Google·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Google SWE interview with a graph traversal problem that had a time-constraint twist. The core challenge was modeling flights as directed edges and doing BFS/DFS while respecting departure/arrival ordering, which sounds straightforward until you're actually in the room.

Questions Asked (1)

Given a list of flights (origin, destination, departure time, arrival time), a start airport, an end airport, and an optional earliest start time, determine if you can travel from start to end using a valid sequence of flights where each flight departs no earlier than the previous flight's arrival time.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I started with plain BFS and forgot the time constraint for the first few minutes, which was embarrassing.

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AI HintsAI Generated

Suggested Approach

Model the problem as a graph where airports are nodes and flights are directed edges with time constraints. Use a modified BFS or Dijkstra-like algorithm to find the earliest arrival time at each airport, ensuring each flight departs after the previous arrival. Return true if the end airport is reachable with a valid sequence.

Pro tip: Clarify edge cases upfront, such as multiple flights between the same airports, flights with same departure and arrival times, and whether the earliest start time is inclusive. Discussing these shows attention to detail and prevents incorrect assumptions.

1. Clarify requirements and constraints

Ask about input size, whether flights can be taken multiple times, if times are in a consistent format, and if the earliest start time is inclusive. Confirm the goal is to determine reachability, not to find the shortest path.

2. Model as a graph problem

Represent airports as nodes and flights as directed edges with departure and arrival times. The problem reduces to finding a path from start to end where each edge's departure time is >= the previous edge's arrival time (and >= earliest start time for the first flight).

3. Choose an algorithm

Use a priority queue (min-heap) to always process the airport with the earliest known arrival time, similar to Dijkstra. Alternatively, sort flights by departure time and use BFS with time tracking. The key is to track the earliest time you can be at each airport.

4. Implement and handle edge cases

Initialize the start airport with the earliest start time. For each flight from the current airport, if its departure time >= current time, update the arrival time at the destination if it's earlier than previously recorded. Handle cases where start equals end, no flights exist, or times are equal.

5. Analyze complexity and trade-offs

Discuss time complexity: O(E log V) with Dijkstra-like approach, where E is number of flights and V is number of airports. Space complexity O(V + E). Mention that sorting flights by departure time can optimize but may not be necessary.

Key Points to Mention

Graph modeling: airports as nodes, flights as time-constrained directed edges.
Use of priority queue (min-heap) to efficiently find earliest arrival times.
Time complexity analysis: O(E log V) with heap, O(E + V) if flights are pre-sorted by departure time.
Handling of earliest start time: initialize start node with this time, and ensure first flight departs no earlier.
Edge cases: start equals end, no path exists, multiple flights between same airports, and flights with zero layover.
Comparison with standard shortest path: here we minimize arrival time, not distance, and constraints are on departure times.

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