Is Find Overlapping Shifts easy or hard?

Find Overlapping Shifts is considered a medium difficulty database problem. The challenge comes from recognizing the correct interval overlap condition and applying a self-join without accidentally matching rows with themselves.

Find Overlapping Shifts Python/Java solution

SQL is the primary solution format for this problem, but Python solutions use Pandas with DataFrame.merge() and boolean filtering to replicate the same overlap logic. Java implementations typically rely on database queries rather than in-memory algorithms.

How to solve Find Overlapping Shifts in O(n)?

Pure O(n) solutions are uncommon in SQL because overlap detection requires comparing intervals. Database engines optimize joins using indexes on employee_id and timestamps, which reduces practical runtime, but the logical comparison complexity remains O(n^2).

What is the best approach for Find Overlapping Shifts?

The standard solution uses a self-join on the shifts table combined with an interval overlap condition. Two shifts overlap if start1 < end2 and start2 < end1. After joining rows from the same employee, filtering and grouping identify the overlapping shifts efficiently.

Is Find Overlapping Shifts asked at Google/Amazon/Meta?

Interval overlap detection is a common database and analytics interview pattern used by companies like Amazon and Google. Variations appear in scheduling systems, booking conflicts, and employee shift management problems.

What data structure is used in Find Overlapping Shifts?

The problem primarily uses relational database tables and join operations. Conceptually it relies on interval comparison logic, implemented through SQL self-joins or DataFrame merges in Pandas.

What is the time complexity of Find Overlapping Shifts?

The typical self-join solution runs in O(n^2) time because each shift may be compared with many other shifts. Space complexity is O(1) in SQL aside from query execution overhead, though Pandas implementations may use O(n^2) memory for the merged dataframe.

3262. Find Overlapping Shifts - Practice Online

Q: Is Find Overlapping Shifts asked at Google/Amazon/Meta?

Interval overlap detection is a common database and analytics interview pattern used by companies like Amazon and Google. Variations appear in scheduling systems, booking conflicts, and employee shift management problems.

Q: What data structure is used in Find Overlapping Shifts?

The problem primarily uses relational database tables and join operations. Conceptually it relies on interval comparison logic, implemented through SQL self-joins or DataFrame merges in Pandas.

Q: What is the time complexity of Find Overlapping Shifts?

The typical self-join solution runs in O(n^2) time because each shift may be compared with many other shifts. Space complexity is O(1) in SQL aside from query execution overhead, though Pandas implementations may use O(n^2) memory for the merged dataframe.

Table: EmployeeShifts

+------------------+---------+
| Column Name      | Type    |
+------------------+---------+
| employee_id      | int     |
| start_time       | time    |
| end_time         | time    |
+------------------+---------+
(employee_id, start_time) is the unique key for this table.
This table contains information about the shifts worked by employees, including the start and end times on a specific date.

Write a solution to count the number of overlapping shifts for each employee. Two shifts are considered overlapping if one shift’s end_time is later than another shift’s start_time.

Return the result table ordered by employee_id in ascending order.

The query result format is in the following example.

Example:

Input:

EmployeeShifts table:

+-------------+------------+----------+
| employee_id | start_time | end_time |
+-------------+------------+----------+
| 1           | 08:00:00   | 12:00:00 |
| 1           | 11:00:00   | 15:00:00 |
| 1           | 14:00:00   | 18:00:00 |
| 2           | 09:00:00   | 17:00:00 |
| 2           | 16:00:00   | 20:00:00 |
| 3           | 10:00:00   | 12:00:00 |
| 3           | 13:00:00   | 15:00:00 |
| 3           | 16:00:00   | 18:00:00 |
| 4           | 08:00:00   | 10:00:00 |
| 4           | 09:00:00   | 11:00:00 |
+-------------+------------+----------+

Output:

+-------------+--------------------+
| employee_id | overlapping_shifts |
+-------------+--------------------+
| 1           | 2                  |
| 2           | 1                  |
| 4           | 1                  |
+-------------+--------------------+

Explanation:

Employee 1 has 3 shifts:
- 08:00:00 to 12:00:00
- 11:00:00 to 15:00:00
- 14:00:00 to 18:00:00
The first shift overlaps with the second, and the second overlaps with the third, resulting in 2 overlapping shifts.
Employee 2 has 2 shifts:
- 09:00:00 to 17:00:00
- 16:00:00 to 20:00:00
These shifts overlap with each other, resulting in 1 overlapping shift.
Employee 3 has 3 shifts:
- 10:00:00 to 12:00:00
- 13:00:00 to 15:00:00
- 16:00:00 to 18:00:00
None of these shifts overlap, so Employee 3 is not included in the output.
Employee 4 has 2 shifts:
- 08:00:00 to 10:00:00
- 09:00:00 to 11:00:00
These shifts overlap with each other, resulting in 1 overlapping shift.

The output shows the employee_id and the count of overlapping shifts for each employee who has at least one overlapping shift, ordered by employee_id in ascending order.