MarketAnalysis-ETL/THC/02 dm_ext_thc_sales_bakup_20260327.sql

-- Databricks notebook source
-- create table dm.dm_ext_thc_sales
-- (
-- yyyymm STRING,
-- audit_cod STRING,
-- pack_cod STRING,
-- corp_cod STRING,
-- sales_value_cal decimal(38,10),
-- sales_unit_cal decimal(38,10),
-- counting_unit decimal(38,10),
-- sales_value_cal_ly decimal(38,10),
-- sales_unit_cal_ly decimal(38,10),
-- counting_unit_ly decimal(38,10)
-- )
-- using delta

-- COMMAND ----------

--------------------------------------------------------------------------
--修改时间：20241106
--修改人：Fanxujia
--修改内容：
--THC原本只有全国数据，现在增加14个省份的数据。
--但是全国数据是半年提供一次，省份数据一个季度提供一次
--目前需求是将季度数据放入模型，而全国数据单独成一个事实表，供PBI单独展示
--这里生成的事实表里全国和省份都包含了，在写入模型的时候用where条件筛选
---------------------------------------------------------------------------
--rawdata年月处理
with thc_rawdata1 as (
select case when right(yq,2) = 'Q1' then concat(left(yq,4),'03')
            when right(yq,2) = 'Q2' then concat(left(yq,4),'06')
            when right(yq,2) = 'Q3' then concat(left(yq,4),'09')
            when right(yq,2) = 'Q4' then concat(left(yq,4),'12')
            else yq
        end as yyyymm,
        city_c,
        pack_cod,
        prod_cod,
        value,
        totalunit,
        countingunit
from dwd.dwd_gnd_ext_thc_rawdata
)
--CHPA渠道对应的公司编码以及名称
,chpa_corp as (
select distinct
PACK_COD,
CORP_COD,
CORP_DES
from dm.dm_ims_td_pack_property
)
--从thc渠道的pack_property里取到对应的iqvia_pack_code、countingunit
--然后corp_cod优先取CHPA渠道的
,thc_corp as (
select t1.pack_cod,
t1.iqvia_pack_code,
t1.countingunit,
coalesce(t2.CORP_COD, t1.CORP_COD) as CORP_COD ,
coalesce(t2.CORP_DES,t1.CORP_DES ) as CORP_DES
from dwd.dwd_gnd_ext_thc_pack_property t1
left join chpa_corp t2
on t1.iqvia_pack_code = t2.PACK_COD
)
--编辑thc渠道的audit_cod
--全国数据设置成CHT
--省份数据使用公共的地理维表里的省份编码作为audit_cod
,thc_geo as (
select 
t1.city_c,
t1.city_e,
t1.province_c,
t1.province_e,
case when t1.province_c = '全国' then 'CHT'
     when t2.geo_key is null then t1.province_e
     else t2.geo_key
end as AUDIT_COD
from dwd.dwd_gnd_ext_thc_geo t1
left join dm.dm_td_geography t2
--THC最细到省，用省关联
on t1.province_c = t2.province_name
and t2.geo_key = t2.province_code
)
--将上面的rawdata、corp、audit信息合到一起
,thc_rawdata2 as (
select t1.yyyymm,
t1.city_c,
t1.pack_cod as pack_cod_old,
t2.iqvia_pack_code as pack_cod,
t2.CORP_COD,
t3.AUDIT_COD,
t1.prod_cod,
t1.value,
t1.totalunit,
t1.totalunit * t2.countingunit as countingunit
from thc_rawdata1 t1
left join thc_corp t2
on t1.pack_cod = t2.pack_cod
left join thc_geo t3
on t1.city_c = t3.city_c
)
--最大的月份，计算ly时会需要
,max_thc_rawdata as (
select max(cast(yyyymm as int)) as max_ym
from thc_rawdata2
)
--计算ly
,thc_rawdata3 as (
select yyyymm,
AUDIT_COD,
pack_cod,
CORP_COD,
value,
totalunit,
countingunit,
0 as value_ly,
0 as totalunit_ly,
0 as countingunit_ly
from thc_rawdata2
union all
select cast(t1.yyyymm as int) + 100 as yyyymm,
t1.AUDIT_COD,
t1.pack_cod,
t1.CORP_COD,
0 as value,
0 as totalunit,
0 as countingunit,
t1.value as value_ly,
t1.totalunit as totalunit_ly,
t1.countingunit as countingunit_ly
from thc_rawdata2 t1
cross join max_thc_rawdata t2
where cast(t1.yyyymm as int) + 100 <= t2.max_ym
)
--按年月、audit_cod、pack_cod、Corp_cod汇总
,thc_rawdata4 as (
select yyyymm,
AUDIT_COD,
pack_cod,
CORP_COD,
sum(value) as SALES_VALUE_CAL,
sum(totalunit) as SALES_UNIT_CAL,
sum(countingunit) as COUNTING_UNIT,
sum(value_ly) as SALES_VALUE_CAL_LY,
sum(totalunit_ly) as SALES_UNIT_CAL_LY,
sum(countingunit_ly) as COUNTING_UNIT_LY
from thc_rawdata3
group by yyyymm,
AUDIT_COD,
pack_cod,
CORP_COD
)

insert overwrite table dm.dm_ext_thc_sales
(
yyyymm,
AUDIT_COD,
pack_cod,
CORP_COD,
SALES_VALUE_CAL,
SALES_UNIT_CAL,
COUNTING_UNIT,
SALES_VALUE_CAL_LY,
SALES_UNIT_CAL_LY,
COUNTING_UNIT_LY
)
select yyyymm,
AUDIT_COD,
pack_cod,
CORP_COD,
SALES_VALUE_CAL,
SALES_UNIT_CAL,
COUNTING_UNIT,
SALES_VALUE_CAL_LY,
SALES_UNIT_CAL_LY,
COUNTING_UNIT_LY
from thc_rawdata4
--取2018年之后的数
where yyyymm >= '201801'