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[Niu Ke's questions -sql big factory interview questions] no1 An audio short video
2022-06-22 03:51:00 【It bond】
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List of articles
- Preface
- SQL1 Average completion rate of each video
- SQL2 The average playback progress is greater than 60% Video category
- SQL3 The forwarding volume of each type of video in recent one month / rate
- SQL4 The monthly rise rate of each creator and the total number of fans to date
- SQL5 During the national day, the number of likes and forwards of each type of video
- SQL6 The hottest video released in the past month top3 video
Preface
SQL Everyone has to use , But output is not measured by SQL In itself , You need to use this tool , To create other value .
SQL1 Average completion rate of each video
Create table statement
DROP TABLE IF EXISTS tb_user_video_log, tb_video_info;
CREATE TABLE tb_user_video_log (
id INT PRIMARY KEY AUTO_INCREMENT COMMENT ' Self increasing ID',
uid INT NOT NULL COMMENT ' user ID',
video_id INT NOT NULL COMMENT ' video ID',
start_time datetime COMMENT ' Start watching time ',
end_time datetime COMMENT ' End viewing time ',
if_follow TINYINT COMMENT ' Whether to pay attention to ',
if_like TINYINT COMMENT ' Do you like it ',
if_retweet TINYINT COMMENT ' Whether to forward ',
comment_id INT COMMENT ' Comment on ID'
) CHARACTER SET utf8 COLLATE utf8_bin;
CREATE TABLE tb_video_info (
id INT PRIMARY KEY AUTO_INCREMENT COMMENT ' Self increasing ID',
video_id INT UNIQUE NOT NULL COMMENT ' video ID',
author INT NOT NULL COMMENT ' Creator ID',
tag VARCHAR(16) NOT NULL COMMENT ' Category label ',
duration INT NOT NULL COMMENT ' Video duration ( Number of seconds )',
release_time datetime NOT NULL COMMENT ' Release time '
)CHARACTER SET utf8 COLLATE utf8_bin;
INSERT INTO tb_user_video_log(uid, video_id, start_time, end_time, if_follow, if_like, if_retweet, comment_id) VALUES
(101, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:30', 0, 1, 1, null),
(102, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:24', 0, 0, 1, null),
(103, 2001, '2021-10-01 11:00:00', '2021-10-01 11:00:34', 0, 1, 0, 1732526),
(101, 2002, '2021-09-01 10:00:00', '2021-09-01 10:00:42', 1, 0, 1, null),
(102, 2002, '2021-10-01 11:00:00', '2021-10-01 11:00:30', 1, 0, 1, null);
INSERT INTO tb_video_info(video_id, author, tag, duration, release_time) VALUES
(2001, 901, ' Movies ', 30, '2021-01-01 7:00:00'),
(2002, 901, ' food ', 60, '2021-01-01 7:00:00'),
(2003, 902, ' tourism ', 90, '2021-01-01 7:00:00');
demand
problem ： Calculation 2021 The completion rate of each video recorded in the year ( The result is three decimal places ), And sort them in descending order according to the completion rate
notes ： Video completion rate refers to the proportion of completed playback times to the total playback times . Simplicity ,
The difference between the end viewing time and the start playing time >= Video duration , It is deemed to have finished playing .
answer
select a.video_id,
round(sum(case when timestampdiff(second,b.start_time,b.end_time)>=a.duration
then 1 else 0 end)/count(b.uid),3)
as avg_com_play_rate
from tb_video_info a join tb_user_video_log b on a.video_id=b.video_id
where YEAR(b.start_time) = 2021
group by a.video_id
order by avg_com_play_rate desc
SQL2 The average playback progress is greater than 60% Video category
Create table statement
DROP TABLE IF EXISTS tb_user_video_log, tb_video_info;
CREATE TABLE tb_user_video_log (
id INT PRIMARY KEY AUTO_INCREMENT COMMENT ' Self increasing ID',
uid INT NOT NULL COMMENT ' user ID',
video_id INT NOT NULL COMMENT ' video ID',
start_time datetime COMMENT ' Start watching time ',
end_time datetime COMMENT ' End viewing time ',
if_follow TINYINT COMMENT ' Whether to pay attention to ',
if_like TINYINT COMMENT ' Do you like it ',
if_retweet TINYINT COMMENT ' Whether to forward ',
comment_id INT COMMENT ' Comment on ID'
) CHARACTER SET utf8 COLLATE utf8_bin;
CREATE TABLE tb_video_info (
id INT PRIMARY KEY AUTO_INCREMENT COMMENT ' Self increasing ID',
video_id INT UNIQUE NOT NULL COMMENT ' video ID',
author INT NOT NULL COMMENT ' Creator ID',
tag VARCHAR(16) NOT NULL COMMENT ' Category label ',
duration INT NOT NULL COMMENT ' Video duration ( Number of seconds )',
release_time datetime NOT NULL COMMENT ' Release time '
)CHARACTER SET utf8 COLLATE utf8_bin;
INSERT INTO tb_user_video_log(uid, video_id, start_time, end_time, if_follow, if_like, if_retweet, comment_id) VALUES
(101, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:30', 0, 1, 1, null),
(102, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:21', 0, 0, 1, null),
(103, 2001, '2021-10-01 11:00:50', '2021-10-01 11:01:20', 0, 1, 0, 1732526),
(102, 2002, '2021-10-01 11:00:00', '2021-10-01 11:00:30', 1, 0, 1, null),
(103, 2002, '2021-10-01 10:59:05', '2021-10-01 11:00:05', 1, 0, 1, null);
INSERT INTO tb_video_info(video_id, author, tag, duration, release_time) VALUES
(2001, 901, ' Movies ', 30, '2021-01-01 7:00:00'),
(2002, 901, ' food ', 60, '2021-01-01 7:00:00'),
(2003, 902, ' tourism ', 90, '2020-01-01 7:00:00');
demand
problem ： Calculate the average playback progress of various videos , Make progress greater than 60% Category output of .
notes ：
Play progress = The broadcast time ÷ Video duration *100%, When the playback time is longer than the video time , The playback progress is recorded as 100%.
Two decimal places are reserved for the result , And sort them in reverse order according to the playback progress .
answer
select
tag,
concat(
ROUND(
avg(
if(
timestampdiff(second,start_time,end_time)>=duration,
1,timestampdiff(second,start_time,end_time)/duration
)
)*100
,2)
,'%') avg_play_progress
from
tb_user_video_log a join tb_video_info b
on a.video_id=b.video_id
group by b.tag
having avg(
if(
timestampdiff(second,start_time,end_time)>=duration,
1,timestampdiff(second,start_time,end_time)/duration
)
)>0.6
order by avg_play_progress desc
SQL3 The forwarding volume of each type of video in recent one month / rate
Create table statement
DROP TABLE IF EXISTS tb_user_video_log, tb_video_info;
CREATE TABLE tb_user_video_log (
id INT PRIMARY KEY AUTO_INCREMENT COMMENT ' Self increasing ID',
uid INT NOT NULL COMMENT ' user ID',
video_id INT NOT NULL COMMENT ' video ID',
start_time datetime COMMENT ' Start watching time ',
end_time datetime COMMENT ' End viewing time ',
if_follow TINYINT COMMENT ' Whether to pay attention to ',
if_like TINYINT COMMENT ' Do you like it ',
if_retweet TINYINT COMMENT ' Whether to forward ',
comment_id INT COMMENT ' Comment on ID'
) CHARACTER SET utf8 COLLATE utf8_bin;
CREATE TABLE tb_video_info (
id INT PRIMARY KEY AUTO_INCREMENT COMMENT ' Self increasing ID',
video_id INT UNIQUE NOT NULL COMMENT ' video ID',
author INT NOT NULL COMMENT ' Creator ID',
tag VARCHAR(16) NOT NULL COMMENT ' Category label ',
duration INT NOT NULL COMMENT ' Video duration ( Number of seconds )',
release_time datetime NOT NULL COMMENT ' Release time '
)CHARACTER SET utf8 COLLATE utf8_bin;
INSERT INTO tb_user_video_log(uid, video_id, start_time, end_time, if_follow, if_like, if_retweet, comment_id) VALUES
(101, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:20', 0, 1, 1, null)
,(102, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:15', 0, 0, 1, null)
,(103, 2001, '2021-10-01 11:00:50', '2021-10-01 11:01:15', 0, 1, 0, 1732526)
,(102, 2002, '2021-09-10 11:00:00', '2021-09-10 11:00:30', 1, 0, 1, null)
,(103, 2002, '2021-10-01 10:59:05', '2021-10-01 11:00:05', 1, 0, 0, null);
INSERT INTO tb_video_info(video_id, author, tag, duration, release_time) VALUES
(2001, 901, ' Movies ', 30, '2021-01-01 7:00:00')
,(2002, 901, ' food ', 60, '2021-01-01 7:00:00')
,(2003, 902, ' tourism ', 90, '2020-01-01 7:00:00');
demand
problem ： Statistics in the last month with user interaction （ Press near... Including the current day 30 God knows ,
such as 10 month 31 Near the sun 30 Tianwei 10.2~10.31 Data between ） in , Forwarding volume and forwarding rate of each type of video （ Retain 3 Decimal place ）.
notes ： Forwarding rate ＝ Forwarding volume ÷ Play volume . The results are sorted in descending order of forwarding rate .
answer
select b.tag, sum(if_retweet) retweet_cnt,
round(sum(if_retweet)/count(1),3) retweet_rate from
tb_user_video_log a
left join tb_video_info b
on a.video_id = b.video_id
where DATEDIFF(DATE((select max(start_time) from tb_user_video_log)) ,
DATE(start_time)) <= 29
group by b.tag
order by 2 desc
SQL4 The monthly rise rate of each creator and the total number of fans to date
Create table statement
DROP TABLE IF EXISTS tb_user_video_log, tb_video_info;
CREATE TABLE tb_user_video_log (
id INT PRIMARY KEY AUTO_INCREMENT COMMENT ' Self increasing ID',
uid INT NOT NULL COMMENT ' user ID',
video_id INT NOT NULL COMMENT ' video ID',
start_time datetime COMMENT ' Start watching time ',
end_time datetime COMMENT ' End viewing time ',
if_follow TINYINT COMMENT ' Whether to pay attention to ',
if_like TINYINT COMMENT ' Do you like it ',
if_retweet TINYINT COMMENT ' Whether to forward ',
comment_id INT COMMENT ' Comment on ID'
) CHARACTER SET utf8 COLLATE utf8_bin;
CREATE TABLE tb_video_info (
id INT PRIMARY KEY AUTO_INCREMENT COMMENT ' Self increasing ID',
video_id INT UNIQUE NOT NULL COMMENT ' video ID',
author INT NOT NULL COMMENT ' Creator ID',
tag VARCHAR(16) NOT NULL COMMENT ' Category label ',
duration INT NOT NULL COMMENT ' Video duration ( Number of seconds )',
release_time datetime NOT NULL COMMENT ' Release time '
)CHARACTER SET utf8 COLLATE utf8_bin;
INSERT INTO tb_user_video_log(uid, video_id, start_time, end_time, if_follow, if_like, if_retweet, comment_id) VALUES
(101, 2001, '2021-09-01 10:00:00', '2021-09-01 10:00:20', 0, 1, 1, null)
,(105, 2002, '2021-09-10 11:00:00', '2021-09-10 11:00:30', 1, 0, 1, null)
,(101, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:20', 1, 1, 1, null)
,(102, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:15', 0, 0, 1, null)
,(103, 2001, '2021-10-01 11:00:50', '2021-10-01 11:01:15', 1, 1, 0, 1732526)
,(106, 2002, '2021-10-01 10:59:05', '2021-10-01 11:00:05', 2, 0, 0, null);
INSERT INTO tb_video_info(video_id, author, tag, duration, release_time) VALUES
(2001, 901, ' Movies ', 30, '2021-01-01 7:00:00')
,(2002, 901, ' Movies ', 60, '2021-01-01 7:00:00')
,(2003, 902, ' tourism ', 90, '2020-01-01 7:00:00')
,(2004, 902, ' beauty ', 90, '2020-01-01 8:00:00');
demand
problem ： Calculation 2021 The monthly fan growth rate of each creator in the year and the total number of fans by the end of the month
notes ：
Powder expansion rate =( Powder addition - Powder loss ) / Play volume . Results by Creator ID、 The total number of fans is sorted in ascending order .
if_follow- Whether to focus on 1 Indicates that the user pays attention to the video creator when watching the video ,
by 0 It indicates that the attention status has not changed before and after this interaction , by 2 It means that the attention has been cancelled during this viewing .
answer
SELECT B.AUTHOR AS AUTHOR,DATE_FORMAT(A.start_time,'%Y-%m') AS MONTH ,
ROUND((COUNT(CASE WHEN A.if_follow=1 THEN 1 END ) -
COUNT(CASE WHEN A.if_follow=2 THEN 1 END ))/COUNT(1) ,3)
AS FANS_GROWTH_RATE,
sum(sum(case when A.if_follow = 1 then 1
when A.if_follow = 2 then -1
else 0 end) ) over (partition by B.author
order by date_format(A.start_time,'%Y-%m'))
fans_total
FROM tb_user_video_log A
LEFT JOIN tb_video_info B
ON A.VIDEO_ID=B.video_id
WHERE year(A.start_time)=2021
and year(A.end_time)=2021
GROUP BY B.AUTHOR,DATE_FORMAT(A.start_time,'%Y-%m')
ORDER BY AUTHOR,fans_total
SQL5 During the national day, the number of likes and forwards of each type of video
Create table statement
DROP TABLE IF EXISTS tb_user_video_log, tb_video_info;
CREATE TABLE tb_user_video_log (
id INT PRIMARY KEY AUTO_INCREMENT COMMENT ' Self increasing ID',
uid INT NOT NULL COMMENT ' user ID',
video_id INT NOT NULL COMMENT ' video ID',
start_time datetime COMMENT ' Start watching time ',
end_time datetime COMMENT ' End viewing time ',
if_follow TINYINT COMMENT ' Whether to pay attention to ',
if_like TINYINT COMMENT ' Do you like it ',
if_retweet TINYINT COMMENT ' Whether to forward ',
comment_id INT COMMENT ' Comment on ID'
) CHARACTER SET utf8 COLLATE utf8_bin;
CREATE TABLE tb_video_info (
id INT PRIMARY KEY AUTO_INCREMENT COMMENT ' Self increasing ID',
video_id INT UNIQUE NOT NULL COMMENT ' video ID',
author INT NOT NULL COMMENT ' Creator ID',
tag VARCHAR(16) NOT NULL COMMENT ' Category label ',
duration INT NOT NULL COMMENT ' Video duration ( Number of seconds )',
release_time datetime NOT NULL COMMENT ' Release time '
)CHARACTER SET utf8 COLLATE utf8_bin;
INSERT INTO tb_user_video_log(uid, video_id, start_time, end_time, if_follow, if_like, if_retweet, comment_id) VALUES
(101, 2001, '2021-09-24 10:00:00', '2021-09-24 10:00:20', 1, 1, 0, null)
,(105, 2002, '2021-09-25 11:00:00', '2021-09-25 11:00:30', 0, 0, 1, null)
,(102, 2002, '2021-09-25 11:00:00', '2021-09-25 11:00:30', 1, 1, 1, null)
,(101, 2002, '2021-09-26 11:00:00', '2021-09-26 11:00:30', 1, 0, 1, null)
,(101, 2002, '2021-09-27 11:00:00', '2021-09-27 11:00:30', 1, 1, 0, null)
,(102, 2002, '2021-09-28 11:00:00', '2021-09-28 11:00:30', 1, 0, 1, null)
,(103, 2002, '2021-09-29 11:00:00', '2021-09-29 11:00:30', 1, 0, 1, null)
,(102, 2002, '2021-09-30 11:00:00', '2021-09-30 11:00:30', 1, 1, 1, null)
,(101, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:20', 1, 1, 0, null)
,(102, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:15', 0, 0, 1, null)
,(103, 2001, '2021-10-01 11:00:50', '2021-10-01 11:01:15', 1, 1, 0, 1732526)
,(106, 2002, '2021-10-02 10:59:05', '2021-10-02 11:00:05', 2, 0, 1, null)
,(107, 2002, '2021-10-02 10:59:05', '2021-10-02 11:00:05', 1, 0, 1, null)
,(108, 2002, '2021-10-02 10:59:05', '2021-10-02 11:00:05', 1, 1, 1, null)
,(109, 2002, '2021-10-03 10:59:05', '2021-10-03 11:00:05', 0, 1, 0, null);
INSERT INTO tb_video_info(video_id, author, tag, duration, release_time) VALUES
(2001, 901, ' tourism ', 30, '2020-01-01 7:00:00')
,(2002, 901, ' tourism ', 60, '2021-01-01 7:00:00')
,(2003, 902, ' Movies ', 90, '2020-01-01 7:00:00')
,(2004, 902, ' beauty ', 90, '2020-01-01 8:00:00');
demand
problem ： Statistics 2021 National Day head 3 The daily total likes of each type of video in the past week and the maximum forwarding volume in a single day in a week ,
The results are in descending order by video category 、 Sort dates in ascending order . Suppose there is enough data in the database ,
At least the national day head under each category 3 Every day of the day and the previous week .
answer
select t1.tag,t1.day,sum(t2.likes),max(t2.retweet)
FROM
(select t2.tag ,left(t1.start_time,10) day,
sum(t1.if_like) as likes,sum(t1.if_retweet) as retweet
from tb_user_video_log t1 left join
tb_video_info t2
on t1.video_id=t2.video_id
group by t2.tag,day) t1
left join
(select t2.tag ,left(t1.start_time,10) day,
sum(t1.if_like) as likes,sum(t1.if_retweet) as retweet
from tb_user_video_log t1 left join
tb_video_info t2
on t1.video_id=t2.video_id
group by t2.tag,day) t2
on t1.tag=t2.tag
where TIMESTAMPDIFF(day,t2.day,t1.day)<7
and TIMESTAMPDIFF(day,t2.day,t1.day)>=0
and t1.day in ("2021-10-01","2021-10-02","2021-10-03")
group by t1.tag,t1.day
SQL6 The hottest video released in the past month top3 video
Create table statement
DROP TABLE IF EXISTS tb_user_video_log, tb_video_info;
CREATE TABLE tb_user_video_log (
id INT PRIMARY KEY AUTO_INCREMENT COMMENT ' Self increasing ID',
uid INT NOT NULL COMMENT ' user ID',
video_id INT NOT NULL COMMENT ' video ID',
start_time datetime COMMENT ' Start watching time ',
end_time datetime COMMENT ' End viewing time ',
if_follow TINYINT COMMENT ' Whether to pay attention to ',
if_like TINYINT COMMENT ' Do you like it ',
if_retweet TINYINT COMMENT ' Whether to forward ',
comment_id INT COMMENT ' Comment on ID'
) CHARACTER SET utf8 COLLATE utf8_bin;
CREATE TABLE tb_video_info (
id INT PRIMARY KEY AUTO_INCREMENT COMMENT ' Self increasing ID',
video_id INT UNIQUE NOT NULL COMMENT ' video ID',
author INT NOT NULL COMMENT ' Creator ID',
tag VARCHAR(16) NOT NULL COMMENT ' Category label ',
duration INT NOT NULL COMMENT ' Video duration ( Number of seconds )',
release_time datetime NOT NULL COMMENT ' Release time '
)CHARACTER SET utf8 COLLATE utf8_bin;
INSERT INTO tb_user_video_log(uid, video_id, start_time, end_time, if_follow, if_like, if_retweet, comment_id) VALUES
(101, 2001, '2021-09-24 10:00:00', '2021-09-24 10:00:30', 1, 1, 1, null)
,(101, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:31', 1, 1, 0, null)
,(102, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:35', 0, 0, 1, null)
,(103, 2001, '2021-10-03 11:00:50', '2021-10-03 11:01:35', 1, 1, 0, 1732526)
,(106, 2002, '2021-10-02 10:59:05', '2021-10-02 11:00:04', 2, 0, 1, null)
,(107, 2002, '2021-10-02 10:59:05', '2021-10-02 11:00:06', 1, 0, 0, null)
,(108, 2002, '2021-10-02 10:59:05', '2021-10-02 11:00:05', 1, 1, 1, null)
,(109, 2002, '2021-10-03 10:59:05', '2021-10-03 11:00:01', 0, 1, 0, null)
,(105, 2002, '2021-09-25 11:00:00', '2021-09-25 11:00:30', 1, 0, 1, null)
,(101, 2003, '2021-09-26 11:00:00', '2021-09-26 11:00:30', 1, 0, 0, null)
,(101, 2003, '2021-09-30 11:00:00', '2021-09-30 11:00:30', 1, 1, 0, null);
INSERT INTO tb_video_info(video_id, author, tag, duration, release_time) VALUES
(2001, 901, ' tourism ', 30, '2021-09-05 7:00:00')
,(2002, 901, ' tourism ', 60, '2021-09-05 7:00:00')
,(2003, 902, ' Movies ', 90, '2021-09-05 7:00:00')
,(2004, 902, ' Movies ', 90, '2021-09-05 8:00:00');
demand
problem ： Find the most popular video released in the past month top3 video .
notes ：
degree of heat =(a* Video completion rate +b* Number of likes +c* comments +d* Forwarding number )* Freshness ;
Freshness =1/( Number of days not played recently +1);
Currently configured parameters a,b,c,d Respectively 100、5、3、2.
The most recent playback date is in end_time- The end viewing time shall prevail , Assuming that T, Press... In the last month [T-29, T] Closed interval statistics .
The heat in the result is kept as an integer , And in descending order of heat .
answer
select t1.video_id
,round(
((sum(case when timestampdiff(second,start_time,end_time)>=duration
then 1 else 0 end ))/count(*)*100
+sum(if_like)*5
+count(comment_id)*3
+sum(if_retweet)*2)
*
1/(DATEDIFF((select date(max(end_time)) from tb_user_video_log),
date(max(end_time)))+1)
) as hot_index
from tb_user_video_log t1,tb_video_info t2
where t1.video_id=t2.video_id
and DATEDIFF(DATE((SELECT MAX(end_time) FROM tb_user_video_log)),
DATE(release_time)) <= 29
group by t1.video_id
order by hot_index desc
limiT 3
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