Flights Tracking: Track and Query Real-Time Flight Status Updates
Interview Experience
Round 1 Coding
Problem
Implement a flight tracking system. Flights have a status that updates over time. Support querying all flights by origin, destination, or status. Also return any flights currently delayed.
python
class FlightTracker:
def add_flight(self, flight_id: str, origin: str, dest: str,
departure: str, arrival: str) -> None:
...
def update_status(self, flight_id: str, status: str,
delay_minutes: int = 0) -> None:
# status: "on_time", "delayed", "landed", "cancelled"
...
def get_delayed(self) -> list[str]:
**returns** flight_ids
...
def flights_from(self, origin: str) -> list[dict]:
...
def flights_to(self, dest: str) -> list[dict]:
...
Example
tracker = FlightTracker()
tracker.add_flight("AA100", "JFK", "LAX", "08:00", "11:30")
tracker.add_flight("UA200", "ORD", "LAX", "09:00", "12:00")
tracker.update_status("AA100", "delayed", 45)
tracker.get_delayed() -> ["AA100"]
tracker.flights_to("LAX") -> [{"id":"AA100",...},{"id":"UA200",...}]
Follow-ups
- How do you efficiently query all flights originating from a given airport if you have 10,000 flights?
- If a flight status changes multiple times, how do you maintain a history of state transitions?
- How would you alert subscribers when a specific flight's status changes?
- Design the schema to store this in a relational database. Which columns would you index?
Full Details
Round 1 Coding
Problem
Implement a flight tracking system. Flights have a status that updates over time. Support querying all flights by origin, destination, or status. Also return any flights currently delayed.
python
class FlightTracker:
def add_flight(self, flight_id: str, origin: str, dest: str,
departure: str, arrival: str) -> None:
...
def update_status(self, flight_id: str, status: str,
delay_minutes: int = 0) -> None:
# status: "on_time", "delayed", "landed", "cancelled"
...
def get_delayed(self) -> list[str]:
**returns** flight_ids
...
def flights_from(self, origin: str) -> list[dict]:
...
def flights_to(self, dest: str) -> list[dict]:
...
Example
tracker = FlightTracker()
tracker.add_flight("AA100", "JFK", "LAX", "08:00", "11:30")
tracker.add_flight("UA200", "ORD", "LAX", "09:00", "12:00")
tracker.update_status("AA100", "delayed", 45)
tracker.get_delayed() -> ["AA100"]
tracker.flights_to("LAX") -> [{"id":"AA100",...},{"id":"UA200",...}]
Follow-ups
- How do you efficiently query all flights originating from a given airport if you have 10,000 flights?
- If a flight status changes multiple times, how do you maintain a history of state transitions?
- How would you alert subscribers when a specific flight's status changes?
- Design the schema to store this in a relational database. Which columns would you index?
Topics
About Ramp Interview Reports
This question was reported by a candidate who interviewed at Ramp. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.
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How To Practice This Type of Question
Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.
Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Ramp reports consistently are the ones worth investing in; one-off niche problems are not.
During Your Ramp Round
Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.
The single most predictive failure mode in Ramp reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.