MongoDB Interview Questions

Background: In a database system like MongoDB, analyzing query performance is crucial to ensure efficient data retrieval. Properly indexing documents can significantly improve query performance.
Problem statement: You are tasked with identifying the top N most accessed documents in a MongoDB collection within a given time frame. The document structure contains fields for documentId, accessCount, and timestamp. Write a function that takes in a collection of documents and returns a list of the top N documents with the highest access counts during the specified period.
Function/class signature:

• def top_accessed_documents(documents: List[Dict[str, Union[str, int]]], start_time: str, end_time: str, N: int) -> List[Dict[str, int]]:



Example 1:


• Input: documents = [{'documentId': 'doc1', 'accessCount': 5, 'timestamp': '2023-09-01T12:00:00Z'}, {'documentId': 'doc2', 'accessCount': 2, 'timestamp': '2023-09-02T12:00:00Z'}, {'documentId': 'doc3', 'accessCount': 8, 'timestamp': '2023-09-01T13:00:00Z'}], start_time = '2023-09-01T00:00:00Z', end_time = '2023-09-02T23:59:59Z', N = 2


• Output: [{'documentId': 'doc3', 'accessCount': 8}, {'documentId': 'doc1', 'accessCount': 5}]


• Explanation: During the given time frame, doc3 had the highest access count followed by doc1.



Example 2:


• Input: documents = [{'documentId': 'doc4', 'accessCount': 10, 'timestamp': '2023-09-01T10:00:00Z'}, {'documentId': 'doc5', 'accessCount': 15, 'timestamp': '2023-09-01T14:00:00Z'}], start_time = '2023-09-01T00:00:00Z', end_time = '2023-09-01T23:59:59Z', N = 1


• Output: [{'documentId': 'doc5', 'accessCount': 15}]`

Constraints:

• 1 <= len(documents) <= 10000


• N > 0 and N <= len(documents)


• timestamp format is ISO 8601


• accessCount >= 0

Solution:

Background: In MongoDB, optimizing data retrieval and storage is critical for performance. This problem relates to efficient algorithms for handling document queries where non-adjacent elements in a dataset have to be processed.
Problem statement: Given an array of integers representing values stored in a MongoDB document, write a function to determine the maximum sum of non-adjacent elements. A non-adjacent element means if you select an element at index i, you cannot select elements at indices i-1 or i+1.
Function/class signature:

• def max_non_adjacent_sum(values: List[int]) -> int:

Example 1:
Input: [3, 2, 5, 10, 7]
Output: 15
Explanation: Select 3 (index 0) and 10 (index 3) to get the maximum sum of 15.
Example 2:
Input: [5, 1, 1, 5]
Output: 10
Explanation: Select 5 (index 0) and 5 (index 3) to get the maximum sum of 10.
Constraints:

• The length of the array values will be between 1 and 1000.


• Each integer in values will be between -1000 and 1000.

Background: MongoDB processes vast amounts of data where efficient querying is crucial for performance. As applications grow, slow database queries can significantly affect user experience.
Problem statement: You are given a SQL query that retrieves customer information from a customers table with thousands of records. The original query takes too long to execute. Your goal is to optimize this query. You need to explain possible optimizations, such as adding indexes, refining the query structure, or altering filter conditions to reduce execution time.
Function/class signature:

• optimize_query(original_query: str) -> str

Example 1:

• Input: "SELECT * FROM customers WHERE country = 'USA' AND age > 30;"


• Output: "CREATE INDEX idx_country_age ON customers (country, age); SELECT * FROM customers WHERE country = 'USA' AND age > 30;"


• Explanation: An index on country and age speeds up the lookup.

Example 2:

• Input: "SELECT * FROM customers WHERE last_purchase_date < '2022-01-01';"


• Output: "CREATE INDEX idx_last_purchase ON customers (last_purchase_date); SELECT * FROM customers WHERE last_purchase_date < '2022-01-01';"


• Explanation: Indexing the last_purchase_date column enhances query performance for historical lookups.

Constraints:

• The original_query length should not exceed 500 characters.


• The customers table can have millions of records.


• The optimization must reduce execution time by at least 50% for high-load scenarios.

Background: MongoDB uses efficient data retrieval and storage mechanisms. Finding the most frequent element in a collection can optimize read operations by informing caching strategies or indexing in documents.
Problem statement: Given a list of elements, your task is to find the most frequent element. If there's a tie, return any of the most frequent elements. The function should handle both integers and strings in the input list.
Function/class signature:

• def most_frequent_element(elements: List[Union[int, str]]) -> Union[int, str]:

Example 1:
Input: ['apple', 'banana', 'apple', 'orange', 'banana', 'banana']
Output: 'banana'
Explanation: 'banana' appears 3 times, which is more than any other element.
Example 2:
Input: [1, 3, 1, 2, 2, 3, 3]
Output: 3
Explanation: In this case, '3' and '1' both appear twice, but '3' is returned as one of the most frequent elements.
Constraints:

• 1 <= len(elements) <= 10^5


• elements can contain integers and strings only.

Background: MongoDB powers many applications with heavy read/write operations, and performance issues can affect end-user experience significantly. Understanding how to efficiently troubleshoot performance issues is crucial for maintaining a healthy database.
Problem statement: You are tasked with developing a function to help troubleshoot a MongoDB database that is experiencing slow performance. The function should take in a list of operations (a list of objects) where each object has properties: type (string, can be 'read' or 'write'), dataSize (int, the size of the operation in bytes), and timestamp (string, timestamp of the operation in ISO format). The function should identify which operations are taking the longest based on their timestamps and data sizes, returning a list of operations that exceed a certain thresholdTime (in milliseconds).
Function/class signature:

• def troubleshoot_slow_db(operations: List[Dict[str, Union[str, int]]], thresholdTime: int) -> List[Dict[str, Union[str, int]]]:

Example 1:
Input:
operations = [{'type': 'read', 'dataSize': 200, 'timestamp': '2023-10-05T12:00:00Z'}, {'type': 'write', 'dataSize': 500, 'timestamp': '2023-10-05T12:00:02Z'}]
thresholdTime = 1000
Output:
[{'type': 'write', 'dataSize': 500, 'timestamp': '2023-10-05T12:00:02Z'}]
Explanation: The write operation took longer than the threshold time.Example 2:
Input:
operations = [{'type': 'read', 'dataSize': 150, 'timestamp': '2023-10-05T12:00:00Z'}, {'type': 'write', 'dataSize': 300, 'timestamp': '2023-10-05T12:00:01Z'}]
thresholdTime = 500
Output:
[]
Explanation: All operations completed faster than the threshold time.Constraints:

• 1 <= len(operations) <= 100


• 1 <= dataSize <= 1000


• timestamp format is always valid.

Background: MongoDB often needs to optimize query performance and data retrieval. By identifying which documents are accessed the most, the system can implement better caching strategies or optimize index structures to enhance overall performance.
Problem statement: Given a list of database access logs, each log containing a documentId, write a function to determine the documentId that was accessed the most frequently. If there are ties, return any of the most frequent documentIds.
Function/class signature:

• def most_frequent_access(logs: List[int]) -> int:

Example 1:
Input: logs = [1, 2, 2, 3, 1, 1, 4]
Output: 1
Explanation: Document 1 was accessed 3 times, which is more than any other document.
Example 2:
Input: logs = [5, 5, 6, 6, 7]
Output: 5 or 6
Explanation: Both Document 5 and Document 6 were accessed 2 times.
Constraints:

• 1 <= len(logs) <= 10^5


• 0 <= logs[i] <= 10^9


• All entries in logs are non-negative integers.

In a multi-tenant environment, MongoDB needs to ensure that the data returned in query results is unique, especially when aggregates are involved. A two pointers approach can efficiently filter out duplicates.
Problem Statement: Given a sorted list of integers, return the list without duplicates.

• Method Signature: def remove_duplicates(nums: List[int]) -> List[int]: - Returns a filtered list of unique numbers.

Example 1:
Input: nums = [1, 1, 2]
Output: [1, 2]
Explanation: The original list contained duplicates.
Example 2:
Input: nums = [0,0,1,1,1,2,2,3,3,4]
Output: [0,1,2,3,4]
Explanation: All duplicates removed from the input list.
Constraints:

• 0 <= len(nums) <= 10^4


• -10^4 <= nums[i] <= 10^4


• The input array is sorted.

In today’s data-driven world, MongoDB needs efficient querying mechanisms to provide real-time data analytics. A sliding window algorithm can help in fetching and analyzing data within a defined time range efficiently.
Problem Statement: Given a list of timestamps in string and a start and end time defining the window, return a list of timestamps that fall within this range.

• Method Signature: def fetch_timestamps(timestamps: List[str], start: str, end: str) -> List[str]: - Returns a filtered list of timestamps.

Example 1:
Input: timestamps = ["2023-10-01T10:00:00Z", "2023-10-01T10:15:00Z", "2023-10-01T10:30:00Z"], start = "2023-10-01T10:05:00Z", end = "2023-10-01T10:25:00Z"
Output: ["2023-10-01T10:15:00Z"]
Explanation: Only one timestamp falls within the provided range.
Example 2:
Input: timestamps = ["2023-10-01T10:00:00Z", "2023-10-01T10:15:00Z", "2023-10-01T10:30:00Z"], start = "2023-10-01T09:00:00Z", end = "2023-10-01T11:00:00Z"
Output: ["2023-10-01T10:00:00Z", "2023-10-01T10:15:00Z", "2023-10-01T10:30:00Z"]
Constraints:

• 1 <= len(timestamps) <= 10^4


• timestamps[i] is in ISO 8601 format.


• All timestamps are distinct.

MongoDB stores items in collections that might be sorted. When searching for items, finding the pivot index of a rotated array can improve caching and querying processes.
Given a rotated sorted array nums, return the index of the pivot where the rotation occurs.

• Function Signature: def find_pivot(nums: List[int]) -> int: Returns the pivot index.

Example 1:
Input: [4,5,6,7,0,1,2]
Output: 3
Explanation: The array is split into two sorted arrays at index 3.Example 2:
Input: [1]
Output: 0
Explanation: The only element does not rotate.Constraints:

• 1 <= nums.length <= 5000


• -10^5 <= nums[i] <= 10^5.

In MongoDB, analyzing logs efficiently is crucial for performance. The ability to find the longest substring of distinct characters in a log entry can help in optimizing error tracking.
Given a string s, return the length of the longest substring without repeating characters.

• Function Signature: def length_of_longest_substring(s: str) -> int: Returns the length of the longest substring.

Example 1:
Input: "abcabcbb"
Output: 3
Explanation: The answer is "abc", with the length being 3.Example 2:
Input: "bbbbb"
Output: 1
Explanation: The answer is "b", with the length being 1.Constraints:

• 0 <= s.length <= 50000


• s consists of English letters, digits, symbols, and spaces.

Handling a high throughput of requests gracefully is critical for both performance and fairness. Implementing a rate limiter can regulate the access to MongoDB APIs efficiently.
Problem Statement: Create a class RateLimiter that allows requests to be allowed to pass if they fall within a defined rate limit.

• Method Signature: class RateLimiter:

- def allow_request(self, user_id: str) -> bool: - Returns True if the request is allowed.
- def get_request_count(self, user_id: str) -> int: - Returns the count of requests made by the user.
Example 1:
Input: rate_limiter = RateLimiter(3, 10)
rate_limiter.allow_request("user1")
Output: True
Explanation: User can make 3 requests in 10 seconds.
Example 2:
Input: rate_limiter.allow_request("user1")
Output: False
Explanation: Only 3 requests are allowed in the 10 seconds.
Constraints:

• 1 <= requests_per_window <= 10^3


• 1 <= window_size <= 10^3


• 1 <= len(user_id) <= 100.

As MongoDB scales, efficient task management is critical for maintaining performance. A job queue can ensure tasks are processed systematically and concurrently.
Problem Statement: Create a class JobQueue that allows adding jobs and processing them in concurrent threads.

• Method Signature: class JobQueue:

- def add_job(self, job: Callable) -> None: - Adds a job to the queue.
- def process_jobs(self, num_threads: int) -> None: - Starts processing jobs using specified threads.
- def get_status(self) -> List[str]: - Returns a list of job statuses ('pending', 'completed').
Example 1:
Input: job_queue = JobQueue()
job_queue.add_job(lambda: sleep(1))
job_queue.process_jobs(2)
Output: job_queue.get_status()
Explanation: The job will be added and processed by 2 threads.
Example 2:
Input: job_queue.add_job(lambda: sleep(2))
Output: job_queue.get_status()
Output: ['pending', 'pending']
Constraints:

• 1 <= num_threads <= 10


• Each job will execute within reasonable limits.

Caching is critical for optimizing query performance in MongoDB. Implementing an LRU (Least Recently Used) cache for quick data retrieval can enhance the efficiency of repeated queries.
Define a class LRUCache that supports get and put operations.

• Class Signature: class LRUCache:


• def __init__(self, capacity: int): Initializes the cache with a specific capacity.


• def get(self, key: int) -> int: Returns the value of the key if present, otherwise return -1.


• def put(self, key: int, value: int) -> None: Updates or inserts the value if the key is not already present. When the cache reaches its capacity, it should invalidate the least recently used item before inserting a new item.

Constraints:

• 1 <= capacity <= 30000


• 0 <= key, value <= 10^4.

MongoDB is centered around efficient data storage and retrieval. Designing an API for basic Create, Read, Update, and Delete operations is essential for service interactions.
Define a class MongoDBApi that simulates these operations on a collection.

• Class Signature: class MongoDBApi:


• def create(self, data: Dict[str, Any]) -> str:: Creates a new record and returns its ID.


• def read(self, record_id: str) -> Dict[str, Any]: Returns the record specified by record_id.


• def update(self, record_id: str, data: Dict[str, Any]) -> bool: Updates the record and returns success status.


• def delete(self, record_id: str) -> bool: Deletes the record and returns success status.

Constraints:

• Records are stored in a dictionary with unique IDs.


• record_id is a string with a length of up to 36 characters.