chore: consolidate PyarrowVersions helpers

google/cloud/bigquery/_helpers.py

@@ -20,7 +20,7 @@
 import math
 import re
 import os
-from typing import Any, Optional, Union
+from typing import Optional, Union
 
 from dateutil import relativedelta
 from google.cloud._helpers import UTC  # type: ignore
@@ -32,10 +32,7 @@
 
 import packaging.version
 
-from google.cloud.bigquery.exceptions import (
-    LegacyBigQueryStorageError,
-    LegacyPyarrowError,
-)
+from google.cloud.bigquery.exceptions import LegacyBigQueryStorageError
 
 _RFC3339_MICROS_NO_ZULU = "%Y-%m-%dT%H:%M:%S.%f"
 _TIMEONLY_WO_MICROS = "%H:%M:%S"
@@ -57,8 +54,6 @@
 
 _MIN_BQ_STORAGE_VERSION = packaging.version.Version("2.0.0")
 
-_MIN_PYARROW_VERSION = packaging.version.Version("3.0.0")
-
 _BQ_STORAGE_OPTIONAL_READ_SESSION_VERSION = packaging.version.Version("2.6.0")
 
 BIGQUERY_EMULATOR_HOST = "BIGQUERY_EMULATOR_HOST"
@@ -126,73 +121,7 @@ def verify_version(self):
             raise LegacyBigQueryStorageError(msg)
 
 
-class PyarrowVersions:
-    """Version comparisons for pyarrow package."""
-
-    def __init__(self):
-        self._installed_version = None
-
-    @property
-    def installed_version(self) -> packaging.version.Version:
-        """Return the parsed version of pyarrow."""
-        if self._installed_version is None:
-            import pyarrow  # type: ignore
-
-            self._installed_version = packaging.version.parse(
-                # Use 0.0.0, since it is earlier than any released version.
-                # Legacy versions also have the same property, but
-                # creating a LegacyVersion has been deprecated.
-                # https://github.com/pypa/packaging/issues/321
-                getattr(pyarrow, "__version__", "0.0.0")
-            )
-
-        return self._installed_version
-
-    @property
-    def use_compliant_nested_type(self) -> bool:
-        return self.installed_version.major >= 4
-
-    def try_import(self, raise_if_error: bool = False) -> Any:
-        """Verify that a recent enough version of pyarrow extra is
-        installed.
-
-        The function assumes that pyarrow extra is installed, and should thus
-        be used in places where this assumption holds.
-
-        Because `pip` can install an outdated version of this extra despite the
-        constraints in `setup.py`, the calling code can use this helper to
-        verify the version compatibility at runtime.
-
-        Returns:
-            The ``pyarrow`` module or ``None``.
-
-        Raises:
-            LegacyPyarrowError:
-                If the pyarrow package is outdated and ``raise_if_error`` is ``True``.
-        """
-        try:
-            import pyarrow
-        except ImportError as exc:  # pragma: NO COVER
-            if raise_if_error:
-                raise LegacyPyarrowError(
-                    f"pyarrow package not found. Install pyarrow version >= {_MIN_PYARROW_VERSION}."
-                ) from exc
-            return None
-
-        if self.installed_version < _MIN_PYARROW_VERSION:
-            if raise_if_error:
-                msg = (
-                    "Dependency pyarrow is outdated, please upgrade "
-                    f"it to version >= {_MIN_PYARROW_VERSION} (version found: {self.installed_version})."
-                )
-                raise LegacyPyarrowError(msg)
-            return None
-
-        return pyarrow
-
-
 BQ_STORAGE_VERSIONS = BQStorageVersions()
-PYARROW_VERSIONS = PyarrowVersions()
 
 
 def _not_null(value, field):

google/cloud/bigquery/_pandas_helpers.py

@@ -23,9 +23,9 @@
 import warnings
 from typing import Any, Union
 
-from packaging import version
-
 from google.cloud.bigquery import _helpers
+from google.cloud.bigquery import _pyarrow_helpers
+from google.cloud.bigquery import _versions_helpers
 from google.cloud.bigquery import schema
 
 try:
@@ -49,7 +49,11 @@
     db_dtypes_import_exception = exc
     date_dtype_name = time_dtype_name = ""  # Use '' rather than None because pytype
 
-pyarrow = _helpers.PYARROW_VERSIONS.try_import()
+pyarrow = _versions_helpers.PYARROW_VERSIONS.try_import()
+
+_BIGNUMERIC_SUPPORT = False
+if pyarrow is not None:
+    _BIGNUMERIC_SUPPORT = True
 
 try:
     # _BaseGeometry is used to detect shapely objevys in `bq_to_arrow_array`
@@ -119,87 +123,6 @@ def __init__(self):
         self.done = False
 
 
-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")
-
-
-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.
-    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()
-        # The exact scale and precision don't matter, see below.
-        pyarrow.decimal128(38, scale=9).id: "NUMERIC",
-    }
-
-    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"
-        _BIGNUMERIC_SUPPORT = True
-    else:
-        _BIGNUMERIC_SUPPORT = False  # pragma: NO COVER
-
-else:  # pragma: NO COVER
-    BQ_TO_ARROW_SCALARS = {}  # pragma: NO COVER
-    ARROW_SCALAR_IDS_TO_BQ = {}  # pragma: NO_COVER
-    _BIGNUMERIC_SUPPORT = False  # pragma: NO COVER
-
-
 BQ_FIELD_TYPE_TO_ARROW_FIELD_METADATA = {
     "GEOGRAPHY": {
         b"ARROW:extension:name": b"google:sqlType:geography",
@@ -240,7 +163,7 @@ def bq_to_arrow_data_type(field):
     if field_type_upper in schema._STRUCT_TYPES:
         return bq_to_arrow_struct_data_type(field)
 
-    data_type_constructor = BQ_TO_ARROW_SCALARS.get(field_type_upper)
+    data_type_constructor = _pyarrow_helpers.bq_to_arrow_scalars(field_type_upper)
     if data_type_constructor is None:
         return None
     return data_type_constructor()
@@ -568,7 +491,9 @@ def augment_schema(dataframe, current_bq_schema):
         if pyarrow.types.is_list(arrow_table.type):
             # `pyarrow.ListType`
             detected_mode = "REPEATED"
-            detected_type = ARROW_SCALAR_IDS_TO_BQ.get(arrow_table.values.type.id)
+            detected_type = _pyarrow_helpers.arrow_scalar_ids_to_bq(
+                arrow_table.values.type.id
+            )
 
             # For timezone-naive datetimes, pyarrow assumes the UTC timezone and adds
             # it to such datetimes, causing them to be recognized as TIMESTAMP type.
@@ -584,7 +509,7 @@ def augment_schema(dataframe, current_bq_schema):
                     detected_type = "DATETIME"
         else:
             detected_mode = field.mode
-            detected_type = ARROW_SCALAR_IDS_TO_BQ.get(arrow_table.type.id)
+            detected_type = _pyarrow_helpers.arrow_scalar_ids_to_bq(arrow_table.type.id)
 
         if detected_type is None:
             unknown_type_fields.append(field)
@@ -705,13 +630,13 @@ def dataframe_to_parquet(
 
             This argument is ignored for ``pyarrow`` versions earlier than ``4.0.0``.
     """
-    pyarrow = _helpers.PYARROW_VERSIONS.try_import(raise_if_error=True)
+    pyarrow = _versions_helpers.PYARROW_VERSIONS.try_import(raise_if_error=True)
 
     import pyarrow.parquet  # type: ignore
 
     kwargs = (
         {"use_compliant_nested_type": parquet_use_compliant_nested_type}
-        if _helpers.PYARROW_VERSIONS.use_compliant_nested_type
+        if _versions_helpers.PYARROW_VERSIONS.use_compliant_nested_type
         else {}
     )
 

google/cloud/bigquery/_pyarrow_helpers.py

@@ -0,0 +1,123 @@
+# 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:
+    # 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()
+        # 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)