Find the salary gap (max salary minus min salary) across all employees in each department.

Problem Statement

<p>Find the <code>salary</code> gap (max <code>salary</code> minus min <code>salary</code>) across all <code>employees</code> in each department.</p>

Examples

Input: employees collection: +-----+-------+--------+ | _id | dept | salary | +-----+-------+--------+ | 1 | Eng | 120000 | | 2 | Eng | 80000 | | 3 | Sales | 70000 | | 4 | Sales | 90000 | | 5 | Eng | 100000 | +-----+-------+--------+

Output: +-------+-----------+-----------+ | _id | maxSalary | minSalary | +-------+-----------+-----------+ | Eng | 120000 | 80000 | | Sales | 90000 | 70000 | +-------+-----------+-----------+

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 "Salary Range Analysis", we query the MongoDB document store. The goal is to find the salary gap (max salary minus min salary) across all employees in each department. 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", maxSalary: { $max: "$salary" }, minSalary: { $min: "$salary" } } }
  ]);
}

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.

Salary Range Analysis

Hard

Find the salary gap (max salary minus min salary) across all employees in each department.

Example Scenarios

1Example 1

Input:

employees collection

_id	dept	salary
1	Eng	120000
2	Eng	80000
3	Sales	70000
4	Sales	90000
5	Eng	100000

Output:

_id	maxSalary	minSalary
Eng	120000	80000
Sales	90000	70000

Explanation:

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

MongoDB Editor

Loading Editor...

Query Results (JSON)

Run your MongoDB code to see results here.