常见大数据面试SQL-查询每个产品每年总销售额

数据仓库晨曦

发布于 2024-06-28 18:10:03

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发布于 2024-06-28 18:10:03

文章被收录于专栏：数据仓库技术数据仓库技术

一、题目

已知有表如下，记录了每个产品id、产品名称、产品销售开始日期、产品销售结束日期以及产品日均销售金额，请计算出每个产品每年的销售金额

样例数据

+-------------+---------------+----------------------+----------------------+----------------------+
| product_id  | product_name  |     period_start     |      period_end      | average_daily_sales  |
+-------------+---------------+----------------------+----------------------+----------------------+
| 1           | LC Phone      | 2019-01-25 00:00:00  | 2019-02-28 00:00:00  | 100                  |
| 2           | LC T-Shirt    | 2018-12-01 00:00:00  | 2020-01-01 00:00:00  | 10                   |
| 3           | LC Keychain   | 2019-12-01 00:00:00  | 2020-01-31 00:00:00  | 1                    |
+-------------+---------------+----------------------+----------------------+----------------------+

期望结果

+------------+--------------+-------------+--------------+
| product_id | product_name | report_year | total_amount |
+------------+--------------+-------------+--------------+
| 1          | LC Phone     |    2019     | 3500         |
| 2          | LC T-Shirt   |    2018     | 310          |
| 2          | LC T-Shirt   |    2019     | 3650         |
| 2          | LC T-Shirt   |    2020     | 10           |
| 3          | LC Keychain  |    2019     | 31           |
| 3          | LC Keychain  |    2020     | 31           |
+------------+--------------+-------------+--------------+

说明

LC Phone 在 2019-01-25 至 2019-02-28 期间销售，该产品销售时间总计35天。销售总额 35*100 = 3500。
LC T-shirt 在 2018-12-01 至 2020-01-01 期间销售，该产品在2018年、2019年、2020年的销售时间分别是31天、365天、1天，2018年、2019年、2020年的销售总额分别是3110=310、36510=3650、1*10=10。
LC Keychain 在 2019-12-01 至 2020-01-31 期间销售，该产品在2019年、2020年的销售时间分别是：31天、31天，2019年、2020年的销售总额分别是31*1=31、31*1=31。

二、分析

题目中给出的是每个产品的开始时间和结束时间，这里最关键的问题在于一个时间段可能跨年,并且不知道跨几年。

维度	评分
题目难度	⭐️⭐️⭐️⭐️
题目清晰度	⭐️⭐️⭐️⭐️⭐️
业务常见度	⭐️⭐️⭐️⭐️

三、SQL

1.构建年份维表

首先我们生成一份年份的维表，包含年份、每年开始日期和每年结束日期，这里有多重方式，由于题目中只有三年，咱们直接union all 拼一个出来.

执行SQL

with dim_year as (select '2018' as year, '2018-01-01' as year_first_day, '2018-12-31' as year_end_day
                  union all
                  select '2019' as year, '2019-01-01' as year_first_day, '2019-12-31' as year_end_day
                  union all
                  select '2020' as year, '2020-01-01' as year_first_day, '2020-12-31' as year_end_day)
select year,year_first_day,year_end_day from dim_year

查询结果

+-------+-----------------+---------------+
| year  | year_first_day  | year_end_day  |
+-------+-----------------+---------------+
| 2018  | 2018-01-01      | 2018-12-31    |
| 2019  | 2019-01-01      | 2019-12-31    |
| 2020  | 2020-01-01      | 2020-12-31    |
+-------+-----------------+---------------+

2.维表与原始数据进行关联

把原始数据与年份维表进行笛卡尔积，得到每年与原始数据的一个交叉值.

执行SQL

set hive.strict.checks.cartesian.product = false;
with dim_year as (select '2018' as year, '2018-01-01' as year_first_day, '2018-12-31' as year_end_day
                  union all
                  select '2019' as year, '2019-01-01' as year_first_day, '2019-12-31' as year_end_day
                  union all
                  select '2020' as year, '2020-01-01' as year_first_day, '2020-12-31' as year_end_day)
select
    product_id,
    product_name,
    period_start,
    period_end,
    average_daily_sales,
    year,
    year_first_day,
    year_end_day
from t_product_sales
left join dim_year

查询结果

+-------------+---------------+----------------------+----------------------+----------------------+-------+-----------------+---------------+
| product_id  | product_name  |     period_start     |      period_end      | average_daily_sales  | year  | year_first_day  | year_end_day  |
+-------------+---------------+----------------------+----------------------+----------------------+-------+-----------------+---------------+
| 1           | LC Phone      | 2019-01-25 00:00:00  | 2019-02-28 00:00:00  | 100                  | 2019  | 2019-01-01      | 2019-12-31    |
| 1           | LC Phone      | 2019-01-25 00:00:00  | 2019-02-28 00:00:00  | 100                  | 2020  | 2020-01-01      | 2020-12-31    |
| 1           | LC Phone      | 2019-01-25 00:00:00  | 2019-02-28 00:00:00  | 100                  | 2018  | 2018-01-01      | 2018-12-31    |
| 2           | LC T-Shirt    | 2018-12-01 00:00:00  | 2020-01-01 00:00:00  | 10                   | 2019  | 2019-01-01      | 2019-12-31    |
| 2           | LC T-Shirt    | 2018-12-01 00:00:00  | 2020-01-01 00:00:00  | 10                   | 2020  | 2020-01-01      | 2020-12-31    |
| 2           | LC T-Shirt    | 2018-12-01 00:00:00  | 2020-01-01 00:00:00  | 10                   | 2018  | 2018-01-01      | 2018-12-31    |
| 3           | LC Keychain   | 2019-12-01 00:00:00  | 2020-01-31 00:00:00  | 1                    | 2019  | 2019-01-01      | 2019-12-31    |
| 3           | LC Keychain   | 2019-12-01 00:00:00  | 2020-01-31 00:00:00  | 1                    | 2020  | 2020-01-01      | 2020-12-31    |
| 3           | LC Keychain   | 2019-12-01 00:00:00  | 2020-01-31 00:00:00  | 1                    | 2018  | 2018-01-01      | 2018-12-31    |
+-------------+---------------+----------------------+----------------------+----------------------+-------+-----------------+---------------+

3.比较计算每年每个产品在售天数

我们先观察2中的结果，可以看到原始记录中每行数据都与所有年都有一行记录。我们从销售日期和每年开始日期中取较大日期 得到一个开始时间，然后从销售截止日期和每年的结束日期取较小日期 得到一个结束日期，然后用结束日期减去开始日期。

执行SQL

with dim_year as (select '2018' as year, '2018-01-01' as year_first_day, '2018-12-31' as year_end_day
                  union all
                  select '2019' as year, '2019-01-01' as year_first_day, '2019-12-31' as year_end_day
                  union all
                  select '2020' as year, '2020-01-01' as year_first_day, '2020-12-31' as year_end_day),
     tmp as (select product_id,
                    product_name,
                    period_start,
                    period_end,
                    average_daily_sales,
                    year,
                    year_first_day,
                    year_end_day,
                    datediff(
                            if(to_date(period_end) > to_date(year_end_day), to_date(year_end_day), to_date(period_end)),
                            if(to_date(period_start) > to_date(year_first_day), to_date(period_start),
                               to_date(year_first_day))) as date_diff
             from t_product_sales
                      left join dim_year)
select product_id,
       product_name,
       year,
       year_first_day,
       year_end_day,
       (date_diff + 1) * average_daily_sales
from tmp
where date_diff > 0

查询结果

+-------------+---------------+----------------------+----------------------+----------------------+-------+-----------------+---------------+------------+
| product_id  | product_name  |     period_start     |      period_end      | average_daily_sales  | year  | year_first_day  | year_end_day  | date_diff  |
+-------------+---------------+----------------------+----------------------+----------------------+-------+-----------------+---------------+------------+
| 1           | LC Phone      | 2019-01-25 00:00:00  | 2019-02-28 00:00:00  | 100                  | 2019  | 2019-01-01      | 2019-12-31    | 34         |
| 1           | LC Phone      | 2019-01-25 00:00:00  | 2019-02-28 00:00:00  | 100                  | 2020  | 2020-01-01      | 2020-12-31    | -307       |
| 1           | LC Phone      | 2019-01-25 00:00:00  | 2019-02-28 00:00:00  | 100                  | 2018  | 2018-01-01      | 2018-12-31    | -25        |
| 2           | LC T-Shirt    | 2018-12-01 00:00:00  | 2020-01-01 00:00:00  | 10                   | 2019  | 2019-01-01      | 2019-12-31    | 364        |
| 2           | LC T-Shirt    | 2018-12-01 00:00:00  | 2020-01-01 00:00:00  | 10                   | 2020  | 2020-01-01      | 2020-12-31    | 0          |
| 2           | LC T-Shirt    | 2018-12-01 00:00:00  | 2020-01-01 00:00:00  | 10                   | 2018  | 2018-01-01      | 2018-12-31    | 30         |
| 3           | LC Keychain   | 2019-12-01 00:00:00  | 2020-01-31 00:00:00  | 1                    | 2019  | 2019-01-01      | 2019-12-31    | 30         |
| 3           | LC Keychain   | 2019-12-01 00:00:00  | 2020-01-31 00:00:00  | 1                    | 2020  | 2020-01-01      | 2020-12-31    | 30         |
| 3           | LC Keychain   | 2019-12-01 00:00:00  | 2020-01-31 00:00:00  | 1                    | 2018  | 2018-01-01      | 2018-12-31    | -335       |
+-------------+---------------+----------------------+----------------------+----------------------+-------+-----------------+---------------+------------+

查看上面结果，就可以发现，只有在当年有销售时间的数据date_diff >=0，如果在当年没有销售时间，则date_diff为负。但是这个date_diff 的值比预期小1，这是因为我们算了日期差，所以我们在结果上+1即可。

4.筛选符合条件数据，计算最终结果

得到有销售的年份和天数之后，乘以每天的销售和即可得到最终结果。

执行SQL

with dim_year as (select '2018' as year, '2018-01-01' as year_first_day, '2018-12-31' as year_end_day
                  union all
                  select '2019' as year, '2019-01-01' as year_first_day, '2019-12-31' as year_end_day
                  union all
                  select '2020' as year, '2020-01-01' as year_first_day, '2020-12-31' as year_end_day),
     tmp as (select product_id
                  , product_name
                  , period_start
                  , period_end
                  , average_daily_sales
                  , year
                  , year_first_day
                  , year_end_day
                  , datediff(if(to_date(period_end)
                                    > to_date(year_end_day)
                                 , to_date(year_end_day)
                                 , to_date(period_end))
             , if(to_date(period_start)
                      > to_date(year_first_day)
                                 , to_date(period_start)
                                 , to_date(year_first_day))) as date_diff
             from t_product_sales
                      left join dim_year)
select product_id,
       product_name,
       year,
       (date_diff + 1) * average_daily_sales as total_amount
from tmp
where date_diff >= 0

查询结果

+-------------+---------------+-------+---------------+
| product_id  | product_name  | year  | total_amount  |
+-------------+---------------+-------+---------------+
| 1           | LC Phone      | 2019  | 3500          |
| 2           | LC T-Shirt    | 2018  | 310           |
| 2           | LC T-Shirt    | 2019  | 3650          |
| 2           | LC T-Shirt    | 2020  | 10            |
| 3           | LC Keychain   | 2019  | 31            |
| 3           | LC Keychain   | 2020  | 31            |
+-------------+---------------+-------+---------------+

四、建表语句和数据插入

--建表语句
create table if not exists t_product_sales
(
    product_id          bigint,
    product_name        string,
    period_start        string,
    period_end          string,
    average_daily_sales bigint
)
    ROW FORMAT DELIMITED
        FIELDS TERMINATED BY ','
    STORED AS orc;

--插入数据

insert into t_product_sales(product_id, product_name, period_start, period_end, average_daily_sales)
values (1, 'LC Phone', '2019-01-25 00:00:00', '2019-02-28 00:00:00', 100),
       (2, 'LC T-Shirt', '2018-12-01 00:00:00', '2020-01-01 00:00:00', 10),
       (3, 'LC Keychain', '2019-12-01 00:00:00', '2020-01-31 00:00:00', 1);

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原始发表：2024-06-27，如有侵权请联系 cloudcommunity@tencent.com 删除

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