Group orders by `customerId`, sum the `total`, and return only customers who spent more than `300`.

Problem Statement

<p>Group <code>orders</code> by <code>customerId</code>, sum the <code>total</code>, and return only <code>customers</code> who spent more than <code>300</code>.</p>

Examples

Input: orders collection: +-----+------------+-------+ | _id | customerId | total | +-----+------------+-------+ | 1 | c1 | 200 | | 2 | c2 | 150 | | 3 | c1 | 200 | | 4 | c3 | 400 | | 5 | c2 | 100 | +-----+------------+-------+

Output: +-----+------------+ | _id | totalSpent | +-----+------------+ | c1 | 400 | | c3 | 400 | +-----+------------+

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 "Match After Group (Having clause equivalent)", we query the MongoDB document store. The goal is to group orders by `customerid`, sum the `total`, and return only customers who spent more than `300`. 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.orders.aggregate([
    { $group: { _id: "$customerId", totalSpent: { $sum: "$total" } } },
    { $match: { totalSpent: { $gt: 300 } } }
  ]);
}

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.

Match After Group (Having clause equivalent)

Medium

Group orders by customerId, sum the total, and return only customers who spent more than 300.

Example Scenarios

1Example 1

Input:

orders collection

_id	customerId	total
1	c1	200
2	c2	150
3	c1	200
4	c3	400
5	c2	100

Output:

_id	totalSpent
c1	400
c3	400

Explanation:

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

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Query Results (JSON)

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