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The Query API.
Simple, expressive and unified.

A flexible, intuitive way to work with data. Deliver applications with minimal code and maximum productivity, in the language and tool of your choice.

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The MongoDB Query API gives developers an intuitive way to handle complex data workloads for any data type, including arrays, geospatial, and time series data, so they can deliver more with less code.

Work in your preferred language
A single interface for operational, analytical, and search workloads

Work easily with any data type

Get complete control over your data with the MongoDB Query API. It’s designed to handle any data structure with ease, including key-value, graph, geospatial, time series, and objects. Optimized for embedded arrays and objects, these expressive querying capabilities unlock the flexibility of the document model.

Query an array

Query nested documents

Boost productivity with a single interface

Unlock the full potential of data operations. Perform key operations, including indexing, querying, and updating; all through a single, powerful API. Modernize your applications by using transactional, search, and analytical queries, without the need to switch between interfaces or struggle with complex query syntax.

Learn about full-text search

Explore analyzing data

Simplify the development process

Query and aggregate data using your preferred coding language. The MongoDB Query API includes drivers to programmatically generate queries and build aggregation pipelines— removing the need to learn a new query language from scratch.

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Feature overview

Simplify working with data

Work with data in an intuitive, familiar way. Deliver data-driven applications with minimal code run queries, build complex aggregation pipelines, and power applications, all in your preferred language.

View Documentation

Expressive querying made easy

Easily query on a range of data types including geospatial, graph, time series, as well as on nested arrays and objects, all while working in your preferred language.

Composable in-database transformations

Build modular, multi-stage aggregation pipelines to run powerful analytics. Query across collections and databases, and transform your data to surface insights and make better decisions.

Full-text search, time series, and more

Use a single interface for creating complex queries that power search workloads, time-series workloads, and event-driven apps. Save time on integrating multiple APIs or complex ETL.

Expressive querying made easy

Easily query on a range of data types including geospatial, graph, time series, as well as on nested arrays and objects, all while working in your preferred language.

Python

Javascript

Java

MongoShell

Code Snippet

Composable in-database transformations

Build modular, multi-stage aggregation pipelines to run powerful analytics. Query across collections and databases, and transform your data to surface insights and make better decisions.

Python

Javascript

Java

MongoShell

Code Snippet

lonely_ne = db.lonely_neighborhoods
popular_ne = db.popular_neighborhoods

pipeline = [
#filter neighborhoods where the population is known
    {"$match": {"population": {"$ne": "unknown"}}}, 
#perform a union with the collection containing popular neighborhoods with an embedded pipeline for filtering neighborhoods where the population is known, and removing documents where the city is an industrial township
    {"$unionWith": {"coll": "popular_neighborhoods", 
			"pipeline": [
					{"$match": {"population": {"$ne": "unknown"}}},
					{$redact: {$cond: {
                            				if: { $eq: [ "$type", "Industrial township"]},
                            		then: "$$PRUNE",
                            		else: "$$DESCEND" }}
                			}
				] }}, 
#finally we are grouping neighborhoods by climate to calculate the total population in popular neighborboods that are not in an industrial township
    {"$group": {"_id": "$climate", "totalPopulation": {"$sum": {"$toLong": "$population"}}}}
]

result = lonely_ne.aggregate(pipeline)

Full-text search, time series, and more

Use a single interface for creating complex queries that power search workloads, time-series workloads, and event-driven apps. Save time on integrating multiple APIs or complex ETL.

Python

Javascript

Java

MongoShell

Code Snippet

Python

Javascript

Java

MongoShell

Code Snippet

Query. Transform. Analyze.

See what the Query API has to offer — and how it has empowered other organizations to transform their applications and workflows.

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Power more data-driven experiences and insights with the rest of our developer data platform.

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Start using the Query API today

Get started in seconds. Use preloaded sample data sets to familiarize yourself with the Query API — and the MongoDB developer data platform.

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BUILD YOUR MONGODB DATABASE WITH:

CRUD
Aggregations
Change streams
Geospatial and graph
Full-text search
Language drivers

.css-9ohd1h{background-color:transparent;color:#00ED64;}The Query API.Simple, expressive and unified.

Work easily with any data type

Boost productivity with a single interface

Simplify the development process

Feature overview

Simplify working with data

Expressive querying made easy

Composable in-database transformations

Full-text search, time series, and more

Expressive querying made easy

Composable in-database transformations

Full-text search, time series, and more

Query. Transform. Analyze.

Get the most out of Atlas

Start using the Query API today

The Query API.
Simple, expressive and unified.