Group events by `hour`, count them, and return only hours with more than 1 event, sorted by count descending.

Problem Statement

<p>Group events by <code>hour</code>, count them, and return only hours with more than 1 event, sorted by count descending.</p>

Examples

Input: events collection: +-----+--------+------+ | _id | type | hour | +-----+--------+------+ | 1 | click | 10 | | 2 | scroll | 10 | | 3 | click | 14 | | 4 | hover | 10 | | 5 | click | 14 | | 6 | scroll | 16 | +-----+--------+------+

Output: +-----+------------+ | _id | eventCount | +-----+------------+ | 10 | 3 | | 14 | 2 | +-----+------------+

Explanation: The query calculates the total count of documents that satisfy the given 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 "Analytics: Hourly Event Count", we query the MongoDB document store. The goal is to group events by `hour`, count them, and return only hours with more than 1 event, sorted by count 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.events.aggregate([
    { $group: { _id: "$hour", eventCount: { $sum: 1 } } },
    { $match: { eventCount: { $gt: 1 } } },
    { $sort: { eventCount: -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.

Analytics: Hourly Event Count

Hard

Group events by hour, count them, and return only hours with more than 1 event, sorted by count descending.

Example Scenarios

1Example 1

Input:

events collection

_id	type	hour
1	click	10
2	scroll	10
3	click	14
4	hover	10
5	click	14
6	scroll	16

Output:

_id	eventCount
10	3
14	2

Explanation:

The query calculates the total count of documents that satisfy the given filter criteria.

MongoDB Editor

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Query Results (JSON)

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