): Unit = {

    /*

    val df: DataFrame = spark_session.read
      .format("jdbc")
      .option("url", "jdbc:mysql://localhost:3306/junsheng?useUnicode=true&characterEncoding=utf-8")
      .option("dbtable", "订单")
      .option("user", "root")
      .option("password", "123456")
      .load()
    df.show()
    * */

    //以jdbc方式连接mysql
    val url="jdbc:mysql://localhost:3306/junsheng?useUnicode=true&characterEncoding=utf-8" //&useSSL=true
    //设置用户名和密码信息
    val prop = new java.util.Properties
    prop.setProperty("user","root")
    prop.setProperty("password","123456")
    //创建sqlContext对象

    //读取dat_order_item表
    val df1: DataFrame = spark_session.read.jdbc(url,"订单明细","订单ID",0,5000000,4,prop)
    val df2: DataFrame = spark_session.read.jdbc(url, "订单", "订单ID", 0, 5000000,4,prop)
    //读取dat_order表

    //将dat_order_item和dat_order DF注册成spark临时表
    df1.createOrReplaceTempView("data1")
    df2.createOrReplaceTempView("data2")
    //使用sqlContext.sql（"XXX"）方式执行查询语句
//    df2.show()
    val ywSQL:String= "SELECT dt1.`订单ID`,dt2.`客户ID`,dt1.`产品ID`,dt1.`单价`,dt1.`数量` " +
  "FROM data1 AS dt1 LEFT JOIN  data2 as dt2 ON dt1.`订单ID`=dt2.`订单ID`"
    val df: DataFrame = spark_session.sql(ywSQL)
    df.rdd.map(lines=>{(lines(0).toString,lines(2).toString.toDouble,lines(4).toString.toInt)})
      .toDF("订单ID","产品单价","订购数量").show()



  }
}