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_pyarrow_helpers.py
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_pyarrow_helpers.py
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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.
"""Shared helper functions for connecting BigQuery and pyarrow."""
from typing import Any
from packaging import version
try:
import pyarrow # type: ignore
except ImportError: # pragma: NO COVER
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: # pragma: NO COVER
# 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)