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

Intermediate

Apr 2026

Summary

Google SWE coding round with a scheduling/greedy problem that looks straightforward but has a sneaky output requirement. The assignment tracking part is what gets you.

Questions Asked (1)

Given N car rental requests each with a pickup and return time, find the minimum number of cars needed to serve all requests without overlap (where return_time == next pickup_time is allowed on the same car). Also produce a valid assignment and store each request in the corresponding car's rental record, sorted chronologically.

Algorithms & Data StructuresSystem Design

Author's notes

The minimum cars part I got pretty fast, it's basically an interval scheduling problem with a min-heap tracking when each car becomes free.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Model each rental request as an interval [pickup, return]. The minimum number of cars equals the maximum number of overlapping intervals, which can be found by sorting events (pickups as +1, returns as -1) and sweeping through them. For assignment, use a min-heap of available cars keyed by their next available time, and for each request, reuse a car if its available time <= pickup time, otherwise allocate a new car.

Pro tip: Clarify that return_time == next pickup_time is allowed, so when checking availability, use <= instead of <. Also, mention that the greedy assignment is optimal because it minimizes the number of cars by always reusing the earliest available car.

1. Understand the problem and edge cases

Restate the problem: given N intervals, find the minimum number of cars to cover all intervals without overlap, allowing back-to-back rentals. Clarify that each request must be assigned to exactly one car, and the assignment must be valid and sorted chronologically per car.

2. Determine the minimum number of cars

Use a sweep-line algorithm: create events for each pickup (+1) and return (-1), sort them by time (with returns before pickups if times equal, to allow same-time reuse), and track the maximum concurrent rentals. This maximum is the minimum number of cars needed.

3. Assign requests to cars

Sort requests by pickup time. Use a min-heap to track cars by their next available time. For each request, if the earliest available car is free (available_time <= pickup_time), assign it and update its available time to return_time; otherwise, create a new car and add it to the heap.

4. Store and sort rental records per car

Maintain a list of rental records for each car. After assignment, sort each car's records by pickup time (or return time) to ensure chronological order. Return the number of cars and the assignments.

5. Analyze complexity and discuss optimizations

Time complexity: O(N log N) due to sorting and heap operations. Space complexity: O(N) for storing events and assignments. Mention that the sweep-line and heap approach is optimal and can be implemented in a single pass after sorting.

Key Points to Mention

Interval scheduling and the sweep-line algorithm for counting maximum overlaps.
Greedy assignment using a min-heap to reuse cars efficiently.
Handling of edge case where return_time equals next pickup_time (use <=).
Sorting events with returns before pickups when times are equal to allow immediate reuse.
Time and space complexity analysis: O(N log N) time, O(N) space.
Proof of optimality: the greedy assignment minimizes the number of cars because it always reuses the earliest available car.

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