Add a `salePrice` field that is `price * 0.85` (15% discount) to all products, then sort by salePrice ascending and limit to 3.

Problem Statement

<p>Add a <code>salePrice</code> field that is <code>price * 0.85</code> (15% discount) to all <code>products</code>, then sort by salePrice ascending and limit to 3.</p>

Examples

Input: products collection: +-----+------+-------+ | _id | name | price | +-----+------+-------+ | 1 | A | 100 | | 2 | B | 200 | | 3 | C | 50 | | 4 | D | 150 | +-----+------+-------+

Output: +-----+------+-------+-----------+ | _id | name | price | salePrice | +-----+------+-------+-----------+ | 3 | C | 50 | 42.5 | | 1 | A | 100 | 85 | | 4 | D | 150 | 127.5 | +-----+------+-------+-----------+

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

Complexity

Time Complexity: -

Space Complexity: -

Hints

💡 Hint 1: Use db.<collection>.aggregate([ ... ]) to run a multi-stage data processing pipeline. 💡 Hint 2: Order pipeline stages logically (e.g. $match early to filter documents before processing). 💡 Hint 3: Convert the aggregation cursor using .toArray() at the end of the function.

Editorial & Approach

Problem Overview & Intuition

To solve "Product Catalog: Price Markup", we query the MongoDB document store. The goal is to add a `saleprice` field that is `price * 0.85` (15% discount) to all products, then sort by saleprice ascending and limit to 3. 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.products.aggregate([
    { $addFields: { salePrice: { $multiply: ["$price", 0.85] } } },
    { $sort: { salePrice: 1 } },
    { $limit: 3 }
  ]);
}

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.

Product Catalog: Price Markup

Hard

Add a salePrice field that is price * 0.85 (15% discount) to all products, then sort by salePrice ascending and limit to 3.

Example Scenarios

1Example 1

Input:

products collection

_id	name	price
1	A	100
2	B	200
3	C	50
4	D	150

Output:

_id	name	price	salePrice
3	C	50	42.5
1	A	100	85
4	D	150	127.5

Explanation:

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

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Query Results (JSON)

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