Create a leaderboard: group `scores` by `playerId`, sum their points, sort descending, and return only the top 3.

Problem Statement

<p>Create a leaderboard: group <code>scores</code> by <code>playerId</code>, sum their points, sort descending, and return only the top 3.</p>

Examples

Input: scores collection: +-----+----------+--------+ | _id | playerId | points | +-----+----------+--------+ | 1 | p1 | 100 | | 2 | p2 | 200 | | 3 | p1 | 150 | | 4 | p3 | 300 | | 5 | p4 | 50 | | 6 | p2 | 100 | +-----+----------+--------+

Output: +-----+-------------+ | _id | totalPoints | +-----+-------------+ | p2 | 300 | | p3 | 300 | | p1 | 250 | +-----+-------------+

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 "Group + Sort + Limit (Leaderboard)", we query the MongoDB document store. The goal is to create a leaderboard: group `scores` by `playerid`, sum their points, sort descending, and return only the top 3. 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.scores.aggregate([
    { $group: { _id: "$playerId", totalPoints: { $sum: "$points" } } },
    { $sort: { totalPoints: -1 } },
    { $limit: 3 }
  ]);
}

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.

Group + Sort + Limit (Leaderboard)

Medium

Create a leaderboard: group scores by playerId, sum their points, sort descending, and return only the top 3.

Example Scenarios

1Example 1

Input:

scores collection

_id	playerId	points
1	p1	100
2	p2	200
3	p1	150
4	p3	300
5	p4	50
6	p2	100

Output:

_id	totalPoints
p2	300
p3	300
p1	250

Explanation:

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

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Query Results (JSON)

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