Busiest Airport by Total Flight Traffic

Medium

An aviation operations analytics platform stores route-level movement data where each row captures how many flights traveled from one airport to another. Table: flights departure_airport: airport ID where flights originate arrival_airport: airport ID where flights terminate flights_count: number of flights observed on that directed route Traffic definition: For each airport_id: total_traffic = total departures + total arrivals Task: Return the airport IDs that have the maximum total_traffic. Output requirements: Return exactly one column: airport_id If multiple airports share the same maximum total_traffic, return all of them Sort by airport_id in ascending order Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

flights:
| departure_airport | arrival_airport | flights_count |
|-------------------|-----------------|---------------|
| 1                 | 2               | 4             |
| 2                 | 1               | 5             |
| 2                 | 4               | 5             |

Output

[
  {"airport_id":2}
]

Explanation

Airport 2 has total traffic 14 (10 departures + 4 arrivals), higher than airport 1 (9) and airport 4 (5).

Example 2

Input

flights:
| departure_airport | arrival_airport | flights_count |
|-------------------|-----------------|---------------|
| 1                 | 2               | 4             |
| 2                 | 1               | 5             |
| 3                 | 4               | 5             |
| 4                 | 3               | 4             |
| 5                 | 6               | 7             |

Output

[
  {"airport_id":1},
  {"airport_id":2},
  {"airport_id":3},
  {"airport_id":4}
]

Explanation

Airports 1, 2, 3, and 4 all reach traffic 9, while airports 5 and 6 each have traffic 7.

Example 3

Input

flights:
| departure_airport | arrival_airport | flights_count |
|-------------------|-----------------|---------------|
| 10                | 11              | 15            |
| 12                | 10              | 8             |
| 13                | 10              | 7             |
| 14                | 15              | 9             |
| 15                | 14              | 9             |

Output

[
  {"airport_id":10}
]

Explanation

Airport 10 sends 15 flights (to airport 11) and receives 15 flights (8 from airport 12 and 7 from airport 13), so its total traffic is 30, which is the maximum.

Constraints

0 <= number of rows in flights <= 200000
1 <= departure_airport, arrival_airport <= 1000000000
1 <= flights_count <= 1000000
(departure_airport, arrival_airport) pairs are unique
departure_airport != arrival_airport
departure_airport and arrival_airport represent valid airport IDs
Return exactly one column: airport_id
Return all airports tied at maximum total traffic
Sort by airport_id ascending
If flights is empty, return an empty result
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with one column: airport_id

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Busiest Airport by Total Flight Traffic

Medium

An aviation operations analytics platform stores route-level movement data where each row captures how many flights traveled from one airport to another. Table: flights departure_airport: airport ID where flights originate arrival_airport: airport ID where flights terminate flights_count: number of flights observed on that directed route Traffic definition: For each airport_id: total_traffic = total departures + total arrivals Task: Return the airport IDs that have the maximum total_traffic. Output requirements: Return exactly one column: airport_id If multiple airports share the same maximum total_traffic, return all of them Sort by airport_id in ascending order Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

flights:
| departure_airport | arrival_airport | flights_count |
|-------------------|-----------------|---------------|
| 1                 | 2               | 4             |
| 2                 | 1               | 5             |
| 2                 | 4               | 5             |

Output

[
  {"airport_id":2}
]

Explanation

Airport 2 has total traffic 14 (10 departures + 4 arrivals), higher than airport 1 (9) and airport 4 (5).

Example 2

Input

flights:
| departure_airport | arrival_airport | flights_count |
|-------------------|-----------------|---------------|
| 1                 | 2               | 4             |
| 2                 | 1               | 5             |
| 3                 | 4               | 5             |
| 4                 | 3               | 4             |
| 5                 | 6               | 7             |

Output

[
  {"airport_id":1},
  {"airport_id":2},
  {"airport_id":3},
  {"airport_id":4}
]

Explanation

Airports 1, 2, 3, and 4 all reach traffic 9, while airports 5 and 6 each have traffic 7.

Example 3

Input

flights:
| departure_airport | arrival_airport | flights_count |
|-------------------|-----------------|---------------|
| 10                | 11              | 15            |
| 12                | 10              | 8             |
| 13                | 10              | 7             |
| 14                | 15              | 9             |
| 15                | 14              | 9             |

Output

[
  {"airport_id":10}
]

Explanation

Airport 10 sends 15 flights (to airport 11) and receives 15 flights (8 from airport 12 and 7 from airport 13), so its total traffic is 30, which is the maximum.

Constraints

0 <= number of rows in flights <= 200000
1 <= departure_airport, arrival_airport <= 1000000000
1 <= flights_count <= 1000000
(departure_airport, arrival_airport) pairs are unique
departure_airport != arrival_airport
departure_airport and arrival_airport represent valid airport IDs
Return exactly one column: airport_id
Return all airports tied at maximum total traffic
Sort by airport_id ascending
If flights is empty, return an empty result
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with one column: airport_id