Google·Software Engineer·Onsite - Coding / Algorithms·Senior

Senior

Apr 2026

Summary

Google SWE interview that went deep into a cyclic meeting rooms problem, starting from a sweep-line baseline and then scaling it up to millions of intervals. The follow-up discussion on distributed approaches was where things got interesting and a bit uncomfortable.

Questions Asked (2)

You have a meeting rooms problem on a 24-hour cyclic clock where intervals can wrap past midnight. Find the maximum number of overlapping meetings using a sweep-line approach.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Got the basic sweep-line down pretty quickly.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem constraints and edge cases, especially how to handle intervals that wrap past midnight. Then, explain how to adapt the sweep-line algorithm to a cyclic timeline, such as by splitting wrapping intervals or duplicating the timeline, and analyze the time and space complexity.

Pro tip: Demonstrate awareness of the cyclic nature by discussing how to avoid double-counting overlaps that span midnight, and mention that you can normalize intervals by splitting them into two non-wrapping intervals or by using a circular sweep with a reference point.

1. Clarify the problem

Ask questions to confirm the input format, whether intervals are inclusive/exclusive, and if meetings can start and end at the same time. Also confirm that the clock is 24-hour cyclic and intervals may wrap.

2. Handle wrapping intervals

Decide on a strategy to linearize the cyclic timeline: either split each wrapping interval into two parts (e.g., [start, 24) and [0, end)) or duplicate the timeline to 48 hours and map intervals accordingly.

3. Apply sweep-line

Create events for each interval start (+1) and end (-1), sort them by time, and sweep through to track the current number of active meetings, updating the maximum.

4. Analyze complexity

State that sorting takes O(n log n) time and the sweep takes O(n) time, with O(n) space for events. Discuss trade-offs of splitting vs. duplicating.

5. Test edge cases

Walk through examples like all meetings wrapping, no overlaps, and meetings that exactly touch midnight to ensure correctness.

Key Points to Mention

Sweep-line algorithm: events sorted by time, increment on start, decrement on end.
Cyclic timeline handling: splitting wrapping intervals or duplicating the timeline.
Time complexity: O(n log n) due to sorting, O(n) for sweep.
Space complexity: O(n) for storing events.
Edge cases: intervals that start and end at midnight, zero-length meetings, all meetings overlapping.
Trade-offs: splitting intervals increases event count but simplifies logic; duplicating timeline may double memory but avoids splitting.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Now assume there are millions of meetings. How would you optimize the solution? Walk through bucketed difference arrays, parallelism, streaming, and distributed merging, and how the cyclic boundary affects each.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

This is where I started fumbling.

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AI HintsAI Generated

Suggested Approach

Start by restating the problem and the scale challenge, then propose a layered optimization: bucketed difference arrays to reduce memory and enable parallelism, streaming to handle continuous input, and distributed merging to combine partial results. For each layer, explain how the cyclic boundary is handled, and discuss trade-offs between latency, throughput, and complexity.

Pro tip: Emphasize that the cyclic boundary is not just an edge case but a fundamental constraint that influences bucketing, merging, and parallelism strategies; showing you've considered it at every layer demonstrates deep systems thinking.

1. Clarify the problem and scale

Confirm the input format (millions of meetings with start/end times), the output (e.g., maximum concurrent meetings), and the cyclic nature (e.g., meetings can wrap around midnight).

2. Bucketed difference arrays

Divide the time range into buckets (e.g., hours) and use a difference array per bucket to count meeting starts and ends. Handle cyclic boundary by splitting meetings that cross midnight into two buckets or by using a circular buffer.

3. Parallelism and streaming

Process buckets in parallel since they are independent, and use streaming to handle continuous input without storing all meetings. For cyclic boundary, ensure buckets at the start and end of the day are synchronized.

4. Distributed merging

Merge partial results from distributed workers by summing difference arrays across buckets, then compute prefix sums to get concurrency. For cyclic boundary, merge the first and last buckets carefully to account for wrap-around.

5. Trade-offs and optimizations

Discuss trade-offs: bucket size vs. memory/accuracy, parallelism overhead, streaming latency, and distributed coordination. Mention optimizations like using a Fenwick tree for dynamic updates or approximate algorithms for very large scale.

Key Points to Mention

Bucketed difference arrays reduce memory and enable parallel processing by dividing time into independent chunks.
Cyclic boundary requires special handling: meetings crossing midnight must be split or the difference array must be treated as circular.
Parallelism: buckets can be processed independently, but merging requires synchronization, especially at boundaries.
Streaming: process meetings as they arrive, updating buckets incrementally, but cyclic boundary complicates state management.
Distributed merging: combine difference arrays from workers via summation, then compute prefix sums; cyclic boundary requires merging first and last buckets.
Trade-offs: bucket size affects memory and accuracy; parallelism adds coordination overhead; streaming reduces memory but increases latency.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.