Seat Reservation Manager

Seat Reservation Manager

What is this problem about?

The Seat Reservation Manager interview question asks you to design a system that manages seat reservations in a venue with n seats (numbered 1 to n). Implement three operations: reserve() which returns the smallest available seat number and marks it reserved, and unreserve(seatNumber) which releases a previously reserved seat. This is a heap-based design problem focused on always serving the minimum available seat.

Why is this asked in interviews?

Microsoft, Meta, Google, and Dropbox ask this design problem because it models real-world ticketing and resource allocation systems. The ability to efficiently retrieve and restore the minimum available resource — in O(log n) per operation — is a fundamental systems design skill. It evaluates whether candidates reach for a min-heap as the right data structure for ordered retrieval, and how to handle both forward-allocation and out-of-order releases.

Algorithmic pattern used

The pattern is min-heap (priority queue) design. Initialize a min-heap with all seats from 1 to n. reserve(): pop and return the minimum from the heap (O(log n)). unreserve(seatNumber): push seatNumber back into the heap (O(log n)). The heap invariant always keeps the smallest available seat at the top, satisfying the "smallest unreserved seat" requirement in O(log n) time per operation.

Example explanation

n = 5. Initial heap: [1, 2, 3, 4, 5].

• reserve() → return 1. Heap: [2, 3, 4, 5].
• reserve() → return 2. Heap: [3, 4, 5].
• unreserve(2) → push 2. Heap: [2, 3, 4, 5].
• reserve() → return 2 (smallest available). Heap: [3, 4, 5].
• reserve() → return 3. Heap: [4, 5].

Common mistakes candidates make

• Using a sorted set or list instead of a heap — while sorted sets allow O(log n) minimum retrieval, a heap is more natural and has lower constant factors for this access pattern.
• Initializing the heap lazily and using a counter variable — this fails when unreserved seats (out of monotone order) need to be returned before higher-numbered available seats.
• Not using Python's heapq (min-heap by default) and forgetting that Java's PriorityQueue is also a min-heap.
• Forgetting to handle the case where reserve() is called more times than seats exist (problem constraints typically prevent this but worth noting).

Interview preparation tip

For the Seat Reservation Manager coding problem, the heap design interview pattern is the key. Preloading all n seats into the heap at initialization is the simplest correct approach. Interviewers at Siemens and Dropbox may ask about trade-offs when n is very large — in that case, use a counter for sequential allocation and only push to the heap when seats are unreserved out of order. Practice explaining this lazy-heap optimization to demonstrate systems-level thinking.