From the `activityLogs`, count the number of actions per user, get the average actions, and return users with more than 2 actions.

Problem Statement

<p>From the <code>activityLogs</code>, count the number of actions per user, get the average actions, and return <code>users</code> with more than 2 actions.</p>

Examples

Input: activityLogs collection: +-----+--------+----------+ | _id | userId | action | +-----+--------+----------+ | 1 | u1 | login | | 2 | u2 | login | | 3 | u1 | purchase | | 4 | u1 | logout | | 5 | u3 | login | | 6 | u2 | purchase | +-----+--------+----------+

Output: +-----+-------------+ | _id | actionCount | +-----+-------------+ | u1 | 3 | +-----+-------------+

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 "User Activity Report", we query the MongoDB document store. The goal is to from the `activitylogs`, count the number of actions per user, get the average actions, and return users with more than 2 actions. 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.activityLogs.aggregate([
    { $group: { _id: "$userId", actionCount: { $sum: 1 } } },
    { $match: { actionCount: { $gt: 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.

User Activity Report

Hard

From the activityLogs, count the number of actions per user, get the average actions, and return users with more than 2 actions.

Example Scenarios

1Example 1

Input:

activityLogs collection

_id	userId	action
1	u1	login
2	u2	login
3	u1	purchase
4	u1	logout
5	u3	login
6	u2	purchase

Output:

_id	actionCount
u1	3

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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