Group all subscriptions by `status` and count how many are in each group. Sort by count descending.

Problem Statement

<p>Group all subscriptions by <code>status</code> and count how many are in each group. Sort by count descending.</p>

Examples

Input: subscriptions collection: +-----+-----------+ | _id | status | +-----+-----------+ | 1 | active | | 2 | active | | 3 | cancelled | | 4 | active | | 5 | cancelled | | 6 | trial | +-----+-----------+

Output: +-----------+-------+ | _id | count | +-----------+-------+ | active | 3 | | cancelled | 2 | | trial | 1 | +-----------+-------+

Explanation: The query calculates the total count of documents that satisfy the given filter criteria.

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 "Churn Analysis: Active vs Cancelled Count", we query the MongoDB document store. The goal is to group all subscriptions by `status` and count how many are in each group. sort by count 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 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.subscriptions.aggregate([
    { $group: { _id: "$status", count: { $sum: 1 } } },
    { $sort: { count: -1 } }
  ]);
}

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.

Churn Analysis: Active vs Cancelled Count

Hard

Group all subscriptions by status and count how many are in each group. Sort by count descending.

Example Scenarios

1Example 1

Input:

subscriptions collection

_id	status
1	active
2	active
3	cancelled
4	active
5	cancelled
6	trial

Output:

_id	count
active	3
cancelled	2
trial	1

Explanation:

The query calculates the total count of documents that satisfy the given filter criteria.

MongoDB Editor

Loading Editor...

Query Results (JSON)

Run your MongoDB code to see results here.