OpenMetadata/openmetadata-docs/content/v1.1.0-snapshot/connectors/database/mongodb/cli.md

title	slug
Run MongoDB Connector using the CLI	/connectors/database/mongodb/cli

Run MongoDB using the metadata CLI

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Feature	Status
Stage	PROD
Metadata	{% icon iconName="check" /%}
Query Usage	{% icon iconName="cross" /%}
Data Profiler	{% icon iconName="cross" /%}
Data Quality	{% icon iconName="cross" /%}
Lineage	{% icon iconName="cross" /%}
DBT	{% icon iconName="cross" /%}
Supported Versions	--

Feature	Status
Lineage	{% icon iconName="cross" /%}
Table-level	{% icon iconName="cross" /%}
Column-level	{% icon iconName="cross" /%}

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In this section, we provide guides and references to use the MongoDB connector.

Configure and schedule MongoDB metadata workflows from the OpenMetadata UI:

• Requirements
• Metadata Ingestion

Requirements

{%inlineCallout icon="description" bold="OpenMetadata 0.12 or later" href="/deployment"%} To deploy OpenMetadata, check the Deployment guides. {%/inlineCallout%}

To run the Ingestion via the UI you'll need to use the OpenMetadata Ingestion Container, which comes shipped with custom Airflow plugins to handle the workflow deployment.

To fetch the metadata from MongoDB to OpenMetadata, the MongoDB user must have access to perform find operation on collection and listCollection operations on database available in MongoDB.

Python Requirements

To run the MongoDB ingestion, you will need to install:

pip3 install "openmetadata-ingestion[mongo]"

Metadata Ingestion

All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to MongoDB.

In order to create and run a Metadata Ingestion workflow, we will follow the steps to create a YAML configuration able to connect to the source, process the Entities if needed, and reach the OpenMetadata server.

The workflow is modeled around the following JSON Schema

1. Define the YAML Config

This is a sample config for MongoDB:

{% codePreview %}

{% codeInfoContainer %}

Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

username: Username to connect to Mongodb. This user must have access to perform find operation on collection and listCollection operations on database available in MongoDB.

{% /codeInfo %}

{% codeInfo srNumber=2 %}

password: Password to connect to MongoDB.

{% /codeInfo %}

{% codeInfo srNumber=3 %}

hostPort: The hostPort parameter specifies the host and port of the MongoDB. This should be specified as a string in the format hostname:port. E.g., localhost:27017.

{% /codeInfo %}

{% codeInfo srNumber=5 %}

connectionURI: MongoDB connection string is a concise string of parameters used to establish a connection between an OpenMetadata and a MongoDB database. For ex. mongodb://username:password@mongodb0.example.com:27017.

{% /codeInfo %}

{% codeInfo srNumber=6 %}

databaseName: Optional name to give to the database in OpenMetadata. If left blank, we will use default as the database name.

{% /codeInfo %}

Source Configuration - Source Config

{% codeInfo srNumber=9 %}

The sourceConfig is defined here:

markDeletedTables: To flag tables as soft-deleted if they are not present anymore in the source system.

includeTables: true or false, to ingest table data. Default is true.

includeViews: true or false, to ingest views definitions.

databaseFilterPattern, schemaFilterPattern, tableFilternPattern: Note that the filter supports regex as include or exclude. You can find examples here

{% /codeInfo %}

Sink Configuration

{% codeInfo srNumber=10 %}

To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest.

{% /codeInfo %}

Workflow Configuration

{% codeInfo srNumber=11 %}

The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.

For a simple, local installation using our docker containers, this looks like:

{% /codeInfo %}

Advanced Configuration

{% codeInfo srNumber=7 %}

Connection Options (Optional): Enter the details for any additional connection options that can be sent to Athena during the connection. These details must be added as Key-Value pairs.

{% /codeInfo %}

{% /codeInfoContainer %}

{% codeBlock fileName="filename.yaml" %}

source:
  type: mongodb
  serviceName: local_mongodb
  serviceConnection:
    config:
      type: MongoDB
      connectionDetails:

        username: username

        password: password

        hostPort: localhost:27017

        # connectionURI: mongodb://username:password@mongodb0.example.com:27017

        # connectionOptions:
        #   key: value

      database: custom_database_name

  sourceConfig:
    config:
      type: DatabaseMetadata
      markDeletedTables: true
      includeTables: true
      includeViews: true
      # includeTags: true
      # databaseFilterPattern:
      #   includes:
      #     - database1
      #     - database2
      #   excludes:
      #     - database3
      #     - database4
      # schemaFilterPattern:
      #   includes:
      #     - schema1
      #     - schema2
      #   excludes:
      #     - schema3
      #     - schema4
      # tableFilterPattern:
      #   includes:
      #     - users
      #     - type_test
      #   excludes:
      #     - table3
      #     - table4

sink:
  type: metadata-rest
  config: {}

workflowConfig:
  openMetadataServerConfig:
    hostPort: "http://localhost:8585/api"
    authProvider: openmetadata
    securityConfig:
      jwtToken: "{bot_jwt_token}"

{% /codeBlock %}

{% /codePreview %}

Workflow Configs for Security Provider

We support different security providers. You can find their definitions here.

Openmetadata JWT Auth

• JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details here.

workflowConfig:
  openMetadataServerConfig:
    hostPort: "http://localhost:8585/api"
    authProvider: openmetadata
    securityConfig:
      jwtToken: "{bot_jwt_token}"

• You can refer to the JWT Troubleshooting section link for any issues in your JWT configuration. If you need information on configuring the ingestion with other security providers in your bots, you can follow this doc link.

2. Run with the CLI

First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:

metadata ingest -c <path-to-yaml>

Note that from connector to connector, this recipe will always be the same. By updating the YAML configuration, you will be able to extract metadata from different sources.

Related

{% tilesContainer %}

{% tile title="Ingest with Airflow" description="Configure the ingestion using Airflow SDK" link="/connectors/database/mongodb/airflow" / %}

{% /tilesContainer %}