Source code for airflow.providers.apache.spark.operators.spark_sql

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from __future__ import annotations

from typing import TYPE_CHECKING, Any, Sequence

from deprecated import deprecated

from airflow.exceptions import AirflowProviderDeprecationWarning
from airflow.models import BaseOperator
from airflow.providers.apache.spark.hooks.spark_sql import SparkSqlHook

    from airflow.utils.context import Context

[docs]class SparkSqlOperator(BaseOperator): """ Execute Spark SQL query. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:SparkSqlOperator` :param sql: The SQL query to execute. (templated) :param conf: arbitrary Spark configuration property :param conn_id: connection_id string :param total_executor_cores: (Standalone & Mesos only) Total cores for all executors (Default: all the available cores on the worker) :param executor_cores: (Standalone & YARN only) Number of cores per executor (Default: 2) :param executor_memory: Memory per executor (e.g. 1000M, 2G) (Default: 1G) :param keytab: Full path to the file that contains the keytab :param master: spark://host:port, mesos://host:port, yarn, or local (Default: The ``host`` and ``port`` set in the Connection, or ``"yarn"``) :param name: Name of the job :param num_executors: Number of executors to launch :param verbose: Whether to pass the verbose flag to spark-sql :param yarn_queue: The YARN queue to submit to (Default: The ``queue`` value set in the Connection, or ``"default"``) """
[docs] template_fields: Sequence[str] = ("sql",)
[docs] template_ext: Sequence[str] = (".sql", ".hql")
[docs] template_fields_renderers = {"sql": "sql"}
def __init__( self, *, sql: str, conf: str | None = None, conn_id: str = "spark_sql_default", total_executor_cores: int | None = None, executor_cores: int | None = None, executor_memory: str | None = None, keytab: str | None = None, principal: str | None = None, master: str | None = None, name: str = "default-name", num_executors: int | None = None, verbose: bool = True, yarn_queue: str | None = None, **kwargs: Any, ) -> None: super().__init__(**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._verbose = verbose self._yarn_queue = yarn_queue self._hook: SparkSqlHook | None = None @property @deprecated( reason="`_sql` is deprecated and will be removed in the future. Please use `sql` instead.", category=AirflowProviderDeprecationWarning, ) def _sql(self): """Alias for ``sql``, used for compatibility (deprecated).""" return self.sql
[docs] def execute(self, context: Context) -> None: """Call the SparkSqlHook to run the provided sql query.""" if self._hook is None: self._hook = self._get_hook() self._hook.run_query()
[docs] def on_kill(self) -> None: if self._hook is None: self._hook = self._get_hook() self._hook.kill()
def _get_hook(self) -> SparkSqlHook: """Get SparkSqlHook.""" return 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, verbose=self._verbose, yarn_queue=self._yarn_queue, )

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