Add a computed field `totalCost` that is `price * quantity` for each item in `lineItems`.

Problem Statement

<p>Add a computed field <code>totalCost</code> that is <code>price * quantity</code> for each item in <code>lineItems</code>.</p>

Examples

Input: lineItems collection: +-----+-----------+-------+----------+ | _id | product | price | quantity | +-----+-----------+-------+----------+ | 1 | Widget | 10 | 5 | | 2 | Gadget | 25 | 2 | | 3 | Doohickey | 15 | 3 | +-----+-----------+-------+----------+

Output: +-----+-----------+-------+----------+-----------+ | _id | product | price | quantity | totalCost | +-----+-----------+-------+----------+-----------+ | 1 | Widget | 10 | 5 | 50 | | 2 | Gadget | 25 | 2 | 50 | | 3 | Doohickey | 15 | 3 | 45 | +-----+-----------+-------+----------+-----------+

Explanation: The query retrieves all documents from the collection that satisfy the specified filter criteria.

Complexity

Time Complexity: -

Space Complexity: -

Hints

💡 Hint 1: Use db.<collection>.aggregate([ ... ]) to run a multi-stage data processing pipeline. 💡 Hint 2: Order pipeline stages logically (e.g. $match early to filter documents before processing). 💡 Hint 3: Convert the aggregation cursor using .toArray() at the end of the function.

Editorial & Approach

Problem Overview & Intuition

To solve "Aggregation: $addFields (Computed Field)", we query the MongoDB document store. The goal is to add a computed field `totalcost` that is `price * quantity` for each item in `lineitems`. 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.lineItems.aggregate([{ $addFields: { totalCost: { $multiply: ["$price", "$quantity"] } } }]);
}

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.

Aggregation: $addFields (Computed Field)

Medium

Add a computed field totalCost that is price * quantity for each item in lineItems.

Example Scenarios

1Example 1

Input:

lineItems collection

_id	product	price	quantity
1	Widget	10	5
2	Gadget	25	2
3	Doohickey	15	3

Output:

_id	product	price	quantity	totalCost
1	Widget	10	5	50
2	Gadget	25	2	50
3	Doohickey	15	3	45

Explanation:

The query retrieves all documents from the collection that satisfy the specified filter criteria.

MongoDB Editor

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Query Results (JSON)

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