Count patients per `department`, sort by count descending, and project `_id` as `department` and show `patientCount`.

Problem Statement

<p>Count <code>patients</code> per <code>department</code>, sort by count descending, and project <code>_id</code> as <code>department</code> and show <code>patientCount</code>.</p>

Examples

Input: patients collection: +-----+------+------------+ | _id | name | department | +-----+------+------------+ | 1 | A | Cardiology | | 2 | B | Neurology | | 3 | C | Cardiology | | 4 | D | Cardiology | | 5 | E | Neurology | +-----+------+------------+

Output: +--------------+ | patientCount | +--------------+ | 3 | | 2 | +--------------+

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 "Hospital: Count Patients Per Department", we query the MongoDB document store. The goal is to count patients per `department`, sort by count descending, and project `_id` as `department` and show `patientcount`. 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.patients.aggregate([
    { $group: { _id: "$department", patientCount: { $sum: 1 } } },
    { $sort: { patientCount: -1 } },
    { $project: { _id: 0, department: 1, patientCount: 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.

Hospital: Count Patients Per Department

Hard

Count patients per department, sort by count descending, and project _id as department and show patientCount.

Example Scenarios

1Example 1

Input:

patients collection

_id	name	department
1	A	Cardiology
2	B	Neurology
3	C	Cardiology
4	D	Cardiology
5	E	Neurology

Output:

patientCount
3
2

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.