动作表: Actions
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| user_id | int |
| post_id | int |
| action_date | date |
| action | enum |
| extra | varchar |
+---------------+---------+
这张表没有主键,并有可能存在重复的行。
action 列的类型是 ENUM,
可能的值为 ('view', 'like', 'reaction', 'comment', 'report', 'share')。
extra 列拥有一些可选信息,
例如:报告理由(a reason for report)或反应类型(a type of reaction)等。
移除表: Removals
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| post_id | int |
| remove_date | date |
+---------------+---------+
这张表的主键是 post_id。
这张表的每一行表示一个被移除的帖子,
原因可能是由于被举报或被管理员审查。
编写一段 SQL 来查找:在被报告为垃圾广告的帖子中,被移除的帖子的每日平均占比,四舍五入到小数点后 2 位。
查询结果的格式如下:
Actions table:
+---------+---------+-------------+--------+--------+
| user_id | post_id | action_date | action | extra |
+---------+---------+-------------+--------+--------+
| 1 | 1 | 2019-07-01 | view | null |
| 1 | 1 | 2019-07-01 | like | null |
| 1 | 1 | 2019-07-01 | share | null |
| 2 | 2 | 2019-07-04 | view | null |
| 2 | 2 | 2019-07-04 | report | spam |
| 3 | 4 | 2019-07-04 | view | null |
| 3 | 4 | 2019-07-04 | report | spam |
| 4 | 3 | 2019-07-02 | view | null |
| 4 | 3 | 2019-07-02 | report | spam |
| 5 | 2 | 2019-07-03 | view | null |
| 5 | 2 | 2019-07-03 | report | racism |
| 5 | 5 | 2019-07-03 | view | null |
| 5 | 5 | 2019-07-03 | report | racism |
+---------+---------+-------------+--------+--------+
Removals table:
+---------+-------------+
| post_id | remove_date |
+---------+-------------+
| 2 | 2019-07-20 |
| 3 | 2019-07-18 |
+---------+-------------+
Result table:
+-----------------------+
| average_daily_percent |
+-----------------------+
| 75.00 |
+-----------------------+
2019-07-04 的垃圾广告移除率是 50%,因为有两张帖子被报告为垃圾广告,但只有一个得到移除。
2019-07-02 的垃圾广告移除率是 100%,因为有一张帖子被举报为垃圾广告并得到移除。
其余几天没有收到垃圾广告的举报,因此平均值为:(50 + 100) / 2 = 75%
注意,输出仅需要一个平均值即可,我们并不关注移除操作的日期。
来源:力扣(LeetCode) 链接:https://leetcode-cn.com/problems/reported-posts-ii 著作权归领扣网络所有。商业转载请联系官方授权,非商业转载请注明出处。
select distinct post_id, action_date
from Actions
where extra='spam'
{"headers": ["post_id", "action_date"],
"values": [
[2, "2019-07-04"],
[4, "2019-07-04"],
[3, "2019-07-02"]]}
# Write your MySQL query statement below
select round(avg(percent), 2) average_daily_percent
from
(
select count(remove_date)/count(*)*100 percent
from
(
select distinct post_id, action_date
from Actions
where extra='spam'
) t left join Removals r
using(post_id)
group by action_date
) t