Group sales by `region` and calculate the total `revenue` for each region.

Problem Statement

<p>Group <code>sales</code> by <code>region</code> and calculate the total <code>revenue</code> for each region.</p>

Examples

Input: sales collection: +-----+--------+---------+ | _id | region | revenue | +-----+--------+---------+ | 1 | North | 1000 | | 2 | South | 1500 | | 3 | North | 2000 | | 4 | South | 800 | +-----+--------+---------+

Output: +-------+--------------+ | _id | totalRevenue | +-------+--------------+ | North | 3000 | | South | 2300 | +-------+--------------+

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 Sum ($group + $sum)", we query the MongoDB document store. The goal is to group sales by `region` and calculate the total `revenue` for each region. 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.sales.aggregate([
    { $group: { _id: "$region", totalRevenue: { $sum: "$revenue" } } }
  ]);
}

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 Sum ($group + $sum)

Medium

Group sales by region and calculate the total revenue for each region.

Example Scenarios

1Example 1

Input:

sales collection

_id	region	revenue
1	North	1000
2	South	1500
3	North	2000
4	South	800

Output:

_id	totalRevenue
North	3000
South	2300

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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