# 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. """Shared helper functions for connecting BigQuery and pyarrow.""" from typing import Any from packaging import version try: import pyarrow # type: ignore except ImportError: pyarrow = None def pyarrow_datetime(): return pyarrow.timestamp("us", tz=None) def pyarrow_numeric(): return pyarrow.decimal128(38, 9) def pyarrow_bignumeric(): # 77th digit is partial. # https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#decimal_types return pyarrow.decimal256(76, 38) def pyarrow_time(): return pyarrow.time64("us") def pyarrow_timestamp(): return pyarrow.timestamp("us", tz="UTC") _BQ_TO_ARROW_SCALARS = {} _ARROW_SCALAR_IDS_TO_BQ = {} if pyarrow: # This dictionary is duplicated in bigquery_storage/test/unite/test_reader.py # When modifying it be sure to update it there as well. # Note(todo!!): type "BIGNUMERIC"'s matching pyarrow type is added in _pandas_helpers.py _BQ_TO_ARROW_SCALARS = { "BOOL": pyarrow.bool_, "BOOLEAN": pyarrow.bool_, "BYTES": pyarrow.binary, "DATE": pyarrow.date32, "DATETIME": pyarrow_datetime, "FLOAT": pyarrow.float64, "FLOAT64": pyarrow.float64, "GEOGRAPHY": pyarrow.string, "INT64": pyarrow.int64, "INTEGER": pyarrow.int64, "NUMERIC": pyarrow_numeric, "STRING": pyarrow.string, "TIME": pyarrow_time, "TIMESTAMP": pyarrow_timestamp, } _ARROW_SCALAR_IDS_TO_BQ = { # https://arrow.apache.org/docs/python/api/datatypes.html#type-classes pyarrow.bool_().id: "BOOL", pyarrow.int8().id: "INT64", pyarrow.int16().id: "INT64", pyarrow.int32().id: "INT64", pyarrow.int64().id: "INT64", pyarrow.uint8().id: "INT64", pyarrow.uint16().id: "INT64", pyarrow.uint32().id: "INT64", pyarrow.uint64().id: "INT64", pyarrow.float16().id: "FLOAT64", pyarrow.float32().id: "FLOAT64", pyarrow.float64().id: "FLOAT64", pyarrow.time32("ms").id: "TIME", pyarrow.time64("ns").id: "TIME", pyarrow.timestamp("ns").id: "TIMESTAMP", pyarrow.date32().id: "DATE", pyarrow.date64().id: "DATETIME", # because millisecond resolution pyarrow.binary().id: "BYTES", pyarrow.string().id: "STRING", # also alias for pyarrow.utf8() pyarrow.large_string().id: "STRING", # The exact scale and precision don't matter, see below. pyarrow.decimal128(38, scale=9).id: "NUMERIC", } # Adds bignumeric support only if pyarrow version >= 3.0.0 # Decimal256 support was added to arrow 3.0.0 # https://arrow.apache.org/blog/2021/01/25/3.0.0-release/ if version.parse(pyarrow.__version__) >= version.parse("3.0.0"): _BQ_TO_ARROW_SCALARS["BIGNUMERIC"] = pyarrow_bignumeric # The exact decimal's scale and precision are not important, as only # the type ID matters, and it's the same for all decimal256 instances. _ARROW_SCALAR_IDS_TO_BQ[pyarrow.decimal256(76, scale=38).id] = "BIGNUMERIC" def bq_to_arrow_scalars(bq_scalar: str): """ Returns: The Arrow scalar type that the input BigQuery scalar type maps to. If it cannot find the BigQuery scalar, return None. """ return _BQ_TO_ARROW_SCALARS.get(bq_scalar) def arrow_scalar_ids_to_bq(arrow_scalar: Any): """ Returns: The BigQuery scalar type that the input arrow scalar type maps to. If it cannot find the arrow scalar, return None. """ return _ARROW_SCALAR_IDS_TO_BQ.get(arrow_scalar)