Find the highest score per `subject` from the `exams` collection.

Problem Statement

<p>Find the highest score per <code>subject</code> from the <code>exams</code> collection.</p>

Examples

Input: exams collection: +-----+---------+---------+-------+ | _id | student | subject | score | +-----+---------+---------+-------+ | 1 | Alice | Math | 92 | | 2 | Bob | Math | 88 | | 3 | Alice | Science | 95 | | 4 | Bob | Science | 91 | +-----+---------+---------+-------+

Output: +---------+--------------+ | _id | highestScore | +---------+--------------+ | Math | 92 | | Science | 95 | +---------+--------------+

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 and Max ($group + $max)", we query the MongoDB document store. The goal is to find the highest score per `subject` from the `exams` collection. 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.exams.aggregate([
    { $group: { _id: "$subject", highestScore: { $max: "$score" } } }
  ]);
}

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 and Max ($group + $max)

Medium

Find the highest score per subject from the exams collection.

Example Scenarios

1Example 1

Input:

exams collection

_id	student	subject	score
1	Alice	Math	92
2	Bob	Math	88
3	Alice	Science	95
4	Bob	Science	91

Output:

_id	highestScore
Math	92
Science	95

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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