:mod:`airflow.contrib.operators.spark_sql_operator`
===================================================

.. py:module:: airflow.contrib.operators.spark_sql_operator


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

.. py:class:: SparkSqlOperator(sql, conf=None, conn_id='spark_sql_default', total_executor_cores=None, executor_cores=None, executor_memory=None, keytab=None, principal=None, master='yarn', name='default-name', num_executors=None, yarn_queue='default', *args, **kwargs)

   Bases: :class:`airflow.models.BaseOperator`

   Execute Spark SQL query

   :param sql: The SQL query to execute. (templated)
   :type sql: str
   :param conf: arbitrary Spark configuration property
   :type conf: str (format: PROP=VALUE)
   :param conn_id: connection_id string
   :type conn_id: str
   :param total_executor_cores: (Standalone & Mesos only) Total cores for all
       executors (Default: all the available cores on the worker)
   :type total_executor_cores: int
   :param executor_cores: (Standalone & YARN only) Number of cores per
       executor (Default: 2)
   :type executor_cores: int
   :param executor_memory: Memory per executor (e.g. 1000M, 2G) (Default: 1G)
   :type executor_memory: str
   :param keytab: Full path to the file that contains the keytab
   :type keytab: str
   :param master: spark://host:port, mesos://host:port, yarn, or local
   :type master: str
   :param name: Name of the job
   :type name: str
   :param num_executors: Number of executors to launch
   :type num_executors: int
   :param verbose: Whether to pass the verbose flag to spark-sql
   :type verbose: bool
   :param yarn_queue: The YARN queue to submit to (Default: "default")
   :type yarn_queue: str

   .. attribute:: template_fields
      :annotation: = ['_sql']

      

   .. attribute:: template_ext
      :annotation: = ['.sql', '.hql']

      

   
   .. method:: execute(self, context)

      Call the SparkSqlHook to run the provided sql query



   
   .. method:: on_kill(self)