Spark应用【根据新df更新旧df】

      // 主键字段保持不变，再转换回来
      var columnMap:Map[String, String] = Map()
      for(key <- keysOpt){
        columnMap += (key + " AS " + key + "S" -> key)
      }
      // keysOpt：主键字段构成的数组
      var columnBackMap:Map[String, String] = Map()
      for(key <- columnName){
        if(!keysOpt.contains(key)){// 只对非主键字段做处理
          columnBackMap += (key + "S" -> key)
        }
      }
      val convertion = columnName.map(key => key + " AS " + key + "S") // columnName:包含该df中所有的字段名称
      val df1_plus = df1.selectExpr(convertion.map(t => columnMap.getOrElse(t, t)): _*) // 修改df1除主键之外的字段，末尾加S
      //df关联
      val df3 = df1_plus.join(df2, keysOpt)

      val df4 = df3.select(columnName.map(c => df2(c)): _*)
      // 重新获取df3中属于df1的字段
      val df1_column_back = df1_plus.columns
      val df5 = df3.select(df1_column_back.map(c => df1_plus(c)): _*)
      // 把转换的字段名称再转换回来
      val df5_plus = df1.selectExpr(df1_column_back.map(t => columnBackMap.getOrElse(t, t)): _*) // 去掉末尾之前添加的S
      // 合并
      val union_Data = df4.union(df5_plus)

结果：

　　在非主键字段名称末尾添加S

　　去掉非主键字段名称末尾添加的S