目录
- [1 题目](#1 题目)
- [2 建表语句](#2 建表语句)
- [3 题解](#3 题解)
1 题目
已知有表 t_product_sales 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 |
+------------+--------------+-------------+--------------+2 建表语句
sql
--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);3 题解
（1）构建年份维表
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执行结果
+-------+-----------------+---------------+
| 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）年份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。
sql
# 这一设置的含义是关闭Hive中的笛卡尔积严格检查。
# 具体来说，Hive默认情况下不允许生成笛卡尔积（即两个没有连接条件的表的笛卡尔积），
# 因为笛卡尔积通常会生成非常大的结果集，可能会导致性能问题或资源耗尽。
# 通过将这个设置设为false，你可以禁用这种严格检查，从而允许在查询中生成笛卡尔积。
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执行结果
+-------------+---------------+----------------------+----------------------+----------------------+-------+-----------------+---------------+
| 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）计算每年每个产品在售天数，计算年销售额
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。
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执行结果
+-------------+---------------+-------+---------------+
| 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 |
+-------------+---------------+-------+---------------+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。