{x}
blog image

Find Total Time Spent by Each Employee

Table: Employees

+-------------+------+
| Column Name | Type |
+-------------+------+
| emp_id      | int  |
| event_day   | date |
| in_time     | int  |
| out_time    | int  |
+-------------+------+
(emp_id, event_day, in_time) is the primary key (combinations of columns with unique values) of this table.
The table shows the employees' entries and exits in an office.
event_day is the day at which this event happened, in_time is the minute at which the employee entered the office, and out_time is the minute at which they left the office.
in_time and out_time are between 1 and 1440.
It is guaranteed that no two events on the same day intersect in time, and in_time < out_time.

 

Write a solution to calculate the total time in minutes spent by each employee on each day at the office. Note that within one day, an employee can enter and leave more than once. The time spent in the office for a single entry is out_time - in_time.

Return the result table in any order.

The result format is in the following example.

 

Example 1:

Input: 
Employees table:
+--------+------------+---------+----------+
| emp_id | event_day  | in_time | out_time |
+--------+------------+---------+----------+
| 1      | 2020-11-28 | 4       | 32       |
| 1      | 2020-11-28 | 55      | 200      |
| 1      | 2020-12-03 | 1       | 42       |
| 2      | 2020-11-28 | 3       | 33       |
| 2      | 2020-12-09 | 47      | 74       |
+--------+------------+---------+----------+
Output: 
+------------+--------+------------+
| day        | emp_id | total_time |
+------------+--------+------------+
| 2020-11-28 | 1      | 173        |
| 2020-11-28 | 2      | 30         |
| 2020-12-03 | 1      | 41         |
| 2020-12-09 | 2      | 27         |
+------------+--------+------------+
Explanation: 
Employee 1 has three events: two on day 2020-11-28 with a total of (32 - 4) + (200 - 55) = 173, and one on day 2020-12-03 with a total of (42 - 1) = 41.
Employee 2 has two events: one on day 2020-11-28 with a total of (33 - 3) = 30, and one on day 2020-12-09 with a total of (74 - 47) = 27.

Solution Explanation:

The problem requires calculating the total time spent by each employee on each day. The input is a table named Employees with columns emp_id, event_day, in_time, and out_time. The solution involves grouping the data by employee ID and event day, then summing the time spent for each group.

Approach:

The most efficient way to solve this problem is using SQL's GROUP BY and SUM() functions. The steps are as follows:

  1. Group the data: We group the Employees table by emp_id and event_day. This ensures that we calculate the total time separately for each employee on each day.

  2. Calculate the time spent per entry: For each row (entry), we calculate the difference between out_time and in_time. This represents the time spent during that specific entry.

  3. Sum the time spent: We use the SUM() function to aggregate the time spent for all entries within each group (employee and day).

  4. Rename columns (optional): For clarity, we rename the event_day column to day.

Code (MySQL):

SELECT event_day AS day, emp_id, SUM(out_time - in_time) AS total_time
FROM Employees
GROUP BY 1, 2;

Explanation of the Code:

  • SELECT event_day AS day, emp_id, SUM(out_time - in_time) AS total_time: This selects the event_day (renamed as day), emp_id, and the sum of the difference between out_time and in_time, aliased as total_time.

  • FROM Employees: This specifies the table we are querying.

  • GROUP BY 1, 2: This groups the results by the first and second columns in the SELECT statement, which are event_day (or day) and emp_id.

Time Complexity Analysis:

The time complexity of this SQL query is dominated by the GROUP BY operation. The time complexity of a GROUP BY operation is generally considered to be O(N log N) or O(N) depending on the specific database implementation and the size of the data, where N is the number of rows in the Employees table. The SUM() operation within each group has a linear time complexity with respect to the number of rows in that group. Overall, the dominant factor is the sorting or hashing required by GROUP BY, giving us an approximate complexity of O(N log N) in the worst case. In practice, optimized database systems often achieve closer to linear time complexity for many datasets.

Space Complexity Analysis:

The space complexity depends on the size of the output. In the worst case, if each employee has a unique entry for every day, the output will be the same size as the input table. Therefore, the space complexity is O(N), where N is the number of rows in the Employees table. The database system itself may use additional temporary space during the GROUP BY and SUM() operations, but this is typically handled internally and not directly reflected in the space complexity analysis from the perspective of the query itself.