Source code for airflow.contrib.operators.databricks_operator

# -*- coding: utf-8 -*-
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
#

import six
import time

from airflow.exceptions import AirflowException
from airflow.contrib.hooks.databricks_hook import DatabricksHook
from airflow.models import BaseOperator


XCOM_RUN_ID_KEY = 'run_id'
XCOM_RUN_PAGE_URL_KEY = 'run_page_url'


[docs]class DatabricksSubmitRunOperator(BaseOperator): """ Submits an Spark job run to Databricks using the `api/2.0/jobs/runs/submit <https://docs.databricks.com/api/latest/jobs.html#runs-submit>`_ 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/2.0/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': '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', 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`` - ``new_cluster`` - ``existing_cluster_id`` - ``libraries`` - ``run_name`` - ``timeout_seconds`` :param json: A JSON object containing API parameters which will be passed directly to the ``api/2.0/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:`jinja-templating`. https://docs.databricks.com/api/latest/jobs.html#runs-submit :type json: dict :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`` should be specified. This field will be templated. .. seealso:: https://docs.databricks.com/api/latest/jobs.html#jobssparkjartask :type spark_jar_task: dict :param notebook_task: The notebook path and parameters for the notebook task. *EITHER* ``spark_jar_task`` *OR* ``notebook_task`` should be specified. This field will be templated. .. seealso:: https://docs.databricks.com/api/latest/jobs.html#jobsnotebooktask :type notebook_task: dict :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. This field will be templated. .. seealso:: https://docs.databricks.com/api/latest/jobs.html#jobsclusterspecnewcluster :type new_cluster: dict :param existing_cluster_id: ID for existing cluster on which to run this task. *EITHER* ``new_cluster`` *OR* ``existing_cluster_id`` should be specified. This field will be templated. :type existing_cluster_id: string :param libraries: Libraries which this run will use. This field will be templated. .. seealso:: https://docs.databricks.com/api/latest/libraries.html#managedlibrarieslibrary :type libraries: list of dicts :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. :type run_name: string :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. :type timeout_seconds: int32 :param databricks_conn_id: The name of the Airflow connection to use. 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. :type databricks_conn_id: string :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. :type polling_period_seconds: int :param databricks_retry_limit: Amount of times retry if the Databricks backend is unreachable. Its value must be greater than or equal to 1. :type databricks_retry_limit: int :param do_xcom_push: Whether we should push run_id and run_page_url to xcom. :type do_xcom_push: boolean """ # Used in airflow.models.BaseOperator template_fields = ('json',) # Databricks brand color (blue) under white text ui_color = '#1CB1C2' ui_fgcolor = '#fff' def __init__( self, json=None, spark_jar_task=None, notebook_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, do_xcom_push=False, **kwargs): """ Creates a new ``DatabricksSubmitRunOperator``. """ super(DatabricksSubmitRunOperator, self).__init__(**kwargs) self.json = json or {} self.databricks_conn_id = databricks_conn_id self.polling_period_seconds = polling_period_seconds self.databricks_retry_limit = databricks_retry_limit if spark_jar_task is not None: self.json['spark_jar_task'] = spark_jar_task if notebook_task is not None: self.json['notebook_task'] = notebook_task if new_cluster is not None: self.json['new_cluster'] = new_cluster if existing_cluster_id is not None: self.json['existing_cluster_id'] = existing_cluster_id if libraries is not None: self.json['libraries'] = libraries if run_name is not None: self.json['run_name'] = run_name if timeout_seconds is not None: self.json['timeout_seconds'] = timeout_seconds if 'run_name' not in self.json: self.json['run_name'] = run_name or kwargs['task_id'] self.json = self._deep_string_coerce(self.json) # This variable will be used in case our task gets killed. self.run_id = None self.do_xcom_push = do_xcom_push def _deep_string_coerce(self, content, json_path='json'): """ Coerces content or all values of content if it is a dict to a string. The function will throw if content contains non-string or non-numeric types. The reason why we have this function is because the ``self.json`` field must be a dict with only string values. This is because ``render_template`` will fail for numerical values. """ c = self._deep_string_coerce if isinstance(content, six.string_types): return content elif isinstance(content, six.integer_types + (float,)): # Databricks can tolerate either numeric or string types in the API backend. return str(content) elif isinstance(content, (list, tuple)): return [c(e, '{0}[{1}]'.format(json_path, i)) for i, e in enumerate(content)] elif isinstance(content, dict): return {k: c(v, '{0}[{1}]'.format(json_path, k)) for k, v in list(content.items())} else: param_type = type(content) msg = 'Type {0} used for parameter {1} is not a number or a string'\ .format(param_type, json_path) raise AirflowException(msg) def _log_run_page_url(self, url): self.log.info('View run status, Spark UI, and logs at %s', url) def get_hook(self): return DatabricksHook( self.databricks_conn_id, retry_limit=self.databricks_retry_limit) def execute(self, context): hook = self.get_hook() self.run_id = hook.submit_run(self.json) if self.do_xcom_push: context['ti'].xcom_push(key=XCOM_RUN_ID_KEY, value=self.run_id) self.log.info('Run submitted with run_id: %s', self.run_id) run_page_url = hook.get_run_page_url(self.run_id) if self.do_xcom_push: context['ti'].xcom_push(key=XCOM_RUN_PAGE_URL_KEY, value=run_page_url) self._log_run_page_url(run_page_url) while True: run_state = hook.get_run_state(self.run_id) if run_state.is_terminal: if run_state.is_successful: self.log.info('%s completed successfully.', self.task_id) self._log_run_page_url(run_page_url) return else: error_message = '{t} failed with terminal state: {s}'.format( t=self.task_id, s=run_state) raise AirflowException(error_message) else: self.log.info('%s in run state: %s', self.task_id, run_state) self._log_run_page_url(run_page_url) self.log.info('Sleeping for %s seconds.', self.polling_period_seconds) time.sleep(self.polling_period_seconds) def on_kill(self): hook = self.get_hook() hook.cancel_run(self.run_id) self.log.info( 'Task: %s with run_id: %s was requested to be cancelled.', self.task_id, self.run_id )