Calculate the average salary per department and return only departments where the average salary exceeds `70000`.

Problem Statement

<p>Calculate the average <code>salary</code> per department and return only departments where the average <code>salary</code> exceeds <code>70000</code>.</p>

Examples

Input: employees collection: +-----+---------+-------------+--------+ | _id | name | dept | salary | +-----+---------+-------------+--------+ | 1 | Alice | Engineering | 120000 | | 2 | Bob | Support | 45000 | | 3 | Charlie | Engineering | 95000 | | 4 | Dana | Support | 50000 | | 5 | Eve | Sales | 80000 | +-----+---------+-------------+--------+

Output: +-------------+-----------+ | _id | avgSalary | +-------------+-----------+ | Engineering | 107500 | | Sales | 80000 | +-------------+-----------+

Explanation: The query retrieves all documents from the collection that satisfy the specified 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 "HR: Departments With Average Salary", we query the MongoDB document store. The goal is to calculate the average salary per department and return only departments where the average salary exceeds `70000`. 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.employees.aggregate([
    { $group: { _id: "$dept", avgSalary: { $avg: "$salary" } } },
    { $match: { avgSalary: { $gt: 70000 } } },
    { $sort: { avgSalary: -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.

HR: Departments With Average Salary

Medium

Calculate the average salary per department and return only departments where the average salary exceeds 70000.

Example Scenarios

1Example 1

Input:

employees collection

_id	name	dept	salary
1	Alice	Engineering	120000
2	Bob	Support	45000
3	Charlie	Engineering	95000
4	Dana	Support	50000
5	Eve	Sales	80000

Output:

_id	avgSalary
Engineering	107500
Sales	80000

Explanation:

The query retrieves all documents from the collection that satisfy the specified filter criteria.

MongoDB Editor

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Query Results (JSON)

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