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# Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import logging
from typing import Callable, Optional, TYPE_CHECKING
if TYPE_CHECKING:
from bigframes.session import Session
import bigframes.series
import google.api_core.exceptions
from google.cloud import bigquery
import bigframes.formatting_helpers as bf_formatting
from bigframes.functions import _function_session as bff_session
from bigframes.functions import function_typing, udf_def
logger = logging.getLogger(__name__)
class UnsupportedTypeError(ValueError):
def __init__(self, type_, supported_types):
self.type = type_
self.supported_types = supported_types
class DatasetMissingError(ValueError):
pass
def get_routine_reference(
routine_ref_str: str, bigquery_client: bigquery.Client, session: Optional[Session]
) -> bigquery.RoutineReference:
try:
# Handle cases "<project_id>.<dataset_name>.<routine_name>" and
# "<dataset_name>.<routine_name>".
return bigquery.RoutineReference.from_string(
routine_ref_str,
default_project=bigquery_client.project,
)
except ValueError:
# Handle case of "<routine_name>".
if not session:
raise DatasetMissingError
dataset_ref = bigquery.DatasetReference(
bigquery_client.project, session._anonymous_dataset.dataset_id
)
return dataset_ref.routine(routine_ref_str)
def remote_function(*args, **kwargs):
function_session = bff_session.FunctionSession()
return function_session.remote_function(*args, **kwargs)
remote_function.__doc__ = bff_session.FunctionSession.remote_function.__doc__
def udf(*args, **kwargs):
function_session = bff_session.FunctionSession()
return function_session.udf(*args, **kwargs)
udf.__doc__ = bff_session.FunctionSession.udf.__doc__
def _try_import_routine(
routine: bigquery.Routine, session: bigframes.Session
) -> BigqueryCallableRoutine:
udf_def = _routine_as_udf_def(routine)
is_remote = (
hasattr(routine, "remote_function_options") and routine.remote_function_options
)
return BigqueryCallableRoutine(udf_def, session, is_managed=not is_remote)
def _try_import_row_routine(
routine: bigquery.Routine, session: bigframes.Session
) -> BigqueryCallableRowRoutine:
udf_def = _routine_as_udf_def(routine, is_row_processor=True)
is_remote = (
hasattr(routine, "remote_function_options") and routine.remote_function_options
)
return BigqueryCallableRowRoutine(udf_def, session, is_managed=not is_remote)
def _routine_as_udf_def(
routine: bigquery.Routine, is_row_processor: bool = False
) -> udf_def.BigqueryUdf:
try:
return udf_def.BigqueryUdf.from_routine(
routine, is_row_processor=is_row_processor
)
except udf_def.ReturnTypeMissingError:
raise bf_formatting.create_exception_with_feedback_link(
ValueError, "Function return type must be specified."
)
except function_typing.UnsupportedTypeError as e:
raise bf_formatting.create_exception_with_feedback_link(
ValueError,
f"Type {e.type} not supported, supported types are {e.supported_types}.",
)
# TODO(b/399894805): Support managed function.
def read_gbq_function(
function_name: str,
*,
session: Session,
is_row_processor: bool = False,
):
"""
Read an existing BigQuery function and prepare it for use in future queries.
"""
bigquery_client = session.bqclient
try:
routine_ref = get_routine_reference(function_name, bigquery_client, session)
except DatasetMissingError:
raise bf_formatting.create_exception_with_feedback_link(
ValueError,
"Project and dataset must be provided, either directly or via session.",
)
# Find the routine and get its arguments.
try:
routine = bigquery_client.get_routine(routine_ref)
except google.api_core.exceptions.NotFound:
raise bf_formatting.create_exception_with_feedback_link(
ValueError, f"Unknown function '{routine_ref}'."
)
# TODO(493293086): Deprecate is_row_processor.
if is_row_processor:
return _try_import_row_routine(routine, session)
else:
return _try_import_routine(routine, session)
class BigqueryCallableRoutine:
"""
A reference to a routine in the context of a session.
Can be used both directly as a callable, or as an input to dataframe ops that take a callable.
"""
def __init__(
self,
udf_def: udf_def.BigqueryUdf,
session: bigframes.Session,
*,
local_func: Optional[Callable] = None,
cloud_function_ref: Optional[str] = None,
is_managed: bool = False,
):
self._udf_def = udf_def
self._session = session
self._local_fun = local_func
self._cloud_function = cloud_function_ref
self._is_managed = is_managed
def __call__(self, *args, **kwargs):
if self._local_fun:
return self._local_fun(*args, **kwargs)
# avoid circular imports
from bigframes.core.compile.sqlglot import sql as sg_sql
import bigframes.session._io.bigquery as bf_io_bigquery
args_string = ", ".join([sg_sql.to_sql(sg_sql.literal(v)) for v in args])
sql = f"SELECT `{str(self._udf_def.routine_ref)}`({args_string})"
iter, job = bf_io_bigquery.start_query_with_client(
self._session.bqclient,
sql=sql,
query_with_job=True,
job_config=bigquery.QueryJobConfig(),
publisher=self._session._publisher,
) # type: ignore
return list(iter.to_arrow().to_pydict().values())[0][0]
@property
def bigframes_bigquery_function(self) -> str:
return str(self._udf_def.routine_ref)
@property
def bigframes_remote_function(self):
return None if self._is_managed else str(self._udf_def.routine_ref)
@property
def is_row_processor(self) -> bool:
return False
@property
def udf_def(self) -> udf_def.BigqueryUdf:
return self._udf_def
@property
def bigframes_cloud_function(self) -> Optional[str]:
return self._cloud_function
@property
def input_dtypes(self):
return tuple(arg.bf_type for arg in self.udf_def.signature.inputs)
@property
def output_dtype(self):
return self.udf_def.signature.output.bf_type
@property
def bigframes_bigquery_function_output_dtype(self):
return self.udf_def.signature.output.emulating_type.bf_type
class BigqueryCallableRowRoutine:
"""
A reference to a routine in the context of a session.
Can be used both directly as a callable, or as an input to dataframe ops that take a callable.
"""
def __init__(
self,
udf_def: udf_def.BigqueryUdf,
session: bigframes.Session,
*,
local_func: Optional[Callable] = None,
cloud_function_ref: Optional[str] = None,
is_managed: bool = False,
):
assert udf_def.signature.is_row_processor
self._udf_def = udf_def
self._session = session
self._local_fun = local_func
self._cloud_function = cloud_function_ref
self._is_managed = is_managed
def __call__(self, *args, **kwargs):
if self._local_fun:
return self._local_fun(*args, **kwargs)
# avoid circular imports
from bigframes.core.compile.sqlglot import sql as sg_sql
import bigframes.session._io.bigquery as bf_io_bigquery
args_string = ", ".join([sg_sql.to_sql(sg_sql.literal(v)) for v in args])
sql = f"SELECT `{str(self._udf_def.routine_ref)}`({args_string})"
iter, job = bf_io_bigquery.start_query_with_client(
self._session.bqclient,
sql=sql,
query_with_job=True,
job_config=bigquery.QueryJobConfig(),
publisher=self._session._publisher,
) # type: ignore
return list(iter.to_arrow().to_pydict().values())[0][0]
@property
def bigframes_bigquery_function(self) -> str:
return str(self._udf_def.routine_ref)
@property
def bigframes_remote_function(self):
return None if self._is_managed else str(self._udf_def.routine_ref)
@property
def is_row_processor(self) -> bool:
return True
@property
def udf_def(self) -> udf_def.BigqueryUdf:
return self._udf_def
@property
def bigframes_cloud_function(self) -> Optional[str]:
return self._cloud_function
@property
def input_dtypes(self):
return tuple(arg.bf_type for arg in self.udf_def.signature.inputs)
@property
def output_dtype(self):
return self.udf_def.signature.output.bf_type
@property
def bigframes_bigquery_function_output_dtype(self):
return self.udf_def.signature.output.emulating_type.bf_type