MongoDB is one of the most popular NoSQL databases, known for its flexibility, scalability, and ability to handle unstructured data. Many top companies rely on MongoDB, making it an essential skill for developers. Whether you’re starting your career or looking to level up, preparing well for your MongoDB interview is the key to success.
In this article, we have compiled a complete list of MongoDB interview questions, covering everything from basics to advanced concepts. These questions will help you build confidence, understand key topics, and ace your interview. Start preparing now to secure your dream job!
MongoDB Basic Interview Questions
MongoDB Basic Interview Questions focus on foundational knowledge, helping you understand how MongoDB operates as a NoSQL database. These questions typically cover key concepts like the differences between SQL and NoSQL databases, how MongoDB structures and stores data, and basic operations like CRUD (Create, Read, Update, Delete).
1. What is MongoDB, and How Does It Differ from Traditional SQL Databases?
- MongoDB is a NoSQL database which means it does not use the traditional table-based relational database structure. Instead of it uses a flexible and document-oriented data model that stores data in BSON (Binary JSON) format.
- Unlike SQL databases that use rows and columns, MongoDB stores data as JSON-like documents, making it easier to handle unstructured data and providing greater flexibility in terms of schema design.
2. Explain BSON and Its Significance in MongoDB.
BSON (Binary JSON) is a binary-encoded serialization format used by MongoDB to store documents. BSON extends JSON by adding support for data types such as dates and binary data and it is designed to be efficient in both storage space and scan speed. The binary format allows MongoDB to be more efficient with data retrieval and storage compared to text-based JSON.
3. Describe the Structure of a MongoDB Document.
A MongoDB document is a set of key-value pairs similar to a JSON object. Each key is a string and the value can be a variety of data types including strings, numbers, arrays, nested documents and more.
Example:
{
"_id": ObjectId("507f1f77bcf86cd799439011"),
"name": "Alice",
"age": 25,
"address": {
"street": "123 Main St",
"city": "Anytown",
"state": "CA"
},
"hobbies": ["reading", "cycling"]
}4. What are Collections And Databases In MongoDB?
- Database: A container for collections, equivalent to a database in SQL.
- Collection: A group of documents, similar to tables in SQL, but schema-less.
- For example, a
users collection can be part of the mydatabase database.
5. How Does MongoDB Ensure High Availability and Scalability?
- MongoDB ensures high availability and scalability through its features like replica sets and sharding.
- Replica sets provide redundancy and failover capabilities by ensuring that data is always available.
- Sharding distributes data across multiple servers, enabling horizontal scalability to handle large volumes of data and high traffic loads.
6. Explain the Concept of Replica Sets in MongoDB.
- A replica set in MongoDB is a group of mongod instances that maintain the same data set.
- A replica set consists of a primary node and multiple secondary nodes.
- The primary node receives all write operations while secondary nodes replicate the primary's data and can serve read operations.
- If the primary node fails, an automatic election process selects a new primary to maintain high availability.
7. What are the Advantages of Using MongoDB Over Other Databases?
- Flexibility: MongoDB's document-oriented model allows for dynamic schemas.
- Scalability: Built-in sharding enables horizontal scaling.
- High Availability: Replica sets provide redundancy and automatic failover.
- Performance: Efficient storage and retrieval with BSON format.
- Ease of Use: JSON-like documents make it easier for developers to interact with data.
8. How to Create a New Database and Collection in MongoDB?
To create a new database and collection in MongoDB, you can use the mongo shell:
use mydatabase
db.createCollection("mycollection")
This command switches to mydatabase (creating it if it doesn't exist) and creates a new collection named mycollection.
9. What is Sharding, and How Does It Work in MongoDB?
Sharding is a method for distributing data across multiple servers in MongoDB. It allows for horizontal scaling by splitting large datasets into smaller, more manageable pieces called shards.
- Each shard is a separate database that holds a portion of the data.
- MongoDB automatically balances data and load across shards, ensuring efficient data distribution and high performance.
10. Explain the Basic Syntax of MongoDB CRUD Operations.
CRUD operations in MongoDB are used to create, read, update, and delete documents.
- Create: db.collection.insertOne({ name: "Alice", age: 25 })
- Read: db.collection.find({ name: "Alice" })
- Update: db.collection.updateOne({ name: "Alice" }, { $set: { age: 26 } })
- Delete: db.collection.deleteOne({ name: "Alice" })
Basic querying in MongoDB involves using the find method to retrieve documents that match certain criteria.
Example:
db.collection.find({ age: { $gte: 20 } })This query retrieves all documents from the collection where the age field is greater than or equal to 20.
12. What is an Index in MongoDB, and How to Create One?
An index in MongoDB is a data structure that improves the speed of data retrieval operations on a collection. You can create an index using the createIndex method.
For example, to create an index on the name field:
db.collection.createIndex({ name: 1 })13. How Does MongoDB Handle Data Consistency?
MongoDB provides several mechanisms to ensure data consistency:
- Journaling: MongoDB uses write-ahead logging to maintain data integrity.
- Write Concerns: It specify the level of acknowledgment requested from MongoDB for write operations (e.g., acknowledgment from primary only, or acknowledgment from primary and secondaries).
- Replica Sets: Replication ensures data is consistent across multiple nodes, and read concerns can be configured to ensure data consistency for read operations.
To perform data import and export in MongoDB, you can use the mongoimport and mongoexport tools. These tools allow you to import data from JSON, CSV or TSV files into MongoDB and export data from MongoDB collections to JSON or CSV files.
Import Data:
mongoimport --db mydatabase --collection mycollection --file data.json
This command imports data from data.json into the mycollection collection in the mydatabase database.
Export Data:
mongoexport --db mydatabase --collection mycollection --out data.json
This command exports data from the mycollection collection in the mydatabase database to data.json.
15. What are MongoDB Aggregation Pipelines and How are They Used?
The aggregation pipeline is a framework for data aggregation, modeled on the concept of data processing pipelines. Documents enter a multi-stage pipeline that transforms the documents into aggregated results. Each stage performs an operation on the input documents and passes the results to the next stage.
db.orders.aggregate([
{ $match: { status: "A" } }, // Stage 1: Filter documents by status
{ $group: { _id: "$cust_id", total: { $sum: "$amount" } } }, // Stage 2: Group by customer ID and sum the amount
{ $sort: { total: -1 } } // Stage 3: Sort by total in descending order
])
In this example:
- Stage 1 ($match) filters documents by status "A".
- Stage 2 ($group) groups documents by customer ID and calculates the total amount for each group.
- Stage 3 ($sort) sorts the results by total amount in descending order.
Aggregation pipelines are powerful and flexible, enabling complex data processing tasks to be executed within MongoDB.
MongoDB Intermediate Interview Questions explore advanced concepts and features, such as schema design, aggregation pipelines, indexing strategies, and transaction management. These questions help gauge your ability to utilize MongoDB efficiently in more complex scenarios.
1. Describe the Aggregation Framework in MongoDB
- The Aggregation Framework in MongoDB is a powerful tool for performing data processing and transformation on documents within a collection.
- It works by passing documents through a multi-stage pipeline, where each stage performs a specific operation on the data, such as filtering, grouping, sorting, reshaping and computing aggregations.
- This framework is particularly useful for creating complex data transformations and analytics directly within the database.
Aggregation operations in MongoDB are performed using the aggregate method. This method takes an array of pipeline stages, each stage representing a step in the data processing pipeline.
Example: Calculate total sales for each product:
db.sales.aggregate([
{ $match: { status: "completed" } }, // Filter completed sales
{ $group: { _id: "$product", totalSales: { $sum: "$amount" } } }, // Group by product and sum the sales amount
{ $sort: { totalSales: -1 } } // Sort by total sales in descending order
])
3. Explain the Concept of Write Concern and Its Importance in MongoDB
Write Concern in MongoDB refers to the level of acknowledgment requested from MongoDB for write operations. It determines how many nodes must confirm the write operation before it is considered successful. Write concern levels range from "acknowledged" (default) to "unacknowledged," "journaled," and various "replica acknowledged" levels.
The importance of write concern lies in balancing between data durability and performance. Higher write concern ensures data is safely written to disk and replicated, but it may impact performance due to the added latency.
4. What are TTL Indexes, and How are They Used in MongoDB?
TTL (Time To Live) Indexes in MongoDB are special indexes that automatically remove documents from a collection after a certain period. They are commonly used for data that needs to expire after a specific time, such as session information, logs, or temporary data. To create a TTL index, you can specify the expiration time in seconds
Example: Remove documents 1 hour after createdAt:
db.sessions.createIndex({ "createdAt": 1 }, { expireAfterSeconds: 3600 })This index will remove documents from the sessions collection 1 hour (3600 seconds) after the createdAt field's value.
5. How to Handle Schema Design and Data Modeling in MongoDB?
Schema design and data modeling in MongoDB involve defining how data is organized and stored in a document-oriented database. Unlike SQL databases, MongoDB offers flexible schema design, which can be both an advantage and a challenge. Key considerations for schema design include:
- Embedding vs. Referencing: Deciding whether to embed related data within a single document or use references between documents.
- Document Structure: Designing documents that align with application query patterns for efficient read and write operations.
- Indexing: Creating indexes to support query performance.
- Data Duplication: Accepting some level of data duplication to optimize for read performance.
- Sharding: Designing the schema to support sharding if horizontal scaling is required.
6. What is GridFS, and When is it Used in MongoDB?
GridFS is a specification for storing and retrieving large files in MongoDB. It is used when files exceed the BSON-document size limit of 16 MB or when you need to perform efficient retrieval of specific file sections.
GridFS splits a large file into smaller chunks and stores each chunk as a separate document within two collections: fs.files and fs.chunks. This allows for efficient storage and retrieval of large files, such as images, videos, or large datasets.
7. Explain the Differences Between WiredTiger and MMAPv1 Storage Engines
| Feature | WiredTiger | MMAPv1 |
|---|
| Concurrency | Document-level concurrency, allowing multiple operations simultaneously. | Collection-level concurrency, limiting performance under heavy write operations. |
| Compression | Supports data compression, reducing storage requirements. | Does not support data compression. |
| Performance | Generally offers better performance and efficiency for most workloads. | Limited performance, especially under heavy workloads. |
| Journaling | Uses write-ahead logging for better data integrity. | Basic journaling; less advanced than WiredTiger. |
| Status | Modern and default storage engine. | Legacy engine, deprecated in favor of WiredTiger. |
| Implementation | Advanced implementation with additional features. | Simple implementation but lacks advanced features. |
8. How to Handle Transactions in MongoDB?
MongoDB supports multi-document ACID transactions by allowing us to perform a series of read and write operations across multiple documents and collections in a transaction. This ensures data consistency and integrity. To use transactions we typically start a session, begin a transaction, perform the operations and then commit or abort the transaction.
Example in JavaScript:
const session = client.startSession();
session.startTransaction();
try {
db.collection1.insertOne({ name: "Alice" }, { session });
db.collection2.insertOne({ name: "Bob" }, { session });
session.commitTransaction();
} catch (error) {
session.abortTransaction();
} finally {
session.endSession();
}
MongoDB Compass is a graphical user interface (GUI) tool for MongoDB that provides an easy way to visualize, explore, and manipulate your data. It offers features such as:
- Schema Visualization: View and analyze your data schema, including field types and distributions.
- Query Building: Build and execute queries using a visual interface.
- Aggregation Pipeline: Construct and run aggregation pipelines.
- Index Management: Create and manage indexes to optimize query performance.
- Performance Monitoring: Monitor database performance, including slow queries and resource utilization.
- Data Validation: Define and enforce schema validation rules to ensure data integrity.
- Data Import/Export: Easily import and export data between MongoDB and JSON/CSV files.
10. What is MongoDB Atlas, and How Does it Differ From Self-Hosted MongoDB?
MongoDB Atlas is a fully managed cloud database service provided by MongoDB. It offers automated deployment, scaling, and management of MongoDB clusters across various cloud providers (AWS, Azure, Google Cloud). Key differences from self-hosted MongoDB include:
- Managed Service: Atlas handles infrastructure management, backups, monitoring, and upgrades.
- Scalability: Easily scale clusters up or down based on demand.
- Security: Built-in security features such as encryption, access controls, and compliance certifications.
- Global Distribution: Deploy clusters across multiple regions for low-latency access and high availability.
- Integrations: Seamless integration with other cloud services and MongoDB tools.
11. How to Implement Access Control and User Authentication in MongoDB?
Access control and user authentication in MongoDB are implemented through a role-based access control (RBAC) system. You create users and assign roles that define their permissions. To set up access control:
- Enable Authentication: Configure MongoDB to require authentication by starting the server with --auth or setting security.authorization to enabled in the configuration file.
- Create Users: Use the db.createUser method to create users with specific roles.
db.createUser({
user: "admin",
pwd: "password",
roles: [{ role: "userAdminAnyDatabase", db: "admin" }]
});- Assign Roles: Assign roles to users that define their permissions, such as read, write, or admin roles for specific databases or collections.
12. What are Capped Collections, and When are They Useful?
Capped collections in MongoDB are fixed-size collections that automatically overwrite the oldest documents when the specified size limit is reached. They maintain insertion order and are useful for scenarios where you need to store a fixed amount of recent data, such as logging, caching, or monitoring data.
Example of creating a capped collection:
db.createCollection("logs", { capped: true, size: 100000 });13. Explain the Concept of Geospatial Indexes in MongoDB
Geospatial indexes in MongoDB are special indexes that support querying of geospatial data, such as locations and coordinates. They enable efficient queries for proximity, intersections, and other spatial relationships. MongoDB supports two types of geospatial indexes: 2d for flat geometries and 2dsphere for spherical geometries.
Example of creating a 2dsphere index:
db.places.createIndex({ location: "2dsphere" });14. How to Handle Backups and Disaster Recovery in MongoDB?
Handling backups and disaster recovery in MongoDB involves regularly creating backups of your data and having a plan for restoring data in case of failure. Methods include:
- Mongodump/Mongorestore: Use the mongodump and mongorestore utilities to create and restore binary backups.
- File System Snapshots: Use file system snapshots to take consistent backups of the data files.
- Cloud Backups: If using MongoDB Atlas, leverage automated backups provided by the service.
- Replica Sets: Use replica sets to ensure data redundancy and high availability. Regularly test the failover and recovery process.
15. Describe the Process of Upgrading MongoDB to a Newer Version
Upgrading MongoDB to a newer version involves several steps to ensure a smooth transition:
- Check Compatibility: Review the release notes and compatibility changes for the new version.
- Backup Data: Create a backup of your data to prevent data loss.
- Upgrade Drivers: Ensure that your application drivers are compatible with the new MongoDB version.
- Upgrade MongoDB: Follow the official MongoDB upgrade instructions, which typically involve stopping the server, installing the new version, and restarting the server.
- Test Application: Thoroughly test your application with the new MongoDB version to identify any issues.
- Monitor: Monitor the database performance and logs to ensure a successful upgrade.
16. What are Change Streams in MongoDB, and How are They Used?
Change Streams in MongoDB allow applications to listen for real-time changes to data in collections, databases, or entire clusters. They provide a powerful way to implement event-driven architectures by capturing insert, update, replace, and delete operations. To use Change Streams, you typically open a change stream cursor and process the change events as they occur.
Example:
const changeStream = db.collection('orders').watch();
changeStream.on('change', (change) => {
console.log(change);
});This example listens for changes in the orders collection and logs the change events.
17. Explain the Use of Hashed Sharding Keys in MongoDB
Hashed Sharding Keys in MongoDB distribute data across shards using a hashed value of the shard key field. This approach ensures an even distribution of data and avoids issues related to data locality or uneven data distribution that can occur with range-based sharding. Hashed sharding is useful for fields with monotonically increasing values, such as timestamps or identifiers.
Example:
db.collection.createIndex({ _id: "hashed" });
sh.shardCollection("mydb.mycollection", { _id: "hashed" });Optimizing MongoDB queries involves several strategies:
- Indexes: Create appropriate indexes to support query patterns.
- Query Projections: Use projections to return only necessary fields.
- Index Hinting: Use index hints to force the query optimizer to use a specific index.
- Query Analysis: Use the explain() method to analyze query execution plans and identify bottlenecks.
- Aggregation Pipeline: Optimize the aggregation pipeline stages to minimize data processing and improve efficiency.
19. Describe the Map-Reduce Functionality in MongoDB
Map-Reduce in MongoDB is a data processing paradigm used to perform complex data aggregation operations. It consists of two phases: the map phase processes each input document and emits key-value pairs, and the reduce phase processes all emitted values for each key and outputs the final result.
Example:
db.collection.mapReduce(
function() { emit(this.category, this.price); },
function(key, values) { return Array.sum(values); },
{ out: "category_totals" }
);
This example calculates the total price for each category in a collection.
Journaling in MongoDB ensures data durability and crash recovery by recording changes to the data in a journal file before applying them to the database files. This mechanism allows MongoDB to recover from unexpected shutdowns or crashes by replaying the journal. While journaling provides data safety, it can impact performance due to the additional I/O operations required to write to the journal file.
21. How to Implement Full-Text Search in MongoDB?
Full-Text Search in MongoDB is implemented using text indexes. These indexes allow you to perform text search queries on string content within documents.
Example:
db.collection.createIndex({ content: "text" });
db.collection.find({ $text: { $search: "mongodb" } });In this example, a text index is created on the content field, and a text search query is performed to find documents containing the word "mongodb."
22. What are the Considerations for Deploying MongoDB in a Production Environment?
Considerations for deploying MongoDB in a production environment include:
- Replication: Set up replica sets for high availability and data redundancy.
- Sharding: Implement sharding for horizontal scaling and to distribute the load.
- Backup and Recovery: Establish a robust backup and recovery strategy.
- Security: Implement authentication, authorization, and encryption.
- Monitoring: Use monitoring tools to track performance and detect issues.
- Capacity Planning: Plan for adequate storage, memory, and CPU resources.
- Maintenance: Regularly update MongoDB to the latest stable version and perform routine maintenance tasks.
23. Explain the Concept of Horizontal Scalability and Its Implementation in MongoDB
- Horizontal Scalability in MongoDB refers to the ability to add more servers to distribute the load and data. This is achieved through sharding, where data is partitioned across multiple shards.
- Each shard is a replica set that holds a subset of the data. Sharding allows MongoDB to handle large datasets and high-throughput operations by distributing the workload.
Monitoring and troubleshooting performance issues in MongoDB involve:
- Monitoring Tools: Use tools like MongoDB Cloud Manager, MongoDB Ops Manager, or third-party monitoring solutions.
- Logs: Analyze MongoDB logs for errors and performance metrics.
- Profiling: Enable database profiling to capture detailed information about operations.
- Explain Plans: Use the explain() method to understand query execution and identify bottlenecks.
- Index Analysis: Review and optimize indexes based on query patterns and usage.
- Resource Utilization: Monitor CPU, memory, and disk I/O usage to identify resource constraints.
25. Describe the Process of Migrating Data from a Relational Database to MongoDB
Migrating data from a relational database to MongoDB involves several steps:
- Schema Design: Redesign the relational schema to fit MongoDB's document-oriented model. Decide on embedding vs. referencing, and plan for indexes and collections.
- Data Export: Export data from the relational database in a format suitable for MongoDB (e.g., CSV, JSON).
- Data Transformation: Transform the data to match the MongoDB schema. This can involve converting data types, restructuring documents, and handling relationships.
- Data Import: Import the transformed data into MongoDB using tools like mongoimport or custom scripts.
- Validation: Validate the imported data to ensure consistency and completeness.
- Application Changes: Update the application code to interact with MongoDB instead of the relational database.
- Testing: Thoroughly test the application and the database to ensure everything works as expected.
- Go Live: Deploy the MongoDB database in production and monitor the transition.
MongoDB Query Based Interview Questions
MongoDB Query-Based Interview Questions focus on your ability to write efficient and optimized queries to interact with databases. These tasks include retrieving specific data using filters, sorting and paginating results, and utilizing projections to select desired fields. Below is a sample dataset we'll use to demonstrate various queries.
Sample Dataset
The following dataset represents a collection named employees, containing documents about employees in an organization. Each document includes details such as the employee's name, age, position, salary, department, and hire date.
"[
{
""_id"": 1,
""name"": ""John Doe"",
""age"": 28,
""position"": ""Software Engineer"",
""salary"": 80000,
""department"": ""Engineering"",
""hire_date"": ISODate(""2021-01-15"")
},
{
""_id"": 2,
""name"": ""Jane Smith"",
""age"": 34,
""position"": ""Project Manager"",
""salary"": 95000,
""department"": ""Engineering"",
""hire_date"": ISODate(""2019-06-23"")
},
{
""_id"": 3,
""name"": ""Emily Johnson"",
""age"": 41,
""position"": ""CTO"",
""salary"": 150000,
""department"": ""Management"",
""hire_date"": ISODate(""2015-03-12"")
},
{
""_id"": 4,
""name"": ""Michael Brown"",
""age"": 29,
""position"": ""Software Engineer"",
""salary"": 85000,
""department"": ""Engineering"",
""hire_date"": ISODate(""2020-07-30"")
},
{
""_id"": 5,
""name"": ""Sarah Davis"",
""age"": 26,
""position"": ""UI/UX Designer"",
""salary"": 70000,
""department"": ""Design"",
""hire_date"": ISODate(""2022-10-12"")
}
]"
1. Find all Employees Who Work in the "Engineering" Department.
Query:
db.employees.find({ department: "Engineering" })Output:
[
{
"_id": 1,
"name": "John Doe",
"age": 28,
"position": "Software Engineer",
"salary": 80000,
"department": "Engineering",
"hire_date": ISODate("2021-01-15")
},
{
"_id": 2,
"name": "Jane Smith",
"age": 34,
"position": "Project Manager",
"salary": 95000,
"department": "Engineering",
"hire_date": ISODate("2019-06-23")
},
{
"_id": 4,
"name": "Michael Brown",
"age": 29,
"position": "Software Engineer",
"salary": 85000,
"department": "Engineering",
"hire_date": ISODate("2020-07-30")
}
]
Explanation: This query finds all employees whose department field is "Engineering".
2. Find the Employee with the Highest Salary.
Query:
db.employees.find().sort({ salary: -1 }).limit(1)Output:
[
{
"_id": 3,
"name": "Emily Johnson",
"age": 41,
"position": "CTO",
"salary": 150000,
"department": "Management",
"hire_date": ISODate("2015-03-12")
}
]
Explanation: This query sorts all employees by salary in descending order and retrieves the top document, which is the employee with the highest salary.
3. Update the Salary of "John Doe" to 90000.
Query:
db.employees.updateOne({ name: "John Doe" }, { $set: { salary: 90000 } })Output:
{ "acknowledged" : true, "matchedCount" : 1, "modifiedCount" : 1 }Explanation: This query updates the salary of the employee named "John Doe" to 90000.
4. Count the Number of Employees in Each Department.
Query:
db.employees.aggregate([
{ $group: { _id: "$department", count: { $sum: 1 } } }
])
Output:
[
{ "_id": "Engineering", "count": 3 },
{ "_id": "Management", "count": 1 },
{ "_id": "Design", "count": 1 }
]
Explanation: This query groups the employees by the department field and counts the number of employees in each department.
5. Add a New Field Bonus to All Employees in the "Engineering" Department with a Value of 5000.
Query:
db.employees.updateMany({ department: "Engineering" }, { $set: { bonus: 5000 } })Output:
{ "acknowledged" : true, "matchedCount" : 3, "modifiedCount" : 3 }Explanation: This query adds a new field bonus with a value of 5000 to all employees in the "Engineering" department.
6. Retrieve All Documents in the Employees Collection and Sort Them by the Length of Their Name in Descending Order.
Query:
db.employees.aggregate([
{ $addFields: { nameLength: { $strLenCP: "$name" } } },
{ $sort: { nameLength: -1 } },
{ $project: { nameLength: 0 } }
])
Output:
[
{
"_id": 2,
"name": "Jane Smith",
"age": 34,
"position": "Project Manager",
"salary": 95000,
"department": "Engineering",
"hire_date": ISODate("2019-06-23")
},
{
"_id": 3,
"name": "Emily Johnson",
"age": 41,
"position": "CTO",
"salary": 150000,
"department": "Management",
"hire_date": ISODate("2015-03-12")
},
{
"_id": 1,
"name": "John Doe",
"age": 28,
"position": "Software Engineer",
"salary": 80000,
"department": "Engineering",
"hire_date": ISODate("2021-01-15")
},
{
"_id": 4,
"name": "Michael Brown",
"age": 29,
"position": "Software Engineer",
"salary": 85000,
"department": "Engineering",
"hire_date": ISODate("2020-07-30")
},
{
"_id": 5,
"name": "Sarah Davis",
"age": 26,
"position": "UI/UX Designer",
"salary": 70000,
"department": "Design",
"hire_date": ISODate("2022-10-12")
}
]
Explanation: This query calculates the length of each employee's name, sorts the documents by this length in descending order, and removes the temporary nameLength field from the output.
7. Find the Average Salary of Employees in the "Engineering" Department.
Query:
db.employees.aggregate([
{ $match: { department: "Engineering" } },
{ $group: { _id: null, averageSalary: { $avg: "$salary" } } }
])
Output:
[
{ "_id": null, "averageSalary": 86666.66666666667 }
]
Explanation: This query filters employees to those in the "Engineering" department and calculates the average salary of these employees.
8. Find the Department with the Highest Average Salary.
Query:
db.employees.aggregate([
{ $group: { _id: "$department", averageSalary: { $avg: "$salary" } } },
{ $sort: { averageSalary: -1 } },
{ $limit: 1 }
])
Output:
[
{ "_id": "Management", "averageSalary": 150000 }
]
Explanation: This query groups employees by department, calculates the average salary for each department, sorts these averages in descending order, and retrieves the department with the highest average salary.
9. Find the Total Number of Employees Hired in Each Year.
Query:
db.employees.aggregate([
{ $group: { _id: { $year: "$hire_date" }, totalHired: { $sum: 1 } } }
])
Output:
[
{ "_id": 2015, "totalHired": 1 },
{ "_id": 2019, "totalHired": 1 },
{ "_id": 2020, "totalHired": 1 },
{ "_id": 2021, "totalHired": 1 },
{ "_id": 2022, "totalHired": 1 }
]
Explanation: This query groups employees by the year they were hired, which is extracted from the hire_date field, and counts the total number of employees hired each year.
10. Find the Highest and Lowest Salary in the "Engineering" Department.
Query:
db.employees.aggregate([
{ $match: { department: "Engineering" } },
{
$group: {
_id: null,
highestSalary: { $max: "$salary" },
lowestSalary: { $min: "$salary" }
}
}
])
Output:
[
{ "_id": null, "highestSalary": 95000, "lowestSalary": 80000 }
]
Explanation: This query filters employees to those in the "Engineering" department, then calculates the highest and lowest salary within this group.
Conclusion
Preparing for a MongoDB interview becomes much simpler with the right resources. This guide has provided a detailed collection of MongoDB interview questions, covering fundamental concepts, advanced topics, and real-world applications. By practicing these questions, you'll enhance your knowledge of MongoDB, strengthen your problem-solving skills, and build the confidence needed to excel in your interview. Stay consistent in your preparation, and you'll be ready to tackle any MongoDB challenge that comes your way!
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MongoDB Tutorial MongoDB is an open source, document-oriented, NoSql database whose data is stored in an organized format. It is scalable and easy to learn, commonly used in modern web and mobile apps, dealing with high volumes of data. MongoDB stores data in BSON format, which lets you store JSON like documents eff
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Introduction
How do Document Databases Work?Document databases are a powerful tool in the world of NoSQL databases, and they play an important role in modern applications, especially where flexibility, scalability, and performance are key requirements. But how exactly do document databases work? In this article, we will go deep into the struc
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How MongoDB works ?MongoDB is an open-source document-oriented database. It is used to store a larger amount of data and also allows you to work with that data. MongoDB is not based on the table-like relational database structure but provides an altogether different mechanism for storage and retrieval of data, that is
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MongoDB: An introductionMongoDB is the most popular NoSQL open source document-oriented database. The term 'NoSQL' means 'non-relational'. This means that MongoDB is not based on a table like relational database structure but provides an altogether different mechanism for storage and retrieval of data. This format of stora
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MongoDB: Getting StartedTerminologies: A MongoDB Database can be called the container for all the collections. A collection is a bunch of MongoDB documents. It is similar to tables in RDBMS.A document is made of fields. It is similar to a tuple in RDBMS, but it has a dynamic schema here. Documents of the same collection ne
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MongoDB - Working and FeaturesMongoDB is a powerful, flexible, and scalable NoSQL database that provides high performance and real-time data processing. Unlike traditional relational databases (RDBMS), MongoDB uses a document-oriented model, allowing developers to store and manage large volumes of unstructured or semi-structured
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Difference between RDBMS and MongoDBRDBMS and MongoDB both are widely used database management systems, but they differ significantly in how they store, manage and retrieve data. RDBMS (Relational Database Management System) is a traditional approach to database management, while MongoDB is a NoSQL (Non-relational) database known for
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MongoDB vs MySQLBoth MongoDB and MySQL are popular database management systems (DBMS), but they are built for different purposes and have distinct features. MongoDB is a NoSQL database, designed for handling unstructured data with high scalability, while MySQL is a traditional relational database management system
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Installation
How to Install and Configure MongoDB in Ubuntu?MongoDB is a popular NoSQL database offering flexibility, scalability, and ease of use. Installing and configuring MongoDB in Ubuntu is a straightforward process, but it requires careful attention in detail to ensure a smooth setup. In this article, we'll learn how to install and configure MongoDB i
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How to Install MongoDB on MacOSMongoDB is a leading open-source NoSQL database, known for its flexibility, scalability and high performance. It is widely used by companies like Adobe, Uber, IBM and Google for big data applications and real-time analytics. Unlike traditional relational databases, MongoDB stores data in documents (
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How to Install MongoDB on Windows?Looking to install MongoDB on your Windows machine? This detailed guide will help you install MongoDB on Windows (Windows Server 2022, 2019, and Windows 11) quickly and efficiently. Whether you are a developer or a beginner, follow this guide for seamless MongoDB installation, including setting up e
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Basics of MongoDB
MongoDB - Database, Collection, and DocumentMongoDB is a popular NoSQL database that offers a flexible, scalable, and high-performance way to store data. In MongoDB, Databases, Collections, and Documents are the fundamental building blocks for data storage and management. Understanding these components is crucial for efficiently working with
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MongoDB CursorIn MongoDB, a cursor is a powerful object that enables us to iterate over the results of a query. When we execute a query using methods like find(), MongoDB returns a cursor object that allows you to efficiently retrieve and process documents from the database one by one. Cursors provide various met
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DataTypes in MongoDBMongoDB, a leading NoSQL database, uses BSON (Binary JSON) format to store documents, offering a wide range of data types that allow flexible and efficient data storage. Understanding the different data types in MongoDB is crucial for designing effective schemas, optimizing queries, and ensuring sea
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What is ObjectId in MongoDBIn MongoDB, each document within a collection is uniquely identified by a field called _id. By default, this field uses the ObjectId format, a 12-byte BSON data type that ensures uniqueness and embeds valuable metadata, such as the creation timestamp. Understanding how ObjectId works is crucial for
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What is a MongoDB Query?A MongoDB query is a request to the database to retrieve specific documents or data based on certain conditions or criteria. It is similar to SQL queries in traditional relational databases, but MongoDB queries are written using JavaScript-like syntax. The most common query operation in MongoDB is t
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MongoDB - Create Database using Mongo ShellMongoDB is a popular NoSQL database that uses collections and documents, which are highly flexible and scalable. Unlike relational databases (RDBMS), MongoDB does not use tables and rows but stores data in a more dynamic, JSON-like format. In this article, we'll explore how to create a MongoDB datab
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MongoDB | Delete Database using MongoShellMongoDB is a NoSQL database system that uses dynamic schemas, making it highly flexible for developers. A MongoDB database acts as a container for collections, where each collection contains documents. In this article, we will explain how to delete databases in MongoDB using the db.dropDatabase() co
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MongoDB CRUD OperationsCRUD operations Create, Read, Update and Delete are essential for interacting with databases. In MongoDB, CRUD operations allow users to perform various actions like inserting new documents, reading data, updating records and deleting documents from collections. Mastering these operations is fundame
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MongoDB Methods
MongoDB - Insert() MethodThe insert() method in MongoDB is a fundamental operation used to add new documents to a collection. It allows inserting one or multiple documents in a single execution with MongoDB automatically generating a unique _id field if not explicitly provided. In this article, We will learn about the Mongo
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MongoDB insertOne() Method - db.Collection.insertOne()MongoDB is a powerful NoSQL database known for its flexibility, scalability, and performance. When working with MongoDB, one of the most common tasks is inserting data into collections. The insertOne() method is an essential tool in this process.In this article, We will learn about the MongoDB inser
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MongoDB insertMany() Method - db.Collection.insertMany()MongoDB insertMany() method is a powerful tool for inserting multiple documents into a collection in one operation. This method is highly versatile, allowing for both ordered and unordered inserts, and provides options for customizing the write concern. In this article, We will learn about insertMan
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MongoDB - Bulk.insert() MethodIn MongoDB, the Bulk.insert() method is used to perform insert operations in bulk. Or in other words, the Bulk.insert() method is used to insert multiple documents in one go. To use Bulk.insert() method the collection in which data has to be inserted must already exist. We will discuss the following
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MongoDB - bulkWrite() MethodThe bulkWrite() method in MongoDB is a powerful tool that allows for the execution of multiple write operations with a single command. This method is particularly useful for efficiently performing batches of operations, reducing the number of round trips to the database server and thus improving per
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MongoDB - Update() MethodMongoDB update operations allow us to modify documents in a collection. These operations can update a single document or multiple documents based on specified criteria. MongoDB offers various update operators to perform specific actions like setting a value, incrementing a value or updating elements
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MongoDB - updateOne() MethodMongoDB's updateOne() method provides a powerful way to update a single document in a collection based on specified criteria. This method is particularly useful when Accuracy is needed in modifying specific documents without affecting others.In this article, We will learn about MongoDB’s updateOne()
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MongoDB updateMany() Method - db.Collection.updateMany()MongoDB updateMany method is a powerful feature used to update multiple documents in a collection that match a specified filter. This method allows developers to efficiently perform bulk update operations, reducing network overhead and improving performanceIn this comprehensive guide, we will explor
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MongoDB - Find() Methodfind() method in MongoDB is a tool for retrieving documents from a collection. It supports various query operators and enabling complex queries. It also allows selecting specific fields to optimize data transfer and benefits from automatic indexing for better performance.In this article, We will lea
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MongoDB - FindAndModify() MethodThe findAndModify() method in MongoDB is a powerful and versatile tool for atomic updates on documents. This method allows us to perform multiple operations such as modifying, removing, or inserting documents while ensuring atomicity, meaning that no other operations can interfere during the modific
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MongoDB - FindOne() MethodMongoDB is a widely used NoSQL database that allows for flexible and scalable data storage. One of its essential methods findOne() which is used to retrieve a single document from a collection that matches the specified query criteria. This method is particularly useful when we need to fetch one spe
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MongoDB - findOneAndDelete() MethodMongoDB is a widely used NoSQL database that provides flexibility and scalability for handling large volumes of data. One of the key methods in MongoDB for document deletion is the findOneAndDelete() method. This method allows us to delete a single document from a collection based on specified crite
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MongoDB - db.collection.findOneAndReplace() MethodThe findOneAndReplace() method in MongoDB is a powerful tool for finding and replacing a single document within a collection. This method replaces the first document that matches the specified criteria with a new one. By default, it returns the original document but this can be configured to return
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MongoDB - db.collection.findOneAndUpdate() MethodThe MongoDB findOneAndUpdate() method is used to update the first matched document in a collection based on the selection criteria. It offers various options such as sorting, upserting, and returning the updated document. This method is a part of MongoDB's CRUD operations and provides an easy-to-use
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MongoDB - sort() MethodThe sort() method in MongoDB is an essential tool for developers to order documents returned by queries in a specified manner. By utilizing the sort() method, we can organize our query results in either ascending (1) or descending (-1) order based on one or more fields. MongoDB supports complex sort
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MongoDB - copyTo() MethodMongoDB copyTo() method is used to duplicate the contents of one collection into another collection within the same database. It's like making a copy of a file on your computer to another location. In this article, We will learn about MongoDB copyTo() Method with the help of examples and so on.Mongo
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MongoDB Count() Method - db.Collection.count()MongoDB's count() method is a powerful tool for retrieving the number of documents in a collection that match a specified query. It offers flexibility in filtering and is useful for obtaining quick counts based on various criteria.In this article, We will explain the MongoDB count() method in detail
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MongoDB - countDocuments() MethodMongoDB provides powerful methods to manage and retrieve data efficiently. One such method is countDocuments(), which allows us to count the number of documents in a collection that match a specified query filter. This method is particularly useful when dealing with large datasets, ensuring accurate
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MongoDB - Drop CollectionIn MongoDB, managing collections is a fundamental aspect of database operations. The MongoDB drop collection command allows us to permanently delete an entire collection along with its documents and indexes. By using the db.collection.drop() method is essential when we need to clear outdated data or
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MongoDB Remove() Method - db.Collection.remove()The MongoDB remove() method allows users to remove documents from a collection based on specific criteria. It is a powerful tool in MongoDB that enables both single and bulk document deletion, offering flexibility in managing your database. It supports various options like removing only one document
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MongoDB - db.collection.deleteone()The MongoDB deleteOne() method is an essential tool for removing a single document from a collection that matches a specified filter. It is widely used for precise deletion tasks, ensuring that we can manage your MongoDB collections effectively by removing specific documents based on certain criteri
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MongoDB - Distinct() MethodThe distinct() method in MongoDB is a powerful tool used to find unique values for a specified field across a single collection. By retrieving all distinct values associated with a specific key, this method helps eliminate duplicates and enables better analysis and reporting on the dataset.In this a
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MongoDB - limit() MethodThe limit() method in MongoDB is a powerful tool used to control the number of documents returned in a query result. It is particularly beneficial when working with large collections as it allows for the restriction of result set sizes thereby improving performance and reducing client load. In this
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MongoDB - skip() MethodWhen working with large datasets in MongoDB, efficient data retrieval becomes crucial. The MongoDB skip() method is an essential tool that allows developers to control which portion of the dataset is returned, improving performance and enabling better data paginationWhat is MongoDB skip()?In MongoDB
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MongoDB | ObjectID() FunctionObjectID() Function: MongoDB uses ObjectID to create unique identifiers for all the documents in the database. It is different than the traditional autoincrementing integer ID, but it comes with its own set of advantages. An ObjectID is a GUID (Globally Unique Identifier). GUIDs are generated random
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MongoDB - db.collection.CreateIndex() MethodMongoDB's createIndex() method is used to create indexes on collections which allows for efficient querying and sorting of data. This method supports various types of indexes like text indexes, 2dsphere indexes, 2d indexes and more. It also provides options to customize the index creation process.In
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createIndexes() Method in MongoDBMongoDB is a highly scalable NoSQL database that allows flexible data storage. One of the most powerful features for improving query performance is indexing. The createIndexes() method in MongoDB allows developers to create various types of indexes which significantly improve query execution speed a
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MongoDB - getIndexes() MethodIn MongoDB, managing indexes is important for optimizing query performance. The getIndexes() method provides a straightforward way to retrieve information about the indexes on a specific collection. Understanding how to use this method effectively helps developers analyze and manage their indexing s
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MongoDB dropIndex() MethodIndexes are important in MongoDB for improving query performance, allowing the database to quickly find the documents that match query criteria. The dropIndex() method in MongoDB enables developers to manage their collection's indexes by removing unnecessary or outdated indexes. However, it's import
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MongoDB - dropIndexes() MethodThe MongoDB dropIndexes command is an important tool for managing and optimizing database performance. By removing unnecessary indexes, we can free up system resources and ensure faster query execution. In this article, we’ll explore the dropIndexes() in MongoDB and explain how to use the MongoDB in
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Comparison Operators
MongoDB - Comparison Query OperatorsMongoDB provides powerful comparison query operators to filter and retrieve documents based on field values. These operators help developers perform precise queries, enabling efficient data retrieval and manipulation. MongoDB uses various comparison query operators to compare the values of the docum
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MongoDB $cmp OperatorThe MongoDB $cmp operator is a powerful tool for comparing two values within documents, commonly used in aggregation pipelines for sorting or conditional operations. It plays a crucial role in sorting, conditional operations, and advanced comparisons inside MongoDB queriesIn this article, We will le
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MongoDB $gt OperatorThe $gt operator in MongoDB is a powerful comparison operator that allows you to query documents where the value of a field is greater than a specified value. It can be used in various methods, such as find, update, and aggregate, making it a flexible tool for data analysis and retrieval.In this gui
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MongoDB - $lt OperatorMongoDB provides powerful query operators to filter and retrieve data efficiently. One such essential operator is the $lt (less than) operator, which allows users to select documents where a specified field’s value is less than a given value. We can use this operator in methods like, find(), update(
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MongoDB - $eq OperatorMongoDB provides a variety of comparison query operators to filter and retrieve documents efficiently. One of the most widely used operators is $eq (equal to operator), which allows users to match exact values in a MongoDB collection.In this article, we will explore the MongoDB $eq operator, its syn
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MongoDB - $lte OperatorMongoDB $lte Operator is one of the comparison operators. $lte operator selects those documents where the field value is less than equal to (<=) the given value. This operator can be used in methods like find(), update(), etc. according to your requirements.. Syntax{field: {$lte: value}}MongoDB $
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MongoDB - $gte OperatorMongoDB $gte or "greater than equals to" operator is one of the comparison operators. $gte operator selects those documents where the field value is greater than equals to(>=) the given value. This operator can be used in methods (like, find(), update(), etc.) according to your requirements. Synt
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MongoDB - $ne OperatorMongoDB $ne or "not equals" operator is one of the comparison operators. The $ne operator selects those documents where the field value is not equal to the given value. It also includes those documents that do not contain the specified field. You can use this operator in methods like find(), update(
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MongoDB $in OperatorMongoDB $in operator provides a powerful way to query documents based on multiple potential values for a specific field within a single query.In this article, We will learn about the MongoDB $in Operator by understanding various examples in detail.MongoDB $in OperatorThe MongoDB $in operator is used
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MongoDB - $nin OperatorMongoDB $nin or " not in" is one of the comparison query operators. The $nin operator selects those documents where the field value is not equal to any of the given values in the array and the field that does not exist. You can use this operator in methods like find(), update(), etc. according to yo
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Logical Operators
Arithmetic Operators
MongoDB $add OperatorThe $add operator in MongoDB is a versatile and essential tool within the aggregation framework. It enables us to perform arithmetic operations like addition on numeric values, as well as concatenate dates and numbers. Whether we are aggregating data or manipulating documents, the $add operator is i
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MongoDB $subtract OperatorMongoDB’s $subtract operator is an essential tool in the aggregation pipeline, allowing users to perform subtraction operations on numbers, dates, and even date-time calculations. This powerful operator simplifies arithmetic operations within the aggregation pipeline and enhances MongoDB's ability t
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MongoDB $multiply OperatorIn MongoDB, the $multiply operator is a powerful tool used in aggregation pipelines to perform multiplication operations. This operator takes one or more expressions as arguments and multiplies them to produce a result.In this article, we will explain the MongoDB $multiply operator, its syntax, usag
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MongoDB $divide OperatorIn MongoDB, the $divide operator is a powerful tool used to perform division between two numerical values. It allows for precise arithmetic operations directly within the database queries, enhancing the capability to manipulate and analyze data. In this article, We will learn about the MongoDB $divi
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MongoDB $abs operatorThe $abs operator in MongoDB is a fundamental arithmetic expression operator used in aggregation pipeline stages. Its primary function is to calculate the absolute value of a specified number. This operation ensures that only positive values are considered, regardless of the number’s sign, making it
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MongoDB $floor OperatorThe MongoDB $floor operator is a powerful tool used in the aggregation pipeline to round numbers down to the nearest integer that is less than or equal to the original number. Whether we're working with employee performance metrics, financial data, or any numerical dataset, the $floor operator helps
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MongoDB $ceil OperatorIn MongoDB, the $ceil operator is a powerful tool used in aggregation pipelines to round numbers up to the nearest integer greater than or equal to the original number. In this article, We will learn about the MongoDB $ceil Operator in detail. MongoDB $ceil OperatorMongoDB $ceil operator is used in
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MongoDB $mod OperatorMongoDB provides different types of arithmetic expression operators that are used in the aggregation pipeline stages and $mod operator is one of them. This operator is used to divide one number by another number and return the remainder. Syntax: { $mod: [ <expression1>, <expression2> ] }
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MongoDB $sqrt OperatorMongoDB provides different types of arithmetic expression operators that are used in the aggregation pipeline stages $sqrt operator is one of them. This operator is used to find the square root of a positive number and returns the result as a double. Syntax: { $sqrt: <number> } Here, the numbe
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MongoDB $pow OperatorMongoDB's $pow operator is a powerful tool within the aggregation framework which is designed to compute exponentiation operations directly on numeric fields. In this article, We will learn about the MongoDB $pow Operator in detail by understanding various examples and so on.MongoDB $pow OperatorThe
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MongoDB $exp OperatorMongoDB's aggregation framework provides a powerful set of tools for data manipulation and processing. One such tool is the $exp operator which allows users to perform exponential calculations within aggregation pipelines. In this article, We will learn about the MongoDB $exp Operator in detail. Mon
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MongoDB $log OperatorThe MongoDB $log operator is used within the aggregation pipeline to calculate the logarithm of a number with a specified base. This operator helps perform logarithmic calculations on fields whether in simple documents or embedded documents. The syntax is straightforward by requiring a number and a
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MongoDB $log10 OperatorIn MongoDB, the $log10 operator is a powerful tool that allows users to perform mathematical computations directly within the database. This operator returns the base 10 logarithm of a specified number and making it invaluable for data analysis and transformation tasks. In this article, We will lear
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MongoDB $ln Operator$in operator in MongoDB is a powerful query tool used to filter documents based on whether a field value matches any value within a specified array. This operator simplifies searching through large datasets by allowing developers to specify multiple values for a single field. In this article, we wil
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Field Update Operators
MongoDB - Field Update OperatorsMongoDB offers a range of powerful field update operators that enable efficient modification of specific fields within documents. These operators allow developers to update specific fields in documents without rewriting the entire document, thus improving performance and operational efficiency.By gu
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MongoDB - $max OperatorThe $max operator in MongoDB is one of the field update operators used to conditionally update fields within a document. It updates a field only if the specified value is greater than the current value, making it highly efficient for managing thresholds and ensuring data accuracy. This operator is v
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MongoDB - $min OperatorMongoDB offers a range of powerful update operators, and one of the most useful is the $min operator. This operator updates a field's value to a specified value, but only if that value is smaller than the current field value. If the specified value is greater than or equal to the current value, no u
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MongoDB - $inc OperatorThe MongoDB $inc operator is one of the most commonly used update operators in MongoDB, especially when it comes to modifying numerical values within documents. It is used to increment or decrement the value of a field by a specific amount, making it highly useful for applications like counters, sco
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MongoDB - $mul OperatorMongoDB $mul operator is a powerful update operator used to multiply the value of a field by a specified number. This operator allows for direct arithmetic operations within the database, making it particularly useful for scenarios that require modifying numeric field values without needing to retri
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MongoDB - Rename Operator ($rename)MongoDB $rename operator is a powerful tool for for efficiently renaming fields within documents. This operator ensures data consistency and helps developers maintain a clear and organized schema, especially when working with large collections. Whether you’re dealing with nested documents, arrays, o
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MongoDB - Current Date Operator ($currentDate)MongoDB provides different types of field update operators to update the values of the fields of the documents and $currentDate operator is one of them. This operator is used to set the value of a field to the current date (either as a timestamp or as a Date). The default type of $currentDate operat
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MongoDB - $setOnInsert OperatorThe $setOnInsert operator in MongoDB is a powerful tool used in updating operations with the upsert option. It allows us to specify values that should be set only when a new document is inserted. In this article, we will learn about the $setOnInsert Operator in MongoDB in detail and so on. MongoDB $
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MongoDB Bitwise Update OperatorThe MongoDB Bitwise Update Operator allows for efficient manipulation of integer fields within MongoDB documents through bitwise operations. In this article, We will learn about the MongoDB Bitwise Update Operator in detail by understanding various examples in detail.MongoDB Bitwise Update OperatorM
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Array Expression Operators