美团大数据面试SQL-每分钟最大直播人数.
一、题目
有如下数据记录直播平台主播上播及下播时间，根据该数据计算出平台每分钟最大直播人数。
+----------+----------------------+----------------------+
| user_id | start_time | end_time |
+----------+----------------------+----------------------+
| 1 | 2024-04-29 01:00:00 | 2024-04-29 02:01:05 |
| 2 | 2024-04-29 01:05:00 | 2024-04-29 02:03:18 |
| 3 | 2024-04-29 02:00:00 | 2024-04-29 04:03:22 |
| 4 | 2024-04-29 03:15:07 | 2024-04-29 04:33:21 |
| 5 | 2024-04-29 03:34:16 | 2024-04-29 06:10:45 |
| 6 | 2024-04-29 05:22:00 | 2024-04-29 07:01:08 |
| 7 | 2024-04-29 06:11:03 | 2024-04-29 09:26:05 |
| 3 | 2024-04-29 08:00:00 | 2024-04-29 12:34:27 |
| 1 | 2024-04-29 11:00:00 | 2024-04-29 16:03:18 |
| 8 | 2024-04-29 15:00:00 | 2024-04-29 17:01:05 |
+----------+----------------------+----------------------+二、分析
查询每分钟最大在线人数，这里处理逻辑基本和最高峰在线人数是一致的，但有一个问题，如果某一分钟内无任何操作记录，则不会出现该分钟的数据，我们就统计不到。所以这里额外考察了生成数据；
| 维度 | 评分 |
|---|---|
| 题目难度 | ⭐️⭐️⭐️⭐️ |
| 题目清晰度 | ⭐️⭐️⭐️⭐️⭐️ |
| 业务常见度 | ⭐️⭐️⭐️⭐️⭐️ |
三、SQL
每分钟最大直播人数
1.首先对原始数据进行处理，生成主播上下播的日志数据，同时增加人数变化字段，主播上播为1，主播下播-1。新数据包含 user_id,action_time,change_cnt
--开播记录
select
user_id,
start_time as action_time,
1 as change_cnt
from t1_livestream_log
union all
--下播记录
select
user_id,
end_time as action_time,
-1 as change_cnt
from t1_livestream_log查询结果
+--------------+----------------------+-----------------+
| _u1.user_id | _u1.action_time | _u1.change_cnt |
+--------------+----------------------+-----------------+
| 1 | 2024-04-29 01:00:00 | 1 |
| 1 | 2024-04-29 02:01:05 | -1 |
| 2 | 2024-04-29 01:05:00 | 1 |
| 2 | 2024-04-29 02:03:18 | -1 |
| 3 | 2024-04-29 02:00:00 | 1 |
| 3 | 2024-04-29 04:03:22 | -1 |
| 4 | 2024-04-29 03:15:07 | 1 |
| 4 | 2024-04-29 04:33:21 | -1 |
| 5 | 2024-04-29 03:34:16 | 1 |
| 5 | 2024-04-29 06:10:45 | -1 |
| 6 | 2024-04-29 05:22:00 | 1 |
| 6 | 2024-04-29 07:01:08 | -1 |
| 7 | 2024-04-29 06:11:03 | 1 |
| 7 | 2024-04-29 09:26:05 | -1 |
| 3 | 2024-04-29 08:00:00 | 1 |
| 3 | 2024-04-29 12:34:27 | -1 |
| 1 | 2024-04-29 11:00:00 | 1 |
| 1 | 2024-04-29 16:03:18 | -1 |
| 8 | 2024-04-29 15:00:00 | 1 |
| 8 | 2024-04-29 17:01:05 | -1 |
+--------------+----------------------+-----------------+2.生成0~24*60-1条记录数据
select idx from (select posexplode(split(space(24*60),' ')) as (idx,value)) t查询结果,这里仅显示前10行数据
+------+
| idx |
+------+
| 0 |
| 1 |
| 2 |
| 3 |
| 4 |
| 5 |
| 6 |
| 7 |
| 8 |
| 9 |
+------+3.根据2生成每分钟一条记录的心跳记录，心跳记录change_cnt= 0 ,代表没有主播上播，也没有主播下播。
SELECT
0 as user_id,
from_unixtime(unix_timestamp('2024-04-29','yyyy-MM-dd')+item*60,'yyyy-MM-dd HH:mm:ss') as action_time,
0 as change_cnt
from (select posexplode(split(space(24*60),' ')) as (item,value)) t
limit 10;查询结果，这里仅显示前10行数据
+----------+----------------------+-------------+
| user_id | action_time | change_cnt |
+----------+----------------------+-------------+
| 0 | 2024-04-29 00:00:00 | 0 |
| 0 | 2024-04-29 00:01:00 | 0 |
| 0 | 2024-04-29 00:02:00 | 0 |
| 0 | 2024-04-29 00:03:00 | 0 |
| 0 | 2024-04-29 00:04:00 | 0 |
| 0 | 2024-04-29 00:05:00 | 0 |
| 0 | 2024-04-29 00:06:00 | 0 |
| 0 | 2024-04-29 00:07:00 | 0 |
| 0 | 2024-04-29 00:08:00 | 0 |
| 0 | 2024-04-29 00:09:00 | 0 |
+----------+----------------------+-------------+4.汇总所有数据之后，对change_cnt累积求和，然后求出每分钟的最大值即可
with t_all as(
--开播记录
select
user_id,
start_time as action_time,
1 as change_cnt
from t1_livestream_log
union all
--下播记录
select
user_id,
end_time as action_time,
-1 as change_cnt
from t1_livestream_log
--心跳数据
union all
SELECT
0 as user_id,
from_unixtime(unix_timestamp('2024-04-29','yyyy-MM-dd')+item*60,'yyyy-MM-dd HH:mm:ss') as action_time,
0 as change_cnt
from (select posexplode(split(space(24*60),' ')) as (item,value)) t
)
select
date_format(action_time,'yyyy-MM-dd HH:mm') as act_minute,
max(online_cnt) as minute_max_cnt
from (select user_id,
action_time,
change_cnt,
sum(change_cnt) over (order by action_time asc) online_cnt
from t_all
)t1
group by date_format(action_time,'yyyy-MM-dd HH:mm')
;查询结果，截取了部分有直播数据进行展示
| act_minute | minute_max_cnt |
+-------------------+-----------------+
| 2024-04-29 16:40 | 1 |
| 2024-04-29 16:41 | 1 |
| 2024-04-29 16:42 | 1 |
| 2024-04-29 16:43 | 1 |
| 2024-04-29 16:44 | 1 |
| 2024-04-29 16:45 | 1 |
| 2024-04-29 16:46 | 1 |
| 2024-04-29 16:47 | 1 |
| 2024-04-29 16:48 | 1 |
| 2024-04-29 16:49 | 1 |
| 2024-04-29 16:50 | 1 |
| 2024-04-29 16:51 | 1 |
| 2024-04-29 16:52 | 1 |
| 2024-04-29 16:53 | 1 |
| 2024-04-29 16:54 | 1 |
| 2024-04-29 16:55 | 1 |
| 2024-04-29 16:56 | 1 |
| 2024-04-29 16:57 | 1 |
| 2024-04-29 16:58 | 1 |四、建表语句和数据插入
CREATE TABLE IF NOT EXISTS t1_livestream_log (
user_id INT, -- 主播ID
start_time STRING, -- 开始时间
end_time STRING -- 结束时间
);
insert into t1_livestream_log(user_id, start_time, end_time) values
(1,'2024-04-29 01:00:00','2024-04-29 02:01:05'),
(2,'2024-04-29 01:05:00','2024-04-29 02:03:18'),
(3,'2024-04-29 02:00:00','2024-04-29 04:03:22'),
(4,'2024-04-29 03:15:07','2024-04-29 04:33:21'),
(5,'2024-04-29 03:34:16','2024-04-29 06:10:45'),
(6,'2024-04-29 05:22:00','2024-04-29 07:01:08'),
(7,'2024-04-29 06:11:03','2024-04-29 09:26:05'),
(3,'2024-04-29 08:00:00','2024-04-29 12:34:27'),
(1,'2024-04-29 11:00:00','2024-04-29 16:03:18'),
(8,'2024-04-29 15:00:00','2024-04-29 17:01:05');
本文同步在微信公众号”数据仓库技术“和个人博客”数据仓库技术 (opens in a new tab)“发表；