Top 50 MongoDB Interview Questions and Answers

MongoDB is better suited for handling unstructured data and offers better scalability and performance compared to MySQL.

• MongoDB is a NoSQL database that allows for flexible and dynamic schema design.

• MongoDB is better suited for handling large amounts of unstructured data, such as social media posts or sensor data.

• MongoDB offers better scalability and performance compared to MySQL, especially for write-heavy applications.

• MongoDB also has better support for distributed database...read more

Bucket collection is a specialized form of collection where delinquent accounts are grouped into buckets based on their age or severity.

• Normal collection involves the process of collecting outstanding payments from customers.

• Bucket collection categorizes delinquent accounts into different buckets based on their age or severity.

• Bucket collection helps prioritize and manage collections more effectively.

• For example, a bank may have buckets like 30 days, 60 days, and 90 days past...read more

Managing databases involves understanding the differences between SQL and NoSQL databases like MongoDB.

• SQL databases use a structured data model while NoSQL databases use an unstructured data model.

• MongoDB is a document-oriented database while SQL databases are table-based.

• SQL databases use a schema to define the structure of data while MongoDB uses dynamic schemas.

• SQL databases are better suited for complex queries and transactions while MongoDB is better for handling large ...read more

Mongo query to increase age by 20% for documents with age field

• Use $exists operator to filter documents with age field

• Use $mul operator to increase age by 20%

• Example: db.users.updateMany({ age: { $exists: true } }, { $mul: { age: 1.2 } })

Indexing in MongoDB is a way to optimize queries by creating a data structure that improves search speed.

• Indexes are created on specific fields in a collection

• They can be created in ascending or descending order

• Indexes can be compound, meaning they are created on multiple fields

• Queries that use indexed fields are faster than those that don't

• Indexes can be created using the createIndex() method

Use GridFS to save large files in MongoDB database.

• GridFS is a specification for storing and retrieving large files in MongoDB.

• It divides the file into smaller chunks and stores them as separate documents.

• GridFS provides a way to query and retrieve the file in chunks or as a whole.

• To save a large file, use the GridFS API provided by the MongoDB driver.

• Example: db.fs.files.insert({filename: 'photo.jpg', contentType: 'image/jpeg'})

Create a docker file to make mongodb available for developers

• Use the official MongoDB Docker image as the base image

• Expose the default MongoDB port (27017)

• Set up any necessary environment variables or configurations

• Build the Docker image using the Dockerfile

• Run the Docker container with the MongoDB image

To connect to MongoDB using Javascript, you can use the official MongoDB Node.js driver.

• Install the MongoDB Node.js driver using npm: npm install mongodb

• Require the MongoDB Node.js driver in your Javascript file: const MongoClient = require('mongodb').MongoClient

• Connect to MongoDB using MongoClient.connect() method

• Specify the MongoDB connection URL and database name in the connect() method

• Access the MongoDB database and perform operations like insert, update, delete, and find

Creating a MongoDB model in Node.js for data storage and retrieval.

• Define a schema using Mongoose library

• Create a model using the schema

• Use the model to interact with the MongoDB database

Optimising MongoDB Database involves indexing, proper query optimization, sharding, and using the right data model.

• Create indexes on fields frequently used in queries to improve query performance.

• Use the explain() method to analyze and optimize query performance.

• Implement sharding to distribute data across multiple servers for scalability.

• Use the appropriate data model for your application to reduce the number of queries needed.

• Regularly monitor and optimize the database perf...read more

Over-indexing in MongoDB can negatively impact performance by increasing memory usage and slowing down query execution.

• Over-indexing can lead to increased memory usage as each index consumes memory.

• Having too many indexes can slow down write operations as each index needs to be updated when a document is inserted, updated, or deleted.

• To prevent over-indexing, carefully analyze query patterns and create indexes only for fields that are frequently queried.

• Regularly review and r...read more

To connect MongoDB with a database, you need to use the MongoDB URI and a MongoDB client library.

• Use the MongoDB URI to specify the connection details such as host, port, username, password, and database name

• Install a MongoDB client library like Mongoose for Node.js or pymongo for Python

• Use the client library to establish a connection to the MongoDB database and perform CRUD operations

MongoDB is a NoSQL database while the relational model is based on tables and SQL.

• MongoDB is schema-less, allowing flexible and dynamic data structures.

• Relational databases use a fixed schema with predefined tables and columns.

• MongoDB uses a document-based model, storing data in JSON-like documents.

• Relational databases use a tabular model, storing data in rows and columns.

• MongoDB supports horizontal scalability and distributed data storage.

• Relational databases are typically v...read more

Different databases have their own strengths and weaknesses.

• MongoDB is a NoSQL database that is great for handling unstructured data.

• PostgreSQL is a relational database that is known for its stability and ACID compliance.

• MySQL is a popular open-source database that is great for web applications.

• Oracle is a powerful database that is often used for enterprise-level applications.

• SQL Server is a Microsoft database that is great for Windows-based applications.

To fetch data from MongoDB, use the find() method with optional query parameters.

• Use the find() method to retrieve data from a MongoDB collection

• You can pass query parameters to filter the results

• Use the findOne() method to retrieve a single document

Key considerations include data modeling, indexing, sharding, and replication.

• Consider the data model carefully to ensure it fits the application's needs.

• Use indexing to improve query performance.

• Plan for sharding to distribute data across multiple servers for scalability.

• Implement replication for high availability and fault tolerance.

MongoDB is scalable due to its ability to horizontally partition data across multiple servers.

• MongoDB uses sharding to distribute data across multiple servers.

• Sharding allows for horizontal scaling by adding more servers to the cluster.

• MongoDB also supports replica sets for high availability and fault tolerance.

• Indexes can be created on any field in a MongoDB document, allowing for efficient querying of large datasets.

CRUD operations in MongoDB include create, read, update, and delete operations on documents in a collection.

• Create: db.collection.insertOne()

• Read: db.collection.find()

• Update: db.collection.updateOne()

• Delete: db.collection.deleteOne()

To join in MongoDB, you can use the $lookup aggregation stage to perform a left outer join between two collections.

• Use the $lookup aggregation stage in MongoDB to join collections.

• Specify the localField and foreignField to define the fields to join on.

• Use the as field to specify the name of the new field that will contain the joined data.

MongoDB is a document-oriented database while Cassandra is a column-family database.

• MongoDB is better suited for complex queries and data modeling.

• Cassandra is better for write-heavy workloads and high availability.

• MongoDB has a flexible schema while Cassandra has a fixed schema.

• MongoDB supports ACID transactions while Cassandra supports eventual consistency.

• MongoDB is more popular for web applications while Cassandra is more popular for IoT and real-time analytics.

Handle large datasets in MongoDB by sharding, indexing, and using aggregation pipelines.

• Use sharding to distribute data across multiple servers for horizontal scaling.

• Create indexes on frequently queried fields to improve query performance.

• Utilize aggregation pipelines for complex data processing and analysis.

• Consider using MongoDB Atlas for managed sharding and scaling capabilities.

DB stands for database. MongoDB is a NoSQL database, while DB is a general term for any type of database.

• DB stands for database, which is a collection of data organized in a way that allows easy access, management, and updating.

• MongoDB is a specific type of database known as a NoSQL database, which stores data in a flexible, JSON-like format.

• MongoDB uses collections and documents to store data, while traditional databases use tables and rows.

• MongoDB is designed for scalabilit...read more

They are used in MongoDB aggregation pipeline to perform various operations like joining, filtering, projecting, etc.

• Used in MongoDB aggregation pipeline for data manipulation

• Perform operations like joining, filtering, projecting, etc.

• Help in combining data from multiple collections

• Use $lookup to perform a left outer join between two collections

Mongoose is an Object Data Modeling (ODM) library for MongoDB that provides a higher level of abstraction and simplifies interactions with the database.

• Mongoose provides schema validation which helps maintain data integrity.

• Mongoose simplifies querying and data manipulation with built-in functions.

• Mongoose supports middleware functions for pre and post processing of data operations.

Indexes in MongoDB are data structures that improve the speed of data retrieval operations.

• Indexes in MongoDB are similar to indexes in relational databases, allowing for faster query performance.

• Indexes can be created on a single field or a combination of fields in a collection.

• Examples of indexes in MongoDB include single field index, compound index, text index, and geospatial index.

Lookup in MongoDB is used to perform a join between two collections.

• Lookup is used to combine data from multiple collections in a single query.

• It is similar to a SQL join operation.

• Lookup requires a local field in the input collection and a foreign field in the from collection.

• Example: db.orders.aggregate([{ $lookup: { from: 'products', localField: 'productId', foreignField: '_id', as: 'productDetails' } }])

MongoDB queries are used to retrieve data from a MongoDB database.

• Use find() method to retrieve documents from a collection

• Use findOne() method to retrieve a single document

• Use sort() method to sort the results

• Use limit() method to limit the number of results returned

MongoDB database algorithms are used for efficient data storage, retrieval, and processing.

• MongoDB uses various algorithms for indexing, sharding, and aggregation.

• Indexing algorithms include B-tree, hash, and text search indexes.

• Sharding algorithms include range-based, hash-based, and zone-based sharding.

• Aggregation algorithms include map-reduce and aggregation pipeline.

• MongoDB also uses algorithms for query optimization and data compression.

Designing a MongoDB schema for a particular scenario

• Identify the entities and their relationships

• Consider the data access patterns

• Normalize or denormalize data based on query requirements

• Use embedded documents or references as needed

MongoDB is a NoSQL database that offers flexibility, scalability, and high performance.

• Schema-less design allows for flexible data modeling

• Supports sharding for horizontal scalability

• High performance due to document-based storage

• Rich query language with support for indexing

• Automatic failover and replication for high availability

The time complexity of MongoDB insert method is O(1) on average.

• Inserting a document into a MongoDB collection has a constant time complexity of O(1) on average.

• This is because MongoDB uses a B-tree data structure to store documents, allowing for fast insertion operations.

• The actual time complexity may vary depending on factors such as the size of the collection and the available system resources.

The $lookup operator in MongoDB is used to perform a left outer join between two collections.

• Used to combine data from two collections based on a common field

• Requires a local field and a foreign field to match documents

• Returns an array of documents that include matched data from both collections

MongoDB is used instead of MySQL for its flexibility, scalability, and ability to handle unstructured data.

• MongoDB is a NoSQL database, making it more flexible for handling unstructured data compared to the structured data model of MySQL.

• MongoDB is schema-less, allowing for easier scalability and changes to the data structure without downtime.

• MongoDB is designed for horizontal scalability, making it suitable for applications with large amounts of data or high traffic.

• MongoDB'...read more

MongoDB is a NoSQL database while SQL is a relational database management system.

• MongoDB is schema-less, allowing for flexible data models.

• SQL databases use structured query language for defining and manipulating data.

• MongoDB is better suited for unstructured data and real-time applications.

• SQL databases are better for complex queries and transactions.

• MongoDB uses JSON-like documents for data storage, while SQL uses tables with rows and columns.

Use find() method to fetch data from MongoDB

• Use db.collection_name.find() to fetch all documents in a collection

• Add criteria inside find() to filter data, like db.collection_name.find({ key: value })

• Use projection to specify which fields to include or exclude, like db.collection_name.find({}, { field1: 1, field2: 0 })

Indexing in MongoDB improves query performance by allowing the database to quickly locate and retrieve specific documents.

• Indexes in MongoDB are similar to indexes in relational databases, allowing for efficient data retrieval.

• Types of indexes in MongoDB include single field, compound, multikey, text, hashed, and geospatial indexes.

• Single field indexes are created on a single field in a document, while compound indexes are created on multiple fields.

• Multikey indexes are used ...read more

MongoDB JavaScript query to find documents with a specific field value

• Use the find() method to query the collection

• Pass in a query object with the field and value to search for

• Example: db.collection.find({field: value})

Improving performance of Mongo DB involves indexing, sharding, and optimizing queries.

• Create indexes on frequently queried fields to improve query performance

• Use sharding to distribute data across multiple servers for better scalability

• Optimize queries by using the explain() method to analyze query execution plans

• Avoid unnecessary data fetching by projecting only required fields in queries

Aggregation pipeline in MongoDB is a framework for data aggregation operations.

• Pipeline stages are used to process documents in a collection.

• Operators like $match, $group, $project are used in pipeline stages.

• Results can be transformed, sorted, grouped, and filtered using aggregation pipeline.

• Example: db.collection.aggregate([{$match: {field: value}}, {$group: {_id: '$field', count: {$sum: 1}}}, {$sort: {count: -1}}])

MySQL is a relational database management system, while MongoDB is a NoSQL database management system.

• MySQL is a relational database, while MongoDB is a document-oriented database.

• MySQL uses structured query language (SQL) for querying data, while MongoDB uses JSON-like documents.

• MySQL is suitable for complex queries and transactions, while MongoDB is better for scalability and flexibility.

• MySQL is ACID-compliant, while MongoDB is not fully ACID-compliant.

• MySQL is widely used...read more

MongoDB is preferred over SQL for its flexibility, scalability, and ease of use in handling unstructured data.

• MongoDB is a NoSQL database, making it easier to work with unstructured data compared to SQL.

• MongoDB is schema-less, allowing for more flexibility in data modeling and changes.

• MongoDB is horizontally scalable, making it easier to handle large amounts of data and high traffic loads.

• MongoDB's document-based data model is more natural for developers working with modern a...read more

Indexing in MongoDB improves query performance by creating indexes on fields in a collection.

• Indexes can be created on single fields or compound fields.

• Indexes can be created in ascending or descending order.

• Indexes can improve query performance by allowing MongoDB to quickly locate documents.

• Examples: db.collection.createIndex({ field: 1 }), db.collection.createIndex({ field1: 1, field2: -1 })

MySQL is a relational database management system, while MongoDB is a NoSQL database. Preference depends on project requirements.

• MySQL is a relational database, while MongoDB is a NoSQL database.

• MySQL uses tables and rows to store data, while MongoDB uses collections and documents.

• MySQL is better suited for complex queries and transactions, while MongoDB is more flexible for unstructured data.

• MySQL is ACID-compliant, ensuring data integrity, while MongoDB sacrifices some of th...read more

MongoDB is a NoSQL database, so it does not use SQL. Instead, it uses a query language similar to JSON.

• MongoDB uses a query language called MongoDB Query Language (MQL) which is similar to JSON.

• Queries in MongoDB are expressed as JSON-like documents.

• There is no need for SQL when working with MongoDB.

Indexes in MongoDB improve query performance by allowing the database to quickly locate and retrieve specific documents.

• Indexes help to speed up query performance by allowing the database to quickly locate specific documents based on the indexed fields.

• Without indexes, MongoDB would have to perform a collection scan, which can be slow and resource-intensive.

• Indexes can be created on single fields or compound fields to further optimize query performance.

• However, indexes come w...read more

I would prefer MySQL for structured data and complex queries, while MongoDB for unstructured data and scalability.

• MySQL is better for structured data and complex queries

• MongoDB is better for unstructured data and scalability

• MySQL supports ACID transactions, while MongoDB is eventually consistent

• MySQL is widely used in traditional relational databases, while MongoDB is popular for NoSQL databases

I have extensive working experience with both MySQL and MongoDB.

• I have worked with MySQL for over 5 years, developing and maintaining databases for various projects.

• I am proficient in writing complex SQL queries, optimizing database performance, and ensuring data integrity.

• I have also worked with MongoDB for 3 years, utilizing its document-oriented model for scalable and flexible data storage.

• I have experience with MongoDB's aggregation framework, indexing, and sharding for e...read more

Cross join combines every row of the first table with every row of the second table. Self join joins a table with itself.

• Cross join results in a Cartesian product, while self join is used to join a table with itself based on a related column.

• Cross join does not require a common column, while self join requires a related column for joining.

• Example of cross join: SELECT * FROM table1 CROSS JOIN table2;

• Example of self join: SELECT a.column1, b.column2 FROM table a, table b WHERE...read more