Find all employees in the `"Engineering"` department with a `salary` greater than `85000`.

Problem Statement

<p>Find all <code>employees</code> in the <code>"Engineering"</code> department with a <code>salary</code> greater than <code>85000</code>.</p>

Examples

Input: employees collection: +-----+---------+-------------+--------+ | _id | name | dept | salary | +-----+---------+-------------+--------+ | 1 | Alice | Engineering | 90000 | | 2 | Bob | Marketing | 95000 | | 3 | Charlie | Engineering | 85000 | | 4 | Dana | Engineering | 110000 | +-----+---------+-------------+--------+

Output: +-----+-------+-------------+--------+ | _id | name | dept | salary | +-----+-------+-------------+--------+ | 1 | Alice | Engineering | 90000 | | 4 | Dana | Engineering | 110000 | +-----+-------+-------------+--------+

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

Complexity

Time Complexity: -

Space Complexity: -

Hints

💡 Hint 1: Use comparison operators ($gt, $gte, $lt, $lte) to filter documents based on numeric or date ranges. 💡 Hint 2: Construct the filter condition: { fieldName: { $gt: value } } inside db.<collection>.find(...). 💡 Hint 3: Chain projection or sorting if requested, and invoke .toArray() to produce the result array.

Editorial & Approach

Problem Overview & Intuition

To solve "Implicit AND (Multiple Conditions)", we query the MongoDB document store. The goal is to find all employees in the `"engineering"` department with a `salary` greater than `85000`. Using a targeted find query, 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 query filter with appropriate comparison operators.
Resolve Cursor: Invoke .toArray() to transform the query cursor into the required array of documents.

Optimal Implementation (MongoDB)

function solve(db) {
  return db.employees.find({ dept: "Engineering", salary: { $gt: 85000 } }).toArray();
}

Complexity Analysis

Time Complexity O(N) collection scan (O(log N) if index is present on filtered fields).

Space Complexity O(K) where K is the number of returned documents in memory.

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.

Implicit AND (Multiple Conditions)

Easy

Find all employees in the "Engineering" department with a salary greater than 85000.

Example Scenarios

1Example 1

Input:

employees collection

_id	name	dept	salary
1	Alice	Engineering	90000
2	Bob	Marketing	95000
3	Charlie	Engineering	85000
4	Dana	Engineering	110000

Output:

_id	name	dept	salary
1	Alice	Engineering	90000
4	Dana	Engineering	110000

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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