Commonly asked MongoDB famous interview questions and answers.

1. How does MongoDB handle large datasets?
   • MongoDB provides horizontal scalability through sharding, allowing for large datasets to be partitioned across multiple servers.
   • It also provides support for distributed data processing using MapReduce and the Aggregation Framework.
2. How does MongoDB handle concurrency control?
   • MongoDB uses a multi-version concurrency control (MVCC) mechanism to ensure data consistency and isolation in concurrent read and write operations.
3. What is the role of a shard key in MongoDB?
   • A shard key is a field or a combination of fields that determines the distribution of data across multiple shards in a sharded cluster.
   • It is used to ensure that related data is stored together on the same shard, allowing for efficient querying and data processing.
4. What is the role of a router in a MongoDB sharded cluster?
   • A router, or mongos, is a process that acts as a proxy between applications and the underlying sharded cluster.
   • It is responsible for directing queries to the appropriate shards and aggregating the results.
5. What is the GridFS in MongoDB?
   • GridFS is a feature in MongoDB that allows for storage and retrieval of files that exceed the BSON document size limit of 16 MB.
   • It stores files in a separate collection and provides a mechanism for dividing large files into smaller chunks for efficient storage and retrieval.
6. What is the difference between a capped collection and a regular collection in MongoDB?
   • A capped collection is a collection that has a fixed size and maintains insertion order. When the collection reaches its maximum size, the oldest documents are removed to make room for new ones.
     • A regular collection, on the other hand, does not have a fixed size and does not maintain insertion order.
   • A capped collection in MongoDB is a type of collection that has a fixed size and a limited set of features, designed for storing data in a circular buffer fashion.
     • Capped collections are created with a maximum size and an optional maximum number of documents, and they maintain insertion order and automatically remove the oldest documents when the maximum size or number is reached.
     • Capped collections are useful for log or event data that needs to be stored in a rolling window, and for implementing tailable cursors that allow for real-time data streaming.
     • Regular collections, on the other hand, have no size or document limit, and support a full range of CRUD operations, indexing, and aggregation.
     • Regular collections are the most common type of collection in MongoDB, and are used for storing application data, user profiles, and other types of data that require flexible schema and rich querying capabilities.

7. What is the use of aggregation pipeline in MongoDB?
   • The aggregation pipeline is a feature in MongoDB that allows for data processing and transformation using a series of stages.
   • Each stage performs a specific operation on the input data, such as filtering, grouping, sorting, and projecting, and passes the result to the next stage in the pipeline.
8. How does MongoDB handle failover in a replica set?
   • MongoDB uses an election process to select a new primary when the current primary node fails.
   • The election process involves a majority vote among the replica set members, with each member having an equal chance of becoming the new primary.
9. How does MongoDB handle data consistency in a sharded cluster?
   • MongoDB uses a distributed transaction protocol called Two-Phase Commit (2PC) to ensure data consistency across shards in a sharded cluster.
   • 2PC involves a coordinator node that coordinates the transaction across multiple shards and ensures that all shards either commit or roll back the transaction.
10. What is the difference between an embedded document and a referenced document in MongoDB?
    • An embedded document is a document that is nested inside another document, while a referenced document is a separate document that is linked to another document using a reference field.
    • Embedded documents can be useful for storing denormalized data or data that is frequently accessed together, while referenced documents can be useful for reducing document size and improving query performance.
11. What is the difference between a horizontal and a vertical scaling in MongoDB?
    • Horizontal scaling involves adding more nodes to a MongoDB cluster to increase the capacity and performance of the database system.
    • Vertical scaling, on the other hand, involves adding more resources to a single node, such as CPU, memory, or storage, to increase its capacity and performance.
12. How does MongoDB handle data replication in a replica set?
    • MongoDB uses a replication protocol called the replication oplog to replicate data across the nodes in a replica set.
    • The oplog is a special capped collection that records all write operations in the primary node and replays them in the secondary nodes.
13. How does MongoDB handle schema design?
    • MongoDB is a schema-less database system, which means that it does not enforce a fixed schema for the documents in a collection.
    • However, it is still important to design a good schema for your data to ensure efficient querying and data processing.
14. What is the role of an index in MongoDB?
    • An index is a data structure that helps to improve query performance by allowing for faster data retrieval.
    • MongoDB supports various types of indexes, such as single-field indexes, compound indexes, geospatial indexes, and text indexes.
15. What is the use of text search in MongoDB?
    • Text search is a feature in MongoDB that allows for full-text search of text fields in a collection.
    • It supports various text search features, such as stemming, stop words, and phrase search, and can be used to implement advanced search functionality in applications.
16. How does MongoDB handle data modeling for relationships between documents?
    • MongoDB supports various data modeling patterns for relationships between documents, such as embedding, referencing, and denormalization.
    • The choice of data modeling pattern depends on the specific requirements of the application and the characteristics of the data.
17. How does MongoDB handle security?
    • MongoDB provides various security features, such as authentication, authorization, encryption, and auditing, to ensure the confidentiality, integrity, and availability of data. It also supports integration with external security systems, such as LDAP and Kerberos.
    • MongoDB provides various security features to protect against unauthorized access and data breaches, such as authentication and authorization, encryption at rest and in transit, role-based access control, and auditing and compliance. It also provides best practices and guidelines for securing MongoDB deployments in different environments and scenarios.
18. What is the role of a connection pool in MongoDB?
    • A connection pool is a mechanism that allows for efficient management and reuse of database connections in applications.
    • MongoDB provides a built-in connection pool for managing connections to the database, which can improve application performance and reduce resource usage.
19. What is the role of a cursor in MongoDB?
    • A cursor is a pointer to the result set of a query in MongoDB.
    • It is used to iterate over the documents in a collection and retrieve them in batches, which can help to reduce memory usage and improve query performance.
20. What is the role of aggregation pipelines in MongoDB?
    • Aggregation pipelines are a powerful feature in MongoDB that allow for complex data processing and analysis.
    • A pipeline is a sequence of stages, where each stage performs a specific operation on the input documents and passes the result to the next stage.
    • The stages can include operations such as filtering, grouping, sorting, projecting, and calculating.
21. How does MongoDB handle sharding?
    • Sharding is a technique used to horizontally scale MongoDB by partitioning data across multiple servers or nodes in a cluster.
    • MongoDB uses a sharding key to determine which shard a document belongs to, and it distributes the data across the shards in a balanced manner to ensure optimal performance and scalability.
22. What is the role of the MongoDB Compass tool?
    • MongoDB Compass is a graphical user interface (GUI) tool that is used to manage and visualize data in MongoDB.
    • It provides features such as data exploration, query building, schema analysis, and performance monitoring, and can be used by developers, DBAs, and analysts to work with MongoDB data.
23. How does MongoDB handle backup and disaster recovery?
    • MongoDB provides various mechanisms for backup and disaster recovery, such as the MongoDB Cloud Backup service, which offers continuous backup and point-in-time recovery, and the MongoDB Management Service, which provides monitoring, backup, and automation features for MongoDB deployments.
24. How does MongoDB handle geographic data?
    • MongoDB provides built-in support for geospatial data, which allows for storing and querying location-based data such as points, lines, and polygons.
    • MongoDB supports various geospatial queries, such as distance queries, intersection queries, and bounding box queries, and can be used to implement location-based applications and services.
25. What is the difference between MongoDB and SQL databases?
    • MongoDB is a NoSQL document-based database, while SQL databases are relational databases.
      • MongoDB uses a flexible document model, which allows for storing and querying unstructured or semi-structured data, while SQL databases use tables with fixed schemas and enforce strict relationships between tables.
    • MongoDB is a NoSQL document-oriented database, while SQL databases are relational databases.
      • MongoDB stores data in documents, which are schema-less and can have nested structures, while SQL databases store data in tables with a fixed schema.
      • MongoDB supports dynamic queries and indexing, while SQL databases require predefined queries and indexing.
      • MongoDB is designed for scalability and high availability, while SQL databases are designed for consistency and ACID transactions.

26. What is the role of a journal in MongoDB?
    • The journal is a feature in MongoDB that ensures durability and consistency of write operations.
    • It is a write-ahead log that records all write operations before they are applied to the data files, which provides a recovery mechanism in case of crashes or failures.
27. How does MongoDB handle concurrency and locking?
    • MongoDB uses a multi-version concurrency control (MVCC) mechanism to handle concurrency and locking.
    • It allows multiple readers to access the same data simultaneously, while writers acquire locks to prevent conflicting writes.
    • MongoDB also supports various isolation levels to control the level of concurrency and consistency.
28. What is the use of GridFS in MongoDB?
    • GridFS is a feature in MongoDB that allows for storing and retrieving large files, such as images, videos, and audio files, as chunks in a separate collection.
    • GridFS provides features such as file streaming, random access, and file metadata, and can be used to implement file-based applications and services.
29. How does MongoDB handle time-series data?
    • MongoDB provides built-in support for time-series data, which allows for storing and querying data based on timestamps or time intervals.
    • MongoDB supports various time-series queries, such as sliding windows, moving averages, and time-bucketed aggregation, and can be used to implement time-series-based applications and services.
30. What is the use of the MongoDB aggregation pipeline stage $lookup?
    • The $lookup stage is used to perform a left outer join between two collections in MongoDB.
    • It allows for retrieving documents from a second collection that match the specified criteria and including them in the result set of the aggregation pipeline.
31. How does MongoDB handle change streams?
    • Change streams are a feature in MongoDB that allows for real-time monitoring and processing of data changes in a collection or database.
    • Change streams provide a stream of change events that can be processed by applications in real-time, and can be used to implement reactive applications and services.
32. What is the use of indexes in MongoDB?
    • Indexes are used to optimize query performance in MongoDB by allowing for faster retrieval of data based on specific criteria.
    • MongoDB supports various types of indexes, such as single field indexes, compound indexes, and multikey indexes, and indexes can be created on any field in a collection.
33. What is the use of the MongoDB WiredTiger storage engine?
    • WiredTiger is the default storage engine in MongoDB since version 3.2.
    • It is a high-performance, transactional, and scalable storage engine that provides features such as compression, encryption, and fine-grained concurrency control, and can be used to improve the performance and reliability of MongoDB deployments.
34. What is the use of the MongoDB aggregation pipeline stage $unwind?
    • The $unwind stage is used to flatten arrays in a document into separate documents in the result set of the aggregation pipeline.
    • It allows for performing aggregation operations on each element of the array individually, and can be used to transform and analyze data in arrays.
35. What is the use of the MongoDB Atlas cloud service?
    • MongoDB Atlas is a fully-managed cloud service for MongoDB that provides features such as automatic scaling, backup and recovery, security and compliance, and monitoring and analytics.
    • It can be used to deploy and manage MongoDB databases in the cloud, and to reduce the operational overhead and costs of managing MongoDB deployments.