Is Find Overlapping Shifts II easy or hard?

Find Overlapping Shifts II is rated Hard because it combines interval reasoning with advanced SQL operations such as self joins, ordering, and window-based merging. Correct overlap conditions and query optimization are the main challenges.

Find Overlapping Shifts II Python/Java solution

This problem is categorized under database queries, so the standard solution uses SQL (MySQL). In application code like Python or Java, the same logic would involve sorting intervals and checking overlaps using interval comparison conditions.

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

Sort shifts by employee and start time, then merge contiguous or overlapping intervals using window functions like LAG or grouping logic. After normalization, perform a self join on the merged intervals with the overlap condition start1 < end2 AND start2 < end1.

What is the best approach for Find Overlapping Shifts II?

The merge + join strategy is the most efficient. First normalize or merge adjacent shifts using ordering and window functions, then run a self join to detect interval overlaps using the condition start1 < end2 AND start2 < end1. This reduces redundant comparisons and scales better than a raw self join.

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

Interval overlap and scheduling queries are common interview themes at companies like Amazon, Google, and Meta. Variants appear in both SQL interviews and algorithm rounds where candidates detect overlapping time ranges efficiently.

What data structure is used in Find Overlapping Shifts II?

The core concept is interval processing. In SQL, this is implemented using relational tables, ordering, and window functions rather than traditional in-memory data structures. The overlap logic mirrors interval comparison used in algorithm problems.

What is the time complexity of Find Overlapping Shifts II?

The optimized SQL approach runs in roughly O(n log n) time due to sorting or window function ordering. A naive self join checks every pair of rows and can reach O(n²) comparisons on large tables.

3268. Find Overlapping Shifts II - Practice Online

Q: What is the best approach for Find Overlapping Shifts II?

The merge + join strategy is the most efficient. First normalize or merge adjacent shifts using ordering and window functions, then run a self join to detect interval overlaps using the condition start1 < end2 AND start2 < end1. This reduces redundant comparisons and scales better than a raw self join.

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

Interval overlap and scheduling queries are common interview themes at companies like Amazon, Google, and Meta. Variants appear in both SQL interviews and algorithm rounds where candidates detect overlapping time ranges efficiently.

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

The core concept is interval processing. In SQL, this is implemented using relational tables, ordering, and window functions rather than traditional in-memory data structures. The overlap logic mirrors interval comparison used in algorithm problems.

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

The optimized SQL approach runs in roughly O(n log n) time due to sorting or window function ordering. A naive self join checks every pair of rows and can reach O(n²) comparisons on large tables.

Table: EmployeeShifts

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

Write a solution to analyze overlapping shifts for each employee. Two shifts are considered overlapping if they occur on the same date and one shift's end_time is later than another shift's start_time.

For each employee, calculate the following:

The maximum number of shifts that overlap at any given time.
The total duration of all overlaps in minutes.

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           | 2023-10-01 09:00:00 | 2023-10-01 17:00:00 |
| 1           | 2023-10-01 15:00:00 | 2023-10-01 23:00:00 |
| 1           | 2023-10-01 16:00:00 | 2023-10-02 00:00:00 |
| 2           | 2023-10-01 09:00:00 | 2023-10-01 17:00:00 |
| 2           | 2023-10-01 11:00:00 | 2023-10-01 19:00:00 |
| 3           | 2023-10-01 09:00:00 | 2023-10-01 17:00:00 |
+-------------+---------------------+---------------------+

Output:

+-------------+---------------------------+------------------------+
| employee_id | max_overlapping_shifts    | total_overlap_duration |
+-------------+---------------------------+------------------------+
| 1           | 3                         | 600                    |
| 2           | 2                         | 360                    |
| 3           | 1                         | 0                      |
+-------------+---------------------------+------------------------+

Explanation:

Employee 1 has 3 shifts:
- 2023-10-01 09:00:00 to 2023-10-01 17:00:00
- 2023-10-01 15:00:00 to 2023-10-01 23:00:00
- 2023-10-01 16:00:00 to 2023-10-02 00:00:00
The maximum number of overlapping shifts is 3 (from 16:00 to 17:00). The total overlap duration is: - 2 hours (15:00-17:00) between 1st and 2nd shifts - 1 hour (16:00-17:00) between 1st and 3rd shifts - 7 hours (16:00-23:00) between 2nd and 3rd shifts Total: 10 hours = 600 minutes
Employee 2 has 2 shifts:
- 2023-10-01 09:00:00 to 2023-10-01 17:00:00
- 2023-10-01 11:00:00 to 2023-10-01 19:00:00
The maximum number of overlapping shifts is 2. The total overlap duration is 6 hours (11:00-17:00) = 360 minutes.
Employee 3 has only 1 shift, so there are no overlaps.

The output table contains the employee_id, the maximum number of simultaneous overlaps, and the total overlap duration in minutes for each employee, ordered by employee_id in ascending order.