Hive SQL 每日场景题

6、从订单明细表(order_detail)统计每个商品销售首年的年份，销售数量和销售总额。

期望结果如下：

sku_id (商品id)	year (销售首年年份)	order_num (首年销量)	order_amount (首年销售金额)
1	2020	2	4000.00
2	2020	26	260.00
3	2020	1	5000.00
4	2021	53	318000.00
5	2021	242	121000.00
6	2020	6	12000.00
7	2020	35	3500.00
8	2020	59	35400.00
9	2021	194	194000.00
10	2020	94	9400.00
11	2020	95	4750.00
12	2020	83	1660.00

需要用到的表：

订单明细表：order_detail

order_detail_id(订单明细id)	order_id(订单id)	sku_id(商品id)	create_date(下单日期)	price(商品单价)	sku_num(商品件数)
1	1	1	2021-09-30	2000.00	2
2	1	3	2021-09-30	5000.00	5
22	10	4	2020-10-02	6000.00	1
23	10	5	2020-10-02	500.00	24
24	10	6	2020-10-02	2000.00	5

代码

with t as (
select 
sku_id
,substr(create_date,1,4) as year
,sum(sku_num) as order_num
,sum(price*sku_num) as order_amount
,rank() over (partition by sku_id order by substr(create_date,1,4) ) as rk
from order_detail
group by 
sku_id
,substr(create_date,1,4)
)
select 
sku_id
,year
,order_num
,order_amount
from t where rk =1

7、从订单明细表(order_detail)中筛选出去年总销量小于100的商品及其销量，假设今天的日期是2022-01-10，不考虑上架时间小于一个月的商品

期望结果如下：

sku_id （商品id）	name （商品名称）	order_num （销量）
1	xiaomi 10	49
3	apple 12	35
4	xiaomi 13	53
6	洗碗机	26

需要用到的表：

商品信息表：sku_info

sku_id(商品id)	name(商品名称)	category_id(分类id)	from_date(上架日期)	price(商品价格)
1	xiaomi 10	1	2020-01-01	2000
6	洗碗机	2	2020-02-01	2000
9	自行车	3	2020-01-01	1000

订单明细表：order_detail

order_detail_id(订单明细id)	order_id(订单id)	sku_id(商品id)	create_date(下单日期)	price(商品单价)	sku_num(商品件数)
1	1	1	2021-09-30	2000.00	2
2	1	3	2021-09-30	5000.00	5
22	10	4	2020-10-02	6000.00	1
23	10	5	2020-10-02	500.00	24
24	10	6	2020-10-02	2000

代码

with t as (
select 
a.sku_id
,b.name
,sum(a.sku_num) as order_num
from order_detail a
left join sku_info b on a.sku_id = b.sku_id
where substr(a.create_date ,1,4)='2021' and  b.from_date < date_add('2022-01-10',-30)
group by 
a.sku_id
,b.name)
select * from t where order_num < 100

8、从用户登录明细表（user_login_detail）中查询每天的新增用户数，若一个用户在某天登录了，且在这一天之前没登录过，则任务该用户为这一天的新增用户。

期望结果如下：

login_date_first （日期）	user_count （新增用户数）
2021-09-21	1
2021-09-22	1
2021-09-23	1
2021-09-24	1
2021-09-25	1
2021-09-26	1
2021-09-27	1
2021-10-04	2
2021-10-06	1

需要用到的表：

用户登录明细表：user_login_detail

user_id(用户id)	ip_address(ip地址)	login_ts(登录时间)	logout_ts(登出时间)
101	180.149.130.161	2021-09-21 08:00:00	2021-09-27 08:30:00
102	120.245.11.2	2021-09-22 09:00:00	2021-09-27 09:30:00
103	27.184.97.3	2021-09-23 10:00:00	2021-09-27 10:30:00

代码

with t as (
select 
user_id
,login_ts
,lag(login_ts,1,'null') over(partition by user_id order by login_ts ) as last_date
from 
(select distinct user_id,substr(login_ts,1,10)as login_ts from user_login_detail)a)

select
login_ts as login_date_first
,sum(if(last_date='null',1,0)) as user_count 
from t where last_date ='null'
group by 
login_ts

9、从订单明细表（order_detail）中统计出每种商品销售件数最多的日期及当日销量，如果有同一商品多日销量并列的情况，取其中的最小日期

期望结果如下：

sku_id （商品id）	create_date （销量最高的日期）	sum_num （销量）
1	2021-09-30	9
2	2021-10-02	5800
3	2021-10-05	9
4	2021-10-07	10
5	2021-10-03	47
6	2021-10-03	8
7	2021-10-05	58
8	2020-10-08	59
9	2021-10-01	45
10	2020-10-08	94
11	2020-10-08	95
12	2021-10-03	20400

需要用到的表：

订单明细表：order_detail

order_detail_id(订单明细id)	order_id(订单id)	sku_id(商品id)	create_date(下单日期)	price(商品单价)	sku_num(商品件数)
1	1	1	2021-09-30	2000.00	2
2	1	3	2021-09-30	5000.00	5
22	10	4	2020-10-02	6000.00	1
23	10	5	2020-10-02	500.00	24
24	10	6	2020-10-02	2000.00	5

代码

with t as (
select 
sku_id
,create_date
,sum(sku_num) as sum_num
from order_detail
group by 
sku_id
,create_date)

select 
sku_id
,create_date
,sum_num
from (
select *
,row_number() over (partition by sku_id order by sum_num desc ) as rk 
from t)a
where rk=1

10、从订单明细表（order_detail）中查询累积销售件数高于其所属品类平均数的商品

期望结果如下：

sku_id	name	sum_num	cate_avg_num
2	手机壳	6044	1546
5	破壁机	242	194
7	热水壶	252	194
8	微波炉	253	194
12	遮阳伞	20682	5373

需要用到的表：

商品信息表：sku_info

sku_id(商品id)	name(商品名称)	category_id(分类id)	from_date(上架日期)	price(商品价格)
1	xiaomi 10	1	2020-01-01	2000
6	洗碗机	2	2020-02-01	2000
9	自行车	3	2020-01-01	1000

订单明细表：order_detail

order_detail_id(订单明细id)	order_id(订单id)	sku_id(商品id)	create_date(下单日期)	price(商品单价)	sku_num(商品件数)
1	1	1	2021-09-30	2000.00	2
2	1	3	2021-09-30	5000.00	5
22	10	4	2020-10-02	6000.00	1
23	10	5	2020-10-02	500.00	24
24	10	6	2020-10-02	2000.00	5

代码

with t1 as (
select 
a.sku_id
,b.name
,b.category_id
,sum(sku_num) as sum_num
from order_detail a 
left join sku_info b  on a.sku_id=b.sku_id
group by 
a.sku_id
,b.name
,b.category_id

),
t2 as (
select 
distinct b.category_id
,cast (sum(a.sku_num) over (partition by b.category_id )/count(distinct a.sku_id) over (partition by b.category_id ) as  int )  as cate_avg_num
from order_detail a 
left join sku_info b  on a.sku_id=b.sku_id
)

select 
t1.sku_id
,t1.name
,t1.sum_num
,t2.cate_avg_num
from  t1 
left join t2 on t1.category_id=t2.category_id
where t1.sum_num > t2.cate_avg_num

11、从用户登录明细表（user_login_detail）和订单信息表（order_info）中查询每个用户的注册日期（首次登录日期）、总登录次数以及其在2021年的登录次数、订单数和订单总额。

期望结果如下：

user_id (用户id)	register_date (注册日期)	total_login_count (累积登录次数)	login_count_2021 (2021年登录次数)	order_count_2021 (2021年下单次数)	order_amount_2021 (2021年订单金额)
101	2021-09-21	5	5	4	143660.00
102	2021-09-22	4	4	4	177850.00
103	2021-09-23	2	2	4	75890.00
104	2021-09-24	4	4	4	89880.00
105	2021-10-04	1	1	4	120100.00
106	2021-10-04	2	2	4	119150.00
107	2021-09-25	4	4	4	124150.00
108	2021-10-06	2	2	4	155770.00
109	2021-09-26	3	3	2	129480.00

需要用到的表：

用户登录明细表：user_login_detail

user_id(用户id)	ip_address(ip地址)	login_ts(登录时间)	logout_ts(登出时间)
101	180.149.130.161	2021-09-21 08:00:00	2021-09-27 08:30:00
102	120.245.11.2	2021-09-22 09:00:00	2021-09-27 09:30:00
103	27.184.97.3	2021-09-23 10:00:00	2021-09-27 10:30:00

订单信息表：order_info

order_id (订单id)	user_id (用户id)	create_date (下单日期)	total_amount (订单金额)
1	101	2021-09-30	29000.00
10	103	2020-10-02	28000.00

代码

with t1 as (
select 
distinct user_id 
,min(substr(login_ts,1,10)) over(partition by user_id rows between unbounded preceding and unbounded following ) as register_date
,count(login_ts) over(partition by user_id rows between unbounded preceding and unbounded following ) as total_login_count
,sum(if(substr(login_ts,1,4)='2021',1,0)) over(partition by user_id rows between unbounded preceding and unbounded following ) as login_count_2021
from user_login_detail
)
,t2 as (
select 
user_id
,count(order_id )  as order_count_2021
,sum(total_amount ) as order_amount_2021
from order_info
where substr(create_date,1,4)='2021'
group by 
user_id
)
select 
t1.user_id
,t1.register_date
,t1.total_login_count
,nvl(t1.login_count_2021,0) as login_count_2021
,nvl(t2.order_count_2021,0) as order_count_2021
,nvl(t2.order_amount_2021,0) as order_amount_2021
from t1 left join  t2 on t1.user_id=t2.user_id
order by t1.user_id