Find all products where `stock` is greater than `0` and `price` is less than `100`, sorted by price ascending.

Problem Statement

<p>Find all <code>products</code> where <code>stock</code> is greater than <code>0</code> and <code>price</code> is less than <code>100</code>, sorted by <code>price</code> ascending.</p>

Examples

Input: products collection: +-----+--------+-------+-------+ | _id | name | price | stock | +-----+--------+-------+-------+ | 1 | Cable | 10 | 100 | | 2 | SSD | 120 | 5 | | 3 | Mouse | 25 | 0 | | 4 | Webcam | 50 | 20 | +-----+--------+-------+-------+

Output: +-----+--------+-------+-------+ | _id | name | price | stock | +-----+--------+-------+-------+ | 1 | Cable | 10 | 100 | | 4 | Webcam | 50 | 20 | +-----+--------+-------+-------+

Explanation: Documents containing the matching array elements are selected from the collection.

Complexity

Time Complexity: -

Space Complexity: -

Hints

💡 Hint 1: Use comparison operators ($gt, $gte, $lt, $lte) to filter documents based on numeric or date ranges. 💡 Hint 2: Construct the filter condition: { fieldName: { $gt: value } } inside db.<collection>.find(...). 💡 Hint 3: Chain projection or sorting if requested, and invoke .toArray() to produce the result array.

Editorial & Approach

Problem Overview & Intuition

To solve "E-commerce: Find Products In Stock", we query the MongoDB document store. The goal is to find all products where `stock` is greater than `0` and `price` is less than `100`, sorted by price ascending. Using a targeted find query, 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 query filter with appropriate comparison operators.
Resolve Cursor: Invoke .toArray() to transform the query cursor into the required array of documents.

Optimal Implementation (MongoDB)

function solve(db) {
  return db.products.find({ stock: { $gt: 0 }, price: { $lt: 100 } }).sort({ price: 1 }).toArray();
}

Complexity Analysis

Time Complexity O(N) collection scan (O(log N) if index is present on filtered fields).

Space Complexity O(K) where K is the number of returned documents in memory.

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.

E-commerce: Find Products In Stock

Medium

Find all products where stock is greater than 0 and price is less than 100, sorted by price ascending.

Example Scenarios

1Example 1

Input:

products collection

_id	name	price	stock
1	Cable	10	100
2	SSD	120	5
3	Mouse	25	0
4	Webcam	50	20

Output:

_id	name	price	stock
1	Cable	10	100
4	Webcam	50	20

Explanation:

Documents containing the matching array elements are selected from the collection.

MongoDB Editor

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Query Results (JSON)

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