Count the number of tickets per `priority` level in the `tickets` collection.

Problem Statement

<p>Count the number of tickets per <code>priority</code> level in the <code>tickets</code> collection.</p>

Examples

Input: tickets collection: +-----+----------+ | _id | priority | +-----+----------+ | 1 | high | | 2 | low | | 3 | high | | 4 | medium | | 5 | low | +-----+----------+

Output: +--------+-------+ | _id | count | +--------+-------+ | high | 2 | | low | 2 | | medium | 1 | +--------+-------+

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 "Group and Count ($group + $sum: 1)", we query the MongoDB document store. The goal is to count the number of tickets per `priority` level in the `tickets` collection. 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.tickets.aggregate([
    { $group: { _id: "$priority", count: { $sum: 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.

Group and Count ($group + $sum: 1)

Medium

Count the number of tickets per priority level in the tickets collection.

Example Scenarios

1Example 1

Input:

tickets collection

_id	priority
1	high
2	low
3	high
4	medium
5	low

Output:

_id	count
high	2
low	2
medium	1

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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