Check if a user with `_id: "u1"` exists. If yes, increment their `loginCount` by 1. Then return the user.

Problem Statement

<p>Check if a user with <code>_id: "u1"</code> exists. If yes, increment their <code>loginCount</code> by 1. Then return the user.</p>

Examples

Input: users collection: +-----+-------+------------+ | _id | name | loginCount | +-----+-------+------------+ | u1 | Alice | 5 | | u2 | Bob | 3 | +-----+-------+------------+

Output: +-----+-------+------------+ | _id | name | loginCount | +-----+-------+------------+ | u1 | Alice | 6 | +-----+-------+------------+

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: Filter documents where a field matches any value in a list using the $in or $nin operator. 💡 Hint 2: Pass the query object to db.<collection>.find({ field: { $in: [val1, val2, ...] } }). 💡 Hint 3: Call .toArray() on the returned cursor so the function returns the array of matching documents.

Editorial & Approach

Problem Overview & Intuition

To solve "Upsert-like: Insert If Missing, Update If Exists", we query the MongoDB document store. The goal is to check if a user with `_id: "u1"` exists. if yes, increment their `logincount` by 1. then 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: "u1" }, { $inc: { loginCount: 1 } });
  return db.users.findOne({ _id: "u1" });
}

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.

Upsert-like: Insert If Missing, Update If Exists

Hard

Check if a user with _id: "u1" exists. If yes, increment their loginCount by 1. Then return the user.

Example Scenarios

1Example 1

Input:

users collection

_id	name	loginCount
u1	Alice	5
u2	Bob	3

Output:

_id	name	loginCount
u1	Alice	6

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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