Is Meeting Rooms II easy or hard?

Meeting Rooms II is generally classified as a medium difficulty problem. The challenge lies in recognizing that overlapping intervals can be converted into chronological events and processed with a sweep-line or priority queue approach.

How to solve Meeting Rooms II in O(n)?

An O(n) style solution is possible if the timeline range is small. Use a difference array: increment at meeting start, decrement at meeting end, and compute a prefix sum across the timeline. The maximum prefix value indicates the required number of rooms. The complexity becomes O(n + R), where R is the time range.

What is the best approach for Meeting Rooms II?

The sweep-line approach using a difference map is the most practical solution. Record +1 at each meeting start and -1 at each meeting end, sort the timestamps, and compute a running sum of active meetings. The maximum running value equals the number of rooms required. This runs in O(n log n) time due to sorting and uses O(n) space.

Is Meeting Rooms II asked at Google/Amazon/Meta?

Meeting Rooms II is a well-known interval scheduling problem frequently reported in interviews at companies like Google, Amazon, Meta, and Microsoft. It tests understanding of sweep-line algorithms, sorting of events, and efficient interval overlap handling.

What data structure is used in Meeting Rooms II?

Common implementations use arrays, hash maps, or heaps depending on the strategy. The sweep-line method uses a map or array to store event changes and a running prefix sum to track active meetings. Heap-based solutions also exist and track the earliest finishing meeting.

What is the time complexity of Meeting Rooms II?

The optimal implementation typically runs in O(n log n) time. The complexity comes from sorting the meeting start and end events before scanning them in chronological order. The scan itself is linear, so the dominant cost is sorting.

Meeting Rooms II Python or Java solution approach?

Both Python and Java implementations usually follow the sweep-line pattern. Insert +1 for each start time and -1 for each end time, sort the keys, and compute the running total of active meetings. The peak value during the scan gives the number of rooms required.

Talentd/FleetCode/Problems/

253. Meeting Rooms II

Medium

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253. Meeting Rooms II

Medium52.6% AcceptancePremium

Array Two Pointers Greedy Sorting Heap (Priority Queue)Prefix Sum

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Given an array of meeting time intervals intervals where intervals[i] = [start_i, end_i], return the minimum number of conference rooms required.

Example 1:

Input: intervals = [[0,30],[5,10],[15,20]]
Output: 2

Example 2:

Input: intervals = [[7,10],[2,4]]
Output: 1

Constraints:

1 <= intervals.length <= 10⁴
0 <= start_i < end_i <= 10⁶

Asked by 42 companies