OpenMetadata/openmetadata-docs/content/v1.1.0/connectors/pipeline/airflow/cli.md

title	slug
Run Airflow Connector using the CLI	/connectors/pipeline/airflow/cli

Run Airflow using the metadata CLI

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

Configure and schedule Airbyte metadata and profiler 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.

Python Requirements

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

pip3 install "openmetadata-ingestion[airflow]"

Note that this installs the same Airflow version that we ship in the Ingestion Container, which is Airflow 2.3.3 from Release 0.12.

The ingestion using Airflow version 2.3.3 as a source package has been tested against Airflow 2.3.3 and Airflow 2.2.5.

Note: we only support officially supported Airflow versions. You can check the version list here.

Metadata Ingestion

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

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 Airbyte:

{% codePreview %}

{% codeInfoContainer %}

Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

connection: Airflow metadata database connection. See these docs for supported backends.

In terms of connection we support the following selections:

• backend: Should not be used from the UI. This is only applicable when ingesting Airflow metadata locally by running the ingestion from a DAG. It will use the current Airflow SQLAlchemy connection to extract the data.
• MySQL, Postgres, MSSQL and SQLite: Pass the required credentials to reach out each of these services. We will create a connection to the pointed database and read Airflow data from there.

hostPort: URL to the Airflow instance.

{% /codeInfo %}

{% codeInfo srNumber=1 %}

numberOfStatus: Number of status we want to look back to in every ingestion (e.g., Past executions from a DAG).

{% /codeInfo %}

{% codeInfo srNumber=1 %}

connection: Airflow metadata database connection. See these docs for supported backends.

In terms of connection we support the following selections:

• backend: Should not be used from the UI. This is only applicable when ingesting Airflow metadata locally by running the ingestion from a DAG. It will use the current Airflow SQLAlchemy connection to extract the data.
• MySQL, Postgres, MSSQL and SQLite: Pass the required credentials to reach out each of these services. We will create a connection to the pointed database and read Airflow data from there.

{% /codeInfo %}

Source Configuration - Source Config

{% codeInfo srNumber=5 %}

The sourceConfig is defined here:

dbServiceNames: Database Service Name for the creation of lineage, if the source supports it.

includeTags: Set the 'Include Tags' toggle to control whether to include tags as part of metadata ingestion.

markDeletedPipelines: Set the Mark Deleted Pipelines toggle to flag pipelines as soft-deleted if they are not present anymore in the source system.

pipelineFilterPattern and chartFilterPattern: Note that the pipelineFilterPattern and chartFilterPattern both support regex as include or exclude.

{% /codeInfo %}

Sink Configuration

{% codeInfo srNumber=6 %}

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

{% /codeInfo %}

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

{% /codeInfoContainer %}

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

source:
  type: airflow
  serviceName: airflow_source
  serviceConnection:
    config:
      type: Airflow

      hostPort: http://localhost:8080

      numberOfStatus: 10

      # Connection needs to be one of Mysql, Postgres, Mssql or Sqlite
      connection:
        type: Mysql
        username: airflow_user
        password: airflow_pass
        databaseSchema: airflow_db
        hostPort: localhost:3306
        # #
        # type: Postgres
        # username: airflow_user
        # password: airflow_pass
        # database: airflow_db
        # hostPort: localhost:3306
        # #
        # type: Mssql
        # username: airflow_user
        # password: airflow_pass
        # database: airflow_db
        # hostPort: localhost:3306
        # uriString: http://... (optional)
        # #
        # type: Sqlite
        # username: airflow_user
        # password: airflow_pass
        # database: airflow_db
        # hostPort: localhost:3306
        # databaseMode: ":memory:" (optional)

  sourceConfig:
    config:
      type: PipelineMetadata
      # markDeletedPipelines: True
      # includeTags: True
      # includeLineage: true
      # pipelineFilterPattern:
      #   includes:
      #     - pipeline1
      #     - pipeline2
      #   excludes:
      #     - pipeline3
      #     - pipeline4

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:

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.