pankajkoti commented on code in PR #39178:
URL: https://github.com/apache/airflow/pull/39178#discussion_r1579436906
##########
airflow/providers/databricks/operators/databricks.py:
##########
@@ -892,3 +892,171 @@ class
DatabricksRunNowDeferrableOperator(DatabricksRunNowOperator):
def __init__(self, *args, **kwargs):
super().__init__(deferrable=True, *args, **kwargs)
+
+
+class DatabricksNotebookOperator(BaseOperator):
+ """
+ Runs a notebook on Databricks using an Airflow operator.
+
+ The DatabricksNotebookOperator allows users to launch and monitor notebook
+ job runs on Databricks as Airflow tasks.
+
+ .. seealso::
+ For more information on how to use this operator, take a look at the
guide:
+ :ref:`howto/operator:DatabricksNotebookOperator`
+
+ :param notebook_path: The path to the notebook in Databricks.
+ :param source: Optional location type of the notebook. When set to
WORKSPACE, the notebook will be retrieved
+ from the local Databricks workspace. When set to GIT, the notebook
will be retrieved from a Git repository
+ defined in git_source. If the value is empty, the task will use
GIT if git_source is defined
+ and WORKSPACE otherwise. For more information please visit
+
https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsCreate
+ :param notebook_params: A dict of key-value pairs to be passed as optional
params to the notebook task.
+ :param notebook_packages: A list of the Python libraries to be installed
on the cluster running the
+ notebook.
+ :param new_cluster: Specs for a new cluster on which this task will be run.
+ :param existing_cluster_id: ID for existing cluster on which to run this
task.
+ :param job_cluster_key: The key for the job cluster.
+ :param polling_period_seconds: Controls the rate which we poll for the
result of this notebook job run.
+ :param databricks_retry_limit: Amount of times to retry if the Databricks
backend is unreachable.
+ :param databricks_retry_delay: Number of seconds to wait between retries.
+ :param databricks_retry_args: An optional dictionary with arguments passed
to ``tenacity.Retrying`` class.
+ :param databricks_conn_id: The name of the Airflow connection to use.
+ """
+
+ template_fields = ("notebook_params",)
+
+ def __init__(
+ self,
+ notebook_path: str,
+ source: str,
+ notebook_params: dict | None = None,
+ notebook_packages: list[dict[str, Any]] | None = None,
+ new_cluster: dict[str, Any] | None = None,
+ existing_cluster_id: str = "",
+ job_cluster_key: str = "",
+ polling_period_seconds: int = 5,
+ databricks_retry_limit: int = 3,
+ databricks_retry_delay: int = 1,
+ databricks_retry_args: dict[Any, Any] | None = None,
+ databricks_conn_id: str = "databricks_default",
+ **kwargs: Any,
+ ):
+ self.notebook_path = notebook_path
+ self.source = source
+ self.notebook_params = notebook_params or {}
+ self.notebook_packages = notebook_packages or []
+ self.new_cluster = new_cluster or {}
+ self.existing_cluster_id = existing_cluster_id
+ self.job_cluster_key = job_cluster_key
+ self.polling_period_seconds = polling_period_seconds
+ self.databricks_retry_limit = databricks_retry_limit
+ self.databricks_retry_delay = databricks_retry_delay
+ self.databricks_retry_args = databricks_retry_args
+ self.databricks_conn_id = databricks_conn_id
+ self.databricks_run_id = ""
+ super().__init__(**kwargs)
+
+ @cached_property
+ def _hook(self):
+ return self._get_hook(caller="DatabricksNotebookOperator")
+
+ def _get_hook(self, caller: str) -> DatabricksHook:
+ return DatabricksHook(
+ self.databricks_conn_id,
+ retry_limit=self.databricks_retry_limit,
+ retry_delay=self.databricks_retry_delay,
+ retry_args=self.databricks_retry_args,
+ caller=caller,
+ )
+
+ def _get_task_timeout_seconds(self) -> int:
+ """
+ Get the timeout seconds value for the Databricks job based on the
execution timeout value provided for the Airflow task.
+
+ By default, tasks in Airflow have an execution_timeout set to None. In
Airflow, when
+ execution_timeout is not defined, the task continues to run
indefinitely. Therefore,
+ to mirror this behavior in the Databricks Jobs API, we set the timeout
to 0, indicating
+ that the job should run indefinitely. This aligns with the default
behavior of Databricks jobs,
+ where a timeout seconds value of 0 signifies an indefinite run
duration.
+ More details can be found in the Databricks documentation:
+ See
https://docs.databricks.com/api/workspace/jobs/submit#timeout_seconds
+ """
+ if self.execution_timeout is None:
+ return 0
+ execution_timeout_seconds = int(self.execution_timeout.total_seconds())
+ if execution_timeout_seconds == 0:
+ raise ValueError(
+ "If you've set an `execution_timeout` for the task, ensure
it's not `0`. Set it instead to "
+ "`None` if you desire the task to run indefinitely."
+ )
+ return execution_timeout_seconds
+
+ def _get_task_base_json(self) -> dict[str, Any]:
+ """Get task base json to be used for task submissions."""
+ return {
+ "timeout_seconds": self._get_task_timeout_seconds(),
+ "email_notifications": {},
+ "notebook_task": {
+ "notebook_path": self.notebook_path,
+ "source": self.source,
+ "base_parameters": self.notebook_params,
+ },
+ "libraries": self.notebook_packages,
+ }
+
+ def _get_databricks_task_id(self, task_id: str):
+ """Get the databricks task ID using dag_id and task_id. Removes
illegal characters."""
+ return f"{self.dag_id}__" + task_id.replace(".", "__")
+
+ def _get_run_json(self):
+ """Get run json to be used for task submissions."""
+ run_json = {
+ "run_name": self._get_databricks_task_id(self.task_id),
+ **self._get_task_base_json(),
+ }
+ if self.new_cluster and self.existing_cluster_id:
+ raise ValueError("Both new_cluster and existing_cluster_id are
set. Only one should be set.")
+ if self.new_cluster:
+ run_json["new_cluster"] = self.new_cluster
+ elif self.existing_cluster_id:
+ run_json["existing_cluster_id"] = self.existing_cluster_id
+ else:
+ raise ValueError("Must specify either existing_cluster_id or
new_cluster.")
+ return run_json
+
+ def launch_notebook_job(self):
+ run_json = self._get_run_json()
+ self.databricks_run_id = self._hook.submit_run(run_json)
+ url = self._hook.get_run_page_url(self.databricks_run_id)
+ self.log.info("Check the job run in Databricks: %s", url)
+ return self.databricks_run_id
+
+ def monitor_databricks_job(self):
+ run = self._hook.get_run(self.databricks_run_id)
+ run_state = RunState(**run["state"])
+ self.log.info("Current state of the job: %s",
run_state.life_cycle_state)
+ while not run_state.is_terminal:
+ time.sleep(self.polling_period_seconds)
+ run = self._hook.get_run(self.databricks_run_id)
+ run_state = RunState(**run["state"])
+ self.log.info(
+ "task %s %s", self._get_databricks_task_id(self.task_id),
run_state.life_cycle_state
+ )
+ self.log.info("Current state of the job: %s",
run_state.life_cycle_state)
+ if run_state.life_cycle_state != "TERMINATED":
+ raise AirflowException(
+ f"Databricks job failed with state
{run_state.life_cycle_state}. "
+ f"Message: {run_state.state_message}"
+ )
+ if not run_state.is_successful:
+ raise AirflowException(
+ "Task failed. Final state %s. Reason: %s",
+ run_state.result_state,
+ run_state.state_message,
+ )
+ self.log.info("Task succeeded. Final state %s.",
run_state.result_state)
+
+ def execute(self, context: Context) -> Any:
+ self.launch_notebook_job()
+ self.monitor_databricks_job()
Review Comment:
yes, nice idea. added with default True
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