Project only the `name` and `price` fields (exclude `_id`) from the `menu` collection.

Problem Statement

<p>Project only the <code>name</code> and <code>price</code> fields (exclude <code>_id</code>) from the <code>menu</code> collection.</p>

Examples

Input: menu collection: +-----+--------+-------+----------+ | _id | name | price | calories | +-----+--------+-------+----------+ | 1 | Burger | 10 | 500 | | 2 | Salad | 8 | 200 | | 3 | Fries | 5 | 400 | +-----+--------+-------+----------+

Output: +--------+-------+ | name | price | +--------+-------+ | Burger | 10 | | Salad | 8 | | Fries | 5 | +--------+-------+

Explanation: The query projects only the requested document fields matching the criteria.

Complexity

Time Complexity: -

Space Complexity: -

Hints

💡 Hint 1: Use the $project stage in an aggregation pipeline to include, exclude, or compute new document fields. 💡 Hint 2: Set fields to 1 (include) or 0 (exclude), and use expression operators to reshape data. 💡 Hint 3: Finalize the pipeline and execute with .toArray() to return the structured document array.

Editorial & Approach

Problem Overview & Intuition

To solve "Aggregation: $project Stage", we query the MongoDB document store. The goal is to project only the `name` and `price` fields (exclude `_id`) from the `menu` collection. 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.menu.aggregate([{ $project: { _id: 0, name: 1, price: 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.

Aggregation: $project Stage

Medium

Project only the name and price fields (exclude _id) from the menu collection.

Example Scenarios

1Example 1

Input:

menu collection

_id	name	price	calories
1	Burger	10	500
2	Salad	8	200
3	Fries	5	400

Output:

name	price
Burger	10
Salad	8
Fries	5

Explanation:

The query projects only the requested document fields matching the criteria.

MongoDB Editor

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Query Results (JSON)

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