301.值班排表
create table date_student (
d varchar(4)
);
create table if not exists student1 (
student varchar(5)
);
insert into date_student values ('0101'),('0102'),('0103'),('0104'),('0105'),('0106'),('0107');
insert into student1 values ('小明'),('小华'),('小红');
如上有日期和姓名表,要求依次组合生成一张排班表
0101 小明
0102 小华
0103 小红
0104 小明
0105 小华
0106 小红
0107 小明
select d,student
from (
select d, row_number() over () %3 as rn
from date_student
) t1
join (
select student,row_number() over () %3 as rw
from student1
) t2
on t1.rn = t2.rw
order by d ;
302.TOP_N条件下计算最近14天趋势
SQL要求在kylin和presto中都能跑通
需求
有表test.table_0526
字段 常驻地resident_prov_name 人次person_num 时间 d
现在要求获取到上海的前20的客源地(按人次获取客源地),获取这些客源地最近14天的热度趋势(以用户输入的结束时间-14天为准,关于热度趋势的计算就是获取占比即可)
请求的区间必须大于14天,否则无法返回14天的趋势
SELECT resident_prov_name, d, CAST(person_nums * 1.0 / SUM(person_nums) OVER (PARTITION BY resident_prov_name ) AS DECIMAL(9, 4)) AS ratio
FROM (
SELECT resident_prov_name, d, person_nums
FROM (
SELECT resident_prov_name, d, person_nums, ROW_NUMBER() OVER (PARTITION BY resident_prov_name ORDER BY d DESC) AS rn
FROM (
SELECT resident_prov_name, d, person_nums
FROM (
SELECT resident_prov_name, d, person_nums, dense_rank() OVER (ORDER BY rw DESC) AS rn
FROM (
SELECT resident_prov_name, d, person_nums, SUM(person_nums) OVER (PARTITION BY resident_prov_name ) AS rw
FROM (
SELECT resident_prov_name, d, SUM(person_num) AS person_nums
FROM test.table_0526
WHERE d >= @startTime
AND d <= @endTime
AND to_country_name = @country
AND to_prov_name = @province
AND to_city_name = @city
AND resident_prov_name IS NOT NULL
GROUP BY resident_prov_name, d
) a
) b
) c
WHERE rn <= 20
) d
) e
WHERE rn <= 14
) f
如果用户请求的区间小于14天,也返还给用户14天的数据,怎么写(下面的这种写法查询一年的可以,但是时间区间过长kylin就报错了)
SELECT resident_prov_name,
d,
CAST(person_nums * 1.0 / SUM(person_nums) OVER (PARTITION BY resident_prov_name) AS DECIMAL(9, 4)) AS ratio
FROM
(SELECT resident_prov_name,
d,
SUM(person_num) AS person_nums,
ROW_NUMBER() OVER (PARTITION BY resident_prov_name
ORDER BY d DESC) AS rn
FROM test.table_0526
WHERE d >= '2019-01-01'
AND d <= @endTime
AND to_country_name = @country
AND to_prov_name = '上海'
AND to_city_name = '上海'
AND resident_prov_name IN
(SELECT resident_prov_name
FROM ctripdi_prodb.adm_order_allbu_share_day
WHERE d >= @startTime
AND d <= @endTime
AND to_country_name = @country
AND to_prov_name = @province
AND to_city_name = @city
AND resident_prov_name IS NOT NULL
GROUP BY resident_prov_name
ORDER BY SUM(person_num) DESC
LIMIT 20)
GROUP BY resident_prov_name,
d) a
WHERE rn <= 14
303.同比计算
给定时间区间,对比去年相同区间,在top_20 view_spot_name中计算同比
select view_spot_name,(lag_persons - persons) * 1.0 /persons as ratio
from
(
SELECT view_spot_name,persons,LAG(persons,1) over(partition by view_spot_name order by lag1 ) as lag_persons
FROM (
(
SELECT 1 as lag1,view_spot_name,sum(person_num) AS persons
FROM test.table0527
WHERE d >= '2020-05-01'
AND d<='2021-05-10'
AND to_country_name = '中国'
AND to_prov_name = '上海'
AND to_city_name = '上海'
AND view_spot_name IS NOT NULL
GROUP BY view_spot_name
ORDER BY sum(person_num) DESC
LIMIT 20
)
union all
(
SELECT 2,view_spot_name,sum(person_num)
FROM test.table0527
WHERE
d >= concat(CAST(CAST(substring('2020-05-01', 1, 4) AS int) - 1 AS varchar), substring('2020-05-01', 5, 7))
AND d <= concat(CAST(CAST(substring('2021-05-10', 1, 4) AS int) - 1 AS varchar), substring('2021-05-10', 5, 7))
AND to_country_name = '中国'
AND to_prov_name = '上海'
AND to_city_name = '上海'
AND view_spot_name IS NOT NULL
GROUP BY view_spot_name
)
) a
) b where lag_persons is not null
这里的问题是
concat(CAST(CAST(substring('2020-05-01', 1, 4) AS int) - 1 AS varchar), substring('2020-05-01', 5, 7))
不走rowkey,如果字段的枚举值过大就会无法执行,所以比较有效的方法是与前端沟通让他们传入时间
或者在底表先进行计算,改变粒度(之前的粒度为天,现在为月份)
另外kylin和presto的时间处理函数
kylin
TIMESTAMAPADD(DAY,-14,date '2021-01-01')
presto
date_add('day',-14,cast('2021-01-01' as date)
date_add('day',-14, date'2021-01-01' )
304.分区表批量补数
新建的一张分区表要补数,一般是一天一天的补数,但是这样比较麻烦,采用动态分区的方法来补数,效率将大大提升,set动态分区的参数,d>= 分区的初始时间,需要注意的是要先跑几个分区对比一下数据,当然这种动态分区补数的方法也是有缺陷的,动态分区的时间不是分区d就不能使用,因为可能其他的时间维度与分区不是一一对应的
如下一般的set参数
set hive.merge.mapfiles=true; --map端
set hive.merge.mapredfiles=true; --reduce端
set hive.map.aggr=true;
SET hive.auto.convert.join=true;
SET hive.exec.max.dynamic.partitions.pernode=9000;
SET hive.exec.max.dynamic.partitions=9000;
set hive.exec.parallel=true;
306.TOP_20%条件下计算词频
有表test_table2,包含 master_hotel_name(酒店名称),,tag_name(评价词),
score(酒店评分),pplrt_num(评价词的词频)
现在的需求是要统计前20%酒店(按评分来排序)的评价词的词频
test_table2是我加工的表,来自于一张评价词词频表和酒店相关扩展表(包含酒店名称,评分等)
SELECT tag_name as tag,sum(pplrt_nums) as pplrt_sum
FROM (
SELECT master_hotel_name,tag_name,pplrt_nums, rn * 1.0 / counts AS a
FROM (
SELECT master_hotel_name,tag_name, score,pplrt_nums, ROW_NUMBER() OVER (ORDER BY score desc ) AS rn, COUNT(1) OVER () AS counts
FROM (
SELECT master_hotel_name,tag_name,SUM(score) as score ,SUM(pplrt_num) AS pplrt_nums
FROM test_table2
WHERE d >= '2021-05-27' and d <= '2021-05-27'
GROUP BY master_hotel_id, master_hotel_name,tag_name
)
)
)
WHERE a <= 0.2
group by tag_name
ORDER by pplrt_sum desc
LIMIT 50
307.每隔15min的记录数
CREATE TABLE if not exists test_0521(
ID INT ,
Times datetime
);
INSERT INTO test_0521
VALUES(1,'2021-05-24 11:01:45'),
(2,'2021-05-24 11:03:15'),
(3,'2021-05-24 11:05:34'),
(4,'2021-05-24 11:09:23'),
(5,'2021-05-24 11:17:45'),
(6,'2021-05-24 11:19:15'),
(7,'2021-05-24 11:29:34'),
(8,'2021-05-24 11:37:23');
如何用SQL查询出每个15分钟的记录数,预期结果如下
2021-05-24 11:00:00,4
2021-05-24 11:15:00,3
2021-05-24 11:30:00,1
.....用mysql写着实有点复杂,这边的主要问题就是对时间的处理,不知道mysql有没有特定的时间处理函数集
select
cast(concat(substr(date_add(date_sub( Times ,INTERVAL minute(Times) minute ),
INTERVAL floor(minute(Times) / 15) * 15 minute),1,17),'00') as datetime),
count(1)
from test_0521
group by
cast(concat(substr(date_add(date_sub( Times ,INTERVAL minute(Times) minute ),
INTERVAL floor(minute(Times) / 15) * 15 minute),1,17),'00') as datetime);
我的思路如下,获取时间的分钟数与15进行比较,这样就正确处理了时间分钟,接着要对时间的秒进行处理,我这里是使用substr来切分concat来补充
308.时间段重叠取数问题
某直播业务会记录主播开播及关播时间。需要计算出每天的峰值及峰值出现时间。数据表如下,粒度为每秒
| user_id | start_time | end_time |
| ------- | ------------------- | ------------------- |
| 1 | 2020-01-07 01:03:01 | 2020-01-07 03:03:01 |
| 2 | 2020-01-07 01:05:01 | 2020-01-07 02:03:01 |
| 3 | 2020-01-07 01:09:01 | 2020-01-07 05:03:01 |
| 1 | 2020-01-07 09:10:01 | 2020-01-07 10:03:01 |
这道题主要考虑就是写一个循环,hive中提供了一个udaf函数,之前写过
WITH a AS (
SELECT '2021-04-20' AS depart_time, '2021-04-10' AS arrive_time
UNION ALL
SELECT '2021-03-11', '2021-03-09'
)
SELECT pos
FROM a
LATERAL VIEW POSEXPLODE(SPLIT(SPACE(DATEDIFF(TO_DATE(a.depart_time), TO_DATE(a.arrive_time)) - 1), ' ')) tf AS pos,val
把时间转换为时间戳即可比较,然后在转过来
select unix_timestamp('2020-09-15 10:35:33');
select from_unixtime(1000000000);
309.连续空余座位
CREATE TABLE T0527
(
seat_id INT,
free INT
)
INSERT INTO T0527 VALUES
(1,1),
(2,0),
(3,1),
(4,1),
(5,1)
seat_id 字段是一个自增的整数，free 字段是布尔类型（'1' 表示空余， '0' 表示已被占据）。连续空余座位的定义是大于等于 2 个连续空余的座位
对于如上样例，你的查询语句应该返回如下结果。
3
4
5
select seat_id
from (
select SEAT_ID,
FREE,
LAG(free, 1, 0) over (ORDER BY seat_id) AS LAG_1,
LEAD(free, 1, 0) over (order by seat_id) as lead_1
from T0527
) t1
where free <> 0 and (lead_1 =1 or LAG_1 =1 );
select seat_id
from (
select seat_id,free,
seat_id - lag(seat_id, 1) over (order by seat_id) as lag_1
from T0527
) t1
where free<> 0 and lag_1 =1 ;
方法三可使用自连接判断abs
310.发货单号的最值
CREATE TABLE T0520
(
shipid INT,
paydate DATE,
payno INT
)
INSERT INTO T0520 VALUES(1001,'2020/11/2',5);
INSERT INTO T0520 VALUES(1001,'2020/11/2',3);
INSERT INTO T0520 VALUES(1001,'2020/11/3',1);
INSERT INTO T0520 VALUES(1001,'2020/11/3',3);
INSERT INTO T0520 VALUES(1002,'2020/11/9',1);
INSERT INTO T0520 VALUES(1002,'2020/11/9',4);
INSERT INTO T0520 VALUES(1002,'2020/11/8',3);
INSERT INTO T0520 VALUES(1002,'2020/11/8',2);
使用两种方法,查询出每个发货单号(shipid)，最早付款时间(paydate)和最小付款单号(payno)
select shipid, paydate, payno
from (
select shipid,
paydate,
payno,
row_number() over (partition by shipid order by paydate asc ,payno asc ) as rn
from T0520
) t1
where rn = 1;
select a.shipid,a.paydate,min(a.payno)
from T0520 a
join (
select shipid,min(paydate) as min_date
from T0520
group by shipid
) b on a.paydate = b.min_date and a.shipid = b.shipid
group by a.shipid,a.paydate;
