def _validate_sqlalchemy(
self, actual_column_type, expected_types_list, execution_engine
):
# Our goal is to be as explicit as possible. We will match the dialect
# if that is possible. If there is no dialect available, we *will*
# match against a top-level SqlAlchemy type.
#
# This is intended to be a conservative approach.
#
# In particular, we *exclude* types that would be valid under an ORM
# such as "float" for postgresql with this approach
if expected_types_list is None:
success = True
else:
types = []
type_module = _get_dialect_type_module(execution_engine=execution_engine)
for type_ in expected_types_list:
try:
type_class = getattr(type_module, type_)
types.append(type_class)
except AttributeError:
logger.debug("Unrecognized type: %s" % type_)
if len(types) == 0:
logger.warning(
"No recognized sqlalchemy types in type_list for current dialect."
)
types = tuple(types)
success = isinstance(actual_column_type, types)
return {
"success": success,
"result": {"observed_value": type(actual_column_type).__name__},
}
def _validate_sqlalchemy(self, actual_column_type, expected_types_list,
execution_engine):
# Our goal is to be as explicit as possible. We will match the dialect
# if that is possible. If there is no dialect available, we *will*
# match against a top-level SqlAlchemy type.
#
# This is intended to be a conservative approach.
#
# In particular, we *exclude* types that would be valid under an ORM
# such as "float" for postgresql with this approach
if expected_types_list is None:
success = True
else:
types = []
type_module = _get_dialect_type_module(
execution_engine=execution_engine)
for type_ in expected_types_list:
try:
if type_module.__name__ == "pyathena.sqlalchemy_athena":
potential_type = get_pyathena_potential_type(
type_module, type_)
# In the case of the PyAthena dialect we need to verify that
# the type returned is indeed a type and not an instance.
if not inspect.isclass(potential_type):
real_type = type(potential_type)
else:
real_type = potential_type
types.append(real_type)
else:
potential_type = getattr(type_module, type_)
types.append(potential_type)
except AttributeError:
logger.debug(f"Unrecognized type: {type_}")
if len(types) == 0:
logger.warning(
"No recognized sqlalchemy types in type_list for current dialect."
)
types = tuple(types)
success = isinstance(actual_column_type, types)
return {
"success": success,
"result": {
"observed_value": type(actual_column_type).__name__
},
}