Increase the price of all products in the `"Electronics"` category by `10`. Return all Electronics products sorted by price.

Problem Statement

<p>Increase the <code>price</code> of all <code>products</code> in the <code>"Electronics"</code> <code>category</code> by <code>10</code>. Return all Electronics <code>products</code> sorted by <code>price</code>.</p>

Examples

Input: products collection: +-----+----------+-------------+-------+ | _id | name | category | price | +-----+----------+-------------+-------+ | 1 | Mouse | Electronics | 25 | | 2 | Desk | Furniture | 150 | | 3 | Keyboard | Electronics | 75 | | 4 | Monitor | Electronics | 300 | +-----+----------+-------------+-------+

Output: +-----+----------+-------------+-------+ | _id | name | category | price | +-----+----------+-------------+-------+ | 1 | Mouse | Electronics | 35 | | 3 | Keyboard | Electronics | 85 | | 4 | Monitor | Electronics | 310 | +-----+----------+-------------+-------+

Explanation: The target document is modified according to the update operators and the updated document is returned.

Complexity

Time Complexity: -

Space Complexity: -

Hints

💡 Hint 1: Filter documents where a field matches any value in a list using the $in or $nin operator. 💡 Hint 2: Pass the query object to db.<collection>.find({ field: { $in: [val1, val2, ...] } }). 💡 Hint 3: Call .toArray() on the returned cursor so the function returns the array of matching documents.

Editorial & Approach

Problem Overview & Intuition

To solve "Batch Price Increase", we query the MongoDB document store. The goal is to increase the price of all products in the `"electronics"` category by `10`. return all electronics products sorted by price. Using a targeted find query, 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 query filter with appropriate comparison operators.
Resolve Cursor: Invoke .toArray() to transform the query cursor into the required array of documents.

Optimal Implementation (MongoDB)

function solve(db) {
  db.products.updateMany({ category: "Electronics" }, { $inc: { price: 10 } });
  return db.products.find({ category: "Electronics" }).sort({ price: 1 }).toArray();
}

Complexity Analysis

Time Complexity O(N) collection scan (O(log N) if index is present on filtered fields).

Space Complexity O(K) where K is the number of returned documents in memory.

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.

Batch Price Increase

Hard

Increase the price of all products in the "Electronics" category by 10. Return all Electronics products sorted by price.

Example Scenarios

1Example 1

Input:

products collection

_id	name	category	price
1	Mouse	Electronics	25
2	Desk	Furniture	150
3	Keyboard	Electronics	75
4	Monitor	Electronics	300

Output:

_id	name	category	price
1	Mouse	Electronics	35
3	Keyboard	Electronics	85
4	Monitor	Electronics	310

Explanation:

The target document is modified according to the update operators and the updated document is returned.

MongoDB Editor

Loading Editor...

Query Results (JSON)

Run your MongoDB code to see results here.