DatabricksSubmitRunOperator¶

Use the DatabricksSubmitRunOperator to submit a new Databricks job via Databricks api/2.0/jobs/runs/submit API endpoint.

Using the Operator¶

There are two ways to instantiate this operator. In the first way, you can take the JSON payload that you typically use to call the api/2.0/jobs/runs/submit endpoint and pass it directly to our DatabricksSubmitRunOperator through the json parameter.

Another 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.

Databricks Airflow Connection Metadata¶
Parameter	Input
spark_jar_task: dict	main class and parameters for the JAR task
notebook_task: dict	notebook path and parameters for the task
spark_python_task: dict	python file path and parameters to run the python file with
spark_submit_task: dict	parameters needed to run a spark-submit command
pipeline_task: dict	parameters needed to run a Delta Live Tables pipeline
new_cluster: dict	specs for a new cluster on which this task will be run
existing_cluster_id: string	ID for existing cluster on which to run this task
libraries: list of dict	libraries which this run will use
run_name: string	run name used for this task
timeout_seconds: integer	The timeout for this run
databricks_conn_id: string	the name of the Airflow connection to use
polling_period_seconds: integer	controls the rate which we poll for the result of this run
databricks_retry_limit: integer	amount of times retry if the Databricks backend is unreachable
databricks_retry_delay: decimal	number of seconds to wait between retries
do_xcom_push: boolean	whether we should push run_id and run_page_url to xcom

An example usage of the DatabricksSubmitRunOperator is as follows:

airflow/providers/databricks/example_dags/example_databricks.py[source]

    # Example of using the JSON parameter to initialize the operator.
    notebook_task = DatabricksSubmitRunOperator(task_id='notebook_task', json=notebook_task_params)

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

airflow/providers/databricks/example_dags/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'}],
    )

DatabricksRunNowOperator¶

Use the DatabricksRunNowOperator to trigger run of existing Databricks job via api/2.0/jobs/runs/run-now API endpoint.

Using the Operator¶

There are two ways to instantiate this operator. In the first way, you can take the JSON payload that you typically use to call the api/2.0/jobs/run-now endpoint and pass it directly to our DatabricksRunNowOperator through the json parameter.

Another way to accomplish the same thing is to use the named parameters of the DatabricksRunNowOperator directly. Note that there is exactly one named parameter for each top level parameter in the jobs/run-now endpoint.

Databricks Airflow Connection Metadata¶
Parameter	Input
job_id: str	ID of the existing Databricks jobs (required)
jar_params: list[str]	A list of parameters for jobs with JAR tasks, e.g. `"jar_params": ["john doe", "35"]`. The parameters will be passed to JAR file as command line parameters. If specified upon run-now, it would overwrite the parameters specified in job setting. The json representation of this field (i.e. `{"jar_params":["john doe","35"]}`) cannot exceed 10,000 bytes. This field will be templated.
notebook_params: dict[str,str]	A dict from keys to values for jobs with notebook task, e.g.``”notebook_params”: {“name”: “john doe”, “age”: “35”}```. The map is passed to the notebook and will be accessible through the `dbutils.widgets.get function`. See Widgets for more information. If not specified upon run-now, the triggered run will use the job’s base parameters. `notebook_params` cannot be specified in conjunction with `jar_params`. The json representation of this field (i.e. `{"notebook_params":{"name":"john doe","age":"35"}}`) cannot exceed 10,000 bytes. This field will be templated.
python_params: list[str]	A list of parameters for jobs with python tasks, e.g. `"python_params": ["john doe", "35"]`. The parameters will be passed to python file as command line parameters. If specified upon run-now, it would overwrite the parameters specified in job setting. The json representation of this field (i.e. `{"python_params":["john doe","35"]}`) cannot exceed 10,000 bytes. This field will be templated.
spark_submit_params: list[str]	A list of parameters for jobs with spark submit task, e.g. `"spark_submit_params": ["--class", "org.apache.spark.examples.SparkPi"]`. The parameters will be passed to spark-submit script as command line parameters. If specified upon run-now, it would overwrite the parameters specified in job setting. The json representation of this field cannot exceed 10,000 bytes. This field will be templated.
timeout_seconds: int	The timeout for this run. By default a value of 0 is used which means to have no timeout. This field will be templated.
databricks_conn_id: string	the name of the Airflow connection to use
polling_period_seconds: integer	controls the rate which we poll for the result of this run
databricks_retry_limit: integer	amount of times retry if the Databricks backend is unreachable
databricks_retry_delay: decimal	number of seconds to wait between retries
do_xcom_push: boolean	whether we should push run_id and run_page_url to xcom