65 Essential MongoDB Interview Questions

MongoDB is a high-performance, open-source, document-oriented NoSQL database that stores data in JSON-like BSON format. It is designed to handle unstructured data and offers dynamic schema capabilities.  

The following are the advantages of using MongoDB: 

a) Flexible and schema-less data model 

b) Horizontal scalability with sharding 

c) High performance due to memory-mapped storage engine 

d) Easy integration with various programming languages 

e) Rich query language support 

MongoDB ensures high availability through replica sets. A replica set consists of multiple nodes, including a primary and a secondary node. If and when the primary node fails, the secondary nodes will be automatically elected as the new primary

Sharding is used to distribute data across multiple machines to achieve horizontal scalability. Each shard contains a subset of data, and MongoDB's balancer ensures data distribution and load balancing across shards. 

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BSON or Binary JSON is a binary representation of JSON documents used in MongoDB for efficient storage and data exchange.  

To create a new database, use the use command followed by the database name. MongoDB creates the database when you insert data into it. 

You can insert documents into a collection using the insertOne() or insertMany() methods. 

findOne() returns a single document that matches the query, while find() returns a cursor to the documents that match the query. 

findOne() returns null if no document matches the query, while find() returns an empty cursor. 

You can update a document using the updateOne() or updateMany() methods. 

The Aggregation Pipeline is a powerful tool for processing and transforming data in MongoDB. It consists of stages, each representing a data processing step. Documents are passed through the stages in sequence, allowing complex data manipulations. 

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The $match stage filters documents based on a specified condition, similar to the find() method. 

You can create indexes using the createIndex() method. Indexes improve query performance and help in optimising data retrieval. 

A covered query is a type of query where all the queried fields and the fields to return are covered by an index, making the query faster.  

TTL indexes automatically delete documents after a certain period, specified in seconds. 

db.collection.createIndex({ createdAt: 1 }, { expireAfterSeconds: 3600 }) 

MongoDB supports multi-document transactions for replica sets and sharded clusters using the startSession() and commitTransaction() methods. 

The $project stage allows you to reshape the documents by specifying which fields to include or exclude from the output. 

The $group stage is used for grouping documents by a specified key and performing aggregate functions on the grouped data. 

A compound index includes multiple fields, offering better query performance for complex queries. 

MongoDB supports text search using the $text operator. 

The $unwind stage is used to transform an array field into multiple documents, one for each array element. 

You can perform a case-insensitive search using regular expressions with the i option. 

The $sort stage is used to reorder the documents based on a specified field. 

You can delete documents using the deleteOne() or deleteMany() methods. 

MongoDB offers various indexing strategies, including single-field indexes, compound indexes, multi-key indexes, and geospatial indexes, to optimise query performance for different use cases.  

The $limit stage restricts the number of documents passed to the next stage in the Aggregation Pipeline. 

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You can use the limit() method to restrict the number of documents returned in a query. 

The explain() method provides information about how a query is executed, including details about the chosen index, execution time, and more. It helps in query optimisation. 

GridFS is a specification used for storing and retrieving large binary files, such as images or videos, in MongoDB.  

You can create a TTL index with a filter to apply the expiration only to specific documents. 

Capped collections are fixed-size collections that automatically overwrite older documents when the collection reaches its maximum size. They are useful for storing logs and other time-sensitive data. 

Indexes in MongoDB are data structures that improve the performance of queries by providing efficient access to data. They help speed up data retrieval and reduce the amount of data MongoDB needs to scan to find the matching documents.  

A covered query is a query where all the fields in the query are part of an index, and the index contains all the data needed to fulfil the query. As a result, MongoDB can return the results directly from the index without accessing the actual documents.  

You can perform a case-insensitive sort using the $collation option.  

The Aggregation Framework is a powerful tool for processing and transforming data in MongoDB. It consists of pipeline stages that allow complex data manipulations, filtering, grouping, and more.  

The $group stage groups documents by a specified key and performs aggregate functions on the grouped data.  

To perform a text search with stemming, use the $text operator along with the $language option. 

In MongoDB, the primary key is the _id field, and it uniquely identifies each document in a collection. Once set, the _id value is immutable and cannot be changed.  

You can create a compound index with a unique constraint by setting the unique option to true. 

Secondary indexes in MongoDB are additional indexes created on fields other than the primary key. They allow for efficient querying on frequently accessed fields.  

You can use the limit() method to restrict the number of documents returned in a query. 

The $skip stage is used to skip a specified number of documents in the Aggregation Pipeline. 

To perform a case-sensitive search, use a regular expression without the i option. 

Journaling in MongoDB is a feature that provides data durability by writing every write operation to a journal before applying it to the database. This ensures that data is not lost in the event of a crash.  

You can use the distinct() method to retrieve distinct values from a field. 

In a NoSQL database like MongoDB, data is stored in a non-tabular, flexible format, usually as JSON-like documents. NoSQL databases are horizontally scalable and better suited for handling unstructured data and large volumes. On the other hand, a relational database follows a tabular structure with predefined schemas and is suitable for structured data with complex relationships between tables.  

MongoDB supports multi-document transactions for replica sets and sharded clusters. Transactions provide all-or-nothing semantics, allowing multiple operations to be grouped into a single transaction that either succeeds completely or fails entirely.  

Map-Reduce is a data processing paradigm used in MongoDB to perform complex data aggregations and transformations. It involves two stages: Map, where data is sorted and grouped based on a key, and Reduce, where the grouped data is aggregated based on the key.  

To perform a case-insensitive text search with stemming, use the $text operator along with the $caseSensitive and $diacriticSensitive options. 

The database profiler is a feature in MongoDB that allows you to record and analyse performance statistics for database operations. It helps in identifying slow queries and potential bottlenecks. 

You can create a compound index with a partial filter expression to apply the index only to specific documents. 

The $graphLookup stage performs a recursive search on a collection, allowing you to perform graph-based queries and retrieve connected data.  

You can backup a MongoDB database using the mongodump command and restore it using the mongorestore command. 

WiredTiger is the default storage engine in MongoDB since version 3.2. It offers significant improvements in performance, compression, and concurrency control, making MongoDB more efficient and scalable.  

You can use the countDocuments() method to retrieve the total count of documents in a collection. 

Change Streams in MongoDB allow applications to listen for real-time changes in a collection. They provide a way to monitor data changes and trigger actions based on those changes.  

To create an index on an array field, specify the field name inside quotes in the createIndex() method. 

The $out stage writes the output of an aggregation pipeline to a specified collection, effectively creating a new collection with the aggregated data.  

MongoDB 4.0 introduced collation support directly in the sort() method. 

Sharding is the process of distributing data across multiple servers or shards to achieve horizontal scalability and handle large amounts of data. While replication is the process of synchronising data across multiple servers to ensure high availability and data redundancy. It involves creating multiple copies (replicas) of data on different nodes. 

You can use the stats() method to find the size of a collection in MongoDB. 

db.collection.stats().size 

You can use the stats() method to find the size of a collection in MongoDB. 

db.collection.stats().size 

The $redact stage allows you to apply access control rules to restrict the documents returned in the aggregation pipeline based on certain conditions.  

To perform a full-text search with weights, use the $text operator along with the $search option and the $meta projection to specify the weights for fields. 

TTL (Time-to-Live) indexes in MongoDB are special indexes that automatically remove documents after a specified amount of time. They are commonly used for data that has an expiration date, such as temporary sessions or log entries.