Sort all flights by `departureTime` ascending using the aggregation pipeline.
Problem Statement
Examples
Input: flights collection: +-----+---------+---------------+ | _id | route | departureTime | +-----+---------+---------------+ | 1 | NYC-LAX | 1400 | | 2 | NYC-SFO | 800 | | 3 | NYC-CHI | 1100 | +-----+---------+---------------+
Output: +-----+---------+---------------+ | _id | route | departureTime | +-----+---------+---------------+ | 2 | NYC-SFO | 800 | | 3 | NYC-CHI | 1100 | | 1 | NYC-LAX | 1400 | +-----+---------+---------------+
Explanation: The collection documents are sorted according to the specified field order.
Complexity
Time Complexity: -
Space Complexity: -
Hints
Editorial & Approach
Problem Overview & Intuition
To solve "Aggregation: $sort Stage", we query the MongoDB document store. The goal is to sort all flights by `departuretime` ascending using the aggregation pipeline. Using an aggregation pipeline, the database engine filters and structures the BSON documents efficiently.
Step-by-Step Approach
- Identify Target Collection: Access the collection through the
dbinstance. - Construct Query / Pipeline: Build the aggregation stages ($match, $group, $sort, etc.).
- Resolve Cursor: Invoke
.toArray()to transform the query cursor into the required array of documents.
Optimal Implementation (MongoDB)
function solve(db) {
return db.flights.aggregate([{ $sort: { departureTime: 1 } }]);
}Complexity Analysis
Key Considerations & Edge Cases
- Empty Collections: If no documents match, the query cleanly returns an empty array
[]. - Missing / NULL Fields: Missing fields in documents are handled safely without throwing runtime exceptions.
- Type Coercion: BSON types (ObjectId, Numbers, Strings) are compared strictly according to MongoDB specifications.