def test_null_types(self):
"""
Test to validate that the numpy protocol handler can deal with null values.
@since 3.3.0
- updated 3.6.0: now numeric types used masked array
@jira_ticket PYTHON-550
@expected_result Numpy can handle non mapped types' null values.
@test_category data_types:serialization
"""
s = self.session
s.row_factory = tuple_factory
s.client_protocol_handler = NumpyProtocolHandler
table = "%s.%s" % (self.keyspace_name, self.function_table_name)
create_table_with_all_types(table, s, 10)
begin_unset = max(
s.execute('select primkey from %s' % (table, ))[0]['primkey']) + 1
keys_null = range(begin_unset, begin_unset + 10)
# scatter some emptry rows in here
insert = "insert into %s (primkey) values (%%s)" % (table, )
execute_concurrent_with_args(s, insert, ((k, ) for k in keys_null))
result = s.execute("select * from %s" % (table, ))[0]
from numpy.ma import masked, MaskedArray
result_keys = result.pop('primkey')
mapped_index = [v[1] for v in sorted(zip(result_keys, count()))]
had_masked = had_none = False
for col_array in result.values():
# these have to be different branches (as opposed to comparing against an 'unset value')
# because None and `masked` have different identity and equals semantics
if isinstance(col_array, MaskedArray):
had_masked = True
[
self.assertIsNot(col_array[i], masked)
for i in mapped_index[:begin_unset]
]
[
self.assertIs(col_array[i], masked)
for i in mapped_index[begin_unset:]
]
else:
had_none = True
[
self.assertIsNotNone(col_array[i])
for i in mapped_index[:begin_unset]
]
[
self.assertIsNone(col_array[i])
for i in mapped_index[begin_unset:]
]
self.assertTrue(had_masked)
self.assertTrue(had_none)
def test_custom_raw_row_results_all_types(self):
"""
Test to validate that custom protocol handlers work with varying types of
results
Connect, create a table with all sorts of data. Query the data, make the sure the custom results handler is
used correctly.
@since 2.7
@jira_ticket PYTHON-313
@expected_result custom protocol handler is invoked with various result types
@test_category data_types:serialization
"""
# Connect using a custom protocol handler that tracks the various types the result message is used with.
session = Cluster(protocol_version=PROTOCOL_VERSION).connect(keyspace="custserdes")
session.client_protocol_handler = CustomProtocolHandlerResultMessageTracked
session.row_factory = tuple_factory
colnames = create_table_with_all_types("alltypes", session, 1)
columns_string = ", ".join(colnames)
# verify data
params = get_all_primitive_params(0)
results = session.execute("SELECT {0} FROM alltypes WHERE primkey=0".format(columns_string))[0]
for expected, actual in zip(params, results):
self.assertEqual(actual, expected)
# Ensure we have covered the various primitive types
self.assertEqual(len(CustomResultMessageTracked.checked_rev_row_set), len(PRIMITIVE_DATATYPES)-1)
session.shutdown()
def setUpClass(cls):
cls.cluster = Cluster(protocol_version=PROTOCOL_VERSION)
cls.session = cls.cluster.connect()
cls.session.execute("CREATE KEYSPACE testspace WITH replication = "
"{ 'class' : 'SimpleStrategy', 'replication_factor': '1'}")
cls.session.set_keyspace("testspace")
cls.colnames = create_table_with_all_types("test_table", cls.session, cls.N_ITEMS)
def test_custom_raw_row_results_all_types(self):
"""
Test to validate that custom protocol handlers work with varying types of
results
Connect, create a table with all sorts of data. Query the data, make the sure the custom results handler is
used correctly.
@since 2.7
@jira_ticket PYTHON-313
@expected_result custom protocol handler is invoked with various result types
@test_category data_types:serialization
"""
# Connect using a custom protocol handler that tracks the various types the result message is used with.
cluster = Cluster(protocol_version=PROTOCOL_VERSION)
session = cluster.connect(keyspace="custserdes")
session.client_protocol_handler = CustomProtocolHandlerResultMessageTracked
session.row_factory = tuple_factory
colnames = create_table_with_all_types("alltypes", session, 1)
columns_string = ", ".join(colnames)
# verify data
params = get_all_primitive_params(0)
results = session.execute(
"SELECT {0} FROM alltypes WHERE primkey=0".format(
columns_string))[0]
for expected, actual in zip(params, results):
self.assertEqual(actual, expected)
# Ensure we have covered the various primitive types
self.assertEqual(len(CustomResultMessageTracked.checked_rev_row_set),
len(PRIMITIVE_DATATYPES) - 1)
cluster.shutdown()
def test_null_types(self):
"""
Test to validate that the numpy protocol handler can deal with null values.
@since 3.3.0
- updated 3.6.0: now numeric types used masked array
@jira_ticket PYTHON-550
@expected_result Numpy can handle non mapped types' null values.
@test_category data_types:serialization
"""
s = self.session
s.row_factory = tuple_factory
s.client_protocol_handler = NumpyProtocolHandler
table = "%s.%s" % (self.keyspace_name, self.function_table_name)
create_table_with_all_types(table, s, 10)
begin_unset = max(s.execute('select primkey from %s' % (table,))[0]['primkey']) + 1
keys_null = range(begin_unset, begin_unset + 10)
# scatter some emptry rows in here
insert = "insert into %s (primkey) values (%%s)" % (table,)
execute_concurrent_with_args(s, insert, ((k,) for k in keys_null))
result = s.execute("select * from %s" % (table,))[0]
from numpy.ma import masked, MaskedArray
result_keys = result.pop('primkey')
mapped_index = [v[1] for v in sorted(zip(result_keys, count()))]
had_masked = had_none = False
for col_array in result.values():
# these have to be different branches (as opposed to comparing against an 'unset value')
# because None and `masked` have different identity and equals semantics
if isinstance(col_array, MaskedArray):
had_masked = True
[self.assertIsNot(col_array[i], masked) for i in mapped_index[:begin_unset]]
[self.assertIs(col_array[i], masked) for i in mapped_index[begin_unset:]]
else:
had_none = True
[self.assertIsNotNone(col_array[i]) for i in mapped_index[:begin_unset]]
[self.assertIsNone(col_array[i]) for i in mapped_index[begin_unset:]]
self.assertTrue(had_masked)
self.assertTrue(had_none)
