148 lines
4.2 KiB
SQL
148 lines
4.2 KiB
SQL
-- 由于收入是按照不同的统计方式选择不同的计算方式
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-- 1.divide_type=1:
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-- order_amount * divide_scale
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-- 2.divide_type=2:
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-- 阶梯（累计）：
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-- 0->10% 50->20% 100->%30
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-- 第一个月20(流水): 20->10%
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-- 第二个月90(流水): 30->10% 50->20% 10->30%
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-- 3.divide_type=3:
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-- 阶梯（按月）：
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-- 0->10% 50->20% 100->%30
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-- 第一个月20(流水): 20->10%
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-- 第二个月90(流水): 50->10% 40->20%
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WITH money_divide_scale_tmp AS (
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SELECT
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relation_id,
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money,
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divide_scale,
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COALESCE(LEAD(money) OVER (PARTITION BY relation_id ORDER BY money), 999999999999.99) AS next_money
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FROM (
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SELECT
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relation_id,
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cast((elem->>'money')::text as numeric) money,
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cast((elem->>'divide_scale')::text as numeric) divide_scale
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FROM (
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SELECT
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relation_id,
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jsonb_array_elements(divide_scale_step) elem
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FROM
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dim.game_product_relation
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WHERE
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divide_type != 1
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) tt
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) ttt
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WHERE
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ttt.money IS NOT NULL
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AND ttt.divide_scale IS NOT NULL
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),
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revenue_df_tmp AS
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(
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-- divide_type=1 money= order_amount * divide_scale
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SELECT
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t2.relation_id,
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SUM(t1.order_amount * t2.divide_scale)
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money,
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t1.ds,
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t1.ds_type,
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t1.ds_name
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FROM
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dwd.order_df t1
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INNER JOIN dim.game_product_relation t2 ON t1.game_channel_id = t2.game_channel_id
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AND t1.game_identity = t2.game_identity
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AND t1.game_platform_id = t2.game_platform_id
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AND t2.divide_type = 1
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where t1.ds_type = 'd'
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GROUP BY
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t2.relation_id,
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t1.ds,
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t1.ds_type,
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t1.ds_name
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UNION ALL
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-- 阶梯（累计）：
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-- 0->10% 50->20% 100->%30
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-- 第一个月20(流水): 20->10%
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-- 第二个月90(流水): 30->10% 50->20% 10->30%
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SELECT
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tt1.relation_id,
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tt1.order_amount * tt2.divide_scale money,
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tt1.ds,
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tt1.ds_type,
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tt1.ds_name
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FROM (
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SELECT
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t2.relation_id,
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t1.ds,
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t1.ds_type,
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t1.ds_name,
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t1.order_amount,
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SUM(t1.order_amount) OVER (PARTITION BY t2.relation_id ORDER BY ds) AS sum_order_amount
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FROM
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dwd.order_df t1
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INNER JOIN dim.game_product_relation t2 ON t1.game_channel_id = t2.game_channel_id
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AND t1.game_identity = t2.game_identity
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AND t1.game_platform_id = t2.game_platform_id
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AND t2.divide_type = 2
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WHERE
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t1.ds_type = 'd') tt1
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INNER JOIN money_divide_scale_tmp tt2 ON tt1.relation_id = tt2.relation_id
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AND tt1.sum_order_amount >= tt2.money
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AND tt1.sum_order_amount <= tt2.next_money
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-- 阶梯（按月）：
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-- 0->10% 50->20% 100->%30
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-- 第一个月20(流水): 20->10%
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-- 第二个月90(流水): 50->10% 40->20%
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-- divide_type=3
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UNION ALL
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SELECT
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tt1.relation_id,
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tt1.order_amount * tt2.divide_scale money,
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tt1.ds,
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tt1.ds_type,
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tt1.ds_name
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FROM (
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SELECT
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t2.relation_id,
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t1.ds,
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t1.ds_type,
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t1.ds_name,
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t1.order_amount,
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SUM(t1.order_amount) OVER (PARTITION BY t2.relation_id, SUBSTR(t1.ds, 1, 6) ORDER BY ds) AS sum_order_amount
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FROM
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dwd.order_df t1
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INNER JOIN dim.game_product_relation t2 ON t1.game_channel_id = t2.game_channel_id
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AND t1.game_identity = t2.game_identity
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AND t1.game_platform_id = t2.game_platform_id
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AND t2.divide_type = 3
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WHERE
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t1.ds_type = 'd') tt1
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INNER JOIN money_divide_scale_tmp tt2 ON tt1.relation_id = tt2.relation_id
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AND tt1.sum_order_amount >= tt2.money
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AND tt1.sum_order_amount <= tt2.next_money
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)
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INSERT INTO dwd.revenue_df(relation_id,money,ds,ds_type,ds_name)
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SELECT relation_id
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,sum(money) money
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,ds
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,ds_type
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,ds_name
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FROM revenue_df_tmp where relation_id='${relation_id}'
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GROUP BY relation_id
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,ds
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,ds_type
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,ds_name
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union all
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SELECT relation_id
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,sum(money) money
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,SUBSTRING(ds, 1, 6) || '00' ds
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,'m' ds_type
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,'月' ds_name
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FROM revenue_df_tmp where relation_id='${relation_id}'
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GROUP BY relation_id
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,SUBSTRING(ds, 1, 6) || '00'
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ON CONFLICT (relation_id,ds,ds_type)
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DO UPDATE SET money = EXCLUDED.money,ds_name = EXCLUDED.ds_name
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; |