astro-provider-databricks 0.3.0

Affordable Databricks Workflows in Apache Airflow

Deprecation Notice

With the release 0.3.0 of the astro-provider-databricks package, this provider stands deprecated and will no longer receive updates. We recommend migrating to the official apache-airflow-providers-databricks>= 6.8.0 for the latest features and support. For the operators and sensors that are deprecated in this repository, migrating to the official Apache Airflow Databricks Provider is as simple as changing the import path in your DAG code as per the below examples.

Previous import path used (Deprecated now)	Suggested import path to use
from astro_databricks.operators.notebook import DatabricksNotebookOperator	from airflow.providers.databricks.operators.databricks import DatabricksNotebookOperator
from astro_databricks.operators.workflow import DatabricksWorkflowTaskGroup	from airflow.providers.databricks.operators.databricks_workflow import DatabricksWorkflowTaskGroup
from astro_databricks.operators.common import DatabricksTaskOperator	from airflow.providers.databricks.operators.databricks import DatabricksTaskOperator
from astro_databricks.plugins.plugin import AstroDatabricksPlugin	from airflow.providers.airflow.providers.databricks.plugins.databricks_workflow import DatabricksWorkflowPlugin

Archives

Databricks Workflows in Airflow

The Astro Databricks Provider is an Apache Airflow provider to write Databricks Workflows using Airflow as the authoring interface. Running your Databricks notebooks as Databricks Workflows can result in a 75% cost reduction ($0.40/DBU for all-purpose compute, $0.07/DBU for Jobs compute).

While this is maintained by Astronomer, it's available to anyone using Airflow - you don't need to be an Astronomer customer to use it.

There are a few advantages to defining your Databricks Workflows in Airflow:

	via Databricks	via Airflow
Authoring interface	Web-based via Databricks UI	Code via Airflow DAG
Workflow compute pricing	✅	✅
Notebook code in source control	✅	✅
Workflow structure in source control	✅	✅
Retry from beginning	✅	✅
Retry single task	✅	✅
Task groups within Workflows		✅
Trigger workflows from other DAGs		✅
Workflow-level parameters		✅

Example

The following Airflow DAG illustrates how to use the DatabricksTaskGroup and DatabricksNotebookOperator to define a Databricks Workflow in Airflow:

from pendulum import datetime

from airflow.decorators import dag, task_group
from airflow.operators.trigger_dagrun import TriggerDagRunOperator
from astro_databricks import DatabricksNotebookOperator, DatabricksWorkflowTaskGroup

# define your cluster spec - can have from 1 to many clusters
job_cluster_spec = [
   {
      "job_cluster_key": "astro_databricks",
      "new_cluster": {
         "cluster_name": "",
         # ...
      },
   }
]

@dag(start_date=datetime(2023, 1, 1), schedule_interval="@daily", catchup=False)
def databricks_workflow_example():
   # the task group is a context manager that will create a Databricks Workflow
   with DatabricksWorkflowTaskGroup(
      group_id="example_databricks_workflow",
      databricks_conn_id="databricks_default",
      job_clusters=job_cluster_spec,
      # you can specify common fields here that get shared to all notebooks
      notebook_packages=[
         { "pypi": { "package": "pandas" } },
      ],
      # notebook_params supports templating
      notebook_params={
         "start_time": "{{ ds }}",
      }
   ) as workflow:
      notebook_1 = DatabricksNotebookOperator(
         task_id="notebook_1",
         databricks_conn_id="databricks_default",
         notebook_path="/Shared/notebook_1",
         source="WORKSPACE",
         # job_cluster_key corresponds to the job_cluster_key in the job_cluster_spec
         job_cluster_key="astro_databricks",
         # you can add to packages & params at the task level
         notebook_packages=[
            { "pypi": { "package": "scikit-learn" } },
         ],
         notebook_params={
            "end_time": "{{ macros.ds_add(ds, 1) }}",
         }
      )

      # you can embed task groups for easier dependency management
      @task_group(group_id="inner_task_group")
      def inner_task_group():
         notebook_2 = DatabricksNotebookOperator(
            task_id="notebook_2",
            databricks_conn_id="databricks_default",
            notebook_path="/Shared/notebook_2",
            source="WORKSPACE",
            job_cluster_key="astro_databricks",
         )

         notebook_3 = DatabricksNotebookOperator(
            task_id="notebook_3",
            databricks_conn_id="databricks_default",
            notebook_path="/Shared/notebook_3",
            source="WORKSPACE",
            job_cluster_key="astro_databricks",
         )

      notebook_4 = DatabricksNotebookOperator(
         task_id="notebook_4",
         databricks_conn_id="databricks_default",
         notebook_path="/Shared/notebook_4",
         source="WORKSPACE",
         job_cluster_key="astro_databricks",
      )

      notebook_1 >> inner_task_group() >> notebook_4

   trigger_workflow_2 = TriggerDagRunOperator(
      task_id="trigger_workflow_2",
      trigger_dag_id="workflow_2",
      execution_date="{{ next_execution_date }}",
   )

   workflow >> trigger_workflow_2

databricks_workflow_example_dag = databricks_workflow_example()

Airflow UI

Databricks UI

Quickstart

Check out the following quickstart guides:

• With the Astro CLI
• Without the Astro CLI

Documentation

The documentation is a work in progress--we aim to follow the Diátaxis system:

• Reference Guide

Changelog

Astro Databricks follows semantic versioning for releases. Read changelog to understand more about the changes introduced to each version.

Contribution guidelines

All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.

Read the Contribution Guidelines for a detailed overview on how to contribute.

Contributors and maintainers should abide by the Contributor Code of Conduct.

License

Apache Licence 2.0