From the `articles` collection, unwind `tags`, group by each tag to count occurrences, and return the top 3 most popular tags.

Problem Statement

<p>From the <code>articles</code> collection, unwind <code>tags</code>, group by each tag to count occurrences, and return the top 3 most popular <code>tags</code>.</p>

Examples

Input: articles collection: +-----+-------+-----------------------------------+ | _id | title | tags | +-----+-------+-----------------------------------+ | 1 | A | ["mongodb","database","nosql"] | | 2 | B | ["mongodb","tutorial"] | | 3 | C | ["nosql","database"] | | 4 | D | ["mongodb","nosql","performance"] | +-----+-------+-----------------------------------+

Output: +----------+-------+ | _id | count | +----------+-------+ | mongodb | 3 | | nosql | 3 | | database | 2 | +----------+-------+

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 + Sort (Tag Popularity)", we query the MongoDB document store. The goal is to from the `articles` collection, unwind `tags`, group by each tag to count occurrences, and return the top 3 most popular tags. 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.articles.aggregate([
    { $unwind: "$tags" },
    { $group: { _id: "$tags", count: { $sum: 1 } } },
    { $sort: { count: -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.

Unwind + Group + Sort (Tag Popularity)

Hard

From the articles collection, unwind tags, group by each tag to count occurrences, and return the top 3 most popular tags.

Example Scenarios

1Example 1

Input:

articles collection

_id	title	tags
1	A	["mongodb","database","nosql"]
2	B	["mongodb","tutorial"]
3	C	["nosql","database"]
4	D	["mongodb","nosql","performance"]

Output:

_id	count
mongodb	3
nosql	3
database	2

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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