Problem

Table: Activity

+—————+———+ | Column Name | Type | +—————+———+ | user_id | int | | session_id | int | | activity_date | date | | activity_type | enum | +—————+———+ This table may have duplicate rows. The activity_type column is an ENUM (category) of type (‘open_session’, ’end_session’, ‘scroll_down’, ‘send_message’). The table shows the user activities for a social media website. Note that each session belongs to exactly one user.

Write a solution to find the daily active user count for a period of 30 days ending 2019-07-27 inclusively. A user was active on someday if they made at least one activity on that day.

Return the result table in any order.

The result format is in the following example.

Examples

Example 1

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Input: 
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   |
+---------+------------+---------------+---------------+
Output: 
+------------+--------------+ 
| day        | active_users |
+------------+--------------+ 
| 2019-07-20 | 2            |
| 2019-07-21 | 2            |
+------------+--------------+ 
Explanation: Note that we do not care about days with zero active users.

## Solution

### Method 1 – Group By and Date Filtering

#### Intuition

We need to count the number of unique users active on each day in the last 30 days ending 2019-07-27. We can filter the date range and group by day.

#### Approach

1. Filter the Activity table for dates between 2019-06-28 and 2019-07-27 (inclusive).
2. For each day, count the distinct user_ids.
3. Return the day and active user count.

#### Code



    

         
         
         
        
            
            
        
            
            
                 
                 
                
            
        
            
            
        
            
            
                 
                 
                
            
        
            
            
        
    


    

        
         
        
        
         
         
        

             
            
             
            
                
            
                 
                     
                
            
                 
                     
                
            
                 
                     
                
            
                 
                     
                
            
            
            
            




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SELECT activity_date AS day, COUNT(DISTINCT user_id) AS active_users
FROM Activity
WHERE activity_date BETWEEN DATE_SUB('2019-07-27', INTERVAL 29 DAY) AND '2019-07-27'
GROUP BY activity_date;





        

        
        
         
        
        
         
         
        

             
            
             
            
                
            
                 
                     
                
            
                 
                     
                
            
                 
                     
                
            
                 
                     
                
            
            
            
            




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SELECT activity_date AS day, COUNT(DISTINCT user_id) AS active_users
FROM Activity
WHERE activity_date BETWEEN '2019-07-27'::date - INTERVAL '29 days' AND '2019-07-27'
GROUP BY activity_date;





        

        
        
         
        
        
    





#### Complexity

- ⏰ Time complexity: `O(n)` — Each row is processed once.
- 🧺 Space complexity: `O(1)` — Only a few variables for aggregation.