Group `vehicles` by `type`, compute average `mileage`, and filter only types with average mileage above `50000`.

Problem Statement

<p>Group <code>vehicles</code> by <code>type</code>, compute average <code>mileage</code>, and filter only types with average mileage above <code>50000</code>.</p>

Examples

Input: vehicles collection: +-----+-------+---------+ | _id | type | mileage | +-----+-------+---------+ | 1 | truck | 80000 | | 2 | sedan | 30000 | | 3 | truck | 60000 | | 4 | sedan | 45000 | | 5 | van | 70000 | +-----+-------+---------+

Output: +-------+------------+ | _id | avgMileage | +-------+------------+ | truck | 70000 | | van | 70000 | +-------+------------+

Explanation: The query retrieves all documents from the collection that satisfy the specified filter criteria.

Complexity

Time Complexity: -

Space Complexity: -

Hints

💡 Hint 1: This problem requires grouping documents by a key and aggregating values. Use db.<collection>.aggregate([...]) with a $group stage. 💡 Hint 2: In the $group stage, set _id to the grouping field (e.g. "$category" or "$dept") and use accumulator operators like $sum, $avg, $max, or $min. 💡 Hint 3: Remember to call .toArray() at the end of the aggregation pipeline to return the resolved documents array.

Editorial & Approach

Problem Overview & Intuition

To solve "Fleet Management: Average Mileage Per Vehicle Type", we query the MongoDB document store. The goal is to group `vehicles` by `type`, compute average `mileage`, and filter only types with average mileage above `50000`. 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 db instance.
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.vehicles.aggregate([
    { $group: { _id: "$type", avgMileage: { $avg: "$mileage" } } },
    { $match: { avgMileage: { $gt: 50000 } } },
    { $sort: { avgMileage: -1 } }
  ]);
}

Complexity Analysis

Time Complexity O(N) pipeline traversal through aggregation stages.

Space Complexity O(M) intermediate document buffer in aggregation pipeline.

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.

Fleet Management: Average Mileage Per Vehicle Type

Hard

Group vehicles by type, compute average mileage, and filter only types with average mileage above 50000.

Example Scenarios

1Example 1

Input:

vehicles collection

_id	type	mileage
1	truck	80000
2	sedan	30000
3	truck	60000
4	sedan	45000
5	van	70000

Output:

_id	avgMileage
truck	70000
van	70000

Explanation:

The query retrieves all documents from the collection that satisfy the specified filter criteria.

MongoDB Editor

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Query Results (JSON)

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