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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()