全国职业院校技能大赛-大数据 离线数据处理模块-指标计算

赛题来源2023年全国职业院校技能大赛赛题第1套任务B中指标计算模块

子任务三：指标计算

编写Scala代码，使用Spark计算相关指标。

注：在指标计算中，不考虑订单信息表中order_status字段的值，将所有订单视为有效订单。计算订单金额或订单总金额时只使用final_total_amount字段。需注意dwd所有的维表取最新的分区。

 第一题

根据dwd层表统计每个省份、每个地区、每个月下单的数量和下单的总金额，存入MySQL数据库shtd_result的provinceeverymonth表中（表结构如下），然后在Linux的MySQL命令行中根据订单总数、订单总金额、省份表主键均为降序排序，查询出前5条，将SQL语句复制粘贴至客户端桌面【Release\任务B提交结果.docx】中对应的任务序号下，将执行结果截图粘贴至客户端桌面【Release\任务B提交结果.docx】中对应的任务序号下

import org.apache.spark.SparkConf
import org.apache.spark.sql.{SaveMode, SparkSession}

object Compute01 {
  def main(args: Array[String]): Unit = {
    System.setProperty("HADOOP_USER_NAME", "atguigu")

    // TODO 创建spark连接
    val conf = new SparkConf().setMaster("local[*]").setAppName("Compute01")
    val spark: SparkSession = SparkSession.builder().config(conf).enableHiveSupport().getOrCreate()

    // 开启动态分区
    spark.conf.set("hive.exec.dynamic.partition.mode", "nonstrict")
    // 关闭打印日志
    spark.sparkContext.setLogLevel("OFF")

    // TODO 执行核心查询SQL
    val result = spark.sql(
      """
        |select
        |    province.id provinceid,
        |    province.name provincename,
        |    region.id regionid,
        |    region.region_name regionname,
        |    sum(final_total_amount)
        |       over(partition by province.id,region.id,year(od.create_time),month(od.create_time)) totalconsumption,
        |    count(od.id)
        |       over(partition by province.id,region.id,year(od.create_time),month(od.create_time)) totalorder,
        |    year(od.create_time) year,
        |    month(od.create_time) month
        |from (
        |     select
        |        id,
        |        province_id,
        |        final_total_amount,
        |        trade_body,
        |        create_time
        |     from dwd.act_order_info
        |) od
        |left join (
        |    select
        |        id,
        |        name,
        |        region_id
        |    from ods.dim_province
        |    where etl_date = (
        |        select max(etl_date)
        |        from ods.dim_province
        |    )
        |) province on od.province_id = province.id
        |left join (
        |    select
        |        id,
        |        region_name
        |    from dwd.dim_region
        |    where etl_date = (
        |       select max(etl_date)
        |       from ods.dim_province
        |    )
        |) region on province.region_id = region.id
        |""".stripMargin)

    // 查看结果
    result.show()

    // TODO 结果保存到指定的表中
    result.write
      .format("jdbc") // 使用jdbc格式写入带mysql
      .mode(SaveMode.Append) // 保存方式为追加
      .option("Driver", "com.mysql.jdbc.Driver")
      .option("url", "jdbc:mysql://shtd_result")
      .option("user", "root")
      .option("password", "000000")
      .option("dbtable", "provinceeverymonth")
      .save()
    // shtd_result.provinceeverymonth
    // TODO 关闭spark连接
    spark.close()
  }
}

结果查询SQL

-- 订单总数
select
    *
from dwd.shtd_result.provinceeverymonth
order by totalorder desc
limit 5;
-- 订单总金额
select
    *
from dwd.shtd_result.provinceeverymonth
order by totalconsumption desc
limit 5;
-- 省份表主键
select
    *
from dwd.shtd_result.provinceeverymonth
order by provinceid desc
limit 5;

第二题

请根据dwd层表计算出2020年4月每个省份的平均订单金额和所有省份平均订单金额相比较结果（“高/低/相同”）,存入MySQL数据库shtd_result的provinceavgcmp表（表结构如下）中，然后在Linux的MySQL命令行中根据省份表主键、该省平均订单金额均为降序排序，查询出前5条，将SQL语句复制粘贴至客户端桌面【Release\任务B提交结果.docx】中对应的任务序号下，将执行结果截图粘贴至客户端桌面【Release\任务B提交结果.docx】中对应的任务序号下

import org.apache.spark.SparkConf
import org.apache.spark.sql.{SaveMode, SparkSession}

object Compute02 {
  def main(args: Array[String]): Unit = {
    System.setProperty("HADOOP_USER_NAME", "atguigu")

    val conf = new SparkConf().setMaster("local[*]").setAppName("Compute02")
    val spark: SparkSession = SparkSession.builder().config(conf).enableHiveSupport().getOrCreate()

    spark.conf.set("hive.exec.dynamic.partition.mode", "nonstrict")
    spark.sparkContext.setLogLevel("OFF")

    val result = spark.sql(
      """
        |select
        |    provinceid,
        |    provincename,
        |    provinceavgconsumption,
        |    allprovinceavgconsumption,
        |    case
        |        when provinceavgconsumption > allprovinceavgconsumption then '高'
        |        when provinceavgconsumption < allprovinceavgconsumption then '低'
        |        else '相同'
        |    end comparison -- 比较结果
        |from
        |(
        |    select
        |        id provinceid,
        |        name provincename
        |    from dwd.dim_province
        |    where etl_date = (
        |        select max(etl_date) from ods.base_province
        |    )
        |) province
        |left join (
        |    select
        |        province_id,
        |        avg(final_total_amount) provinceavgconsumption -- 该省平均订单金额
        |    from ods.order_info
        |    where create_time between '2020-04-01' and '2020-04-30'
        |    group by dwd.act_order_info
        |) od on od.province_id = province.provinceid
        |left join (
        |    select
        |        province_id,
        |        avg(final_total_amount) allprovinceavgconsumption -- 所有省平均订单金额
        |    from dwd.act_order_info
        |    where create_time between '2020-06-01' and '2022-06-30'
        |) avgorder on avgorder.province_id = province.provinceid
        |""".stripMargin)

    result
      .write
      .format("jdbc")
      .mode(SaveMode.Append)
      .option("Driver", "com.mysql.jdbc.Driver")
      .option("url", "jdbc:mysql://shtd_result")
      .option("user", "root")
      .option("password", "000000")
      .option("dbtable", "provinceavgcmp")
      .save()

    spark.close()
  }
}

结果查询SQL

-- 省份表主键
select *
from shtd_result.provinceavgcmp
order by provinceid desc
limit 5;
-- 该省平均订单金额
select
    *
from shtd_result.provinceavgcmp
order by provinceavgconsumption desc
limit 5;

第三题

根据dwd层表统计在两天内连续下单并且下单金额保持增长的用户，存入MySQL数据库shtd_result的usercontinueorder表(表结构如下)中，然后在Linux的MySQL命令行中根据订单总数、订单总金额、客户主键均为降序排序，查询出前5条，将SQL语句复制粘贴至客户端桌面【Release\任务B提交结果.docx】中对应的任务序号下，将执行结果截图粘贴至客户端桌面【Release\任务B提交结果.docx】中对应的任务序号下

import org.apache.spark.SparkConf
import org.apache.spark.sql.{SaveMode, SparkSession}

object Compute03 {
  def main(args: Array[String]): Unit = {
    System.setProperty("HADOOP_USER_NAME", "atguigu")

    val conf = new SparkConf().setMaster("local[*]").setAppName("Compute03")
    val spark: SparkSession = SparkSession.builder().config(conf).enableHiveSupport().getOrCreate()

    spark.conf.set("hive.exec.dynamic.partition.mode", "nonstrict")
    spark.sparkContext.setLogLevel("OFF")

    val result = spark.sql(
      """
        | select
        |     userid,
        |     username,
        |     buy_date_first,
        |     buy_date_second,
        |     concat(buy_date_first, '_', buy_date_second) day,
        |     totalconsumption,
        |     totalorder
        | from (
        |     select
        |      od1.user_id userid,
        |      od2.consignee username,
        |      buy_date_first,
        |      buy_date_second,
        |      totalconsumption,
        |      od1.totalorder,
        |      datediff(buy_date_second, buy_date_first) part_date_num,
        |      if (buy_amount_second - total_amount > 0, 1, 0) part_amount_increase
        |     from (
        |         select
        |             user_id ,
        |             create_time buy_date_first, -- 获取当前时间的下一天
        |             count(id) totalorder,
        |             lead(create_time, 1, "9999-12-31 00:00:00") over (partition by user_id order by create_time) buy_date_second,
        |             lead(final_total_amount) over(partition by user_id order by create_time) buy_amount_second,
        |             sum(total_amount) over (partition by user_id) totalconsumption
        |         from dwd.act_order_info
        |         group by user_id, date_format(create_time, 'yyyyMMdd')
        |     ) od1
        |     left join (
        |     select
        |         user_id,
        |         consignee,
        |         final_total_amount
        |     from dwd.act_order_info
        |     ) od2 on od1.user_id = od2.user_id
        | )
        | where part_date_num = 1        -- 连续两天的订单
        | and part_amount_increase = 1   -- 订单金额保持增长
        |""".stripMargin)

    result
      .write
      .format("jdbc")
      .mode(SaveMode.Append)
      .option("Driver", "com.mysql.jdbc.Driver")
      .option("url", "jdbc:mysql://shtd_result")
      .option("user", "root")
      .option("password", "000000")
      .option("dbtable", "provinceavgcmp")
      .save()

    spark.close()
  }
}

结果查询sql

-- 订单总数
select
    *
from shtd_result.usercontinueorder
order by totalorder desc
limit 5;
-- 订单总金额
select
    *
from shtd_result.usercontinueorder
order by totalconsumption
limit 5;
-- 客户主键
select
    *
from shtd_result.usercontinueorder
order by userid desc
limit 5;

指标计算部分的难点就是多表查询的部分已经开窗函数的合理运用，因此熟练掌握HiveSQL中高级函数的部分是非常重要的，不然此部分将会很难完成文章来源地址https://www.toymoban.com/news/detail-791431.html

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