The MongoDB Data API can be used to query and update data in a MongoDB database without the need for language specific drivers.
Language drivers should be used when possible, but the MongoDB Data API comes in handy when drivers are not available or drivers are overkill for the application.
The MongoDB Data API is a pre-configured set of HTTPS endpoints that can be used to read and write data to a MongoDB Atlas database.
With the MongoDB Data API, you can create, read, update, delete, or aggregate documents in a MongoDB Atlas database.
In order to use the Data API, you must first enable the functionality from the Atlas UI.
From the MongoDB Atlas dashboard, navigate to Data API in the left menu.
Select the data source(s) you would like to enable the API on and click Enable the Data API.
By default, no access is granted. Select the access level you'd like to grant the Data API. The choices are: No Access, Read Only, Read and Write, or Custom Access.
In order to authenticate with the Data API, you must first create a Data API key.
Click Create API Key, enter a name for the key, then click Generate API Key.
Be sure to copy the API key and save it somewhere safe. You will not get another chance to see this key again.
We can now use the Data API to send a request to the database.
In the next example, we'll use curl to find the first document in the movies
collection of our sample_mflix
database. We loaded this sample data in the Intro to Aggregations section.
To run this example, you'll need your App Id, API Key, and Cluster name.
You can find your App Id in the URL Endpoint field of the Data API page in the MongoDB Atlas UI.
curl --location --request POST 'https://data.mongodb-api.com/app/<DATA API APP ID>/endpoint/data/v1/action/findOne' \
--header 'Content-Type: application/json' \
--header 'Access-Control-Request-Headers: *' \
--header 'api-key: <DATA API KEY>' \
--data-raw '{
"dataSource":"<CLUSTER NAME>",
"database":"sample_mflix",
"collection":"movies",
"projection": {"title": 1}
}'
Try it Yourself »
In the previous example, we used the findOne
endpoint in our URL.
There are several endpoints available for use with the Data API.
All endpoints start with the Base URL: https://data.mongodb-api.com/app/<Data API App ID>/endpoint/data/v1/action/
POST Base_URL/findOne
The findOne
endpoint is used to find a single document in a collection.
{
"dataSource": "<data source name>",
"database": "<database name>",
"collection": "<collection name>",
"filter": <query filter>,
"projection": <projection>
}
POST Base_URL/find
The find
endpoint is used to find multiple documents in a collection.
{
"dataSource": "<data source name>",
"database": "<database name>",
"collection": "<collection name>",
"filter": <query filter>,
"projection": <projection>,
"sort": <sort expression>,
"limit": <number>,
"skip": <number>
}
POST Base_URL/insertOne
The insertOne
endpoint is used to insert a single document into a collection.
{
"dataSource": "<data source name>",
"database": "<database name>",
"collection": "<collection name>",
"document": <document>
}
POST Base_URL/insertMany
The insertMany
endpoint is used to insert multiple documents into a collection.
{
"dataSource": "<data source name>",
"database": "<database name>",
"collection": "<collection name>",
"documents": [<document>, <document>, ...]
}
POST Base_URL/updateOne
{
"dataSource": "<data source name>",
"database": "<database name>",
"collection": "<collection name>",
"filter": <query filter>,
"update": <update expression>,
"upsert": true|false
}
POST Base_URL/updateMany
{
"dataSource": "<data source name>",
"database": "<database name>",
"collection": "<collection name>",
"filter": <query filter>,
"update": <update expression>,
"upsert": true|false
}
POST Base_URL/deleteOne
{
"dataSource": "<data source name>",
"database": "<database name>",
"collection": "<collection name>",
"filter": <query filter>
}
POST Base_URL/deleteMany
{
"dataSource": "<data source name>",
"database": "<database name>",
"collection": "<collection name>",
"filter": <query filter>
}
POST Base_URL/aggregate
{
"dataSource": "<data source name>",
"database": "<database name>",
"collection": "<collection name>",
"pipeline": [<pipeline expression>, ...]
}