Commonly asked MongoDB famous interview questions and answers.

1. What is MongoDB?
   • MongoDB is a document-oriented NoSQL database that stores data in JSON-like documents with dynamic schemas.
2. What are the advantages of using MongoDB over a relational database?
   • MongoDB provides a flexible schema design that allows for easy and dynamic document structure changes, better performance for large-scale data, and horizontal scalability with automatic sharding.
3. What is a document in MongoDB?
   • A document in MongoDB is a unit of data storage that is roughly equivalent to a row in a relational database.
4. What is a collection in MongoDB?
   • A collection in MongoDB is a group of documents that are stored together in a MongoDB database.
5. What is the syntax for inserting a document in MongoDB?
   • The syntax for inserting a document in MongoDB is:
     • collection_name.insertOne({field1: value1, field2: value2, …})

6. What is the difference between updateOne() and updateMany() methods?
   • The updateOne() method updates the first document that matches the specified filter while updateMany() method updates all the documents that match the specified filter.
7. What is the difference between findOne() and find() methods?
   • The findOne() method returns a single document that matches the specified filter while the find() method returns a cursor to all documents that match the specified filter.
8. What is the difference between a primary key and a unique key in MongoDB?
   • A primary key is a unique identifier for a document in a collection. MongoDB automatically creates a unique _id field for each document as its primary key.
   • A unique key is a field or combination of fields that must be unique across all documents in a collection.
9. What is sharding in MongoDB?
   • Sharding is the process of horizontally scaling a MongoDB database by partitioning data across multiple servers called shards.
   • Sharding is a technique in MongoDB for distributing data across multiple servers, or shards, to improve performance, scalability, and availability.
   • Each shard contains a subset of the data, and MongoDB automatically routes queries and updates to the appropriate shards based on a shard key.
   • Sharding can be used to support large-scale applications with high data volume, throughput, and concurrency, and to handle horizontal scaling and fault tolerance.
   • Sharding is a mechanism in MongoDB for distributing data across multiple nodes or shards to achieve horizontal scalability and handle large volumes of data.
   • Each shard contains a subset of the data, and queries are routed to the appropriate shard based on the value of a shard key.
   • Sharding requires a cluster of MongoDB instances, a shard key that evenly distributes data, and a configuration server that maps the shards to the data.
   • Sharding can be used to increase the capacity, availability, and performance of MongoDB, but also requires careful planning and monitoring.
   • Sharding is a technique in MongoDB for distributing data across multiple servers, or shards, in order to improve scalability and performance.
   • Sharding allows MongoDB to handle large amounts of data and high traffic by partitioning the data into smaller chunks, called shards, and storing each shard on a separate server.
   • Each shard is a self-contained subset of the data, and can be replicated to provide high availability and fault tolerance.
   • MongoDB uses a sharding key, which is a field or combination of fields in the documents, to determine which shard to store each document on.
   • The sharding key should be chosen carefully to ensure even distribution of data and optimal query performance.
10. What is aggregation in MongoDB?
    • Aggregation is a framework for data analysis in MongoDB that allows users to perform complex queries on data and get meaningful results.
11. What is the syntax for creating an index in MongoDB?
    • The syntax for creating an index in MongoDB is:
      • collection_name.createIndex({field: 1})
    • Where 1 is the index type for ascending order and -1 is for descending order.
12. What is MongoDB Atlas?
    • MongoDB Atlas is a fully-managed cloud database service for MongoDB that provides a scalable, secure, and reliable way to run MongoDB databases in the cloud.
    • MongoDB Atlas supports various cloud providers, such as AWS, Azure, and Google Cloud, and provides features such as automated backups, point-in-time recovery, monitoring, alerting, and security controls.
    • MongoDB Atlas simplifies database administration and reduces operational overhead, allowing developers to focus on building applications.
    • MongoDB Atlas is a cloud-based database service provided by MongoDB Inc. that offers a fully managed and scalable solution for deploying, managing, and monitoring MongoDB clusters.
    • MongoDB Atlas supports multiple cloud providers, such as AWS, Azure, and Google Cloud Platform, and provides features such as automatic scaling, backups, and disaster recovery.
    • MongoDB Atlas also supports advanced security features, such as VPC peering, network isolation, and client-side field level encryption. MongoDB Atlas can be accessed through a web-based console, a REST API, or a command-line interface.
13. What is a replica set in MongoDB?
    • A replica set is a group of MongoDB servers that maintain the same data set.
    • It provides redundancy and high availability, allowing automatic failover in case of a primary node failure.
    • A replica set is a group of MongoDB instances that store the same data and provide high availability and fault tolerance.
    • A replica set consists of one primary node that accepts write operations and multiple secondary nodes that replicate the data from the primary and can serve read operations.
    • If the primary node fails, the replica set automatically elects a new primary from the available secondaries.
    • Replica sets can be configured with different levels of durability and consistency, depending on the requirements of the application.
14. How does MongoDB ensure data consistency in a replica set?
    • MongoDB uses the concept of a primary node, which is responsible for all write operations.
    • The primary node then replicates data changes to the secondary nodes, ensuring data consistency.
15. What is the difference between a replica set and a sharded cluster in MongoDB?
    • A replica set provides high availability and redundancy by replicating data across multiple nodes.
      • A sharded cluster, on the other hand, provides horizontal scalability by partitioning data across multiple shards.
    • A replica set is a group of MongoDB instances that maintain a copy of the same data for high availability and data redundancy.
      • A sharded cluster, on the other hand, is a distributed database system that stores data across multiple shards, each consisting of a replica set.
    • A replica set is a group of MongoDB servers that maintain a copy of the same data and elect a primary server for handling write operations.
      • Replica sets provide high availability, durability, and failover, but do not provide horizontal scaling.
      • A sharded cluster is a group of MongoDB servers that distribute data across multiple shards and enable horizontal scaling, but do not provide automatic failover.
      • Sharded clusters require more configuration and management than replica sets, but can support very large-scale applications.
    • A replica set in MongoDB is a group of MongoDB instances that maintain the same data set and provide high availability and fault tolerance by electing a primary node to receive all write operations and allowing the secondary nodes to replicate the data asynchronously or synchronously.
      • A replica set is designed to provide scalability and load balancing within a single data center or geographic region.
      • On the other hand, a sharded cluster in MongoDB is a distributed system that partitions data across multiple nodes and allows horizontal scaling across multiple data centers or regions.
      • A sharded cluster is designed to provide high performance and scalability by allowing queries to be distributed across multiple shards, each of which can handle a subset of the data.

16. How does MongoDB ensure security?
    • MongoDB provides several security features, including authentication, role-based access control, encryption at rest, and transport encryption using SSL/TLS.
17. What is the difference between SQL and MongoDB?
    • SQL is a relational database management system that stores data in tables with a fixed schema.
    • MongoDB, on the other hand, is a document-oriented NoSQL database that stores data in JSON-like documents with dynamic schemas.
18. What is GridFS in MongoDB?
    • GridFS is a file storage system in MongoDB that allows users to store and retrieve large files, such as images, videos, and audio files.
    • GridFS is a specification in MongoDB for storing and retrieving large files, such as images, videos, and audio, that exceed the BSON document size limit of 16 MB.
    • GridFS divides a file into smaller chunks and stores them as separate documents in two collections: one for the metadata and one for the chunks.
    • GridFS provides features for efficient reading and writing of large files, as well as for querying and deleting files based on metadata criteria.
19. What is a TTL index in MongoDB?
    • A TTL (time-to-live) index is a special type of index in MongoDB that automatically deletes documents after a certain amount of time has passed.
    • A TTL (time-to-live) index is a type of index in MongoDB that automatically deletes documents based on a specified expiration time. TTL indexes can be used to remove stale data, such as logs, sessions, or temporary data, without requiring manual intervention or maintenance.
    • TTL indexes are created on a single field of type Date, and MongoDB periodically scans the index to identify and delete expired documents. TTL indexes can be configured to expire documents after a certain time interval, such as seconds, minutes, hours, or days.
    • A TTL (time-to-live) index is a type of index in MongoDB that automatically deletes documents after a certain amount of time has passed since their creation or last modification.
    • TTL indexes are useful for storing data that has an expiration date, such as session tokens, cache entries, or temporary documents.
    • TTL indexes can be created by specifying the expireAfterSeconds option and the field that contains the expiration time.
    • The TTL index is checked periodically by a background task, and any expired documents are deleted from the collection.
20. How do you backup and restore a MongoDB database?
    • MongoDB provides several backup and restore mechanisms, including mongodump and mongorestore command-line tools, as well as cloud-based solutions like MongoDB Atlas Backup.
21. What is the difference between find() and aggregate() methods in MongoDB?
    • The find() method is used to retrieve documents from a collection based on a query, while the aggregate() method is used to perform more complex data analysis operations, such as grouping, sorting, and counting data.
22. What is a covered query in MongoDB?
    • A covered query is a query that can be satisfied entirely using an index, without having to access the actual documents in the collection. This can significantly improve query performance.
    • A covered query in MongoDB is a query that can be satisfied entirely using the index without the need to access the actual documents in the collection.
    • A covered query is a highly optimized type of query that can significantly improve the performance and scalability of the database.
    • A covered query can be achieved by ensuring that all the fields that are queried or projected are covered by the index, and that the index is used to satisfy the query by the query optimizer.
    • A covered query can reduce the amount of disk I/O, memory usage, and network traffic, and can speed up the response time and throughput of the database.
    • A covered query in MongoDB is a type of query that can be fully satisfied using only the index keys and does not require any further access to the documents in the collection.
    • A covered query is also known as an index-only query, as it only needs to access the index data and not the actual document data.
    • Covered queries can provide significant performance benefits, as they reduce the amount of I/O and memory required to fetch and process the query results.
    • To create a covered query, the query must be selective enough to match a subset of the documents in the collection, and the projection must include only the indexed fields.
    • MongoDB supports covered queries on single-field indexes and compound indexes, as well as on text indexes and geo-spatial indexes with specific query patterns.
23. What is a text index in MongoDB?
    • A text index is a special type of index in MongoDB that allows for efficient text search queries.
    • It supports several text-specific features, such as stemming, stop words, and case-insensitive search.
24. What is a compound index in MongoDB?
    • A compound index is an index that includes multiple fields in a single index.
    • This can be useful for queries that involve multiple fields, as it can improve query performance.
25. What is the difference between the $and and $or operators in MongoDB?
    • The $and operator is used to combine multiple conditions, while the $or operator is used to specify that at least one of several conditions must be true.
26. How does MongoDB handle transactions?
    • MongoDB supports multi-document transactions in replica sets and sharded clusters, using the two-phase commit protocol.
    • MongoDB introduced support for multi-document transactions in version 4.0, which allows for atomic and consistent operations across multiple documents in a collection or multiple collections within a single replica set.
    • Transactions are used to ensure data consistency and reliability in complex operations that involve multiple write operations.
    • MongoDB supports multi-document transactions in version 4.0 and later, using the WiredTiger storage engine.
    • Transactions in MongoDB are ACID-compliant, meaning they ensure atomicity, consistency, isolation, and durability of the data changes.
    • Transactions can span multiple collections or shards within a replica set or a sharded cluster, and can be initiated by using the startTransaction, commitTransaction, and abortTransaction commands.
    • MongoDB also provides a retryable writes feature that ensures that write operations are retried in case of transient errors.
27. What is a change stream in MongoDB?
    • A change stream is a real-time feed of changes to a MongoDB collection.
    • It allows applications to receive notifications when data changes, enabling reactive programming and real-time analytics.
28. What is the difference between a local and a global secondary index in MongoDB?
    • A local secondary index is an index that is created on a single shard in a sharded cluster and only applies to the data stored on that shard. A global secondary index, on the other hand, is an index that is created on all shards in a sharded cluster and applies to all data stored in the cluster.
29. How does MongoDB handle schema evolution?
    • MongoDB provides a flexible schema design that allows for easy and dynamic document structure changes, without requiring downtime or manual schema migrations.
    • MongoDB supports flexible and dynamic schemas that can evolve over time as the requirements of the application change.
    • MongoDB does not enforce a strict schema or require a predefined schema for all documents in a collection.
    • Instead, MongoDB allows documents to have different structures and fields, and allows the addition or removal of fields at any time.
    • MongoDB also supports indexes on nested fields and arrays, and allows partial updates of documents using the $set and $unset operators.
    • MongoDB provides several tools and APIs for schema validation, migration, and exploration, such as the validate command, the mongodump and mongorestore utilities, and the aggregation framework.
30. What is the difference between MongoDB and Cassandra?
    • Cassandra is a distributed NoSQL database that is designed for high write throughput and fault tolerance, while MongoDB is a document-oriented NoSQL database that is designed for flexible and scalable data storage and querying.
31. What is the difference between a hot backup and a cold backup in MongoDB?
    • A hot backup is a backup that is taken while the database is still running, allowing for continuous data availability.
    • A cold backup, on the other hand, is a backup that is taken while the database is offline, resulting in temporary data unavailability.
32. How does MongoDB handle indexing of arrays?
    • MongoDB allows for indexing of array fields, which can improve query performance.
    • Indexing of arrays can be done using multikey indexes or text indexes, depending on the type of array field.
33. What is the difference between a primary key and a unique index in MongoDB?
    • A primary key is a field or a combination of fields that uniquely identifies each document in a collection.
    • A unique index, on the other hand, is an index that ensures that no two documents in a collection have the same value for a specified field or combination of fields.
34. How does MongoDB handle query optimization?
    • MongoDB uses a query optimizer to determine the most efficient execution plan for each query.
    • The optimizer uses the available indexes and statistics about the data to select the optimal execution plan.
35. What is the maximum size of a MongoDB document?
    • The maximum size of a MongoDB document is 16 megabytes.