The easiest and most natural way to work with data
Why use MongoDB Query API?
Flexible
Purpose-built to handle data of any structure - query, transform, and analyze it as your schema evolves.
Efficient
Quickly index, query, and update any element of a document in place.
Easy to use
Modular and composable to programmatically generate queries in your programming language of choice.
[MongoDB Query API] has stood out as being incredibly powerful in terms of its malleability (e.g., by allowing you to order and repeat stages in your pipeline to your heart's content)
Douglas Smithers, Training Consultant at Ingeniq
The most beautiful part is the data model. Everything is a natural JSON document. So for the developers, it is easy, really easy for them to work with quickly. Spending time on building business value, rather than data modeling.
Filip Dadgar, Principal Solutions Architect and IT-Manager at Toyota Material Handling Europe[MongoDB Query API] has stood out as being incredibly powerful in terms of its malleability (e.g., by allowing you to order and repeat stages in your pipeline to your heart's content)
Douglas Smithers, Training Consultant at IngeniqThe most beautiful part is the data model. Everything is a natural JSON document. So for the developers, it is easy, really easy for them to work with quickly. Spending time on building business value, rather than data modeling.
Filip Dadgar, Principal Solutions Architect and IT-Manager at Toyota Material Handling Europe
For our use case, MongoDB delivered higher scalability and better price/performance. We also found the document data model to provide richer schema flexibility than alternatives. It was more natural for our developers to work with, which increased their productivity.
Sander Van Loo, Director of Data Delivery & Index Administration Services at IHS MarkitWhat can I do with MongoDB Query API?
Query and update
Quickly query and update documents in your collection with powerful operators like $updateMany and $set.
Index
Optimize your queries with many index types, for example, compound, multikey, wildcard, geospatial, and more - no matter how deeply nested the fields.
Transform and analyze
Create highly sophisticated real-time analytics against live data in the same database cluster, with no ETL or duplication.
Search
Deploy advanced full-text search capabilities built for the MongoDB document model with no additional infrastructure to manage.
Change streams
Build real-time, event-driven applications that can react to any data changes in the database and automatically trigger server-side logic.
Join and union
Utilize the power of aggregation stages like $lookup and $unionWith to join and blend multiple collections for analytics and data exploration.
Geospatial
Put your application on the map and easily run queries against geospatial data.
Graph
Use operators like $graphLookup to quickly traverse connected data sets.
On-Demand materialized views
Pre-compute and cache common queries, dynamically updating result sets as new data is processed.
What's New
We're constantly enriching the MongoDB Query API
Practical MongoDB Aggregations
Learn how to develop effective and optimal data manipulation and analytics pipelines with this book, using the MongoDB Aggregation Framework.
Learn More →
New Aggregation Operators
Check out 9 new operators that were added like $accumulator and $replaceOne
Learn More →
New MongoDB drivers for Rust and Swift
We have released Rust and Swift drivers! Take a look at our docs page to get started.
Learn More →
Frequently Asked Questions
How is it different from SQL?
The MongoDB Query API is designed to work with rapidly changing data of any structure, where SQL is designed to access flat, tabular data structures.
Is this a new language I will have to learn?
Nope! We have language drivers that allow queries to be programmatically generated and tools for low code development of more complex aggregation pipelines.
What is the difference between MongoDB Query API and the aggregation pipeline?
The aggregation pipeline is part of the MongoDB Query API. The API supports CRUD operations against your data, and the aggregation pipeline is modeled on the concept of Unix pipes. Data flows through a series of composable aggregation pipeline stages where it is transformed and processed, allowing you to create sophisticated real-time analytics against your data in-place without having to move it out to specialized analytics databases.
What tools can I use to write queries?
You can use visual tools such as Compass Aggregation Pipeline Builder, Atlas Data Explorer, ODMs for programming language drivers, VS code extension, or 3rd party IDEs.
I am used to SQL. How does the MongoDB Query API map to it?
Check out our documentation page here to see the SQL to MongoDB mapping chart.
How can I learn to use MongoDB Query API?
Check out our in-depth documentation page or enroll in one of our MongoDB University Courses.