OpenMetadata/openmetadata-docs/content/v0.13.3/connectors/dashboard/superset/airflow.md

title	slug
Run Superset Connector using Airflow SDK	/connectors/dashboard/superset/airflow

Run Superset using the Airflow SDK

Stage	PROD
Dashboards	{% icon iconName="check" /%}
Charts	{% icon iconName="check" /%}
Owners	{% icon iconName="check" /%}
Tags	{% icon iconName="cross" /%}
Lineage	{% icon iconName="check" /%}

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

Configure and schedule Superset 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.

The ingestion also works with Superset 2.0.0 🎉

Note:

API Connection: To extract metadata from Superset via API, user must have at least can read on Chart & can read on Dashboard permissions.

Database Connection: To extract metadata from Superset via MySQL or Postgres database, database user must have at least SELECT priviledge on dashboards & slices tables within superset schema.

Python Requirements

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

pip3 install "openmetadata-ingestion[superset]"

Metadata Ingestion

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

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 Superset:

{% codePreview %}

{% codeInfoContainer %}

Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

hostPort: The Host and Post parameter is common for all three modes of authentication which specifies the host and port of the Superset instance. This should be specified as a string in the format http://hostname:port or https://hostname:port. For example, you might set the hostPort parameter to https://org.superset.com:8088.

connection: Add the connection details to fetch metadata from Superset either through APIs or Database.

For Superset API Connection:

Superset API connection is the default mode of authentication where we fetch the metadata using Superset APIs.

Note: Superset only supports basic or ldap authentication through APIs so if you have SSO enabled on your Superset instance then this mode of authentication will not work for you and you can opt for MySQL or Postgres Connection to fetch metadata directly from the database in the backend of Superset.

username: Username to connect to Superset, for ex. user@organization.com. This user should have access to relevant dashboards and charts in Superset to fetch the metadata.

password: Password of the user account to connect with Superset.

provider: Choose between db(default) or ldap mode of Authentication provider for the Superset service. This parameter is used internally to connect to Superset's REST API.

{% /codeInfo %}

{% codeInfo srNumber=2 %}

For MySQL Connection:

You can use Mysql Connection when you have SSO enabled and your Superset is backed by Mysql database.

username: Specify the User to connect to MySQL. It should have enough privileges to read all the metadata. Make sure the user has select privileges on dashboards, tables & slices tables of superset schema.

password: Password to connect to MySQL.

hostPort: Enter the fully qualified hostname and port number for your MySQL deployment in the Host and Port field.

• databaseSchema: Enter the database schema which is associated with the Superset instance..

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

Connection Arguments (Optional): Enter the details for any additional connection arguments such as security or protocol configs that can be sent to MySQL during the connection. These details must be added as Key-Value pairs.

• In case you are using Single-Sign-On (SSO) for authentication, add the authenticator details in the Connection Arguments as a Key-Value pair as follows: "authenticator" : "sso_login_url"

{% /codeInfo %}

{% codeInfo srNumber=3 %}

For Postgres Connection:

You can use Postgres Connection when you have SSO enabled and your Superset is backed by Postgres database.

• username: Specify the User to connect to Postgres. Make sure the user has select privileges on dashboards, tables & slices tables of superset schema.

password: Password to connect to Postgres.

hostPort: Enter the fully qualified hostname and port number for your Postgres deployment in the Host and Port field.

• database: Initial Postgres database to connect to. Specify the name of database associated with Superset instance.

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

Connection Arguments (Optional): Enter the details for any additional connection arguments such as security or protocol configs that can be sent to Postgres during the connection. These details must be added as Key-Value pairs.

• In case you are using Single-Sign-On (SSO) for authentication, add the authenticator details in the Connection Arguments as a Key-Value pair as follows: "authenticator" : "sso_login_url"

{% /codeInfo %}

Source Configuration - Source Config

{% codeInfo srNumber=4 %}

The sourceConfig is defined here:

dbServiceNames: Database Service Name for the creation of lineage, if the source supports it. dashboardFilterPattern, chartFilterPattern: Note that the they 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. markDeletedDashboards: Set the Mark Deleted Dashboards toggle to flag dashboards as soft-deleted if they are not present anymore in the source system.

{% /codeInfo %}

Sink Configuration

{% codeInfo srNumber=5 %}

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

{% /codeInfo %}

Workflow Configuration

{% codeInfo srNumber=6 %}

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 %}

{% /codeInfoContainer %}

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

source:
  type: superset
  serviceName: local_superset
  serviceConnection:
    config:
      type: Superset

      hostPort: http://localhost:8080
      connection:
        # For Superset API Connection
        username: admin
        password: admin
        provider: db # or provider: ldap

        # For MySQL Connection
        # type: Mysql
        # username: <username>
        # password: <password>
        # hostPort: <hostPort>
        # databaseSchema: superset

        # For Postgres Connection
        # type: Postgres
        # username: username
        # password: password
        # hostPort: localhost:5432
        # database: superset

  sourceConfig:
    config:
      type: DashboardMetadata
      overrideOwner: True
      # dbServiceNames:
      #   - service1
      #   - service2
      # dashboardFilterPattern:
      #   includes:
      #     - dashboard1
      #     - dashboard2
      #   excludes:
      #     - dashboard3
      #     - dashboard4
      # chartFilterPattern:
      #   includes:
      #     - chart1
      #     - chart2
      #   excludes:
      #     - chart3
      #     - chart4

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. Prepare the Ingestion DAG

Create a Python file in your Airflow DAGs directory with the following contents:

{% codePreview %}

{% codeInfoContainer %}

{% codeInfo srNumber=7 %}

Import necessary modules

The Workflow class that is being imported is a part of a metadata ingestion framework, which defines a process of getting data from different sources and ingesting it into a central metadata repository.

Here we are also importing all the basic requirements to parse YAMLs, handle dates and build our DAG.

{% /codeInfo %}

{% codeInfo srNumber=8 %}

Default arguments for all tasks in the Airflow DAG.

• Default arguments dictionary contains default arguments for tasks in the DAG, including the owner's name, email address, number of retries, retry delay, and execution timeout.

{% /codeInfo %}

{% codeInfo srNumber=9 %}

• config: Specifies config for the metadata ingestion as we prepare above.

{% /codeInfo %}

{% codeInfo srNumber=10 %}

• metadata_ingestion_workflow(): This code defines a function metadata_ingestion_workflow() that loads a YAML configuration, creates a Workflow object, executes the workflow, checks its status, prints the status to the console, and stops the workflow.

{% /codeInfo %}

{% codeInfo srNumber=11 %}

• DAG: creates a DAG using the Airflow framework, and tune the DAG configurations to whatever fits with your requirements
• For more Airflow DAGs creation details visit here.

{% /codeInfo %}

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.

{% /codeInfoContainer %}

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

import pathlib
import yaml
from datetime import timedelta
from airflow import DAG
from metadata.config.common import load_config_file
from metadata.ingestion.api.workflow import Workflow
from airflow.utils.dates import days_ago

try:
    from airflow.operators.python import PythonOperator
except ModuleNotFoundError:
    from airflow.operators.python_operator import PythonOperator

default_args = {
    "owner": "user_name",
    "email": ["username@org.com"],
    "email_on_failure": False,
    "retries": 3,
    "retry_delay": timedelta(minutes=5),
    "execution_timeout": timedelta(minutes=60)
}

config = """
<your YAML configuration>
"""

def metadata_ingestion_workflow():
    workflow_config = yaml.safe_load(config)
    workflow = Workflow.create(workflow_config)
    workflow.execute()
    workflow.raise_from_status()
    workflow.print_status()
    workflow.stop()

with DAG(
    "sample_data",
    default_args=default_args,
    description="An example DAG which runs a OpenMetadata ingestion workflow",
    start_date=days_ago(1),
    is_paused_upon_creation=False,
    schedule_interval='*/5 * * * *',
    catchup=False,
) as dag:
    ingest_task = PythonOperator(
        task_id="ingest_using_recipe",
        python_callable=metadata_ingestion_workflow,
    )

{% /codeBlock %}

{% /codePreview %}