From `orders` with an `items` array of product names, unwind items, then count how many times each item appears across all orders.

Problem Statement

<p>From <code>orders</code> with an <code>items</code> array of product names, unwind items, then count how many times each item appears across all <code>orders</code>.</p>

Examples

Input: orders collection: +-----+----------+---------------------------------+ | _id | customer | items | +-----+----------+---------------------------------+ | 1 | A | ["Laptop","Mouse"] | | 2 | B | ["Laptop","Keyboard","Monitor"] | | 3 | C | ["Mouse","Keyboard"] | +-----+----------+---------------------------------+

Output: +----------+-------+ | _id | count | +----------+-------+ | Laptop | 2 | | Mouse | 2 | | Keyboard | 2 | | Monitor | 1 | +----------+-------+

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 "Pipeline: Unwind + Match + Group", we query the MongoDB document store. The goal is to from `orders` with an `items` array of product names, unwind items, then count how many times each item appears across all orders. 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.orders.aggregate([
    { $unwind: "$items" },
    { $group: { _id: "$items", count: { $sum: 1 } } },
    { $sort: { count: -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.

Pipeline: Unwind + Match + Group

Hard

From orders with an items array of product names, unwind items, then count how many times each item appears across all orders.

Example Scenarios

1Example 1

Input:

orders collection

_id	customer	items
1	A	["Laptop","Mouse"]
2	B	["Laptop","Keyboard","Monitor"]
3	C	["Mouse","Keyboard"]

Output:

_id	count
Laptop	2
Mouse	2
Keyboard	2
Monitor	1

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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