Recently while practicing interval problems, I noticed something interesting:
A lot of interview questions are actually small twists of the same underlying pattern.
Take the classic Merge Intervals problem:
intervals = [[1,3], [2,5], [6,8], [7,9]]
The first important step is almost always:
intervals.sort(key=lambda x: x[0])
Once intervals are sorted, overlap decisions become local.
Then the core logic becomes:
if start <= merged[-1][1]:
which simply means:
- current interval overlaps with previous interval
and we merge them.
Final result:
[[1,5], [6,9]]
Pretty straightforward.
But interviewers often twist the same idea into a different question.
One famous variation is:
“Given meeting intervals, find the minimum number of meeting rooms required.”
Example:
[[1,10], [2,7], [3,19], [8,12]]
Now the goal is no longer merging.
Instead, while processing intervals, we need to figure out:
- how many meetings are active simultaneously at any moment.
Because that directly tells us:
- how many rooms are required.
At one point here:
are all active together.
So answer becomes:
3 rooms
That’s what makes interval problems interesting in interviews.
The pattern stays similar:
- sort,
- process left to right,
- reason about overlaps,
but the objective changes completely.
I wrote a detailed article around this covering:
- Merge Intervals intuition,
- Meeting Rooms II,
- sweep-line thinking,
- Python templates,
- and how to recognize interval patterns during interviews.
You can read detailed article here.