Source code for airflow.contrib.operators.spark_sql_operator

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from airflow.models import BaseOperator
from airflow.utils.decorators import apply_defaults
from airflow.contrib.hooks.spark_sql_hook import SparkSqlHook

[docs]class SparkSqlOperator(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 """
[docs] template_fields = ["_sql"]
[docs] template_ext = [".sql", ".hql"]
@apply_defaults def __init__(self, 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): super(SparkSqlOperator, self).__init__(*args, **kwargs) self._sql = sql self._conf = conf self._conn_id = conn_id self._total_executor_cores = total_executor_cores self._executor_cores = executor_cores self._executor_memory = executor_memory self._keytab = keytab self._principal = principal self._master = master self._name = name self._num_executors = num_executors self._yarn_queue = yarn_queue self._hook = None
[docs] def execute(self, context): """ Call the SparkSqlHook to run the provided sql query """ self._hook = SparkSqlHook(sql=self._sql, conf=self._conf, conn_id=self._conn_id, total_executor_cores=self._total_executor_cores, executor_cores=self._executor_cores, executor_memory=self._executor_memory, keytab=self._keytab, principal=self._principal, name=self._name, num_executors=self._num_executors, master=self._master, yarn_queue=self._yarn_queue ) self._hook.run_query()
[docs] def on_kill(self): self._hook.kill()