DatabricksSubmitRunOperator

Use the DatabricksSubmitRunOperator to submit a new Databricks job via Databricks api/2.1/jobs/runs/submit API endpoint.

Using the Operator

There are three ways to instantiate this operator. In the first way, you can take the JSON payload that you typically use to call the api/2.1/jobs/runs/submit endpoint and pass it directly to our DatabricksSubmitRunOperator through the json parameter. With this approach you get full control over the underlying payload to Jobs REST API, including execution of Databricks jobs with multiple tasks, but it’s harder to detect errors because of the lack of the type checking.

json = {
    "new_cluster": {"spark_version": "2.1.0-db3-scala2.11", "num_workers": 2},
    "notebook_task": {
        "notebook_path": "/Users/airflow@example.com/PrepareData",
    },
}
notebook_run = DatabricksSubmitRunOperator(task_id="notebook_run", json=json)

The second way to accomplish the same thing is to use the named parameters of the DatabricksSubmitRunOperator directly. Note that there is exactly one named parameter for each top level parameter in the runs/submit endpoint. When using named parameters you must to specify following:

  • Task specification - it should be one of:

    • spark_jar_task - main class and parameters for the JAR task

    • notebook_task - notebook path and parameters for the task

    • spark_python_task - python file path and parameters to run the python file with

    • spark_submit_task - parameters needed to run a spark-submit command

    • pipeline_task - parameters needed to run a Delta Live Tables pipeline

    • dbt_task - parameters needed to run a dbt project

  • Cluster specification - it should be one of: * new_cluster - specs for a new cluster on which this task will be run * existing_cluster_id - ID for existing cluster on which to run this task

  • pipeline_task - may refer to either a pipeline_id or pipeline_name

In the case where both the json parameter AND the named parameters are provided, they will be merged together. If there are conflicts during the merge, the named parameters will take precedence and override the top level json keys.

Currently the named parameters that DatabricksSubmitRunOperator supports are

  • spark_jar_task

  • notebook_task

  • spark_python_task

  • spark_submit_task

  • pipeline_task

  • dbt_task

  • git_source

  • new_cluster

  • existing_cluster_id

  • libraries

  • run_name

  • timeout_seconds

new_cluster = {"spark_version": "10.1.x-scala2.12", "num_workers": 2}
notebook_task = {
    "notebook_path": "/Users/airflow@example.com/PrepareData",
}
notebook_run = DatabricksSubmitRunOperator(
    task_id="notebook_run", new_cluster=new_cluster, notebook_task=notebook_task
)

Another way to do is use the param tasks to pass array of objects to instantiate this operator. Here the value of tasks param that is used to invoke api/2.1/jobs/runs/submit endpoint is passed through the tasks param in DatabricksSubmitRunOperator. Instead of invoking single task, you can pass array of task and submit a one-time run.

tasks = [
    {
        "new_cluster": {"spark_version": "2.1.0-db3-scala2.11", "num_workers": 2},
        "notebook_task": {"notebook_path": "/Users/airflow@example.com/PrepareData"},
    }
]
notebook_run = DatabricksSubmitRunOperator(task_id="notebook_run", tasks=tasks)

Examples

Specifying parameters as JSON

An example usage of the DatabricksSubmitRunOperator is as follows:

tests/system/databricks/example_databricks.py[source]

    # Example of using the JSON parameter to initialize the operator.
    new_cluster = {
        "spark_version": "9.1.x-scala2.12",
        "node_type_id": "r3.xlarge",
        "aws_attributes": {"availability": "ON_DEMAND"},
        "num_workers": 8,
    }

    notebook_task_params = {
        "new_cluster": new_cluster,
        "notebook_task": {
            "notebook_path": "/Users/airflow@example.com/PrepareData",
        },
    }

    notebook_task = DatabricksSubmitRunOperator(task_id="notebook_task", json=notebook_task_params)

Using named parameters

You can also use named parameters to initialize the operator and run the job.

tests/system/databricks/example_databricks.py[source]

    # Example of using the named parameters of DatabricksSubmitRunOperator
    # to initialize the operator.
    spark_jar_task = DatabricksSubmitRunOperator(
        task_id="spark_jar_task",
        new_cluster=new_cluster,
        spark_jar_task={"main_class_name": "com.example.ProcessData"},
        libraries=[{"jar": "dbfs:/lib/etl-0.1.jar"}],
    )

DatabricksSubmitRunDeferrableOperator

Deferrable version of the DatabricksSubmitRunOperator operator.

It allows to utilize Airflow workers more effectively using new functionality introduced in Airflow 2.2.0

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