From `courses` with a `ratings` array, unwind ratings, then compute the average, min, and max rating per course.

Problem Statement

<p>From <code>courses</code> with a <code>ratings</code> array, unwind ratings, then compute the average, min, and max <code>rating</code> per course.</p>

Examples

Input: courses collection: +-----+-------------+-----------+ | _id | name | ratings | +-----+-------------+-----------+ | 1 | MongoDB 101 | [4,5,3,4] | | 2 | React Pro | [5,5,4] | | 3 | Node Basics | [3,2,4,3] | +-----+-------------+-----------+

Output: +-------------+-----------+-----------+-------------------+ | _id | minRating | maxRating | avgRating | +-------------+-----------+-----------+-------------------+ | React Pro | 4 | 5 | 4.666666666666667 | | MongoDB 101 | 3 | 5 | 4 | | Node Basics | 2 | 4 | 3 | +-------------+-----------+-----------+-------------------+

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 "Unwind + Group + Multiple Accumulators", we query the MongoDB document store. The goal is to from `courses` with a `ratings` array, unwind ratings, then compute the average, min, and max rating per course. 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.courses.aggregate([
    { $unwind: "$ratings" },
    { $group: { _id: "$name", avgRating: { $avg: "$ratings" }, minRating: { $min: "$ratings" }, maxRating: { $max: "$ratings" } } },
    { $sort: { avgRating: -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.

Unwind + Group + Multiple Accumulators

Hard

From courses with a ratings array, unwind ratings, then compute the average, min, and max rating per course.

Example Scenarios

1Example 1

Input:

courses collection

_id	name	ratings
1	MongoDB 101	[4,5,3,4]
2	React Pro	[5,5,4]
3	Node Basics	[3,2,4,3]

Output:

_id	minRating	maxRating	avgRating
React Pro	4	5	4.666666666666667
MongoDB 101	3	5	4
Node Basics	2	4	3

Explanation:

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

MongoDB Editor

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Query Results (JSON)

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