Unwind the `skills` array from `developers` collection, then count how many developers have each skill.

Problem Statement

<p>Unwind the <code>skills</code> array from <code>developers</code> collection, then count how many developers have each skill.</p>

Examples

Input: developers collection: +-----+---------+----------------------+ | _id | name | skills | +-----+---------+----------------------+ | 1 | Alice | ["JS","Python"] | | 2 | Bob | ["JS","Go"] | | 3 | Charlie | ["Python","Go","JS"] | +-----+---------+----------------------+

Output: +--------+-------+ | _id | count | +--------+-------+ | JS | 3 | | Python | 2 | | Go | 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 "Unwind and Count Tags", we query the MongoDB document store. The goal is to unwind the `skills` array from `developers` collection, then count how many developers have each skill. 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.developers.aggregate([
    { $unwind: "$skills" },
    { $group: { _id: "$skills", count: { $sum: 1 } } },
    { $sort: { count: -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 and Count Tags

Medium

Unwind the skills array from developers collection, then count how many developers have each skill.

Example Scenarios

1Example 1

Input:

developers collection

_id	name	skills
1	Alice	["JS","Python"]
2	Bob	["JS","Go"]
3	Charlie	["Python","Go","JS"]

Output:

_id	count
JS	3
Python	2
Go	2

Explanation:

The query calculates the total count of documents that satisfy the given filter criteria.

MongoDB Editor

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Query Results (JSON)

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