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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 inspect
import logging
import typing
from typing import cast, Optional, TYPE_CHECKING
import warnings
import bigframes_vendored.ibis.expr.datatypes as ibis_dtypes
import bigframes_vendored.ibis.expr.operations.udf as ibis_udf
if TYPE_CHECKING:
from bigframes.session import Session
import bigframes_vendored.constants as constants
import google.api_core.exceptions
import google.api_core.retry
from google.cloud import bigquery
import google.iam.v1
import bigframes.core.compile.ibis_types
import bigframes.dtypes
import bigframes.exceptions as bfe
import bigframes.functions.function_template
from . import _function_session as bff_session
from . import _utils
logger = logging.getLogger(__name__)
class UnsupportedTypeError(ValueError):
def __init__(self, type_, supported_types):
self.type = type_
self.supported_types = supported_types
class ReturnTypeMissingError(ValueError):
pass
# TODO: Move this to compile folder
def ibis_signature_from_routine(routine: bigquery.Routine) -> _utils.IbisSignature:
if routine.return_type:
ibis_output_type = (
bigframes.core.compile.ibis_types.ibis_type_from_bigquery_type(
routine.return_type
)
)
else:
raise ReturnTypeMissingError
ibis_output_type_override: Optional[ibis_dtypes.DataType] = None
if python_output_type := _utils.get_python_output_type_from_bigframes_metadata(
routine.description
):
if not isinstance(ibis_output_type, ibis_dtypes.String):
raise TypeError(
"An explicit output_type should be provided only for a BigQuery function with STRING output."
)
if typing.get_origin(python_output_type) is list:
ibis_output_type_override = bigframes.core.compile.ibis_types.ibis_array_output_type_from_python_type(
cast(type, python_output_type)
)
else:
raise TypeError(
"Currently only list of a type is supported as python output type."
)
return _utils.IbisSignature(
parameter_names=[arg.name for arg in routine.arguments],
input_types=[
bigframes.core.compile.ibis_types.ibis_type_from_bigquery_type(
arg.data_type
)
if arg.data_type
else None
for arg in routine.arguments
],
output_type=ibis_output_type,
output_type_override=ibis_output_type_override,
)
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):
remote_function_session = bff_session.FunctionSession()
return remote_function_session.remote_function(*args, **kwargs)
remote_function.__doc__ = bff_session.FunctionSession.remote_function.__doc__
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
ibis_client = session.ibis_client
try:
routine_ref = get_routine_reference(function_name, bigquery_client, session)
except DatasetMissingError:
raise ValueError(
"Project and dataset must be provided, either directly or via session. "
f"{constants.FEEDBACK_LINK}"
)
# Find the routine and get its arguments.
try:
routine = bigquery_client.get_routine(routine_ref)
except google.api_core.exceptions.NotFound:
raise ValueError(f"Unknown function '{routine_ref}'. {constants.FEEDBACK_LINK}")
if is_row_processor and len(routine.arguments) > 1:
raise ValueError(
"A multi-input function cannot be a row processor. A row processor function "
"takes in a single input representing the row."
)
try:
ibis_signature = ibis_signature_from_routine(routine)
except ReturnTypeMissingError:
raise ValueError(
f"Function return type must be specified. {constants.FEEDBACK_LINK}"
)
except bigframes.core.compile.ibis_types.UnsupportedTypeError as e:
raise ValueError(
f"Type {e.type} not supported, supported types are {e.supported_types}. "
f"{constants.FEEDBACK_LINK}"
)
# The name "args" conflicts with the Ibis operator, so we use
# non-standard names for the arguments here.
def func(*bigframes_args, **bigframes_kwargs):
f"""Bigframes function {str(routine_ref)}."""
nonlocal node # type: ignore
expr = node(*bigframes_args, **bigframes_kwargs) # type: ignore
return ibis_client.execute(expr)
func.__signature__ = inspect.signature(func).replace( # type: ignore
parameters=[
# TODO(shobs): Find a better way to support functions with param
# named "name". This causes an issue in the ibis compilation.
inspect.Parameter(
f"bigframes_{name}",
inspect.Parameter.POSITIONAL_OR_KEYWORD,
)
for name in ibis_signature.parameter_names
]
)
# TODO: Move ibis logic to compiler step
func.__name__ = routine_ref.routine_id
node = ibis_udf.scalar.builtin(
func,
name=routine_ref.routine_id,
catalog=routine_ref.project,
database=routine_ref.dataset_id,
signature=(ibis_signature.input_types, ibis_signature.output_type),
) # type: ignore
func.bigframes_remote_function = str(routine_ref) # type: ignore
# set input bigframes data types
has_unknown_dtypes = False
function_input_dtypes = []
for ibis_type in ibis_signature.input_types:
input_dtype = cast(bigframes.dtypes.Dtype, bigframes.dtypes.DEFAULT_DTYPE)
if ibis_type is None:
has_unknown_dtypes = True
else:
input_dtype = (
bigframes.core.compile.ibis_types.ibis_dtype_to_bigframes_dtype(
ibis_type
)
)
function_input_dtypes.append(input_dtype)
if has_unknown_dtypes:
msg = (
"The function has one or more missing input data types. BigQuery DataFrames "
f"will assume default data type {bigframes.dtypes.DEFAULT_DTYPE} for them."
)
warnings.warn(msg, category=bfe.UnknownDataTypeWarning)
func.input_dtypes = tuple(function_input_dtypes) # type: ignore
func.output_dtype = bigframes.core.compile.ibis_types.ibis_dtype_to_bigframes_dtype( # type: ignore
ibis_signature.output_type_override
if ibis_signature.output_type_override
else ibis_signature.output_type
)
func.bigframes_bigquery_function_output_dtype = bigframes.core.compile.ibis_types.ibis_dtype_to_bigframes_dtype(ibis_signature.output_type) # type: ignore
func.is_row_processor = is_row_processor # type: ignore
func.ibis_node = node # type: ignore
return func