def test_spark_process_runcmd(self, mock_popen):
# Given
mock_popen.return_value.stdout = six.StringIO(
'Spark-sql communicates using stdout')
mock_popen.return_value.stderr = six.StringIO('stderr')
mock_popen.return_value.wait.return_value = 0
# When
hook = SparkSqlHook(conn_id='spark_default', sql='SELECT 1')
with patch.object(hook.log, 'debug') as mock_debug:
with patch.object(hook.log, 'info') as mock_info:
hook.run_query()
mock_debug.assert_called_with('Spark-Sql cmd: %s', [
'spark-sql', '-e', 'SELECT 1', '--master', 'yarn',
'--name', 'default-name', '--verbose', '--queue', 'default'
])
mock_info.assert_called_with(
'Spark-sql communicates using stdout')
# Then
self.assertEqual(
mock_popen.mock_calls[0],
call([
'spark-sql', '-e', 'SELECT 1', '--master', 'yarn', '--name',
'default-name', '--verbose', '--queue', 'default'
],
stderr=-2,
stdout=-1))
def test_spark_process_runcmd(self, mock_popen):
# Given
mock_popen.return_value.stdout = six.StringIO('Spark-sql communicates using stdout')
mock_popen.return_value.stderr = six.StringIO('stderr')
mock_popen.return_value.wait.return_value = 0
# When
hook = SparkSqlHook(
conn_id='spark_default',
sql='SELECT 1'
)
with patch.object(hook.log, 'debug') as mock_debug:
with patch.object(hook.log, 'info') as mock_info:
hook.run_query()
mock_debug.assert_called_with(
'Spark-Sql cmd: %s',
['spark-sql', '-e', 'SELECT 1', '--master', 'yarn', '--name', 'default-name', '--verbose', '--queue', 'default']
)
mock_info.assert_called_with(
'Spark-sql communicates using stdout'
)
# Then
self.assertEqual(
mock_popen.mock_calls[0],
call(['spark-sql', '-e', 'SELECT 1', '--master', 'yarn', '--name', 'default-name', '--verbose', '--queue', 'default'], stderr=-2, stdout=-1)
)
def test_build_command(self):
hook = SparkSqlHook(**self._config)
# The subprocess requires an array but we build the cmd by joining on a space
cmd = ' '.join(hook._prepare_command(""))
# Check all the parameters
assert "--executor-cores {}".format(
self._config['executor_cores']) in cmd
assert "--executor-memory {}".format(
self._config['executor_memory']) in cmd
assert "--keytab {}".format(self._config['keytab']) in cmd
assert "--name {}".format(self._config['name']) in cmd
assert "--num-executors {}".format(
self._config['num_executors']) in cmd
sql_path = get_after('-f', hook._prepare_command(""))
assert self._config['sql'].strip() == sql_path
# Check if all config settings are there
for kv in self._config['conf'].split(","):
k, v = kv.split('=')
assert "--conf {0}={1}".format(k, v) in cmd
if self._config['verbose']:
assert "--verbose" in cmd
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,
executor_cores=self._executor_cores,
executor_memory=self._executor_memory,
keytab=self._keytab,
name=self._name,
num_executors=self._num_executors,
master=self._master,
yarn_queue=self._yarn_queue)
self._hook.run_query()
def test_spark_process_runcmd_with_list(self, mock_popen):
# Given
mock_popen.return_value.stdout = six.StringIO('Spark-sql communicates using stdout')
mock_popen.return_value.stderr = six.StringIO('stderr')
mock_popen.return_value.wait.return_value = 0
# When
hook = SparkSqlHook(
conn_id='spark_default',
sql='SELECT 1'
)
hook.run_query(['--deploy-mode', 'cluster'])
# Then
self.assertEqual(
mock_popen.mock_calls[0],
call(['spark-sql', '-e', 'SELECT 1', '--master', 'yarn', '--name', 'default-name', '--verbose',
'--queue', 'default', '--deploy-mode', 'cluster'], stderr=-2, stdout=-1)
)
def test_build_command(self):
hook = SparkSqlHook(**self._config)
# The subprocess requires an array but we build the cmd by joining on a space
cmd = ' '.join(hook._prepare_command(""))
# Check all the parameters
assert "--executor-cores {}".format(self._config['executor_cores']) in cmd
assert "--executor-memory {}".format(self._config['executor_memory']) in cmd
assert "--keytab {}".format(self._config['keytab']) in cmd
assert "--name {}".format(self._config['name']) in cmd
assert "--num-executors {}".format(self._config['num_executors']) in cmd
sql_path = get_after('-f', hook._prepare_command(""))
assert self._config['sql'].strip() == sql_path
# Check if all config settings are there
for kv in self._config['conf'].split(","):
k, v = kv.split('=')
assert "--conf {0}={1}".format(k, v) in cmd
if self._config['verbose']:
assert "--verbose" in cmd
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,
executor_cores=self._executor_cores,
executor_memory=self._executor_memory,
keytab=self._keytab,
name=self._name,
num_executors=self._num_executors,
master=self._master,
yarn_queue=self._yarn_queue
)
self._hook.run_query()
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
"""
template_fields = ["_sql"]
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
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()
def on_kill(self):
self._hook.kill()
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
"""
template_fields = ["_sql"]
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().__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
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()
def on_kill(self):
self._hook.kill()