以上这段sql有什么问题吗?讲讲其中什么逻辑

### SQL 代码分析与问题总结

#### 主要功能
该SQL脚本主要用于处理和汇总账单数据，具体包括以下几个步骤：

1. **读取原始数据**：从多个表中读取订单详情、账单记录等数据。
2. **清洗和转换数据**：使用UDF（用户定义函数）解析JSON字段，并进行必要的数据清洗和转换。
3. **生成中间结果表**：创建临时视图以存储中间计算结果。
4. **聚合和汇总数据**：按不同的维度（如省份、城市、产品编号等）进行数据聚合和汇总。
5. **输出结果**：将最终结果保存到指定的HDFS路径。

#### 具体逻辑与潜在问题

1. **日期参数提取**

   val date = args(0)
   val year = date.substring(0, 4) // yyyy
   val yearmonth_start = date.substring(0, 4) + "01" // yyyymm01
   val yearmonth_end = date.substring(0, 6) // yyyymm

- **问题**：`yearmonth_start` 和 `yearmonth_end` 的命名有些混淆。建议更改为 `start_month` 和 `end_month` 以提高可读性。

2. **读取订单详情并解析JSON字段**

   val frame = spark.sql(
     s"""
        |select
        |  atomic_power_orders,
        |  app_id,
        |  application_id,
        |  whether
        |from ods_spyw.t_ods_tyyy_application_detail
        |where month='${yearmonth_end}'
        |  and del_flag='0'
        |  and atomic_power_orders is not null
        """.stripMargin)
     .withColumn("orderNo", getAppidOrderNoJson($"atomic_power_orders"))
     .createOrReplaceTempView("clear_json_object")

- **问题**：`getAppidOrderNoJson` UDF 需要确保在所有情况下都能正确处理空值或无效的JSON字符串。

3. **生成订单详情表**

   spark.sql(
     s"""
        |select
        |  app_id,
        |  application_id,
        |  order_code,
        |  whether
        |from clear_json_object
        |lateral view explode(split(orderNo, '/')) v_ods_ehome_reqinterfacelog as order_code
        """.stripMargin)
     .createOrReplaceTempView("application_detail")

- **问题**：`split(orderNo, '/')` 假设 `orderNo` 字段中的订单号是用斜杠分隔的。如果实际数据格式不同，可能会导致错误。

4. **标记订单是否为“多记”**

   spark.sql(
     s"""
        |with flag_data as (
        |  select
        |    order_code,
        |    if(count(distinct application_id) >= 2, concat(order_code, '-', count(distinct application_id), '-多记'), '单记') as flag
        |  from application_detail
        |  where order_code is not null and order_code != ''
        |  group by order_code
        |)
        |select
        |  order_code,
        |  app_id,
        |  app_type,
        |  app_name,
        |  flag,
        |  business_model_flag
        |from (
        |  select
        |    t0.app_id,
        |    t1.app_name,
        |    t0.application_id,
        |    t0.order_code,
        |    t1.id,
        |    t1.business_model_flag,
        |    t1.app_type,
        |    t0.flag
        |  from (
        |    select
        |      data_flag0.app_id,
        |      data_flag0.application_id,
        |      data_flag0.order_code,
        |      data_flag1.flag
        |    from (
        |      select
        |        app_id,
        |        application_id,
        |        order_code
        |      from application_detail
        |      where order_code is not null and order_code != ''
        |      group by application_id, order_code, app_id
        |    ) data_flag0
        |    left join (
        |      select
        |        order_code,
        |        flag
        |      from flag_data
        |    ) data_flag1
        |    on data_flag0.order_code = data_flag1.order_code
        |  ) t0
        |  left join (
        |    select
        |      id,
        |      business_model_flag,
        |      case
        |        when app_type = '1' then '云眼平台'
        |        when app_type = '2' then '云眼应用'
        |        when app_type = '2' then '云眼国标'
        |        else '其他'
        |      end as app_type,
        |      app_name
        |    from ods_spyw.t_ods_tyyy_application
        |    where month = '${yearmonth_end}'
        |      and del_flag = '0'
        |    group by id, business_model_flag, app_name, app_type
        |  ) t1
        |  on t0.application_id = t1.id
        |) st0
        |group by order_code, app_id, app_type, app_name, flag, business_model_flag
        """.stripMargin)
     .createOrReplaceTempView("application_detail")

- **问题**：`case when app_type = '2' then '云眼应用' when app_type = '2' then '云眼国标'` 这里有一个重复条件，可能导致逻辑错误。应该检查并修正。

5. **读取账单记录并过滤**

   spark.sql(
     s"""
        |select
        |  t0.order_code,
        |  sum(t0.payable_price) as payable_price,
        |  t0.product_no,
        |  t0.settlement_status_str,
        |  t0.apply_status_str,
        |  t0.consumer_company_province,
        |  t0.consumer_company_city_name
        |from (
        |  select
        |    order_code,
        |    payable_price,
        |    product_no,
        |    settlement_status_str,
        |    apply_status_str,
        |    consumer_company_province,
        |    consumer_company_city_name
        |  from ods_spw.t_ods_tyyy_ebp_bill
        |  where replace(payment_day, '-', '') = '${yearmonth_end}'
        |    and product_no in ('PRO20240223092833350', 'PRO20240223090418735', ...)
        |    and apply_status_str = '对账完成'
        |    and settlement_status_str = '开始结算'
        |) t0
        |inner join (
        |  select
        |    order_code
        |  from ods_spyw.t_ods_tyyy_ebp_order
        |  where month = '202410'
        |) t1
        |on t0.order_code = t1.order_code
        |group by t0.order_code, t0.payable_price, t0.product_no, t0.settlement_status_str, t0.apply_status_str, t0.consumer_company_province, t0.consumer_company_city_name
        """.stripMargin)
     .createOrReplaceTempView("ebp_order_detail")

- **问题**：`group by` 子句中包含了 `t0.payable_price`，这通常会导致每个 `payable_price` 被单独分组。如果目的是按 `order_code` 分组并求和 `payable_price`，应去掉 `t0.payable_price`。

6. **生成宽表**

   spark.sql(
     """
        |select /*+ mapjoin(ebp_order_detail)*/
        |  ebp_order_detail.order_code,
        |  ebp_order_detail.payable_price,
        |  ebp_order_detail.product_no,
        |  ebp_order_detail.settlement_status_str,
        |  ebp_order_detail.apply_status_str,
        |  ebp_order_detail.consumer_company_province,
        |  ebp_order_detail.consumer_company_city_name,
        |  application_detail.order_code,
        |  application_detail.app_id,
        |  application_detail.app_type,
        |  application_detail.app_name,
        |  application_detail.flag,
        |  application_detail.business_model_flag,
        |  1 as num_flag,
        |  '${yearmonth_end}' as month
        |from ebp_order_detail
        |left join application_detail
        |on ebp_order_detail.order_code = application_detail.order_code
        """.stripMargin)
     .createOrReplaceTempView("wide_table")

7. **聚合和汇总数据**

   spark.sql(
     """
        |select
        |  consumer_company_province,
        |  consumer_company_city_name,
        |  app_type,
        |  sum(payable_price) as payable_price
        |from (
        |  select
        |    consumer_company_province,
        |    consumer_company_city_name,
        |    app_type,
        |    flag,
        |    (sum(payable_price) / if(flag = '单记', 1, sum(num_flag))) as payable_price
        |  from wide_table
        |  group by consumer_company_province, consumer_company_city_name, app_type, flag
        |) t0
        |group by consumer_company_province, consumer_company_city_name, app_type
        """.stripMargin)
     .show(1000, false)
     .write.mode(SaveMode.Overwrite).option("header", "true").option("nullValue", "NULL").csv(s"$HDFS_PATH/year_province_apptype/$date")

- **问题**：`sum(payable_price) / if(flag = '单记', 1, sum(num_flag))` 中的 `sum(num_flag)` 可能会导致除零错误。需要确保 `num_flag` 不为零。

8. **按月份、省份、城市、产品编号聚合**

   spark.sql(
     """
        |select
        |  month,
        |  consumer_company_province,
        |  consumer_company_city_name,
        |  product_no,
        |  sum(payable_price) as payable_price
        |from (
        |  select
        |    month,
        |    consumer_company_province,
        |    consumer_company_city_name,
        |    product_no,
        |    flag,
        |    (sum(payable_price) / if(flag = '单记', 1, sum(num_flag))) as payable_price
        |  from wide_table
        |  group by month, consumer_company_province, consumer_company_city_name, product_no, flag
        |) t0
        |group by month, consumer_company_province, consumer_company_city_name, product_no
        """.stripMargin)
     .show(1000)

9. **按月份、产品编号聚合**

   spark.sql(
     """
        |select
        |  month,
        |  product_no,
        |  sum(payable_price) as payable_price
        |from (
        |  select
        |    month,
        |    product_no,
        |    flag,
        |    (sum(payable_price) / if(flag = '单记', 1, sum(num_flag))) as payable_price
        |  from wide_table
        |  group by month, product_no, flag
        |) t0
        |group by month, product_no
        """.stripMargin)
     .show(1000, false)

10. **按月份、标志聚合**

    spark.sql(
      """
         |select
         |  month,
         |  flag,
         |  sum(payable_price) as sum_payable_price,
         |  if(flag = '单记', 1, sum(num_flag)) as num_flag,
         |  (sum(payable_price) / if(flag = '单记', 1, sum(num_flag))) as payable_price
         |from wide_table
         |group by month, flag
         """.stripMargin)
      .show(1000)

### 总结
- **命名规范**：改善变量命名以提高代码可读性。
- **逻辑错误**：修正重复条件和不必要的 `group by` 子句。
- **数据处理**：确保所有可能的边界情况都得到处理，特别是空值和除零错误。
- **性能优化**：考虑使用广播连接（broadcast join）来优化JOIN操作，特别是在小表和大表之间进行JOIN时。