Copy data from or to MongoDB using Azure Data Factory or Synapse Analytics

APPLIES TO: Azure Data Factory Azure Synapse Analytics

This article outlines how to use the Copy Activity in Azure Data Factory Synapse Analytics pipelines to copy data from and to a MongoDB database. It builds on the copy activity overview article that presents a general overview of copy activity.

Supported capabilities

This MongoDB connector is supported for the following capabilities:

① Azure integration runtime ② Self-hosted integration runtime

For a list of data stores that are supported as sources/sinks, see the Supported data stores table.

Specifically, this MongoDB connector supports versions up to 4.2. If your work requires versions newer than 4.2, consider using MongoDB Atlas with the MongoDB Atlas connector, which provides more comprehensive support and features.

Prerequisites

If your data store is located inside an on-premises network, an Azure virtual network, or Amazon Virtual Private Cloud, you need to configure a self-hosted integration runtime to connect to it.

If your data store is a managed cloud data service, you can use the Azure Integration Runtime. If the access is restricted to IPs that are approved in the firewall rules, you can add Azure Integration Runtime IPs to the allow list.

You can also use the managed virtual network integration runtime feature in Azure Data Factory to access the on-premises network without installing and configuring a self-hosted integration runtime.

For more information about the network security mechanisms and options supported by Data Factory, see Data access strategies.

Getting started

To perform the Copy activity with a pipeline, you can use one of the following tools or SDKs:

• The Copy Data tool
• The Azure portal
• The .NET SDK
• The Python SDK
• Azure PowerShell
• The REST API
• The Azure Resource Manager template

Create a linked service to MongoDB using UI

Use the following steps to create a linked service to MongoDB in the Azure portal UI.

1. Browse to the Manage tab in your Azure Data Factory or Synapse workspace and select Linked Services, then click New:

   • Azure Data Factory
   • Azure Synapse

   

2. Search for MongoDB and select the MongoDB connector.

   

3. Configure the service details, test the connection, and create the new linked service.

   

Connector configuration details

The following sections provide details about properties that are used to define Data Factory entities specific to MongoDB connector.

Linked service properties

The following properties are supported for MongoDB linked service:

Example:

{
    "name": "MongoDBLinkedService",
    "properties": {
        "type": "MongoDbV2",
        "typeProperties": {
            "connectionString": "mongodb://[username:password@]host[:port][/[database][?options]]",
            "database": "myDatabase"
        },
        "connectVia": {
            "referenceName": "<name of Integration Runtime>",
            "type": "IntegrationRuntimeReference"
        }
    }
}

Dataset properties

For a full list of sections and properties that are available for defining datasets, see Datasets and linked services. The following properties are supported for MongoDB dataset:

Example:

{
    "name": "MongoDbDataset",
    "properties": {
        "type": "MongoDbV2Collection",
        "typeProperties": {
            "collectionName": "<Collection name>"
        },
        "schema": [],
        "linkedServiceName": {
            "referenceName": "<MongoDB linked service name>",
            "type": "LinkedServiceReference"
        }
    }
}

Copy activity properties

For a full list of sections and properties available for defining activities, see the Pipelines article. This section provides a list of properties supported by MongoDB source and sink.

MongoDB as source

The following properties are supported in the copy activity source section:

Example:

"activities":[
    {
        "name": "CopyFromMongoDB",
        "type": "Copy",
        "inputs": [
            {
                "referenceName": "<MongoDB input dataset name>",
                "type": "DatasetReference"
            }
        ],
        "outputs": [
            {
                "referenceName": "<output dataset name>",
                "type": "DatasetReference"
            }
        ],
        "typeProperties": {
            "source": {
                "type": "MongoDbV2Source",
                "filter": "{datetimeData: {$gte: ISODate(\"2018-12-11T00:00:00.000Z\"),$lt: ISODate(\"2018-12-12T00:00:00.000Z\")}, _id: ObjectId(\"5acd7c3d0000000000000000\") }",
                "cursorMethods": {
                    "project": "{ _id : 1, name : 1, age: 1, datetimeData: 1 }",
                    "sort": "{ age : 1 }",
                    "skip": 3,
                    "limit": 3
                }
            },
            "sink": {
                "type": "<sink type>"
            }
        }
    }
]

MongoDB as sink

The following properties are supported in the Copy Activity sink section:

Example

"activities":[
    {
        "name": "CopyToMongoDB",
        "type": "Copy",
        "inputs": [
            {
                "referenceName": "<input dataset name>",
                "type": "DatasetReference"
            }
        ],
        "outputs": [
            {
                "referenceName": "<Document DB output dataset name>",
                "type": "DatasetReference"
            }
        ],
        "typeProperties": {
            "source": {
                "type": "<source type>"
            },
            "sink": {
                "type": "MongoDbV2Sink",
                "writeBehavior": "upsert"
            }
        }
    }
]

Import and export JSON documents

You can use this MongoDB connector to easily:

• Copy documents between two MongoDB collections as-is.
• Import JSON documents from various sources to MongoDB, including from Azure Cosmos DB, Azure Blob storage, Azure Data Lake Store, and other supported file-based stores.
• Export JSON documents from a MongoDB collection to various file-based stores.

To achieve such schema-agnostic copy, skip the "structure" (also called schema) section in dataset and schema mapping in copy activity.

Schema mapping

To copy data from MongoDB to tabular sink or reversed, refer to schema mapping.

Upgrade the MongoDB linked service

Here are steps that help you upgrade your linked service and related queries:

1. Create a new MongoDB linked service and configure it by referring to Linked service properties.

2. If you use SQL queries in your pipelines that refer to the old MongoDB linked service, replace them with the equivalent MongoDB queries. See the following table for the replacement examples:

   SQL query	Equivalent MongoDB query
   SELECT * FROM users	db.users.find({})
   SELECT username, age FROM users	db.users.find({}, {username: 1, age: 1})
   SELECT username AS User, age AS Age, statusNumber AS Status, CASE WHEN Status = 0 THEN "Pending" CASE WHEN Status = 1 THEN "Finished" ELSE "Unknown" END AS statusEnum LastUpdatedTime + interval '2' hour AS NewLastUpdatedTime FROM users	db.users.aggregate([{ $project: { _id: 0, User: "$username", Age: "$age", Status: "$statusNumber", statusEnum: { $switch: { branches: [ { case: { $eq: ["$Status", 0] }, then: "Pending" }, { case: { $eq: ["$Status", 1] }, then: "Finished" } ], default: "Unknown" } }, NewLastUpdatedTime: { $add: ["$LastUpdatedTime", 2 * 60 * 60 * 1000] } } }])
   SELECT employees.name, departments.name AS department_name FROM employees LEFT JOIN departments ON employees.department_id = departments.id;	db.employees.aggregate([ { $lookup: { from: "departments", localField: "department_id", foreignField: "_id", as: "department" } }, { $unwind: "$department" }, { $project: { _id: 0, name: 1, department_name: "$department.name" } } ])

Related content

For a list of data stores supported as sources and sinks by the copy activity, see supported data stores.