Elastic MongoDB connector reference

The Elastic MongoDB connector is a connector for MongoDB data sources.

This connector is available as a native connector in Elastic versions 8.5.0 and later. To use this connector as a native connector, satisfy all native connector requirements.

This connector is also available as a connector client from the Python connectors framework. To use this connector as a connector client, satisfy all connector client requirements.

This connector has no additional prerequisites beyond the shared requirements, linked above.

To use this connector as a native connector, use the Connector workflow. See Native connectors.

To use this connector as a connector client, see Connector clients and frameworks.

For additional operations, see Usage.

An example is available for this connector. See MongoDB connector tutorial.

It’s not possible to use expressions like new Date() inside an aggregation pipeline. These expressions won’t be evaluated by the underlying MongoDB client, but will be passed as a string to the MongoDB instance. A possible workaround is to use aggregation variables.

Incorrect (new Date() will be interpreted as string):

{
    "aggregate": {
        "pipeline": [
            {
                "$match": {
                  "expiresAt": {
                    "$gte": "new Date()"
                  }
                }
            }
        ]
    }
}

Correct (usage of $$NOW):

{
  "aggregate": {
    "pipeline": [
      {
        "$addFields": {
          "current_date": {
            "$toDate": "$$NOW"
          }
        }
      },
      {
        "$match": {
          "$expr": {
            "$gte": [
              "$expiresAt",
              "$current_date"
            ]
          }
        }
      }
    ]
  }
}

See Known issues for any issues affecting all connectors.

See Troubleshooting.

See Security.

This connector is compatible with MongoDB Atlas and MongoDB 3.6 and later.

The data source and your Elastic deployment must be able to communicate with each other over a network.

Each time you create an index to be managed by this connector, you will create a new connector configuration. You will need some or all of the following information about the data source.

Follow these instructions to deploy the MongoDB connector using Docker.

curl https://raw.githubusercontent.com/elastic/connectors-python/main/config.yml --output ~/connectors-python-config/config.yml

elasticsearch:
  host: http://host.docker.internal:9200
  username: elastic
  password: <YOUR_PASSWORD>

connector_id: <CONNECTOR_ID_FROM_KIBANA>
service_type: mongodb

sources:
  # UNCOMMENT "mongodb" below to enable the MongoDB connector

  #mongodb: connectors.sources.mongo:MongoDataSource
  #s3: connectors.sources.s3:S3DataSource
  #dir: connectors.sources.directory:DirectoryDataSource
  #mysql: connectors.sources.mysql:MySqlDataSource
  #network_drive: connectors.sources.network_drive:NASDataSource
  #google_cloud_storage: connectors.sources.google_cloud_storage:GoogleCloudStorageDataSource
  #azure_blob_storage: connectors.sources.azure_blob_storage:AzureBlobStorageDataSource
  #postgresql: connectors.sources.postgresql:PostgreSQLDataSource
  #oracle: connectors.sources.oracle:OracleDataSource
  #mssql: connectors.sources.mssql:MSSQLDataSource

docker run \
-v ~/connectors-python-config:/config \
--network "elastic" \
--tty \
--rm \
docker.elastic.co/enterprise-search/elastic-connectors:8.8.2.0-SNAPSHOT \
/app/bin/elastic-ingest \
-c /config/config.yml

Refer to this guide in the Python framework repository for more details.

The following describes the default syncing behavior for this connector. Use sync rules and ingest pipelines to customize syncing for specific indices.

All documents in the configured MongoDB database and collection are extracted and transformed into documents in your Elasticsearch index.

This results in Elasticsearch documents that closely match the original MongoDB documents.

The Elasticsearch mapping is created when the first document is created.

Each sync is a "full" sync. For each MongoDB document discovered:

This is recursive, because embedded documents can themselves contain embedded documents.

The following sections describe Sync rules for this connector.

Advanced rules for MongoDB can be used to express either find queries or aggregation pipelines. They can also be used to tune options available when issuing these queries/pipelines.

For find queries, the structure of this JSON DSL should look like:

{
	"find":{
		"filter": {
			// find query goes here
		},
		"options":{
			// query options go here
		}
	}
}

For example:

{
	"find": {
		"filter": {
			"$text": {
				"$search": "garden",
				"$caseSensitive": false
			}
		},
		"skip": 10,
		"limit": 1000
	}
}

find queries also support additional options, for example the projection object:

{
  "find": {
    "filter": {
      "languages": [
        "English"
      ],
      "runtime": {
        "$gt":90
      }
    },
    "projection":{
      "tomatoes": 1
    }
  }
}

Where the available options are:

Similarly, for aggregation pipelines, the structure of the JSON DSL should look like:

{
	"aggregate":{
		"pipeline": [
			// pipeline elements go here
		],
		"options": {
            // pipeline options go here
		}
    }
}

Where the available options are:

See Content extraction.

This connector is included in the Python connectors framework.

View the source code for this connector (branch 8.8, compatible with Elastic 8.8).

As part of the 8.8.0 release the MongoDB connector was moved from the Ruby connectors framework to the Python connectors framework.

This change introduces minor formatting modifications to data ingested from MongoDB:

If your MongoDB connector stopped working after migrating from 8.7.x to 8.8.x, read the workaround outlined in Known issues. If that does not work, we recommend deleting the search index attached to this connector and re-creating a MongoDB connector from scratch.