MongoDB Medium 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.