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

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

# Run MongoDB using the metadata CLI

{% multiTablesWrapper %}

| 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" /%} |

{% /multiTablesWrapper %}

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

Configure and schedule MongoDB metadata workflows from the OpenMetadata UI:

- [Requirements](#requirements)
- [Metadata Ingestion](#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:

```bash
pip3 install "openmetadata-ingestion[mongo]"
```

## Metadata Ingestion

All connectors are defined as JSON Schemas.
[Here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/database/mongoDBConnection.json)
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](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/workflow.json)

### 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](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/databaseServiceMetadataPipeline.json):

**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](/connectors/ingestion/workflows/metadata/filter-patterns/database)

{% /codeInfo %}

#### Sink Configuration

{% codeInfo srNumber=10 %}

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

{% /codeInfo %}

{% partial file="workflow-config.md" /%}

#### 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" %}

```yaml
source:
  type: mongodb
  serviceName: local_mongodb
  serviceConnection:
    config:
      type: MongoDB
      connectionDetails:
```
```yaml {% srNumber=1 %}
        username: username
```
```yaml {% srNumber=2 %}
        password: password
```
```yaml {% srNumber=3 %}
        hostPort: localhost:27017
```
```yaml {% srNumber=5 %}
        # connectionURI: mongodb://username:password@mongodb0.example.com:27017
```
```yaml {% srNumber=7 %}
        # connectionOptions:
        #   key: value
```
```yaml {% srNumber=6 %}
      database: custom_database_name
```

```yaml {% srNumber=9 %}
  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
```

```yaml {% srNumber=10 %}
sink:
  type: metadata-rest
  config: {}
```

{% partial file="workflow-config-yaml.md" /%}

{% /codeBlock %}

{% /codePreview %}

### 2. Run with the CLI

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

```bash
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 %}