项目实战从0到1之hive（16）hive求出场率，环比以及共同通话时长

一、求出场率与出厂次数

1、有如下数据：（建表语句+sql查询）

id names

1 aa,bb,cc,dd,ee

2 aa,bb,ff,ww,qq

3 aa,cc,rr,yy

4 aa,bb,dd,oo,pp

2、求英雄的出场排名top3的出场次数及出场率

create table if not exists t_names(

id int,

names array

)

row format delimited

fields terminated by ‘	’

collection items terminated by ‘,’

;

select *

from (

select name,cc,cc / (sum(cc) over()) as ccl,

rank() over(sort by cc desc) as rk

from (

select

name,

count(1) as cc

from t_names lateral view explode(names) tt as name

group by

name

) a

) aa

where aa.rk <= 3

;

二、求通话时长

1、有如下通话记录：

Zhangsan Wangwu 01:01:01

Zhangsan Zhaoliu 00:11:21

Zhangsan Yuqi 00:19:01

Zhangsan Jingba 00:21:01

Zhangsan Wuxi 01:31:17

Wangwu Zhaoliu 00:51:01

Wangwu Zhaoliu 01:11:19

Wangwu Yuqi 00:00:21

Wangwu Yuqi 00:23:01

Yuqi Zhaoliu 01:18:01

Yuqi Wuxi 00:18:00

Jingba Wangwu 00:01:01

Jingba Wangwu 00:00:06

Jingba Wangwu 00:02:04

Jingba Wangwu 00:02:54

Wangwu Yuqi 01:00:13

Wangwu Yuqi 00:01:01

Wangwu Zhangsan 00:01:01

2、统计两个人的通话总时长（用户之间互相通话的时长）

create table relations(

fromstr string,

tostr string,

time string

)

row format delimited

fields terminated by ’ ’

;

select

fromstr,

tostr,

sum(duration) as durations

from (

Select

Case when fromstr >= tostr then fromstr else tostr end fromstr,

Case when fromstr >= tostr then tostr else fromstr end tostr,

Split(time,’:’)[0] * 60 * 60 + Split(time,’:’)[1] * 60 + Split(time,’:’)[2] duration

from relations

) a

group by fromstr,tostr

;

三、求出每个店铺的当月销售额和累计到当月的总销售额

1、有如下销售数据：（建表语句+sql查询）

店铺 月份 金额

a,01,150

a,01,200

b,01,1000

b,01,800

c,01,250

c,01,220

b,01,6000

a,02,2000

a,02,3000

b,02,1000

b,02,1500

c,02,350

c,02,280

a,03,350

a,03,250

2、编写Hive的HQL语句求出每个店铺的当月销售额和累计到当月的总销售额

create table t_store(

name string,

months int,

money int

)

row format delimited fields terminated by “,”;

select name,months,amoney,sum(amoney) over(distribute by name sort by months asc rows between unbounded preceding and current row) as totalmomey

from (

Select name,months,sum(money) as amoney

From t_store

Group by name,months

) a

；

四、统计amt连续3个月，环比增长>50%的user

user_id month amt
1,20170101,100
3,20170101,20
4,20170101,30
1,20170102,200
2,20170102,240
3,20170102,30
4,20170102,2
1,20170101,180
2,20170101,250
3,20170101,30
4,20170101,260
…
…

select user_id
from(
select
user_id,month,mon_amt,pre_mon_amt,
sum(case when ((mon_amt - pre_mon_amt) / pre_mon_amt * 100) > 50
and datediff(to_date(month,‘yyyymm’),to_date(pre2_month,‘yyyymm’),‘mm’) = 2
then 1
else 0 end) over(partition by user_id order by month asc rows between current row and 2 following) as flag
from (
select
user_id,
substr(month,0,6) as month,
sum(amt) as mon_amt,
lag(sum(amt),1,0.00001) over(partition by user_id order by substr(month,0,6) asc ) as pre_mon_amt,
substr(lag(substr(month,0,6),2,‘199001’) over(partition by user_id order by substr(month,0,6) asc),0,6) as pre_2_mon
from amt
group by user_id,substr(month,0,6)
) t1
) t2
where t2.flag >=3;