Hive笔试题实战
短视频
题目一：计算各个视频的平均完播率
有用户-视频互动表tb_user_video_log：
| id |
uid |
video_id |
start_time |
end_time |
if_follow |
if_like |
if_retweet |
comment_id |
| 1 |
101 |
2001 |
2021-10-01 10:00:00 |
2021-10-01 10:00:30 |
0 |
1 |
1 |
NULL |
| 2 |
102 |
2001 |
2021-10-01 10:00:00 |
2021-10-01 10:00:24 |
0 |
0 |
1 |
NULL |
| 3 |
103 |
2001 |
2021-10-01 11:00:00 |
2021-10-01 11:00:34 |
0 |
1 |
0 |
1732526 |
| 4 |
101 |
2002 |
2021-09-01 10:00:00 |
2021-9-01 10:00:42 |
1 |
0 |
1 |
NULL |
| 5 |
102 |
2002 |
2021-10-01 11:00:00 |
2021-10-01 10:00:30 |
1 |
0 |
1 |
NULL |
uid-用户ID，video_id-视频ID，start_time-开始观看时间，end_time-结束观看时间，if_follow-是否关注，if_like-是否点赞，if_retweet-是否转发，comment_id-评论ID。
有短视频信息表tb_video_info：
| id |
video_id |
author |
tag |
duration |
release_time |
| 1 |
2001 |
901 |
影视 |
30 |
2021-01-01 07:00:00 |
| 2 |
2002 |
901 |
美食 |
60 |
2021-01-01 07:00:00 |
| 3 |
2003 |
902 |
旅游 |
90 |
2021-01-01 07:00:00 |
video_id-视频ID，author-创作者ID，tag-类别标签，duration-视频时长(秒)，release_time-发布时间。
问题：计算2021年里有播放记录的每个视频的完播率(结果保留三位小数)，并按完播率降序排序。输出结果如下：
| video_id |
avg_comp_play_rate |
| 2001 |
0.667 |
| 2002 |
0.000 |
注：视频完播率是指完成播放次数占总播放次数的比例。简单起见，结束观看时间与开始播放时间的差≥视频时长时，视为完成播放。
-- 建立用户-视频互动表
drop table if exists tb_user_video_log;
create table tb_user_video_log (
id int comment '自增ID',
uid int comment '用户ID',
video_id int comment '视频ID',
start_time string COMMENT '开始观看时间',
end_time string COMMENT '结束观看时间',
if_follow int comment '是否关注',
if_like int comment '是否点赞',
if_retweet int comment '是否转发',
comment_id int comment '评论ID'
) comment '用户-视频互动表'
row format delimited fields terminated by ',';
-- 建立短视频信息表
drop table if exists tb_video_info;
create table tb_video_info (
id int comment '自增ID',
video_id int comment '视频ID',
author int comment '创作者ID',
tag string comment '类别标签',
duration int comment '视频时长(秒数)',
release_time string comment '发布时间'
) comment '短视频信息表'
row format delimited fields terminated by ',';
-- 插入数据
insert into tb_user_video_log
values (1, 101, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:30', 0, 1, 1, null),
(2, 102, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:24', 0, 0, 1, null),
(3, 103, 2001, '2021-10-01 11:00:00', '2021-10-01 11:00:34', 0, 1, 0, 1732526),
(4, 101, 2002, '2021-09-01 10:00:00', '2021-09-01 10:00:42', 1, 0, 1, null),
(5, 102, 2002, '2021-10-01 11:00:00', '2021-10-01 11:00:30', 1, 0, 1, null);
insert into tb_video_info
values (1, 2001, 901, '影视', 30, '2021-01-01 7:00:00'),
(2, 2002, 901, '美食', 60, '2021-01-01 7:00:00'),
(3, 2003, 902, '旅游', 90, '2021-01-01 7:00:00');
参考答案：
-- 第一步：找出2021年有过播放的视频
select * from tb_user_video_log where year(start_time) = 2021;
-- 第二步：计算(每一个视频的)完播次数。完播：结束时间-起始时间>=视频时长
select a.video_id as video_id,
sum(if(unix_timestamp(a.end_time) - unix_timestamp(a.start_time) >= b.duration, 1, 0))
from (
select * from tb_user_video_log where year(start_time) = 2021
) a left join tb_video_info b on a.video_id = b.video_id
group by a.video_id;
-- 第三步：计算完播率。完播次数/总的播放次数
select a.video_id as video_id,
sum(if(unix_timestamp(a.end_time) - unix_timestamp(a.start_time) >= b.duration, 1, 0)) / count(*)
from (
select * from tb_user_video_log where year(start_time) = 2021
) a left join tb_video_info b on a.video_id = b.video_id
group by a.video_id;
-- 第四步：保留三位小数，还需要降序排序
select a.video_id as video_id,
round(sum(if(unix_timestamp(a.end_time) - unix_timestamp(a.start_time) >= b.duration, 1, 0)) / count(*), 3) as avg_comp_play_rate
from (
select * from tb_user_video_log where year(start_time) = 2021
) a left join tb_video_info b on a.video_id = b.video_id
group by a.video_id
order by avg_comp_play_rate desc;题目二：平均播放进度大于60%的视频类别
有用户-视频互动表tb_user_video_log：
| id |
uid |
video_id |
start_time |
end_time |
if_follow |
if_like |
if_retweet |
comment_id |
| 1 |
101 |
2001 |
2021-10-01 10:00:00 |
2021-10-01 10:00:30 |
0 |
1 |
1 |
NULL |
| 2 |
102 |
2001 |
2021-10-01 10:00:00 |
2021-10-01 10:00:21 |
0 |
0 |
1 |
NULL |
| 3 |
103 |
2001 |
2021-10-01 11:00:50 |
2021-10-01 11:01:20 |
0 |
1 |
0 |
1732526 |
| 4 |
102 |
2002 |
2021-10-01 11:00:00 |
2021-10-01 11:00:30 |
1 |
0 |
1 |
NULL |
| 5 |
103 |
2002 |
2021-10-01 10:59:05 |
2021-10-01 11:00:05 |
1 |
0 |
1 |
NULL |
uid-用户ID，video_id-视频ID，start_time-开始观看时间，end_time-结束观看时间，if_follow-是否关注，if_like-是否点赞，if_retweet-是否转发，comment_id-评论ID。
有短视频信息表tb_video_info：
| id |
video_id |
author |
tag |
duration |
release_time |
| 1 |
2001 |
901 |
影视 |
30 |
2021-01-01 07:00:00 |
| 2 |
2002 |
901 |
美食 |
60 |
2021-01-01 07:00:00 |
| 3 |
2003 |
902 |
旅游 |
90 |
2021-01-01 07:00:00 |
video_id-视频ID，author-创作者ID，tag-类别标签，duration-视频时长(秒)，release_time-发布时间。
问题：计算各类视频的平均播放进度，将进度大于60%的类别输出(结果保留两位小数，并按播放进度倒序排序)。示例数据的输出结果如下：
| tag |
avg_play_progress |
| 影视 |
90.00% |
| 美食 |
75.00% |
注：播放进度=播放时长÷视频时长*100%，当播放时长大于视频时长时，播放进度均记为100%。
例如：影视类视频2001被用户101、102、103看过，播放进度分别为：30秒（100%）、21秒（70%）、30秒（100%），平均播放进度为(100%+70%+100%)/3=90.00%（保留两位小数）。
-- 建立用户-视频互动表
drop table if exists tb_user_video_log;
create table tb_user_video_log (
id int comment '自增ID',
uid int comment '用户ID',
video_id int comment '视频ID',
start_time string COMMENT '开始观看时间',
end_time string COMMENT '结束观看时间',
if_follow int comment '是否关注',
if_like int comment '是否点赞',
if_retweet int comment '是否转发',
comment_id int comment '评论ID'
) comment '用户-视频互动表'
row format delimited fields terminated by ',';
-- 建立短视频信息表
drop table if exists tb_video_info;
create table tb_video_info (
id int comment '自增ID',
video_id int comment '视频ID',
author int comment '创作者ID',
tag string comment '类别标签',
duration int comment '视频时长(秒数)',
release_time string comment '发布时间'
) comment '短视频信息表'
row format delimited fields terminated by ',';
-- 插入数据
insert into tb_user_video_log
values (1, 101, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:30', 0, 1, 1, null),
(2, 102, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:21', 0, 0, 1, null),
(3, 103, 2001, '2021-10-01 11:00:50', '2021-10-01 11:01:20', 0, 1, 0, 1732526),
(4, 102, 2002, '2021-10-01 11:00:00', '2021-10-01 11:00:30', 1, 0, 1, null),
(5, 103, 2002, '2021-10-01 10:59:05', '2021-10-01 11:00:05', 1, 0, 1, null);
insert into tb_video_info
values (1, 2001, 901, '影视', 30, '2021-01-01 7:00:00'),
(2, 2002, 901, '美食', 60, '2021-01-01 7:00:00'),
(3, 2003, 902, '旅游', 90, '2021-01-01 7:00:00');
参考答案：
-- 第一步：计算每次播放的播放时长
select video_id, unix_timestamp(end_time) - unix_timestamp(start_time) as total_time from tb_user_video_log;
-- 第二步：计算每一次的播放进度
select a.video_id as video_id,
if(a.total_time / b.duration > 1, 1, a.total_time / b.duration) as play_progress
from (
select video_id, unix_timestamp(end_time) - unix_timestamp(start_time) as total_time from tb_user_video_log
) a left join tb_video_info b on a.video_id = b.video_id;
-- 第三步：计算各类视频的平均播放进度
select b.tag,
avg(if(a.total_time / b.duration > 1, 1, a.total_time / b.duration)) as avg_play_progress
from (
select video_id, unix_timestamp(end_time) - unix_timestamp(start_time) as total_time from tb_user_video_log
) a left join tb_video_info b on a.video_id = b.video_id
group by b.tag;
-- 第四步：过滤，排序
select b.tag,
avg(if(a.total_time / b.duration > 1, 1, a.total_time / b.duration)) as avg_play_progress
from (
select video_id, unix_timestamp(end_time) - unix_timestamp(start_time) as total_time from tb_user_video_log
) a left join tb_video_info b on a.video_id = b.video_id
group by b.tag having avg_play_progress > 0.6 order by avg_play_progress desc;
-- 第五步：百分比
select tag,
concat(round(avg_play_progress * 100, 2), '%') as avg_play_progress
from (
select b.tag as tag,
avg(if(a.total_time / b.duration > 1, 1, a.total_time / b.duration)) as avg_play_progress
from (
select video_id, unix_timestamp(end_time) - unix_timestamp(start_time) as total_time from tb_user_video_log
) a left join tb_video_info b on a.video_id = b.video_id
group by b.tag
having avg_play_progress > 0.6
order by avg_play_progress desc
)t;题目三：每类视频近一个月的转发量/率
有用户-视频互动表tb_user_video_log：
| id |
uid |
video_id |
start_time |
end_time |
if_follow |
if_like |
if_retweet |
comment_id |
| 1 |
101 |
2001 |
2021-10-01 10:00:00 |
2021-10-01 10:00:20 |
0 |
1 |
1 |
NULL |
| 2 |
102 |
2001 |
2021-10-01 10:00:00 |
2021-10-01 10:00:15 |
0 |
0 |
1 |
NULL |
| 3 |
103 |
2001 |
2021-10-01 11:00:50 |
2021-10-01 11:01:15 |
0 |
1 |
0 |
1732526 |
| 4 |
102 |
2002 |
2021-09-10 11:00:00 |
2021-09-10 11:00:30 |
1 |
0 |
1 |
NULL |
| 5 |
103 |
2002 |
2021-10-01 10:59:05 |
2021-10-01 11:00:05 |
1 |
0 |
0 |
NULL |
uid-用户ID，video_id-视频ID，start_time-开始观看时间，end_time-结束观看时间，if_follow-是否关注，if_like-是否点赞，if_retweet-是否转发，comment_id-评论ID。
有短视频信息表tb_video_info：
| id |
video_id |
author |
tag |
duration |
release_time |
| 1 |
2001 |
901 |
影视 |
30 |
2021-01-01 07:00:00 |
| 2 |
2002 |
901 |
美食 |
60 |
2021-01-01 07:00:00 |
| 3 |
2003 |
902 |
旅游 |
90 |
2021-01-01 07:00:00 |
video_id-视频ID，author-创作者ID，tag-类别标签，duration-视频时长(秒)，release_time-发布时间。
问题：统计在有用户互动的最近一个月（按包含当天在内的近30天算，比如10月31日的近30天为10.2~10.31之间的数据）中，每类视频的转发量和转发率（保留3位小数）。输出结果如下：
| tag |
retweet_cut |
retweet_rate |
| 影视 |
2 |
0.667 |
| 美食 |
1 |
0.500 |
注：转发率＝转发量÷播放量。结果按转发率降序排序。
解释：由表tb_user_video_log的数据可得，数据转储当天为2021年10月1日。近30天内，影视类视频2001共有3次播放记录，被转发2次，转发率为0.667；美食类视频2002共有2次播放记录，1次被转发，转发率为0.500。
-- 建立用户-视频互动表
drop table if exists tb_user_video_log;
create table tb_user_video_log (
id int comment '自增ID',
uid int comment '用户ID',
video_id int comment '视频ID',
start_time string COMMENT '开始观看时间',
end_time string COMMENT '结束观看时间',
if_follow int comment '是否关注',
if_like int comment '是否点赞',
if_retweet int comment '是否转发',
comment_id int comment '评论ID'
) comment '用户-视频互动表'
row format delimited fields terminated by ',';
-- 建立短视频信息表
drop table if exists tb_video_info;
create table tb_video_info (
id int comment '自增ID',
video_id int comment '视频ID',
author int comment '创作者ID',
tag string comment '类别标签',
duration int comment '视频时长(秒数)',
release_time string comment '发布时间'
) comment '短视频信息表'
row format delimited fields terminated by ',';
-- 插入数据
insert into tb_user_video_log
values (1, 101, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:20', 0, 1, 1, null),
(2, 102, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:15', 0, 0, 1, null),
(3, 103, 2001, '2021-10-01 11:00:50', '2021-10-01 11:01:15', 0, 1, 0, 1732526),
(4, 102, 2002, '2021-09-10 11:00:00', '2021-09-10 11:00:30', 1, 0, 1, null),
(5, 103, 2002, '2021-10-01 10:59:05', '2021-10-01 11:00:05', 1, 0, 0, null);
insert into tb_video_info
values (1, 2001, 901, '影视', 30, '2021-01-01 7:00:00'),
(2, 2002, 901, '美食', 60, '2021-01-01 7:00:00'),
(3, 2003, 902, '旅游', 90, '2021-01-01 7:00:00');
参考答案：
-- 1. 找出最后一次的播放时间
select max(start_time) from tb_user_video_log;
-- 2. 基于最后一次的播放时间，向前推29天(包含当天在内的近30天算)，获取到近30天内的所有播放记录
select *
from tb_user_video_log a,
(select max(start_time) as last_date from tb_user_video_log) b
where datediff(b.last_date, a.start_time) <= 29;
-- 3. 计算每一类视频的转发量和转发率
select t2.tag as tag,
sum(if_retweet) as retweet_cut,
round(sum(if_retweet) / count(*), 3) as retweet_rate
from (
select *
from tb_user_video_log a,
(select max(start_time) as last_date from tb_user_video_log) b
where datediff(b.last_date, a.start_time) <= 29
) t1 left join tb_video_info t2 on t1.video_id = t2.video_id
group by t2.tag order by retweet_rate desc;题目四：每个创作者每月的涨粉率及截止当前的总粉丝量
有用户-视频互动表tb_user_video_log：
| id |
uid |
video_id |
start_time |
end_time |
if_follow |
if_like |
if_retweet |
comment_id |
| 1 |
101 |
2001 |
2021-09-01 10:00:00 |
2021-09-01 10:00:20 |
0 |
1 |
1 |
NULL |
| 2 |
105 |
2002 |
2021-09-10 11:00:00 |
2021-09-10 11:00:30 |
1 |
0 |
1 |
NULL |
| 3 |
101 |
2001 |
2021-10-01 10:00:00 |
2021-10-01 10:00:20 |
1 |
1 |
1 |
NULL |
| 4 |
102 |
2001 |
2021-10-01 10:00:00 |
2021-10-01 10:00:15 |
0 |
0 |
1 |
NULL |
| 5 |
103 |
2001 |
2021-10-01 11:00:50 |
2021-10-01 11:01:15 |
1 |
1 |
0 |
1732526 |
| 6 |
106 |
2002 |
2021-10-01 10:59:05 |
021-10-01 11:00:05 |
2 |
0 |
0 |
NULL |
uid-用户ID，video_id-视频ID，start_time-开始观看时间，end_time-结束观看时间，if_follow-是否关注，if_like-是否点赞，if_retweet-是否转发，comment_id-评论ID。
有短视频信息表tb_video_info：
| id |
video_id |
author |
tag |
duration |
release_time |
| 1 |
2001 |
901 |
影视 |
30 |
2021-01-01 07:00:00 |
| 2 |
2002 |
901 |
美食 |
60 |
2021-01-01 07:00:00 |
| 3 |
2003 |
902 |
旅游 |
90 |
2021-01-01 07:00:00 |
| 4 |
2004 |
902 |
美女 |
90 |
2020-01-01 08:00:00 |
video_id-视频ID，author-创作者ID，tag-类别标签，duration-视频时长(秒)，release_time-发布时间。
问题：计算2021年里每个创作者每月的涨粉率及截止当月的总粉丝量。输出结果如下：
| author |
month |
fans_growth_rate |
total_fans |
| 901 |
2021-09 |
0.500 |
1 |
| 901 |
2021-10 |
0.250 |
2 |
注：涨粉率=(加粉量 - 掉粉量) / 播放量。结果按创作者ID、总粉丝量升序排序。if_follow-是否关注，为1表示用户观看视频中关注了视频创作者，为0表示此次互动前后关注状态未发生变化，为2表示本次观看过程中取消了关注。
解释：示例数据中表tb_user_video_log里只有视频2001和2002的播放记录，都来自创作者901，播放时间在2021年9月和10月；其中9月里加粉量为1，掉粉量为0，播放量为2，因此涨粉率为0.500（保留3位小数）；其中10月里加粉量为2，掉份量为1，播放量为4，因此涨粉率为0.250，截止当前总粉丝数为2。
-- 建立用户-视频互动表
drop table if exists tb_user_video_log;
create table tb_user_video_log (
id int comment '自增ID',
uid int comment '用户ID',
video_id int comment '视频ID',
start_time string COMMENT '开始观看时间',
end_time string COMMENT '结束观看时间',
if_follow int comment '是否关注',
if_like int comment '是否点赞',
if_retweet int comment '是否转发',
comment_id int comment '评论ID'
) comment '用户-视频互动表'
row format delimited fields terminated by ',';
-- 建立短视频信息表
drop table if exists tb_video_info;
create table tb_video_info (
id int comment '自增ID',
video_id int comment '视频ID',
author int comment '创作者ID',
tag string comment '类别标签',
duration int comment '视频时长(秒数)',
release_time string comment '发布时间'
) comment '短视频信息表'
row format delimited fields terminated by ',';
-- 插入数据
insert into tb_user_video_log
values (1, 101, 2001, '2021-09-01 10:00:00', '2021-09-01 10:00:20', 0, 1, 1, null),
(2, 105, 2002, '2021-09-10 11:00:00', '2021-09-10 11:00:30', 1, 0, 1, null),
(3, 101, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:20', 1, 1, 1, null),
(4, 102, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:15', 0, 0, 1, null),
(5, 103, 2001, '2021-10-01 11:00:50', '2021-10-01 11:01:15', 1, 1, 0, 1732526),
(6, 106, 2002, '2021-10-01 10:59:05', '2021-10-01 11:00:05', 2, 0, 0, null);
insert into tb_video_info
VALUES (1, 2001, 901, '影视', 30, '2021-01-01 7:00:00'),
(2, 2002, 901, '影视', 60, '2021-01-01 7:00:00'),
(3, 2003, 902, '旅游', 90, '2020-01-01 7:00:00'),
(4, 2004, 902, '美女', 90, '2020-01-01 8:00:00');
参考答案：
-- 1. 获取2021年的数据，日期整理成月的形式
select video_id, date_format(start_time, 'yyyy-MM') as m, if_follow
from tb_user_video_log
where year(start_time) = 2021;
-- 2. 计算每一个作者每一个月的粉丝变化数量以及视频的播放次数
select b.author as author,
a.m as m,
sum(if(a.if_follow = 2, -1, a.if_follow)) as total_fans_m,
count(*) as total_play_m
from (
select video_id, date_format(start_time, 'yyyy-MM') as m, if_follow
from tb_user_video_log
where year(start_time) = 2021
) a left join tb_video_info b on a.video_id = b.video_id
group by b.author, a.m;
-- 3. 计算每一个作者到当前月的粉丝变化率以及总粉丝量
select author,
m as `month`,
round(total_fans_m / total_play_m, 3) as fans_growth_rate,
sum(total_fans_m) over (partition by author order by m rows between unbounded preceding and current row ) as total_fans
from (
select b.author as author,
a.m as m,
sum(if(a.if_follow = 2, -1, a.if_follow)) as total_fans_m,
count(*) as total_play_m
from (
select video_id, date_format(start_time, 'yyyy-MM') as m, if_follow
from tb_user_video_log
where year(start_time) = 2021) a left join tb_video_info b on a.video_id = b.video_id
group by b.author, a.m
) t order by author, total_fans;题目五：国庆期间每类视频点赞量和转发量
有用户-视频互动表tb_user_video_log：
| id |
uid |
video_id |
start_time |
end_time |
if_follow |
if_like |
if_retweet |
comment_id |
| 1 |
101 |
2001 |
2021-09-24 10:00:00 |
2021-09-24 10:00:20 |
1 |
1 |
0 |
NULL |
| 2 |
105 |
2002 |
2021-09-25 11:00:00 |
2021-09-25 11:00:30 |
0 |
0 |
1 |
NULL |
| 3 |
102 |
2002 |
2021-09-25 11:00:00 |
2021-09-25 11:00:30 |
1 |
1 |
1 |
NULL |
| 4 |
101 |
2002 |
2021-09-26 11:00:00 |
2021-09-26 11:00:30 |
1 |
0 |
1 |
NULL |
| 5 |
101 |
2002 |
2021-09-27 11:00:00 |
2021-09-27 11:00:30 |
1 |
1 |
0 |
NULL |
| 6 |
102 |
2002 |
2021-09-28 11:00:00 |
2021-09-28 11:00:30 |
1 |
0 |
1 |
NULL |
| 7 |
103 |
2002 |
2021-09-29 11:00:00 |
2021-10-02 11:00:30 |
1 |
0 |
1 |
NULL |
| 8 |
102 |
2002 |
2021-09-30 11:00:00 |
2021-09-30 11:00:30 |
1 |
1 |
1 |
NULL |
| 9 |
101 |
2001 |
2021-10-01 10:00:00 |
2021-10-01 10:00:20 |
1 |
1 |
0 |
NULL |
| 10 |
102 |
2001 |
2021-10-01 10:00:00 |
2021-10-01 10:00:15 |
0 |
0 |
1 |
NULL |
| 11 |
103 |
2001 |
2021-10-01 11:00:50 |
2021-10-01 11:01:15 |
1 |
1 |
0 |
1732526 |
| 12 |
106 |
2002 |
2021-10-02 10:59:05 |
2021-10-02 11:00:05 |
2 |
0 |
1 |
NULL |
| 13 |
107 |
2002 |
2021-10-02 10:59:05 |
2021-10-02 11:00:05 |
1 |
0 |
1 |
NULL |
| 14 |
108 |
2002 |
2021-10-02 10:59:05 |
2021-10-02 11:00:05 |
1 |
1 |
1 |
NULL |
| 15 |
109 |
2002 |
2021-10-03 10:59:05 |
2021-10-03 11:00:05 |
0 |
1 |
0 |
NULL |
uid-用户ID，video_id-视频ID，start_time-开始观看时间，end_time-结束观看时间，if_follow-是否关注，if_like-是否点赞，if_retweet-是否转发，comment_id-评论ID。
有短视频信息表tb_video_info：
| id |
video_id |
author |
tag |
duration |
release_time |
| 1 |
2001 |
901 |
影视 |
30 |
2021-01-01 07:00:00 |
| 2 |
2002 |
901 |
美食 |
60 |
2021-01-01 07:00:00 |
| 3 |
2003 |
902 |
旅游 |
90 |
2021-01-01 07:00:00 |
| 4 |
2004 |
902 |
美女 |
90 |
2020-01-01 08:00:00 |
video_id-视频ID，author-创作者ID，tag-类别标签，duration-视频时长(秒)，release_time-发布时间。
问题：统计2021年国庆头3天每类视频每天的近一周总点赞量和一周内最大单天转发量，结果按视频类别降序、日期升序排序。假设数据库中数据足够多，至少每个类别下国庆头3天及之前一周的每天都有播放记录。结果如下：
| tag |
dt |
sum_like_cnt_7d |
max_retweet_cnt_7d |
| 旅游 |
2021-10-01 |
5 |
2 |
| 旅游 |
2021-10-02 |
5 |
3 |
| 旅游 |
2021-10-03 |
6 |
3 |
解释：由表tb_user_video_log里的数据可得只有旅游类视频的播放，2021年9月25到10月3日每天的点赞量和转发量如下：
| tag |
dt |
like_cnt |
retweet_cnt |
| 旅游 |
2021-09-25 |
1 |
2 |
| 旅游 |
2021-09-26 |
0 |
1 |
| 旅游 |
2021-09-27 |
1 |
0 |
| 旅游 |
2021-09-28 |
0 |
1 |
| 旅游 |
2021-09-29 |
0 |
1 |
| 旅游 |
2021-09-30 |
1 |
1 |
| 旅游 |
2021-10-01 |
2 |
1 |
| 旅游 |
2021-10-02 |
1 |
3 |
| 旅游 |
2021-10-03 |
1 |
0 |
因此国庆头3天（10.01~10.03）里10.01的近7天（9.25~10.01）总点赞量为5次，单天最大转发量为2次（9月25那天最大）；同理可得10.02和10.03的两个指标。
-- 建立用户-视频互动表
drop table if exists tb_user_video_log;
create table tb_user_video_log (
id int comment '自增ID',
uid int comment '用户ID',
video_id int comment '视频ID',
start_time string COMMENT '开始观看时间',
end_time string COMMENT '结束观看时间',
if_follow int comment '是否关注',
if_like int comment '是否点赞',
if_retweet int comment '是否转发',
comment_id int comment '评论ID'
) comment '用户-视频互动表'
row format delimited fields terminated by ',';
-- 建立短视频信息表
drop table if exists tb_video_info;
create table tb_video_info (
id int comment '自增ID',
video_id int comment '视频ID',
author int comment '创作者ID',
tag string comment '类别标签',
duration int comment '视频时长(秒数)',
release_time string comment '发布时间'
) comment '短视频信息表'
row format delimited fields terminated by ',';
-- 插入数据
insert into tb_user_video_log
values (1, 101, 2001, '2021-09-24 10:00:00', '2021-09-24 10:00:20', 1, 1, 0, null),
(2, 105, 2002, '2021-09-25 11:00:00', '2021-09-25 11:00:30', 0, 0, 1, null),
(3, 102, 2002, '2021-09-25 11:00:00', '2021-09-25 11:00:30', 1, 1, 1, null),
(4, 101, 2002, '2021-09-26 11:00:00', '2021-09-26 11:00:30', 1, 0, 1, null),
(5, 101, 2002, '2021-09-27 11:00:00', '2021-09-27 11:00:30', 1, 1, 0, null),
(6, 102, 2002, '2021-09-28 11:00:00', '2021-09-28 11:00:30', 1, 0, 1, null),
(7, 103, 2002, '2021-09-29 11:00:00', '2021-09-29 11:00:30', 1, 0, 1, null),
(8, 102, 2002, '2021-09-30 11:00:00', '2021-09-30 11:00:30', 1, 1, 1, null),
(9, 101, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:20', 1, 1, 0, null),
(10, 102, 2001, '2021-10-01 10:00:00', '2021-10-01 10:00:15', 0, 0, 1, null),
(11, 103, 2001, '2021-10-01 11:00:50', '2021-10-01 11:01:15', 1, 1, 0, 1732526),
(12, 106, 2002, '2021-10-02 10:59:05', '2021-10-02 11:00:05', 2, 0, 1, null),
(13, 107, 2002, '2021-10-02 10:59:05', '2021-10-02 11:00:05', 1, 0, 1, null),
(14, 108, 2002, '2021-10-02 10:59:05', '2021-10-02 11:00:05', 1, 1, 1, null),
(15, 109, 2002, '2021-10-03 10:59:05', '2021-10-03 11:00:05', 0, 1, 0, null);
insert into tb_video_info
VALUES (1, 2001, 901, '影视', 30, '2021-01-01 7:00:00'),
(2, 2002, 901, '影视', 60, '2021-01-01 7:00:00'),
(3, 2003, 902, '旅游', 90, '2020-01-01 7:00:00'),
(4, 2004, 902, '美女', 90, '2020-01-01 8:00:00');
参考答案：
-- 1. 锁定数据范围：2021-09.25~2021-10-03
select video_id, date(start_time), if_like, if_retweet
from tb_user_video_log
where datediff('2021-10-03', start_time) < 9;
-- 2. 统计每一类视频每天的点赞量和转发量
select b.tag as tag,
a.dt as dt,
sum(a.if_like) as total_like_d,
sum(a.if_retweet) as total_retweet_d
from (
select video_id, date(start_time) as dt, if_like, if_retweet
from tb_user_video_log
where datediff('2021-10-03', start_time) < 9
) a left join tb_video_info b on a.video_id = b.video_id
group by b.tag, a.dt;
-- 3. 统计最近7天的点赞总量和最大转发量
select tag,
dt,
sum(total_like_d) over (partition by tag order by dt rows between 6 preceding and current row ) as sum_like_cnt_7d,
max(total_retweet_d) over (partition by tag order by dt rows between 6 preceding and current row) as max_retweet_cnt_7d
from (
select b.tag as tag,
a.dt as dt,
sum(a.if_like) as total_like_d,
sum(a.if_retweet) as total_retweet_d
from (
select video_id, date(start_time) as dt, if_like, if_retweet
from tb_user_video_log
where datediff('2021-10-03', start_time) < 9
) a left join tb_video_info b on a.video_id = b.video_id
group by b.tag, a.dt
) t1;
-- 4. 过滤出10-01~10-03
select *
from (
select tag,
dt,
sum(total_like_d) over (partition by tag order by dt rows between 6 preceding and current row ) as sum_like_cnt_7d,
max(total_retweet_d) over (partition by tag order by dt rows between 6 preceding and current row) as max_retweet_cnt_7d
from (
select b.tag as tag,
a.dt as dt,
sum(a.if_like) as total_like_d,
sum(a.if_retweet) as total_retweet_d
from (
select video_id, date(start_time) as dt, if_like, if_retweet
from tb_user_video_log
where datediff('2021-10-03', start_time) < 9
) a left join tb_video_info b on a.video_id = b.video_id
group by b.tag, a.dt
) t1
) t2 where month(dt) = 10
order by tag desc, dt asc;题目六：近一个月发布的视频中热度最高的top3视频
有用户-视频互动表tb_user_video_log：
| id |
uid |
video_id |
start_time |
end_time |
if_follow |
if_like |
if_retweet |
comment_id |
| 1 |
101 |
2001 |
2021-09-24 10:00:00 |
2021-09-24 10:00:30 |
1 |
1 |
1 |
NULL |
| 2 |
101 |
2001 |
2021-10-01 10:00:00 |
2021-10-01 10:00:31 |
1 |
1 |
0 |
NULL |