REPLAY EPISODE: A candidate takes on their very first coding mock interview. The problem: designing a seat allocation algorithm for a cinema that can scale to billions of rows. Given a list of reserved seats, the task is to calculate how many groups of four people can still sit together, factoring in aisle breaks, adjacency rules, and edge cases.

The interviewer, a Staff-level engineer with experience at Google and other top-tier Bay Area companies, guides the candidate to optimize their solution by leveraging sparsity, reducing space complexity, and thinking carefully about logical overlap between seating groups. Along the way, they also explore how to make the code cleaner, more modular, and easier to analyze.

The feedback at the end breaks down what the candidate did well, what edge cases tripped them up, and how they can sharpen their real-time reasoning for future interviews.

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See the interviewer’s feedback and transcript here: https://interviewing.io/mocks/google-python-seat-allocation-at-scale

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