--- title: Run Airflow Connector using the CLI slug: /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](#requirements) - [Metadata Ingestion](#metadata-ingestion) ## Requirements To deploy OpenMetadata, check the Deployment guides. 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: ```bash 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 that we only support officially supported Airflow versions. You can check the version list [here](https://airflow.apache.org/docs/apache-airflow/stable/installation/supported-versions.html). ## 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/pipeline/airbyteConnection.json) 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](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 Airbyte: ```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: { } workflowConfig: # loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR openMetadataServerConfig: hostPort: authProvider: ``` #### Source Configuration - Service Connection - **hostPort**: URL to the Airflow instance. - **numberOfStatus**: Number of status we want to look back to in every ingestion (e.g., Past executions from a DAG). - **connection**: Airflow metadata database connection. See these [docs](https://airflow.apache.org/docs/apache-airflow/stable/howto/set-up-database.html) 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. #### Source Configuration - Source Config The `sourceConfig` is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/pipelineServiceMetadataPipeline.json): - `dbServiceNames`: Database Service Name for the creation of lineage, if the source supports it. - `pipelineFilterPattern` and `chartFilterPattern`: Note that the `pipelineFilterPattern` and `chartFilterPattern` both support regex as include or exclude. E.g., - `includeTags`: Set the Include tags toggle to control whether or not 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. ```yaml pipelineFilterPattern: includes: - users - type_test ``` #### Sink Configuration To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`. #### Workflow Configuration 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: ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: openmetadata securityConfig: jwtToken: '{bot_jwt_token}' ``` We support different security providers. You can find their definitions [here](https://github.com/open-metadata/OpenMetadata/tree/main/openmetadata-spec/src/main/resources/json/schema/security/client). You can find the different implementation of the ingestion below. ### Openmetadata JWT Auth ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: openmetadata securityConfig: jwtToken: '{bot_jwt_token}' ``` ### Auth0 SSO ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: auth0 securityConfig: clientId: '{your_client_id}' secretKey: '{your_client_secret}' domain: '{your_domain}' ``` ### Azure SSO ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: azure securityConfig: clientSecret: '{your_client_secret}' authority: '{your_authority_url}' clientId: '{your_client_id}' scopes: - your_scopes ``` ### Custom OIDC SSO ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: custom-oidc securityConfig: clientId: '{your_client_id}' secretKey: '{your_client_secret}' domain: '{your_domain}' ``` ### Google SSO ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: google securityConfig: secretKey: '{path-to-json-creds}' ``` ### Okta SSO ```yaml workflowConfig: openMetadataServerConfig: hostPort: http://localhost:8585/api authProvider: okta securityConfig: clientId: "{CLIENT_ID - SPA APP}" orgURL: "{ISSUER_URL}/v1/token" privateKey: "{public/private keypair}" email: "{email}" scopes: - token ``` ### Amazon Cognito SSO The ingestion can be configured by [Enabling JWT Tokens](https://docs.open-metadata.org/deployment/security/enable-jwt-tokens) ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: auth0 securityConfig: clientId: '{your_client_id}' secretKey: '{your_client_secret}' domain: '{your_domain}' ``` ### OneLogin SSO Which uses Custom OIDC for the ingestion ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: custom-oidc securityConfig: clientId: '{your_client_id}' secretKey: '{your_client_secret}' domain: '{your_domain}' ``` ### KeyCloak SSO Which uses Custom OIDC for the ingestion ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: custom-oidc securityConfig: clientId: '{your_client_id}' secretKey: '{your_client_secret}' domain: '{your_domain}' ``` ### 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 ``` 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.