Group active subscriptions by `plan`, calculate total revenue, and sort by revenue descending.
Problem Statement
Examples
Input: subscriptions collection: +-----+------------+-------+-----------+ | _id | plan | price | status | +-----+------------+-------+-----------+ | 1 | Starter | 29 | active | | 2 | Pro | 99 | active | | 3 | Pro | 99 | active | | 4 | Starter | 29 | cancelled | | 5 | Enterprise | 499 | active | +-----+------------+-------+-----------+
Output: +------------+--------------+ | _id | totalRevenue | +------------+--------------+ | Enterprise | 499 | | Pro | 198 | | Starter | 29 | +------------+--------------+
Explanation: The query retrieves all documents from the collection that satisfy the specified filter criteria.
Complexity
Time Complexity: -
Space Complexity: -
Hints
Editorial & Approach
Problem Overview & Intuition
To solve "Revenue Per Plan Tier", we query the MongoDB document store. The goal is to group active subscriptions by `plan`, calculate total revenue, and sort by revenue descending. 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
dbinstance. - 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.subscriptions.aggregate([
{ $match: { status: "active" } },
{ $group: { _id: "$plan", totalRevenue: { $sum: "$price" } } },
{ $sort: { totalRevenue: -1 } }
]);
}
Complexity Analysis
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.