Find the cheapest product price per `category` from the `products` collection.

Problem Statement

<p>Find the cheapest product <code>price</code> per <code>category</code> from the <code>products</code> collection.</p>

Examples

Input: products collection: +-----+--------+-------------+-------+ | _id | name | category | price | +-----+--------+-------------+-------+ | 1 | Laptop | Electronics | 999 | | 2 | Phone | Electronics | 699 | | 3 | Desk | Furniture | 250 | | 4 | Chair | Furniture | 150 | +-----+--------+-------------+-------+

Output: +-------------+----------+ | _id | cheapest | +-------------+----------+ | Electronics | 699 | | Furniture | 150 | +-------------+----------+

Explanation: The aggregation pipeline processes the documents through stages to compute the grouped results.

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 "Group and Min ($group + $min)", we query the MongoDB document store. The goal is to find the cheapest product price per `category` from the `products` 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.products.aggregate([
    { $group: { _id: "$category", cheapest: { $min: "$price" } } }
  ]);
}

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.

Group and Min ($group + $min)

Medium

Find the cheapest product price per category from the products collection.

Example Scenarios

1Example 1

Input:

products collection

_id	name	category	price
1	Laptop	Electronics	999
2	Phone	Electronics	699
3	Desk	Furniture	250
4	Chair	Furniture	150

Output:

_id	cheapest
Electronics	699
Furniture	150

Explanation:

The aggregation pipeline processes the documents through stages to compute the grouped results.

MongoDB Editor

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Query Results (JSON)

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