From `employees`: match department `"Engineering"`, project `name` and `salary`, add a `bonus` field (salary * 0.1), sort by bonus descending, skip 1, and limit to 2.

Problem Statement

<p>From <code>employees</code>: match department <code>"Engineering"</code>, project <code>name</code> and <code>salary</code>, add a <code>bonus</code> field (<code>salary</code> * 0.1), sort by bonus descending, skip 1, and limit to 2.</p>

Examples

Input: employees collection: +-----+---------+-------------+--------+ | _id | name | dept | salary | +-----+---------+-------------+--------+ | 1 | Alice | Engineering | 120000 | | 2 | Bob | Marketing | 80000 | | 3 | Charlie | Engineering | 95000 | | 4 | Dana | Engineering | 110000 | | 5 | Eve | Engineering | 85000 | +-----+---------+-------------+--------+

Output: +---------+--------+-------+ | name | salary | bonus | +---------+--------+-------+ | Dana | 110000 | 11000 | | Charlie | 95000 | 9500 | +---------+--------+-------+

Explanation: The aggregation pipeline processes the documents through stages to compute the grouped results.

Complexity

Time Complexity: -

Space Complexity: -

Hints

💡 Hint 1: Use the $project stage in an aggregation pipeline to include, exclude, or compute new document fields. 💡 Hint 2: Set fields to 1 (include) or 0 (exclude), and use expression operators to reshape data. 💡 Hint 3: Finalize the pipeline and execute with .toArray() to return the structured document array.

Editorial & Approach

Problem Overview & Intuition

To solve "Full Pipeline: Match + Project + AddFields + Sort + Skip + Limit", we query the MongoDB document store. The goal is to from `employees`: match department `"engineering"`, project `name` and `salary`, add a `bonus` field (salary * 0.1), sort by bonus descending, skip 1, and limit to 2. 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([
    { $match: { dept: "Engineering" } },
    { $project: { _id: 0, name: 1, salary: 1 } },
    { $addFields: { bonus: { $multiply: ["$salary", 0.1] } } },
    { $sort: { bonus: -1 } },
    { $skip: 1 },
    { $limit: 2 }
  ]);
}

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.

Full Pipeline: Match + Project + AddFields + Sort + Skip + Limit

Hard

From employees: match department "Engineering", project name and salary, add a bonus field (salary * 0.1), sort by bonus descending, skip 1, and limit to 2.

Example Scenarios

1Example 1

Input:

employees collection

_id	name	dept	salary
1	Alice	Engineering	120000
2	Bob	Marketing	80000
3	Charlie	Engineering	95000
4	Dana	Engineering	110000
5	Eve	Engineering	85000

Output:

name	salary	bonus
Dana	110000	11000
Charlie	95000	9500

Explanation:

The aggregation pipeline processes the documents through stages to compute the grouped results.

MongoDB Editor

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Query Results (JSON)

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