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< p > Table: < code > Activity< / code > < / p >
< pre >
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| user_id | int |
| session_id | int |
| activity_date | date |
| activity_type | enum |
+---------------+---------+
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This table may have duplicate rows.
The activity_type column is an ENUM (category) of type (' open_session' , ' end_session' , ' scroll_down' , ' send_message' ).
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The table shows the user activities for a social media website.
Note that each session belongs to exactly one user.
< / pre >
< p > < / p >
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< p > Write a solution to find the daily active user count for a period of < code > 30< / code > days ending < code > 2019-07-27< / code > inclusively. A user was active on someday if they made at least one activity on that day.< / p >
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< p > Return the result table in < strong > any order< / strong > .< / p >
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< p > The result format is in the following example.< / p >
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< p > < / p >
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< p > < strong class = "example" > Example 1:< / strong > < / p >
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< pre >
< strong > Input:< / strong >
Activity table:
+---------+------------+---------------+---------------+
| user_id | session_id | activity_date | activity_type |
+---------+------------+---------------+---------------+
| 1 | 1 | 2019-07-20 | open_session |
| 1 | 1 | 2019-07-20 | scroll_down |
| 1 | 1 | 2019-07-20 | end_session |
| 2 | 4 | 2019-07-20 | open_session |
| 2 | 4 | 2019-07-21 | send_message |
| 2 | 4 | 2019-07-21 | end_session |
| 3 | 2 | 2019-07-21 | open_session |
| 3 | 2 | 2019-07-21 | send_message |
| 3 | 2 | 2019-07-21 | end_session |
| 4 | 3 | 2019-06-25 | open_session |
| 4 | 3 | 2019-06-25 | end_session |
+---------+------------+---------------+---------------+
< strong > Output:< / strong >
+------------+--------------+
| day | active_users |
+------------+--------------+
| 2019-07-20 | 2 |
| 2019-07-21 | 2 |
+------------+--------------+
< strong > Explanation:< / strong > Note that we do not care about days with zero active users.
< / pre >