From `apiLogs`, group by `tenantId` to get total `callsCount`, sort descending, and return the top 2 consumers.

Problem Statement

<p>From <code>apiLogs</code>, group by <code>tenantId</code> to get total <code>callsCount</code>, sort descending, and return the top 2 consumers.</p>

Examples

Input: apiLogs collection: +-----+----------+------------+ | _id | tenantId | callsCount | +-----+----------+------------+ | 1 | t1 | 1500 | | 2 | t2 | 3000 | | 3 | t1 | 2200 | | 4 | t3 | 500 | +-----+----------+------------+

Output: +-----+------------+ | _id | totalCalls | +-----+------------+ | t1 | 3700 | | t2 | 3000 | +-----+------------+

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

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 "Top API Consumers (Group + Sort + Limit)", we query the MongoDB document store. The goal is to from `apilogs`, group by `tenantid` to get total `callscount`, sort descending, and return the top 2 consumers. 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.apiLogs.aggregate([
    { $group: { _id: "$tenantId", totalCalls: { $sum: "$callsCount" } } },
    { $sort: { totalCalls: -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.

Top API Consumers (Group + Sort + Limit)

Hard

From apiLogs, group by tenantId to get total callsCount, sort descending, and return the top 2 consumers.

Example Scenarios

1Example 1

Input:

apiLogs collection

_id	tenantId	callsCount
1	t1	1500
2	t2	3000
3	t1	2200
4	t3	500

Output:

_id	totalCalls
t1	3700
t2	3000

Explanation:

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

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Query Results (JSON)

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