From `sales`, compute the `totalSales` (sum of amount), `averageSale` (avg of amount), `largestSale` (max amount), and `smallestSale` (min amount) across ALL sales.

Problem Statement

<p>From <code>sales</code>, compute the <code>totalSales</code> (sum of amount), <code>averageSale</code> (avg of amount), <code>largestSale</code> (max amount), and <code>smallestSale</code> (min amount) across ALL <code>sales</code>.</p>

Examples

Input: sales collection: +-----+--------+ | _id | amount | +-----+--------+ | 1 | 100 | | 2 | 250 | | 3 | 75 | | 4 | 500 | | 5 | 150 | +-----+--------+

Output: +------+------------+-------------+--------------+-------------+ | _id | totalSales | largestSale | smallestSale | averageSale | +------+------------+-------------+--------------+-------------+ | null | 1075 | 500 | 75 | 215 | +------+------------+-------------+--------------+-------------+

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 "Sales Dashboard Summary", we query the MongoDB document store. The goal is to from `sales`, compute the `totalsales` (sum of amount), `averagesale` (avg of amount), `largestsale` (max amount), and `smallestsale` (min amount) across all sales. 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.sales.aggregate([
    { $group: {
      _id: null,
      totalSales: { $sum: "$amount" },
      averageSale: { $avg: "$amount" },
      largestSale: { $max: "$amount" },
      smallestSale: { $min: "$amount" }
    }}
  ]);
}

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.

Sales Dashboard Summary

Hard

From sales, compute the totalSales (sum of amount), averageSale (avg of amount), largestSale (max amount), and smallestSale (min amount) across ALL sales.

Example Scenarios

1Example 1

Input:

sales collection

_id	amount
1	100
2	250
3	75
4	500
5	150

Output:

_id	totalSales	largestSale	smallestSale	averageSale
null	1075	500	75	215

Explanation:

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

MongoDB Editor

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Query Results (JSON)

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