Add a new address `{ type: "work", city: "NYC" }` to the `addresses` array of user `_id: 1`. Return the user.

Problem Statement

<p>Add a new address <code>{ type: "work", city: "NYC" }</code> to the <code>addresses</code> array of user <code>_id: 1</code>. Return the user.</p>

Examples

Input: users collection: +-----+-------+-----------------------------------+ | _id | name | addresses | +-----+-------+-----------------------------------+ | 1 | Alice | [{"type":"home","city":"Boston"}] | +-----+-------+-----------------------------------+

Output: +-----+-------+----------------------------------------------------------------+ | _id | name | addresses | +-----+-------+----------------------------------------------------------------+ | 1 | Alice | [{"type":"home","city":"Boston"},{"type":"work","city":"NYC"}] | +-----+-------+----------------------------------------------------------------+

Explanation: The target document is modified according to the update operators and the updated document is returned.

Complexity

Time Complexity: -

Space Complexity: -

Hints

💡 Hint 1: Use db.<collection>.find(<filter>) to query the documents in the collection. 💡 Hint 2: Pass an empty object {} to retrieve all documents, or specify key-value pairs for exact matching. 💡 Hint 3: Chain .toArray() to resolve the MongoDB cursor into a JavaScript array: return db.<collection>.find(...).toArray();

Editorial & Approach

Problem Overview & Intuition

To solve "Push Nested Object to Array", we query the MongoDB document store. The goal is to add a new address `{ type: "work", city: "nyc" }` to the `addresses` array of user `_id: 1`. return the user. 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) {
  db.users.updateOne({ _id: 1 }, { $push: { addresses: { type: "work", city: "NYC" } } });
  return db.users.findOne({ _id: 1 });
}

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.

Push Nested Object to Array

Hard

Add a new address { type: "work", city: "NYC" } to the addresses array of user _id: 1. Return the user.

Example Scenarios

1Example 1

Input:

users collection

_id	name	addresses
1	Alice	[{"type":"home","city":"Boston"}]

Output:

_id	name	addresses
1	Alice	[{"type":"home","city":"Boston"},{"type":"work","city":"NYC"}]

Explanation:

The target document is modified according to the update operators and the updated document is returned.

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Query Results (JSON)

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