:py:mod:`airflow.providers.databricks.operators.databricks`
===========================================================

.. py:module:: airflow.providers.databricks.operators.databricks

.. autoapi-nested-parse::

   This module contains Databricks operators.


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   airflow.providers.databricks.operators.databricks.DatabricksJobRunLink
   airflow.providers.databricks.operators.databricks.DatabricksSubmitRunOperator
   airflow.providers.databricks.operators.databricks.DatabricksSubmitRunDeferrableOperator
   airflow.providers.databricks.operators.databricks.DatabricksRunNowOperator
   airflow.providers.databricks.operators.databricks.DatabricksRunNowDeferrableOperator


Attributes
~~~~~~~~~~

.. autoapisummary::

   airflow.providers.databricks.operators.databricks.DEFER_METHOD_NAME
   airflow.providers.databricks.operators.databricks.XCOM_RUN_ID_KEY
   airflow.providers.databricks.operators.databricks.XCOM_RUN_PAGE_URL_KEY


.. py:data:: DEFER_METHOD_NAME
   :annotation: = execute_complete

   
.. py:data:: XCOM_RUN_ID_KEY
   :annotation: = run_id

   
.. py:data:: XCOM_RUN_PAGE_URL_KEY
   :annotation: = run_page_url

   
.. py:class:: DatabricksJobRunLink

   Bases: :py:obj:`airflow.models.BaseOperatorLink`

   Constructs a link to monitor a Databricks Job Run.

   .. py:attribute:: name
      :annotation: = See Databricks Job Run

      
   .. py:method:: get_link(self, operator, dttm=None, *, ti_key = None)

      Link to external system.

      Note: The old signature of this function was ``(self, operator, dttm: datetime)``. That is still
      supported at runtime but is deprecated.

      :param operator: airflow operator
      :param ti_key: TaskInstance ID to return link for
      :return: link to external system


.. py:class:: DatabricksSubmitRunOperator(*, json = None, tasks = None, spark_jar_task = None, notebook_task = None, spark_python_task = None, spark_submit_task = None, pipeline_task = None, new_cluster = None, existing_cluster_id = None, libraries = None, run_name = None, timeout_seconds = None, databricks_conn_id = 'databricks_default', polling_period_seconds = 30, databricks_retry_limit = 3, databricks_retry_delay = 1, databricks_retry_args = None, do_xcom_push = True, idempotency_token = None, access_control_list = None, wait_for_termination = True, git_source = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Submits a Spark job run to Databricks using the
   `api/2.1/jobs/runs/submit
   <https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunsSubmit>`_
   API endpoint.

   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/2.1/jobs/runs/submit`` endpoint and pass it directly
   to our ``DatabricksSubmitRunOperator`` through the ``json`` parameter.
   For example ::

       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)

   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. In this method, your code would look like this: ::

       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)

   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_jar_task``
       - ``spark_submit_task``
       - ``pipeline_task``
       - ``new_cluster``
       - ``existing_cluster_id``
       - ``libraries``
       - ``run_name``
       - ``timeout_seconds``

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:DatabricksSubmitRunOperator`

   :param json: A JSON object containing API parameters which will be passed
       directly to the ``api/2.1/jobs/runs/submit`` endpoint. The other named parameters
       (i.e. ``spark_jar_task``, ``notebook_task``..) to this operator will
       be merged with this json dictionary if they are provided.
       If there are conflicts during the merge, the named parameters will
       take precedence and override the top level json keys. (templated)

       .. seealso::
           For more information about templating see :ref:`concepts:jinja-templating`.
           https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunsSubmit
   :param spark_jar_task: The main class and parameters for the JAR task. Note that
       the actual JAR is specified in the ``libraries``.
       *EITHER* ``spark_jar_task`` *OR* ``notebook_task`` *OR* ``spark_python_task``
       *OR* ``spark_submit_task`` *OR* ``pipeline_task`` should be specified.
       This field will be templated.

       .. seealso::
           https://docs.databricks.com/dev-tools/api/2.0/jobs.html#jobssparkjartask
   :param notebook_task: The notebook path and parameters for the notebook task.
       *EITHER* ``spark_jar_task`` *OR* ``notebook_task`` *OR* ``spark_python_task``
       *OR* ``spark_submit_task`` *OR* ``pipeline_task`` should be specified.
       This field will be templated.

       .. seealso::
           https://docs.databricks.com/dev-tools/api/2.0/jobs.html#jobsnotebooktask
   :param spark_python_task: The python file path and parameters to run the python file with.
       *EITHER* ``spark_jar_task`` *OR* ``notebook_task`` *OR* ``spark_python_task``
       *OR* ``spark_submit_task`` *OR* ``pipeline_task`` should be specified.
       This field will be templated.

       .. seealso::
           https://docs.databricks.com/dev-tools/api/2.0/jobs.html#jobssparkpythontask
   :param spark_submit_task: Parameters needed to run a spark-submit command.
       *EITHER* ``spark_jar_task`` *OR* ``notebook_task`` *OR* ``spark_python_task``
       *OR* ``spark_submit_task`` *OR* ``pipeline_task`` should be specified.
       This field will be templated.

       .. seealso::
           https://docs.databricks.com/dev-tools/api/2.0/jobs.html#jobssparksubmittask
   :param pipeline_task: Parameters needed to execute a Delta Live Tables pipeline task.
       The provided dictionary must contain at least ``pipeline_id`` field!
       *EITHER* ``spark_jar_task`` *OR* ``notebook_task`` *OR* ``spark_python_task``
       *OR* ``spark_submit_task`` *OR* ``pipeline_task`` should be specified.
       This field will be templated.

       .. seealso::
           https://docs.databricks.com/dev-tools/api/2.0/jobs.html#jobspipelinetask
   :param new_cluster: Specs for a new cluster on which this task will be run.
       *EITHER* ``new_cluster`` *OR* ``existing_cluster_id`` should be specified
       (except when ``pipeline_task`` is used).
       This field will be templated.

       .. seealso::
           https://docs.databricks.com/dev-tools/api/2.0/jobs.html#jobsclusterspecnewcluster
   :param existing_cluster_id: ID for existing cluster on which to run this task.
       *EITHER* ``new_cluster`` *OR* ``existing_cluster_id`` should be specified
       (except when ``pipeline_task`` is used).
       This field will be templated.
   :param libraries: Libraries which this run will use.
       This field will be templated.

       .. seealso::
           https://docs.databricks.com/dev-tools/api/2.0/jobs.html#managedlibrarieslibrary
   :param run_name: The run name used for this task.
       By default this will be set to the Airflow ``task_id``. This ``task_id`` is a
       required parameter of the superclass ``BaseOperator``.
       This field will be templated.
   :param idempotency_token: an optional token that can be used to guarantee the idempotency of job run
       requests. If a run with the provided token already exists, the request does not create a new run but
       returns the ID of the existing run instead.  This token must have at most 64 characters.
   :param access_control_list: optional list of dictionaries representing Access Control List (ACL) for
       a given job run.  Each dictionary consists of following field - specific subject (``user_name`` for
       users, or ``group_name`` for groups), and ``permission_level`` for that subject.  See Jobs API
       documentation for more details.
   :param wait_for_termination: if we should wait for termination of the job run. ``True`` by default.
   :param timeout_seconds: The timeout for this run. By default a value of 0 is used
       which means to have no timeout.
       This field will be templated.
   :param databricks_conn_id: Reference to the :ref:`Databricks connection <howto/connection:databricks>`.
       By default and in the common case this will be ``databricks_default``. To use
       token based authentication, provide the key ``token`` in the extra field for the
       connection and create the key ``host`` and leave the ``host`` field empty.
   :param polling_period_seconds: Controls the rate which we poll for the result of
       this run. By default the operator will poll every 30 seconds.
   :param databricks_retry_limit: Amount of times retry if the Databricks backend is
       unreachable. Its value must be greater than or equal to 1.
   :param databricks_retry_delay: Number of seconds to wait between retries (it
           might be a floating point number).
   :param databricks_retry_args: An optional dictionary with arguments passed to ``tenacity.Retrying`` class.
   :param do_xcom_push: Whether we should push run_id and run_page_url to xcom.
   :param git_source: Optional specification of a remote git repository from which
       supported task types are retrieved.

       .. seealso::
           https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunsSubmit

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['json']

      
   .. py:attribute:: template_ext
      :annotation: :Sequence[str] = ['.json-tpl']

      
   .. py:attribute:: ui_color
      :annotation: = #1CB1C2

      
   .. py:attribute:: ui_fgcolor
      :annotation: = #fff

      
   .. py:attribute:: operator_extra_links
      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


   .. py:method:: on_kill(self)

      Override this method to cleanup subprocesses when a task instance
      gets killed. Any use of the threading, subprocess or multiprocessing
      module within an operator needs to be cleaned up or it will leave
      ghost processes behind.


.. py:class:: DatabricksSubmitRunDeferrableOperator(*, json = None, tasks = None, spark_jar_task = None, notebook_task = None, spark_python_task = None, spark_submit_task = None, pipeline_task = None, new_cluster = None, existing_cluster_id = None, libraries = None, run_name = None, timeout_seconds = None, databricks_conn_id = 'databricks_default', polling_period_seconds = 30, databricks_retry_limit = 3, databricks_retry_delay = 1, databricks_retry_args = None, do_xcom_push = True, idempotency_token = None, access_control_list = None, wait_for_termination = True, git_source = None, **kwargs)

   Bases: :py:obj:`DatabricksSubmitRunOperator`

   Deferrable version of ``DatabricksSubmitRunOperator``

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


   .. py:method:: execute_complete(self, context, event)


.. py:class:: DatabricksRunNowOperator(*, job_id = None, job_name = None, json = None, notebook_params = None, python_params = None, jar_params = None, spark_submit_params = None, python_named_parameters = None, idempotency_token = None, databricks_conn_id = 'databricks_default', polling_period_seconds = 30, databricks_retry_limit = 3, databricks_retry_delay = 1, databricks_retry_args = None, do_xcom_push = True, wait_for_termination = True, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Runs an existing Spark job run to Databricks using the
   `api/2.1/jobs/run-now
   <https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunNow>`_
   API endpoint.

   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/2.1/jobs/run-now`` endpoint and pass it directly
   to our ``DatabricksRunNowOperator`` through the ``json`` parameter.
   For example ::

       json = {
         "job_id": 42,
         "notebook_params": {
           "dry-run": "true",
           "oldest-time-to-consider": "1457570074236"
         }
       }

       notebook_run = DatabricksRunNowOperator(task_id='notebook_run', json=json)

   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 ``run-now``
   endpoint. In this method, your code would look like this: ::

       job_id=42

       notebook_params = {
           "dry-run": "true",
           "oldest-time-to-consider": "1457570074236"
       }

       python_params = ["douglas adams", "42"]

       jar_params = ["douglas adams", "42"]

       spark_submit_params = ["--class", "org.apache.spark.examples.SparkPi"]

       notebook_run = DatabricksRunNowOperator(
           job_id=job_id,
           notebook_params=notebook_params,
           python_params=python_params,
           jar_params=jar_params,
           spark_submit_params=spark_submit_params
       )

   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 ``DatabricksRunNowOperator`` supports are
       - ``job_id``
       - ``job_name``
       - ``json``
       - ``notebook_params``
       - ``python_params``
       - ``python_named_parameters``
       - ``jar_params``
       - ``spark_submit_params``
       - ``idempotency_token``

   :param job_id: the job_id of the existing Databricks job.
       This field will be templated.

       .. seealso::
           https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunNow
   :param job_name: the name of the existing Databricks job.
       It must exist only one job with the specified name.
       ``job_id`` and ``job_name`` are mutually exclusive.
       This field will be templated.
   :param json: A JSON object containing API parameters which will be passed
       directly to the ``api/2.1/jobs/run-now`` endpoint. The other named parameters
       (i.e. ``notebook_params``, ``spark_submit_params``..) to this operator will
       be merged with this json dictionary if they are provided.
       If there are conflicts during the merge, the named parameters will
       take precedence and override the top level json keys. (templated)

       .. seealso::
           For more information about templating see :ref:`concepts:jinja-templating`.
           https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunNow
   :param notebook_params: 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.

       .. seealso::
           https://docs.databricks.com/user-guide/notebooks/widgets.html
   :param python_params: 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.

       .. seealso::
           https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunNow
   :param python_named_parameters: A list of parameters for jobs with python wheel tasks,
       e.g. "python_named_parameters": {"name": "john doe", "age":  "35"}.
       If specified upon run-now, it would overwrite the parameters specified in job setting.
       This field will be templated.

       .. seealso::
           https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunNow
   :param jar_params: 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.

       .. seealso::
           https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunNow
   :param spark_submit_params: 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.

       .. seealso::
           https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunNow
   :param idempotency_token: an optional token that can be used to guarantee the idempotency of job run
       requests. If a run with the provided token already exists, the request does not create a new run but
       returns the ID of the existing run instead.  This token must have at most 64 characters.
   :param databricks_conn_id: Reference to the :ref:`Databricks connection <howto/connection:databricks>`.
       By default and in the common case this will be ``databricks_default``. To use
       token based authentication, provide the key ``token`` in the extra field for the
       connection and create the key ``host`` and leave the ``host`` field empty.
   :param polling_period_seconds: Controls the rate which we poll for the result of
       this run. By default the operator will poll every 30 seconds.
   :param databricks_retry_limit: Amount of times retry if the Databricks backend is
       unreachable. Its value must be greater than or equal to 1.
   :param databricks_retry_delay: Number of seconds to wait between retries (it
           might be a floating point number).
   :param databricks_retry_args: An optional dictionary with arguments passed to ``tenacity.Retrying`` class.
   :param do_xcom_push: Whether we should push run_id and run_page_url to xcom.
   :param wait_for_termination: if we should wait for termination of the job run. ``True`` by default.

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['json']

      
   .. py:attribute:: template_ext
      :annotation: :Sequence[str] = ['.json-tpl']

      
   .. py:attribute:: ui_color
      :annotation: = #1CB1C2

      
   .. py:attribute:: ui_fgcolor
      :annotation: = #fff

      
   .. py:attribute:: operator_extra_links
      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


   .. py:method:: on_kill(self)

      Override this method to cleanup subprocesses when a task instance
      gets killed. Any use of the threading, subprocess or multiprocessing
      module within an operator needs to be cleaned up or it will leave
      ghost processes behind.


.. py:class:: DatabricksRunNowDeferrableOperator(*, job_id = None, job_name = None, json = None, notebook_params = None, python_params = None, jar_params = None, spark_submit_params = None, python_named_parameters = None, idempotency_token = None, databricks_conn_id = 'databricks_default', polling_period_seconds = 30, databricks_retry_limit = 3, databricks_retry_delay = 1, databricks_retry_args = None, do_xcom_push = True, wait_for_termination = True, **kwargs)

   Bases: :py:obj:`DatabricksRunNowOperator`

   Deferrable version of ``DatabricksRunNowOperator``

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


   .. py:method:: execute_complete(self, context, event)