For each product `category`, calculate the `totalRevenue` (sum of price), `avgPrice` (average price), `cheapest` (min price), and `mostExpensive` (max price).

Problem Statement

<p>For each product <code>category</code>, calculate the <code>totalRevenue</code> (sum of <code>price</code>), <code>avgPrice</code> (average <code>price</code>), <code>cheapest</code> (min <code>price</code>), and <code>mostExpensive</code> (max <code>price</code>).</p>

Examples

Input: products collection: +-----+----------+-------+ | _id | category | price | +-----+----------+-------+ | 1 | A | 10 | | 2 | A | 30 | | 3 | B | 50 | | 4 | A | 20 | | 5 | B | 40 | +-----+----------+-------+

Output: +-----+--------------+----------+---------------+----------+ | _id | totalRevenue | cheapest | mostExpensive | avgPrice | +-----+--------------+----------+---------------+----------+ | A | 60 | 10 | 30 | 20 | | B | 90 | 40 | 50 | 45 | +-----+--------------+----------+---------------+----------+

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 "Multi-Accumulator Group", we query the MongoDB document store. The goal is to for each product `category`, calculate the `totalrevenue` (sum of price), `avgprice` (average price), `cheapest` (min price), and `mostexpensive` (max price). 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",
      totalRevenue: { $sum: "$price" },
      avgPrice: { $avg: "$price" },
      cheapest: { $min: "$price" },
      mostExpensive: { $max: "$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.

Multi-Accumulator Group

Hard

For each product category, calculate the totalRevenue (sum of price), avgPrice (average price), cheapest (min price), and mostExpensive (max price).

Example Scenarios

1Example 1

Input:

products collection

_id	category	price
1	A	10
2	A	30
3	B	50
4	A	20
5	B	40

Output:

_id	totalRevenue	cheapest	mostExpensive	avgPrice
A	60	10	30	20
B	90	40	50	45

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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