Add a computed field `engagementRate` = `(likes + comments) / views` for each post. Sort by engagementRate descending.

Problem Statement

<p>Add a computed field <code>engagementRate</code> = <code>(likes + <code>comments</code>) / views</code> for each post. Sort by engagementRate descending.</p>

Examples

Input: posts collection: +-----+-------+-------+----------+-------+ | _id | title | likes | comments | views | +-----+-------+-------+----------+-------+ | 1 | A | 100 | 20 | 1000 | | 2 | B | 50 | 80 | 500 | | 3 | C | 200 | 50 | 5000 | +-----+-------+-------+----------+-------+

Output: +-----+-------+-------+----------+-------+-----------------+ | _id | title | likes | comments | views | totalEngagement | +-----+-------+-------+----------+-------+-----------------+ | 3 | C | 200 | 50 | 5000 | 250 | | 2 | B | 50 | 80 | 500 | 130 | | 1 | A | 100 | 20 | 1000 | 120 | +-----+-------+-------+----------+-------+-----------------+

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

Complexity

Time Complexity: -

Space Complexity: -

Hints

💡 Hint 1: Use db.<collection>.aggregate([ ... ]) to run a multi-stage data processing pipeline. 💡 Hint 2: Order pipeline stages logically (e.g. $match early to filter documents before processing). 💡 Hint 3: Convert the aggregation cursor using .toArray() at the end of the function.

Editorial & Approach

Problem Overview & Intuition

To solve "Social Media: Post Engagement Rate", we query the MongoDB document store. The goal is to add a computed field `engagementrate` = `(likes + comments) / views` for each post. sort by engagementrate descending. 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.posts.aggregate([
    { $addFields: { totalEngagement: { $add: ["$likes", "$comments"] } } },
    { $sort: { totalEngagement: -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.

Social Media: Post Engagement Rate

Hard

Add a computed field engagementRate = (likes + comments) / views for each post. Sort by engagementRate descending.

Example Scenarios

1Example 1

Input:

posts collection

_id	title	likes	comments	views
1	A	100	20	1000
2	B	50	80	500
3	C	200	50	5000

Output:

_id	title	likes	comments	views	totalEngagement
3	C	200	50	5000	250
2	B	50	80	500	130
1	A	100	20	1000	120

Explanation:

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

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Query Results (JSON)

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