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Posted to github@beam.apache.org by GitBox <gi...@apache.org> on 2022/08/29 23:14:10 UTC
[GitHub] [beam] TheNeuralBit commented on a diff in pull request #22575: Add BatchConverter implementations for pandas types, use Batched DoFns in DataFrame convert utilities
TheNeuralBit commented on code in PR #22575:
URL: https://github.com/apache/beam/pull/22575#discussion_r957857602
##########
sdks/python/apache_beam/typehints/pandas_type_compatibility_test.py:
##########
@@ -0,0 +1,191 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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.
+#
+
+"""Unit tests for pandas batched type converters."""
+
+import unittest
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+from parameterized import parameterized
+from parameterized import parameterized_class
+
+from apache_beam.typehints import row_type
+from apache_beam.typehints import typehints
+from apache_beam.typehints.batch import BatchConverter
+
+
+@parameterized_class([
+ {
+ 'batch_typehint': pd.DataFrame,
+ 'element_typehint': row_type.RowTypeConstraint.from_fields([
+ ('foo', int),
+ ('bar', float),
+ ('baz', str),
+ ]),
+ 'match_index': False,
+ 'batch': pd.DataFrame({
+ 'foo': pd.Series(range(100), dtype='int64'),
+ 'bar': pd.Series([i / 100 for i in range(100)], dtype='float64'),
+ 'baz': pd.Series([str(i) for i in range(100)],
+ dtype=pd.StringDtype()),
+ }),
+ },
+ {
+ 'batch_typehint': pd.DataFrame,
+ 'element_typehint': row_type.RowTypeConstraint.from_fields(
+ [
+ ('an_index', int),
+ ('foo', int),
+ ('bar', float),
+ ('baz', str),
+ ],
+ field_options={'an_index': [('beam:dataframe:index', None)]},
+ ),
+ 'match_index': True,
+ 'batch': pd.DataFrame({
+ 'foo': pd.Series(range(100), dtype='int64'),
+ 'bar': pd.Series([i / 100 for i in range(100)], dtype='float64'),
+ 'baz': pd.Series([str(i) for i in range(100)],
+ dtype=pd.StringDtype()),
+ }).set_index(pd.Int64Index(range(123, 223), name='an_index')),
+ },
+ {
+ 'batch_typehint': pd.DataFrame,
+ 'element_typehint': row_type.RowTypeConstraint.from_fields(
+ [
+ ('an_index', int),
+ ('another_index', int),
+ ('foo', int),
+ ('bar', float),
+ ('baz', str),
+ ],
+ field_options={
+ 'an_index': [('beam:dataframe:index', None)],
+ 'another_index': [('beam:dataframe:index', None)],
+ }),
+ 'match_index': True,
+ 'batch': pd.DataFrame({
+ 'foo': pd.Series(range(100), dtype='int64'),
+ 'bar': pd.Series([i / 100 for i in range(100)], dtype='float64'),
+ 'baz': pd.Series([str(i) for i in range(100)],
+ dtype=pd.StringDtype()),
+ }).set_index([
+ pd.Int64Index(range(123, 223), name='an_index'),
+ pd.Int64Index(range(475, 575), name='another_index'),
+ ]),
+ },
+ {
+ 'batch_typehint': pd.Series,
+ 'element_typehint': int,
+ 'match_index': False,
+ 'batch': pd.Series(range(500)),
+ },
+ {
+ 'batch_typehint': pd.Series,
+ 'element_typehint': str,
+ 'batch': pd.Series(['foo', 'bar', 'baz', 'def', 'ghi', 'abc'] * 10,
+ dtype=pd.StringDtype()),
+ },
+ {
+ 'batch_typehint': pd.Series,
+ 'element_typehint': Optional[np.int64],
+ 'batch': pd.Series((i if i % 11 else None for i in range(500)),
+ dtype=pd.Int64Dtype()),
+ },
+ {
+ 'batch_typehint': pd.Series,
+ 'element_typehint': Optional[str],
+ 'batch': pd.Series(['foo', None, 'bar', 'baz', None, 'def', 'ghi'] * 10,
+ dtype=pd.StringDtype()),
+ },
+])
+class DataFrameBatchConverterTest(unittest.TestCase):
+ def create_batch_converter(self):
+ return BatchConverter.from_typehints(
+ element_type=self.element_typehint, batch_type=self.batch_typehint)
+
+ def setUp(self):
+ self.converter = self.create_batch_converter()
+ self.normalized_batch_typehint = typehints.normalize(self.batch_typehint)
+ self.normalized_element_typehint = typehints.normalize(
+ self.element_typehint)
+
+ def equality_check(self, left, right):
+ if isinstance(left, pd.DataFrame):
+ if self.match_index:
+ pd.testing.assert_frame_equal(left.sort_index(), right.sort_index())
+ else:
+ pd.testing.assert_frame_equal(
+ left.sort_values(by=list(left.columns)).reset_index(drop=True),
+ right.sort_values(by=list(right.columns)).reset_index(drop=True))
+ elif isinstance(left, pd.Series):
+ pd.testing.assert_series_equal(
+ left.sort_values().reset_index(drop=True),
+ right.sort_values().reset_index(drop=True))
+ else:
+ raise TypeError(f"Encountered unexpected type, left is a {type(left)!r}")
+
+ def test_typehint_validates(self):
+ typehints.validate_composite_type_param(self.batch_typehint, '')
+ typehints.validate_composite_type_param(self.element_typehint, '')
+
+ def test_type_check(self):
Review Comment:
Done!
##########
sdks/python/apache_beam/typehints/pandas_type_compatibility.py:
##########
@@ -0,0 +1,299 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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.
+#
+
+r"""Utilities for converting between Beam schemas and pandas DataFrames.
+
+Imposes a mapping between native Python typings (specifically those compatible
+with :mod:`apache_beam.typehints.schemas`), and common pandas dtypes::
+
+ pandas dtype Python typing
+ np.int{8,16,32,64} <-----> np.int{8,16,32,64}*
+ pd.Int{8,16,32,64}Dtype <-----> Optional[np.int{8,16,32,64}]*
+ np.float{32,64} <-----> Optional[np.float{32,64}]
+ \--- np.float{32,64}
+ Not supported <------ Optional[bytes]
+ np.bool <-----> np.bool
+ np.dtype('S') <-----> bytes
+ pd.BooleanDType() <-----> Optional[bool]
+ pd.StringDType() <-----> Optional[str]
+ \--- str
+ np.object <-----> Any
+
+ * int, float, bool are treated the same as np.int64, np.float64, np.bool
+
+Note that when converting to pandas dtypes, any types not specified here are
+shunted to ``np.object``.
+
+Similarly when converting from pandas to Python types, types that aren't
+otherwise specified here are shunted to ``Any``. Notably, this includes
+``np.datetime64``.
+
+Pandas does not support hierarchical data natively. Currently, all structured
+types (``Sequence``, ``Mapping``, nested ``NamedTuple`` types), are
+shunted to ``np.object`` like all other unknown types. In the future these
+types may be given special consideration.
+
+Note utilities in this package are for internal use only, we make no backward
+compatibility guarantees, except for the type mapping itself.
+"""
+
+from typing import Any
+from typing import List
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+
+from apache_beam.typehints.batch import BatchConverter
+from apache_beam.typehints.row_type import RowTypeConstraint
+from apache_beam.typehints.typehints import is_nullable
+from apache_beam.typehints.typehints import normalize
+
+# No public API currently, this just exists to register BatchConverter
+# implementations.
+__all__ = []
+
+# Name for a valueless field-level option which, when present, indicates that
+# a field should map to an index in the Beam DataFrame API.
+INDEX_OPTION_NAME = 'beam:dataframe:index'
+
+# Generate type map (presented visually in the docstring)
+_BIDIRECTIONAL = [
+ (bool, bool),
+ (np.int8, np.int8),
+ (np.int16, np.int16),
+ (np.int32, np.int32),
+ (np.int64, np.int64),
+ (pd.Int8Dtype(), Optional[np.int8]),
+ (pd.Int16Dtype(), Optional[np.int16]),
+ (pd.Int32Dtype(), Optional[np.int32]),
+ (pd.Int64Dtype(), Optional[np.int64]),
+ (np.float32, Optional[np.float32]),
+ (np.float64, Optional[np.float64]),
+ (object, Any),
+ (pd.StringDtype(), Optional[str]),
+ (pd.BooleanDtype(), Optional[bool]),
+]
+
+PANDAS_TO_BEAM = {
+ pd.Series([], dtype=dtype).dtype: fieldtype
+ for dtype,
+ fieldtype in _BIDIRECTIONAL
+}
+BEAM_TO_PANDAS = {fieldtype: dtype for dtype, fieldtype in _BIDIRECTIONAL}
+
+# Shunt non-nullable Beam types to the same pandas types as their non-nullable
+# equivalents for FLOATs, DOUBLEs, and STRINGs. pandas has no non-nullable dtype
+# for these.
+OPTIONAL_SHUNTS = [np.float32, np.float64, str]
+
+for typehint in OPTIONAL_SHUNTS:
+ BEAM_TO_PANDAS[typehint] = BEAM_TO_PANDAS[Optional[typehint]]
+
+# int, float -> int64, np.float64
+BEAM_TO_PANDAS[int] = BEAM_TO_PANDAS[np.int64]
+BEAM_TO_PANDAS[Optional[int]] = BEAM_TO_PANDAS[Optional[np.int64]]
+BEAM_TO_PANDAS[float] = BEAM_TO_PANDAS[np.float64]
+BEAM_TO_PANDAS[Optional[float]] = BEAM_TO_PANDAS[Optional[np.float64]]
+
+BEAM_TO_PANDAS[bytes] = 'bytes'
+
+# Add shunts for normalized (Beam) typehints as well
+BEAM_TO_PANDAS.update({
+ normalize(typehint): pandas_dtype
+ for (typehint, pandas_dtype) in BEAM_TO_PANDAS.items()
+})
+
+
+def dtype_from_typehint(typehint):
+ # Default to np.object. This is lossy, we won't be able to recover
+ # the type at the output.
+ return BEAM_TO_PANDAS.get(typehint, object)
+
+
+def dtype_to_fieldtype(dtype):
+ fieldtype = PANDAS_TO_BEAM.get(dtype)
+
+ if fieldtype is not None:
+ return fieldtype
+ elif dtype.kind == 'S':
+ return bytes
+ else:
+ return Any
+
+
+@BatchConverter.register
+def create_pandas_batch_converter(
+ element_type: type, batch_type: type) -> BatchConverter:
+ if batch_type == pd.DataFrame:
+ return DataFrameBatchConverter.from_typehints(
+ element_type=element_type, batch_type=batch_type)
+ elif batch_type == pd.Series:
+ return SeriesBatchConverter.from_typehints(
+ element_type=element_type, batch_type=batch_type)
+
+ return None
+
+
+class DataFrameBatchConverter(BatchConverter):
+ def __init__(
+ self,
+ element_type: RowTypeConstraint,
+ ):
+ super().__init__(pd.DataFrame, element_type)
+ self._columns = [name for name, _ in element_type._fields]
+
+ @staticmethod
+ def from_typehints(element_type,
+ batch_type) -> Optional['DataFrameBatchConverter']:
+ if not batch_type == pd.DataFrame:
+ return None
+
+ if not isinstance(element_type, RowTypeConstraint):
+ element_type = RowTypeConstraint.from_user_type(element_type)
+ if element_type is None:
+ return None
+
+ index_columns = [
+ field_name
+ for (field_name, field_options) in element_type._field_options.items()
+ if any(key == INDEX_OPTION_NAME for key, value in field_options)
+ ]
+
+ if index_columns:
+ return DataFrameBatchConverterKeepIndex(element_type, index_columns)
+ else:
+ return DataFrameBatchConverterDropIndex(element_type)
+
+ def _get_series(self, batch: pd.DataFrame):
+ raise NotImplementedError
+
+ def explode_batch(self, batch: pd.DataFrame):
+ # TODO: Only do null checks for nullable types
+ def make_null_checking_generator(series):
+ nulls = pd.isnull(series)
+ return (None if isnull else value for isnull, value in zip(nulls, series))
+
+ all_series = self._get_series(batch)
+ iterators = [make_null_checking_generator(series) for series in all_series]
+
+ for values in zip(*iterators):
+ yield self._element_type.user_type(
+ **{column: value
+ for column, value in zip(self._columns, values)})
+
+ def combine_batches(self, batches: List[pd.DataFrame]):
+ return pd.concat(batches)
+
+ def estimate_byte_size(self, batch: pd.DataFrame):
+ return batch.memory_usage().sum()
+
+ def get_length(self, batch: pd.DataFrame):
+ return len(batch)
Review Comment:
Done, thank you! I also filed #22950 - we should have a standard test suite to test all the `BatchConverter` implementations.
##########
sdks/python/apache_beam/typehints/pandas_type_compatibility.py:
##########
@@ -0,0 +1,299 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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.
+#
+
+r"""Utilities for converting between Beam schemas and pandas DataFrames.
+
+Imposes a mapping between native Python typings (specifically those compatible
+with :mod:`apache_beam.typehints.schemas`), and common pandas dtypes::
+
+ pandas dtype Python typing
+ np.int{8,16,32,64} <-----> np.int{8,16,32,64}*
+ pd.Int{8,16,32,64}Dtype <-----> Optional[np.int{8,16,32,64}]*
+ np.float{32,64} <-----> Optional[np.float{32,64}]
+ \--- np.float{32,64}
+ Not supported <------ Optional[bytes]
+ np.bool <-----> np.bool
+ np.dtype('S') <-----> bytes
+ pd.BooleanDType() <-----> Optional[bool]
+ pd.StringDType() <-----> Optional[str]
+ \--- str
+ np.object <-----> Any
+
+ * int, float, bool are treated the same as np.int64, np.float64, np.bool
+
+Note that when converting to pandas dtypes, any types not specified here are
+shunted to ``np.object``.
+
+Similarly when converting from pandas to Python types, types that aren't
+otherwise specified here are shunted to ``Any``. Notably, this includes
+``np.datetime64``.
+
+Pandas does not support hierarchical data natively. Currently, all structured
+types (``Sequence``, ``Mapping``, nested ``NamedTuple`` types), are
+shunted to ``np.object`` like all other unknown types. In the future these
+types may be given special consideration.
+
+Note utilities in this package are for internal use only, we make no backward
+compatibility guarantees, except for the type mapping itself.
+"""
+
+from typing import Any
+from typing import List
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+
+from apache_beam.typehints.batch import BatchConverter
+from apache_beam.typehints.row_type import RowTypeConstraint
+from apache_beam.typehints.typehints import is_nullable
+from apache_beam.typehints.typehints import normalize
+
+# No public API currently, this just exists to register BatchConverter
+# implementations.
+__all__ = []
+
+# Name for a valueless field-level option which, when present, indicates that
+# a field should map to an index in the Beam DataFrame API.
+INDEX_OPTION_NAME = 'beam:dataframe:index'
+
+# Generate type map (presented visually in the docstring)
+_BIDIRECTIONAL = [
+ (bool, bool),
+ (np.int8, np.int8),
+ (np.int16, np.int16),
+ (np.int32, np.int32),
+ (np.int64, np.int64),
+ (pd.Int8Dtype(), Optional[np.int8]),
+ (pd.Int16Dtype(), Optional[np.int16]),
+ (pd.Int32Dtype(), Optional[np.int32]),
+ (pd.Int64Dtype(), Optional[np.int64]),
+ (np.float32, Optional[np.float32]),
+ (np.float64, Optional[np.float64]),
+ (object, Any),
+ (pd.StringDtype(), Optional[str]),
+ (pd.BooleanDtype(), Optional[bool]),
+]
+
+PANDAS_TO_BEAM = {
+ pd.Series([], dtype=dtype).dtype: fieldtype
+ for dtype,
+ fieldtype in _BIDIRECTIONAL
+}
+BEAM_TO_PANDAS = {fieldtype: dtype for dtype, fieldtype in _BIDIRECTIONAL}
+
+# Shunt non-nullable Beam types to the same pandas types as their non-nullable
+# equivalents for FLOATs, DOUBLEs, and STRINGs. pandas has no non-nullable dtype
+# for these.
+OPTIONAL_SHUNTS = [np.float32, np.float64, str]
+
+for typehint in OPTIONAL_SHUNTS:
+ BEAM_TO_PANDAS[typehint] = BEAM_TO_PANDAS[Optional[typehint]]
+
+# int, float -> int64, np.float64
+BEAM_TO_PANDAS[int] = BEAM_TO_PANDAS[np.int64]
+BEAM_TO_PANDAS[Optional[int]] = BEAM_TO_PANDAS[Optional[np.int64]]
+BEAM_TO_PANDAS[float] = BEAM_TO_PANDAS[np.float64]
+BEAM_TO_PANDAS[Optional[float]] = BEAM_TO_PANDAS[Optional[np.float64]]
+
+BEAM_TO_PANDAS[bytes] = 'bytes'
+
+# Add shunts for normalized (Beam) typehints as well
+BEAM_TO_PANDAS.update({
+ normalize(typehint): pandas_dtype
+ for (typehint, pandas_dtype) in BEAM_TO_PANDAS.items()
+})
+
+
+def dtype_from_typehint(typehint):
+ # Default to np.object. This is lossy, we won't be able to recover
+ # the type at the output.
+ return BEAM_TO_PANDAS.get(typehint, object)
+
+
+def dtype_to_fieldtype(dtype):
+ fieldtype = PANDAS_TO_BEAM.get(dtype)
+
+ if fieldtype is not None:
+ return fieldtype
+ elif dtype.kind == 'S':
+ return bytes
+ else:
+ return Any
+
+
+@BatchConverter.register
+def create_pandas_batch_converter(
+ element_type: type, batch_type: type) -> BatchConverter:
+ if batch_type == pd.DataFrame:
+ return DataFrameBatchConverter.from_typehints(
+ element_type=element_type, batch_type=batch_type)
+ elif batch_type == pd.Series:
+ return SeriesBatchConverter.from_typehints(
+ element_type=element_type, batch_type=batch_type)
+
+ return None
+
+
+class DataFrameBatchConverter(BatchConverter):
+ def __init__(
+ self,
+ element_type: RowTypeConstraint,
+ ):
+ super().__init__(pd.DataFrame, element_type)
+ self._columns = [name for name, _ in element_type._fields]
+
+ @staticmethod
+ def from_typehints(element_type,
+ batch_type) -> Optional['DataFrameBatchConverter']:
+ if not batch_type == pd.DataFrame:
+ return None
+
+ if not isinstance(element_type, RowTypeConstraint):
+ element_type = RowTypeConstraint.from_user_type(element_type)
+ if element_type is None:
+ return None
+
+ index_columns = [
+ field_name
+ for (field_name, field_options) in element_type._field_options.items()
+ if any(key == INDEX_OPTION_NAME for key, value in field_options)
+ ]
+
+ if index_columns:
+ return DataFrameBatchConverterKeepIndex(element_type, index_columns)
+ else:
+ return DataFrameBatchConverterDropIndex(element_type)
+
+ def _get_series(self, batch: pd.DataFrame):
+ raise NotImplementedError
+
+ def explode_batch(self, batch: pd.DataFrame):
+ # TODO: Only do null checks for nullable types
+ def make_null_checking_generator(series):
+ nulls = pd.isnull(series)
+ return (None if isnull else value for isnull, value in zip(nulls, series))
+
+ all_series = self._get_series(batch)
+ iterators = [make_null_checking_generator(series) for series in all_series]
+
+ for values in zip(*iterators):
Review Comment:
Im not quite sure what this would look like, could you clarify?
Related: It would be good to add a microbenchmark for produce_batch and explode_batch so we can easily evaluate alternative implementations. But I'd prefer to leave that as future work. For now this just preserves the implementation from `apache_beam.dataframe.schemas`.
##########
sdks/python/apache_beam/typehints/pandas_type_compatibility.py:
##########
@@ -0,0 +1,299 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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.
+#
+
+r"""Utilities for converting between Beam schemas and pandas DataFrames.
+
+Imposes a mapping between native Python typings (specifically those compatible
+with :mod:`apache_beam.typehints.schemas`), and common pandas dtypes::
+
+ pandas dtype Python typing
+ np.int{8,16,32,64} <-----> np.int{8,16,32,64}*
+ pd.Int{8,16,32,64}Dtype <-----> Optional[np.int{8,16,32,64}]*
+ np.float{32,64} <-----> Optional[np.float{32,64}]
+ \--- np.float{32,64}
+ Not supported <------ Optional[bytes]
+ np.bool <-----> np.bool
+ np.dtype('S') <-----> bytes
+ pd.BooleanDType() <-----> Optional[bool]
+ pd.StringDType() <-----> Optional[str]
+ \--- str
+ np.object <-----> Any
+
+ * int, float, bool are treated the same as np.int64, np.float64, np.bool
+
+Note that when converting to pandas dtypes, any types not specified here are
+shunted to ``np.object``.
+
+Similarly when converting from pandas to Python types, types that aren't
+otherwise specified here are shunted to ``Any``. Notably, this includes
+``np.datetime64``.
+
+Pandas does not support hierarchical data natively. Currently, all structured
+types (``Sequence``, ``Mapping``, nested ``NamedTuple`` types), are
+shunted to ``np.object`` like all other unknown types. In the future these
+types may be given special consideration.
+
+Note utilities in this package are for internal use only, we make no backward
+compatibility guarantees, except for the type mapping itself.
+"""
+
+from typing import Any
+from typing import List
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+
+from apache_beam.typehints.batch import BatchConverter
+from apache_beam.typehints.row_type import RowTypeConstraint
+from apache_beam.typehints.typehints import is_nullable
+from apache_beam.typehints.typehints import normalize
+
+# No public API currently, this just exists to register BatchConverter
+# implementations.
+__all__ = []
+
+# Name for a valueless field-level option which, when present, indicates that
+# a field should map to an index in the Beam DataFrame API.
+INDEX_OPTION_NAME = 'beam:dataframe:index'
+
+# Generate type map (presented visually in the docstring)
+_BIDIRECTIONAL = [
+ (bool, bool),
+ (np.int8, np.int8),
+ (np.int16, np.int16),
+ (np.int32, np.int32),
+ (np.int64, np.int64),
+ (pd.Int8Dtype(), Optional[np.int8]),
+ (pd.Int16Dtype(), Optional[np.int16]),
+ (pd.Int32Dtype(), Optional[np.int32]),
+ (pd.Int64Dtype(), Optional[np.int64]),
+ (np.float32, Optional[np.float32]),
+ (np.float64, Optional[np.float64]),
+ (object, Any),
+ (pd.StringDtype(), Optional[str]),
+ (pd.BooleanDtype(), Optional[bool]),
+]
+
+PANDAS_TO_BEAM = {
+ pd.Series([], dtype=dtype).dtype: fieldtype
+ for dtype,
+ fieldtype in _BIDIRECTIONAL
+}
+BEAM_TO_PANDAS = {fieldtype: dtype for dtype, fieldtype in _BIDIRECTIONAL}
+
+# Shunt non-nullable Beam types to the same pandas types as their non-nullable
+# equivalents for FLOATs, DOUBLEs, and STRINGs. pandas has no non-nullable dtype
+# for these.
+OPTIONAL_SHUNTS = [np.float32, np.float64, str]
+
+for typehint in OPTIONAL_SHUNTS:
+ BEAM_TO_PANDAS[typehint] = BEAM_TO_PANDAS[Optional[typehint]]
+
+# int, float -> int64, np.float64
+BEAM_TO_PANDAS[int] = BEAM_TO_PANDAS[np.int64]
+BEAM_TO_PANDAS[Optional[int]] = BEAM_TO_PANDAS[Optional[np.int64]]
+BEAM_TO_PANDAS[float] = BEAM_TO_PANDAS[np.float64]
+BEAM_TO_PANDAS[Optional[float]] = BEAM_TO_PANDAS[Optional[np.float64]]
+
+BEAM_TO_PANDAS[bytes] = 'bytes'
+
+# Add shunts for normalized (Beam) typehints as well
+BEAM_TO_PANDAS.update({
+ normalize(typehint): pandas_dtype
+ for (typehint, pandas_dtype) in BEAM_TO_PANDAS.items()
+})
+
+
+def dtype_from_typehint(typehint):
+ # Default to np.object. This is lossy, we won't be able to recover
+ # the type at the output.
+ return BEAM_TO_PANDAS.get(typehint, object)
+
+
+def dtype_to_fieldtype(dtype):
+ fieldtype = PANDAS_TO_BEAM.get(dtype)
+
+ if fieldtype is not None:
+ return fieldtype
+ elif dtype.kind == 'S':
+ return bytes
+ else:
+ return Any
+
+
+@BatchConverter.register
+def create_pandas_batch_converter(
+ element_type: type, batch_type: type) -> BatchConverter:
+ if batch_type == pd.DataFrame:
+ return DataFrameBatchConverter.from_typehints(
+ element_type=element_type, batch_type=batch_type)
+ elif batch_type == pd.Series:
+ return SeriesBatchConverter.from_typehints(
+ element_type=element_type, batch_type=batch_type)
+
+ return None
+
+
+class DataFrameBatchConverter(BatchConverter):
+ def __init__(
+ self,
+ element_type: RowTypeConstraint,
+ ):
+ super().__init__(pd.DataFrame, element_type)
+ self._columns = [name for name, _ in element_type._fields]
+
+ @staticmethod
+ def from_typehints(element_type,
+ batch_type) -> Optional['DataFrameBatchConverter']:
+ if not batch_type == pd.DataFrame:
+ return None
+
+ if not isinstance(element_type, RowTypeConstraint):
+ element_type = RowTypeConstraint.from_user_type(element_type)
+ if element_type is None:
+ return None
+
+ index_columns = [
+ field_name
+ for (field_name, field_options) in element_type._field_options.items()
+ if any(key == INDEX_OPTION_NAME for key, value in field_options)
+ ]
+
+ if index_columns:
+ return DataFrameBatchConverterKeepIndex(element_type, index_columns)
+ else:
+ return DataFrameBatchConverterDropIndex(element_type)
+
+ def _get_series(self, batch: pd.DataFrame):
+ raise NotImplementedError
+
+ def explode_batch(self, batch: pd.DataFrame):
+ # TODO: Only do null checks for nullable types
+ def make_null_checking_generator(series):
+ nulls = pd.isnull(series)
+ return (None if isnull else value for isnull, value in zip(nulls, series))
+
+ all_series = self._get_series(batch)
+ iterators = [make_null_checking_generator(series) for series in all_series]
+
+ for values in zip(*iterators):
+ yield self._element_type.user_type(
+ **{column: value
+ for column, value in zip(self._columns, values)})
+
+ def combine_batches(self, batches: List[pd.DataFrame]):
+ return pd.concat(batches)
+
+ def estimate_byte_size(self, batch: pd.DataFrame):
+ return batch.memory_usage().sum()
+
+ def get_length(self, batch: pd.DataFrame):
+ return len(batch)
+
+
+class DataFrameBatchConverterDropIndex(DataFrameBatchConverter):
+ """A DataFrameBatchConverter that assumes the DataFrame index has no meaning.
+
+ When producing a DataFrame from Rows, a meaningless index will be generated.
+ When exploding a DataFrame into Rows, the index will be dropped.
+ """
+ def _get_series(self, batch: pd.DataFrame):
+ return [batch[column] for column in batch.columns]
+
+ def produce_batch(self, elements):
+ # Note from_records has an index= parameter
+ batch = pd.DataFrame.from_records(elements, columns=self._columns)
+
+ for column, typehint in self._element_type._fields:
+ batch[column] = batch[column].astype(dtype_from_typehint(typehint))
+
+ return batch
+
+
+class DataFrameBatchConverterKeepIndex(DataFrameBatchConverter):
+ """A DataFrameBatchConverter that preserves the DataFrame index.
+
+ This is tracked via options on the Beam schema. Each field in the schema that
+ should map to the index is tagged in an option with name 'dataframe:index'.
+ """
+ def __init__(self, element_type: RowTypeConstraint, index_columns: List[Any]):
+ super().__init__(element_type)
+ self._index_columns = index_columns
+
+ def _get_series(self, batch: pd.DataFrame):
+ assert list(batch.index.names) == self._index_columns
+ return [
+ batch.index.get_level_values(i) for i in range(len(batch.index.names))
+ ] + [batch[column] for column in batch.columns]
+
+ def produce_batch(self, elements):
+ # Note from_records has an index= parameter
+ batch = pd.DataFrame.from_records(elements, columns=self._columns)
Review Comment:
Yeah that's right, I think the above comment was just a note to self as I was iterating on this. I dropped the comment.
##########
sdks/python/apache_beam/typehints/pandas_type_compatibility.py:
##########
@@ -0,0 +1,299 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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.
+#
+
+r"""Utilities for converting between Beam schemas and pandas DataFrames.
+
+Imposes a mapping between native Python typings (specifically those compatible
+with :mod:`apache_beam.typehints.schemas`), and common pandas dtypes::
+
+ pandas dtype Python typing
+ np.int{8,16,32,64} <-----> np.int{8,16,32,64}*
+ pd.Int{8,16,32,64}Dtype <-----> Optional[np.int{8,16,32,64}]*
+ np.float{32,64} <-----> Optional[np.float{32,64}]
+ \--- np.float{32,64}
+ Not supported <------ Optional[bytes]
+ np.bool <-----> np.bool
+ np.dtype('S') <-----> bytes
+ pd.BooleanDType() <-----> Optional[bool]
+ pd.StringDType() <-----> Optional[str]
+ \--- str
+ np.object <-----> Any
+
+ * int, float, bool are treated the same as np.int64, np.float64, np.bool
+
+Note that when converting to pandas dtypes, any types not specified here are
+shunted to ``np.object``.
+
+Similarly when converting from pandas to Python types, types that aren't
+otherwise specified here are shunted to ``Any``. Notably, this includes
+``np.datetime64``.
+
+Pandas does not support hierarchical data natively. Currently, all structured
+types (``Sequence``, ``Mapping``, nested ``NamedTuple`` types), are
+shunted to ``np.object`` like all other unknown types. In the future these
+types may be given special consideration.
+
+Note utilities in this package are for internal use only, we make no backward
+compatibility guarantees, except for the type mapping itself.
+"""
+
+from typing import Any
+from typing import List
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+
+from apache_beam.typehints.batch import BatchConverter
+from apache_beam.typehints.row_type import RowTypeConstraint
+from apache_beam.typehints.typehints import is_nullable
+from apache_beam.typehints.typehints import normalize
+
+# No public API currently, this just exists to register BatchConverter
+# implementations.
+__all__ = []
+
+# Name for a valueless field-level option which, when present, indicates that
+# a field should map to an index in the Beam DataFrame API.
+INDEX_OPTION_NAME = 'beam:dataframe:index'
+
+# Generate type map (presented visually in the docstring)
+_BIDIRECTIONAL = [
+ (bool, bool),
+ (np.int8, np.int8),
+ (np.int16, np.int16),
+ (np.int32, np.int32),
+ (np.int64, np.int64),
+ (pd.Int8Dtype(), Optional[np.int8]),
+ (pd.Int16Dtype(), Optional[np.int16]),
+ (pd.Int32Dtype(), Optional[np.int32]),
+ (pd.Int64Dtype(), Optional[np.int64]),
+ (np.float32, Optional[np.float32]),
+ (np.float64, Optional[np.float64]),
+ (object, Any),
+ (pd.StringDtype(), Optional[str]),
+ (pd.BooleanDtype(), Optional[bool]),
+]
+
+PANDAS_TO_BEAM = {
+ pd.Series([], dtype=dtype).dtype: fieldtype
+ for dtype,
+ fieldtype in _BIDIRECTIONAL
+}
+BEAM_TO_PANDAS = {fieldtype: dtype for dtype, fieldtype in _BIDIRECTIONAL}
+
+# Shunt non-nullable Beam types to the same pandas types as their non-nullable
+# equivalents for FLOATs, DOUBLEs, and STRINGs. pandas has no non-nullable dtype
+# for these.
+OPTIONAL_SHUNTS = [np.float32, np.float64, str]
+
+for typehint in OPTIONAL_SHUNTS:
+ BEAM_TO_PANDAS[typehint] = BEAM_TO_PANDAS[Optional[typehint]]
+
+# int, float -> int64, np.float64
+BEAM_TO_PANDAS[int] = BEAM_TO_PANDAS[np.int64]
+BEAM_TO_PANDAS[Optional[int]] = BEAM_TO_PANDAS[Optional[np.int64]]
+BEAM_TO_PANDAS[float] = BEAM_TO_PANDAS[np.float64]
+BEAM_TO_PANDAS[Optional[float]] = BEAM_TO_PANDAS[Optional[np.float64]]
+
+BEAM_TO_PANDAS[bytes] = 'bytes'
+
+# Add shunts for normalized (Beam) typehints as well
+BEAM_TO_PANDAS.update({
+ normalize(typehint): pandas_dtype
+ for (typehint, pandas_dtype) in BEAM_TO_PANDAS.items()
+})
+
+
+def dtype_from_typehint(typehint):
+ # Default to np.object. This is lossy, we won't be able to recover
+ # the type at the output.
+ return BEAM_TO_PANDAS.get(typehint, object)
+
+
+def dtype_to_fieldtype(dtype):
+ fieldtype = PANDAS_TO_BEAM.get(dtype)
+
+ if fieldtype is not None:
+ return fieldtype
+ elif dtype.kind == 'S':
+ return bytes
+ else:
+ return Any
+
+
+@BatchConverter.register
+def create_pandas_batch_converter(
+ element_type: type, batch_type: type) -> BatchConverter:
+ if batch_type == pd.DataFrame:
+ return DataFrameBatchConverter.from_typehints(
+ element_type=element_type, batch_type=batch_type)
+ elif batch_type == pd.Series:
+ return SeriesBatchConverter.from_typehints(
+ element_type=element_type, batch_type=batch_type)
+
+ return None
+
+
+class DataFrameBatchConverter(BatchConverter):
+ def __init__(
+ self,
+ element_type: RowTypeConstraint,
+ ):
+ super().__init__(pd.DataFrame, element_type)
+ self._columns = [name for name, _ in element_type._fields]
+
+ @staticmethod
+ def from_typehints(element_type,
+ batch_type) -> Optional['DataFrameBatchConverter']:
+ if not batch_type == pd.DataFrame:
+ return None
+
+ if not isinstance(element_type, RowTypeConstraint):
+ element_type = RowTypeConstraint.from_user_type(element_type)
+ if element_type is None:
+ return None
+
+ index_columns = [
+ field_name
+ for (field_name, field_options) in element_type._field_options.items()
+ if any(key == INDEX_OPTION_NAME for key, value in field_options)
+ ]
+
+ if index_columns:
+ return DataFrameBatchConverterKeepIndex(element_type, index_columns)
+ else:
+ return DataFrameBatchConverterDropIndex(element_type)
+
+ def _get_series(self, batch: pd.DataFrame):
+ raise NotImplementedError
+
+ def explode_batch(self, batch: pd.DataFrame):
+ # TODO: Only do null checks for nullable types
Review Comment:
There is now :) (https://github.com/apache/beam/issues/22948)
##########
sdks/python/apache_beam/typehints/pandas_type_compatibility_test.py:
##########
@@ -0,0 +1,191 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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.
+#
+
+"""Unit tests for pandas batched type converters."""
+
+import unittest
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+from parameterized import parameterized
+from parameterized import parameterized_class
+
+from apache_beam.typehints import row_type
+from apache_beam.typehints import typehints
+from apache_beam.typehints.batch import BatchConverter
+
+
+@parameterized_class([
+ {
+ 'batch_typehint': pd.DataFrame,
+ 'element_typehint': row_type.RowTypeConstraint.from_fields([
+ ('foo', int),
+ ('bar', float),
+ ('baz', str),
+ ]),
+ 'match_index': False,
+ 'batch': pd.DataFrame({
+ 'foo': pd.Series(range(100), dtype='int64'),
+ 'bar': pd.Series([i / 100 for i in range(100)], dtype='float64'),
+ 'baz': pd.Series([str(i) for i in range(100)],
+ dtype=pd.StringDtype()),
+ }),
+ },
+ {
+ 'batch_typehint': pd.DataFrame,
+ 'element_typehint': row_type.RowTypeConstraint.from_fields(
+ [
+ ('an_index', int),
+ ('foo', int),
+ ('bar', float),
+ ('baz', str),
+ ],
+ field_options={'an_index': [('beam:dataframe:index', None)]},
+ ),
+ 'match_index': True,
+ 'batch': pd.DataFrame({
+ 'foo': pd.Series(range(100), dtype='int64'),
+ 'bar': pd.Series([i / 100 for i in range(100)], dtype='float64'),
+ 'baz': pd.Series([str(i) for i in range(100)],
+ dtype=pd.StringDtype()),
+ }).set_index(pd.Int64Index(range(123, 223), name='an_index')),
+ },
+ {
+ 'batch_typehint': pd.DataFrame,
+ 'element_typehint': row_type.RowTypeConstraint.from_fields(
+ [
+ ('an_index', int),
+ ('another_index', int),
+ ('foo', int),
+ ('bar', float),
+ ('baz', str),
+ ],
+ field_options={
+ 'an_index': [('beam:dataframe:index', None)],
+ 'another_index': [('beam:dataframe:index', None)],
+ }),
+ 'match_index': True,
+ 'batch': pd.DataFrame({
+ 'foo': pd.Series(range(100), dtype='int64'),
+ 'bar': pd.Series([i / 100 for i in range(100)], dtype='float64'),
+ 'baz': pd.Series([str(i) for i in range(100)],
+ dtype=pd.StringDtype()),
+ }).set_index([
+ pd.Int64Index(range(123, 223), name='an_index'),
+ pd.Int64Index(range(475, 575), name='another_index'),
+ ]),
+ },
+ {
+ 'batch_typehint': pd.Series,
+ 'element_typehint': int,
+ 'match_index': False,
+ 'batch': pd.Series(range(500)),
+ },
+ {
+ 'batch_typehint': pd.Series,
+ 'element_typehint': str,
+ 'batch': pd.Series(['foo', 'bar', 'baz', 'def', 'ghi', 'abc'] * 10,
+ dtype=pd.StringDtype()),
+ },
+ {
+ 'batch_typehint': pd.Series,
+ 'element_typehint': Optional[np.int64],
+ 'batch': pd.Series((i if i % 11 else None for i in range(500)),
+ dtype=pd.Int64Dtype()),
+ },
+ {
+ 'batch_typehint': pd.Series,
+ 'element_typehint': Optional[str],
+ 'batch': pd.Series(['foo', None, 'bar', 'baz', None, 'def', 'ghi'] * 10,
+ dtype=pd.StringDtype()),
+ },
+])
+class DataFrameBatchConverterTest(unittest.TestCase):
+ def create_batch_converter(self):
+ return BatchConverter.from_typehints(
+ element_type=self.element_typehint, batch_type=self.batch_typehint)
+
+ def setUp(self):
+ self.converter = self.create_batch_converter()
+ self.normalized_batch_typehint = typehints.normalize(self.batch_typehint)
+ self.normalized_element_typehint = typehints.normalize(
+ self.element_typehint)
+
+ def equality_check(self, left, right):
+ if isinstance(left, pd.DataFrame):
+ if self.match_index:
+ pd.testing.assert_frame_equal(left.sort_index(), right.sort_index())
+ else:
+ pd.testing.assert_frame_equal(
+ left.sort_values(by=list(left.columns)).reset_index(drop=True),
+ right.sort_values(by=list(right.columns)).reset_index(drop=True))
+ elif isinstance(left, pd.Series):
+ pd.testing.assert_series_equal(
+ left.sort_values().reset_index(drop=True),
+ right.sort_values().reset_index(drop=True))
+ else:
+ raise TypeError(f"Encountered unexpected type, left is a {type(left)!r}")
+
+ def test_typehint_validates(self):
+ typehints.validate_composite_type_param(self.batch_typehint, '')
+ typehints.validate_composite_type_param(self.element_typehint, '')
+
+ def test_type_check(self):
+ typehints.check_constraint(self.normalized_batch_typehint, self.batch)
+
+ def test_type_check_element(self):
+ for element in self.converter.explode_batch(self.batch):
+ typehints.check_constraint(self.normalized_element_typehint, element)
+
+ def test_explode_rebatch(self):
+ exploded = list(self.converter.explode_batch(self.batch))
+ rebatched = self.converter.produce_batch(exploded)
+
+ typehints.check_constraint(self.normalized_batch_typehint, rebatched)
+ self.equality_check(self.batch, rebatched)
+
+ @parameterized.expand([
+ (2, ),
+ (3, ),
+ (10, ),
+ ])
+ def test_combine_batches(self, N):
+ elements = list(self.converter.explode_batch(self.batch))
+
+ # Split elements into N contiguous partitions, create a batch out of each
+ batches = [
+ self.converter.produce_batch(
+ elements[len(elements) * i // N:len(elements) * (i + 1) // N])
+ for i in range(N)
+ ]
+
+ # Combine the batches, output should be equivalent to the original batch
+ combined = self.converter.combine_batches(batches)
+
+ self.equality_check(self.batch, combined)
+
+ def test_equals(self):
+ self.assertTrue(self.converter == self.create_batch_converter())
+ self.assertTrue(self.create_batch_converter() == self.converter)
Review Comment:
This is just being overly cautious - in theory the instances on either side could be a different type and could have a different `__eq__` implementation.
##########
sdks/python/apache_beam/typehints/pandas_type_compatibility.py:
##########
@@ -0,0 +1,299 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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.
+#
+
+r"""Utilities for converting between Beam schemas and pandas DataFrames.
+
+Imposes a mapping between native Python typings (specifically those compatible
+with :mod:`apache_beam.typehints.schemas`), and common pandas dtypes::
+
+ pandas dtype Python typing
+ np.int{8,16,32,64} <-----> np.int{8,16,32,64}*
+ pd.Int{8,16,32,64}Dtype <-----> Optional[np.int{8,16,32,64}]*
+ np.float{32,64} <-----> Optional[np.float{32,64}]
+ \--- np.float{32,64}
+ Not supported <------ Optional[bytes]
+ np.bool <-----> np.bool
+ np.dtype('S') <-----> bytes
+ pd.BooleanDType() <-----> Optional[bool]
+ pd.StringDType() <-----> Optional[str]
+ \--- str
+ np.object <-----> Any
+
+ * int, float, bool are treated the same as np.int64, np.float64, np.bool
+
+Note that when converting to pandas dtypes, any types not specified here are
+shunted to ``np.object``.
+
+Similarly when converting from pandas to Python types, types that aren't
+otherwise specified here are shunted to ``Any``. Notably, this includes
+``np.datetime64``.
+
+Pandas does not support hierarchical data natively. Currently, all structured
+types (``Sequence``, ``Mapping``, nested ``NamedTuple`` types), are
+shunted to ``np.object`` like all other unknown types. In the future these
+types may be given special consideration.
+
+Note utilities in this package are for internal use only, we make no backward
+compatibility guarantees, except for the type mapping itself.
+"""
+
+from typing import Any
+from typing import List
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+
+from apache_beam.typehints.batch import BatchConverter
+from apache_beam.typehints.row_type import RowTypeConstraint
+from apache_beam.typehints.typehints import is_nullable
+from apache_beam.typehints.typehints import normalize
+
+# No public API currently, this just exists to register BatchConverter
+# implementations.
+__all__ = []
+
+# Name for a valueless field-level option which, when present, indicates that
+# a field should map to an index in the Beam DataFrame API.
+INDEX_OPTION_NAME = 'beam:dataframe:index'
+
+# Generate type map (presented visually in the docstring)
+_BIDIRECTIONAL = [
+ (bool, bool),
+ (np.int8, np.int8),
+ (np.int16, np.int16),
+ (np.int32, np.int32),
+ (np.int64, np.int64),
+ (pd.Int8Dtype(), Optional[np.int8]),
+ (pd.Int16Dtype(), Optional[np.int16]),
+ (pd.Int32Dtype(), Optional[np.int32]),
+ (pd.Int64Dtype(), Optional[np.int64]),
+ (np.float32, Optional[np.float32]),
+ (np.float64, Optional[np.float64]),
+ (object, Any),
+ (pd.StringDtype(), Optional[str]),
+ (pd.BooleanDtype(), Optional[bool]),
+]
+
+PANDAS_TO_BEAM = {
+ pd.Series([], dtype=dtype).dtype: fieldtype
+ for dtype,
+ fieldtype in _BIDIRECTIONAL
+}
+BEAM_TO_PANDAS = {fieldtype: dtype for dtype, fieldtype in _BIDIRECTIONAL}
+
+# Shunt non-nullable Beam types to the same pandas types as their non-nullable
+# equivalents for FLOATs, DOUBLEs, and STRINGs. pandas has no non-nullable dtype
+# for these.
+OPTIONAL_SHUNTS = [np.float32, np.float64, str]
+
+for typehint in OPTIONAL_SHUNTS:
+ BEAM_TO_PANDAS[typehint] = BEAM_TO_PANDAS[Optional[typehint]]
+
+# int, float -> int64, np.float64
+BEAM_TO_PANDAS[int] = BEAM_TO_PANDAS[np.int64]
+BEAM_TO_PANDAS[Optional[int]] = BEAM_TO_PANDAS[Optional[np.int64]]
+BEAM_TO_PANDAS[float] = BEAM_TO_PANDAS[np.float64]
+BEAM_TO_PANDAS[Optional[float]] = BEAM_TO_PANDAS[Optional[np.float64]]
+
+BEAM_TO_PANDAS[bytes] = 'bytes'
+
+# Add shunts for normalized (Beam) typehints as well
+BEAM_TO_PANDAS.update({
+ normalize(typehint): pandas_dtype
+ for (typehint, pandas_dtype) in BEAM_TO_PANDAS.items()
+})
+
+
+def dtype_from_typehint(typehint):
+ # Default to np.object. This is lossy, we won't be able to recover
+ # the type at the output.
+ return BEAM_TO_PANDAS.get(typehint, object)
+
+
+def dtype_to_fieldtype(dtype):
+ fieldtype = PANDAS_TO_BEAM.get(dtype)
+
+ if fieldtype is not None:
+ return fieldtype
+ elif dtype.kind == 'S':
+ return bytes
+ else:
+ return Any
+
+
+@BatchConverter.register
+def create_pandas_batch_converter(
+ element_type: type, batch_type: type) -> BatchConverter:
+ if batch_type == pd.DataFrame:
+ return DataFrameBatchConverter.from_typehints(
+ element_type=element_type, batch_type=batch_type)
+ elif batch_type == pd.Series:
+ return SeriesBatchConverter.from_typehints(
+ element_type=element_type, batch_type=batch_type)
+
+ return None
Review Comment:
yep, this is handled when we try construction all the registered implementations: https://github.com/apache/beam/blob/7cc48e9dc3f8c44b0cc5dcfd2cd4eca51096ecdc/sdks/python/apache_beam/typehints/batch.py#L77-L90
Note this is very naive right now. In the future this should include helpful debug information to handle cases where one or more implementations _almost_ matches. Tracked in #21654
##########
sdks/python/apache_beam/dataframe/convert.py:
##########
@@ -94,14 +119,58 @@ def _make_unbatched_pcoll(
label += " with indexes"
if label not in UNBATCHED_CACHE:
- UNBATCHED_CACHE[label] = pc | label >> schemas.UnbatchPandas(
- expr.proxy(), include_indexes=include_indexes)
+ proxy = expr.proxy()
+ shim_dofn: beam.DoFn
+ if isinstance(proxy, pd.DataFrame):
+ shim_dofn = DataFrameToRowsFn(proxy, include_indexes)
+ elif isinstance(proxy, pd.Series):
+ if include_indexes:
+ warnings.warn(
+ "Pipeline is converting a DeferredSeries to PCollection "
+ "with include_indexes=True. Note that this parameter is "
+ "_not_ respected for DeferredSeries conversion. To "
+ "include the index with your data, produce a"
+ "DeferredDataFrame instead.")
+
+ shim_dofn = SeriesToElementsFn(proxy)
+ else:
+ raise TypeError(f"Proxy '{proxy}' has unsupported type '{type(proxy)}'")
+
+ UNBATCHED_CACHE[label] = pc | label >> beam.ParDo(shim_dofn)
# Note unbatched cache is keyed by the expression id as well as parameters
# for the unbatching (i.e. include_indexes)
return UNBATCHED_CACHE[label]
+class DataFrameToRowsFn(beam.DoFn):
+ def __init__(self, proxy, include_indexes):
+ self._proxy = proxy
+ self._include_indexes = include_indexes
+
+ @beam.DoFn.yields_batches
+ def process(self, element: pd.DataFrame) -> Iterable[pd.DataFrame]:
+ yield element
+
+ def infer_output_type(self, input_element_type):
+ return element_typehint_from_dataframe_proxy(
+ self._proxy, self._include_indexes)
+
+
+class SeriesToElementsFn(beam.DoFn):
+ def __init__(self, proxy):
+ self._proxy = proxy
+
+ @beam.DoFn.yields_batches
+ def process(self, element: pd.Series) -> Iterable[pd.Series]:
+ yield element
+
+ def infer_output_type(self, input_element_type):
+ # Raise a TypeError if proxy has an unknown type
Review Comment:
Oops, this comment references behavior that was removed in https://github.com/apache/beam/commit/2b0597e9309711807669f5d22fa29042b944c077
Now we will just shunt to Any in this case. I removed the comment. Thanks for raising this!
##########
sdks/python/apache_beam/typehints/pandas_type_compatibility.py:
##########
@@ -0,0 +1,299 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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.
+#
+
+r"""Utilities for converting between Beam schemas and pandas DataFrames.
+
+Imposes a mapping between native Python typings (specifically those compatible
+with :mod:`apache_beam.typehints.schemas`), and common pandas dtypes::
+
+ pandas dtype Python typing
+ np.int{8,16,32,64} <-----> np.int{8,16,32,64}*
+ pd.Int{8,16,32,64}Dtype <-----> Optional[np.int{8,16,32,64}]*
+ np.float{32,64} <-----> Optional[np.float{32,64}]
+ \--- np.float{32,64}
+ Not supported <------ Optional[bytes]
+ np.bool <-----> np.bool
+ np.dtype('S') <-----> bytes
+ pd.BooleanDType() <-----> Optional[bool]
+ pd.StringDType() <-----> Optional[str]
+ \--- str
+ np.object <-----> Any
+
+ * int, float, bool are treated the same as np.int64, np.float64, np.bool
+
+Note that when converting to pandas dtypes, any types not specified here are
+shunted to ``np.object``.
+
+Similarly when converting from pandas to Python types, types that aren't
+otherwise specified here are shunted to ``Any``. Notably, this includes
+``np.datetime64``.
+
+Pandas does not support hierarchical data natively. Currently, all structured
+types (``Sequence``, ``Mapping``, nested ``NamedTuple`` types), are
+shunted to ``np.object`` like all other unknown types. In the future these
+types may be given special consideration.
+
+Note utilities in this package are for internal use only, we make no backward
+compatibility guarantees, except for the type mapping itself.
+"""
+
+from typing import Any
+from typing import List
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+
+from apache_beam.typehints.batch import BatchConverter
+from apache_beam.typehints.row_type import RowTypeConstraint
+from apache_beam.typehints.typehints import is_nullable
+from apache_beam.typehints.typehints import normalize
+
+# No public API currently, this just exists to register BatchConverter
+# implementations.
+__all__ = []
+
+# Name for a valueless field-level option which, when present, indicates that
+# a field should map to an index in the Beam DataFrame API.
+INDEX_OPTION_NAME = 'beam:dataframe:index'
+
+# Generate type map (presented visually in the docstring)
+_BIDIRECTIONAL = [
+ (bool, bool),
+ (np.int8, np.int8),
+ (np.int16, np.int16),
+ (np.int32, np.int32),
+ (np.int64, np.int64),
+ (pd.Int8Dtype(), Optional[np.int8]),
+ (pd.Int16Dtype(), Optional[np.int16]),
+ (pd.Int32Dtype(), Optional[np.int32]),
+ (pd.Int64Dtype(), Optional[np.int64]),
+ (np.float32, Optional[np.float32]),
+ (np.float64, Optional[np.float64]),
+ (object, Any),
+ (pd.StringDtype(), Optional[str]),
+ (pd.BooleanDtype(), Optional[bool]),
+]
+
+PANDAS_TO_BEAM = {
+ pd.Series([], dtype=dtype).dtype: fieldtype
+ for dtype,
+ fieldtype in _BIDIRECTIONAL
+}
+BEAM_TO_PANDAS = {fieldtype: dtype for dtype, fieldtype in _BIDIRECTIONAL}
+
+# Shunt non-nullable Beam types to the same pandas types as their non-nullable
+# equivalents for FLOATs, DOUBLEs, and STRINGs. pandas has no non-nullable dtype
+# for these.
+OPTIONAL_SHUNTS = [np.float32, np.float64, str]
+
+for typehint in OPTIONAL_SHUNTS:
+ BEAM_TO_PANDAS[typehint] = BEAM_TO_PANDAS[Optional[typehint]]
+
+# int, float -> int64, np.float64
+BEAM_TO_PANDAS[int] = BEAM_TO_PANDAS[np.int64]
+BEAM_TO_PANDAS[Optional[int]] = BEAM_TO_PANDAS[Optional[np.int64]]
+BEAM_TO_PANDAS[float] = BEAM_TO_PANDAS[np.float64]
+BEAM_TO_PANDAS[Optional[float]] = BEAM_TO_PANDAS[Optional[np.float64]]
+
+BEAM_TO_PANDAS[bytes] = 'bytes'
+
+# Add shunts for normalized (Beam) typehints as well
+BEAM_TO_PANDAS.update({
+ normalize(typehint): pandas_dtype
+ for (typehint, pandas_dtype) in BEAM_TO_PANDAS.items()
+})
+
+
+def dtype_from_typehint(typehint):
+ # Default to np.object. This is lossy, we won't be able to recover
+ # the type at the output.
+ return BEAM_TO_PANDAS.get(typehint, object)
+
+
+def dtype_to_fieldtype(dtype):
+ fieldtype = PANDAS_TO_BEAM.get(dtype)
+
+ if fieldtype is not None:
+ return fieldtype
+ elif dtype.kind == 'S':
+ return bytes
+ else:
+ return Any
+
+
+@BatchConverter.register
+def create_pandas_batch_converter(
+ element_type: type, batch_type: type) -> BatchConverter:
+ if batch_type == pd.DataFrame:
+ return DataFrameBatchConverter.from_typehints(
+ element_type=element_type, batch_type=batch_type)
+ elif batch_type == pd.Series:
+ return SeriesBatchConverter.from_typehints(
+ element_type=element_type, batch_type=batch_type)
+
+ return None
+
+
+class DataFrameBatchConverter(BatchConverter):
+ def __init__(
+ self,
+ element_type: RowTypeConstraint,
+ ):
+ super().__init__(pd.DataFrame, element_type)
+ self._columns = [name for name, _ in element_type._fields]
+
+ @staticmethod
+ def from_typehints(element_type,
+ batch_type) -> Optional['DataFrameBatchConverter']:
+ if not batch_type == pd.DataFrame:
+ return None
+
+ if not isinstance(element_type, RowTypeConstraint):
+ element_type = RowTypeConstraint.from_user_type(element_type)
+ if element_type is None:
+ return None
+
+ index_columns = [
+ field_name
+ for (field_name, field_options) in element_type._field_options.items()
+ if any(key == INDEX_OPTION_NAME for key, value in field_options)
+ ]
+
+ if index_columns:
+ return DataFrameBatchConverterKeepIndex(element_type, index_columns)
+ else:
+ return DataFrameBatchConverterDropIndex(element_type)
+
+ def _get_series(self, batch: pd.DataFrame):
+ raise NotImplementedError
+
+ def explode_batch(self, batch: pd.DataFrame):
+ # TODO: Only do null checks for nullable types
+ def make_null_checking_generator(series):
+ nulls = pd.isnull(series)
+ return (None if isnull else value for isnull, value in zip(nulls, series))
+
+ all_series = self._get_series(batch)
+ iterators = [make_null_checking_generator(series) for series in all_series]
+
+ for values in zip(*iterators):
+ yield self._element_type.user_type(
+ **{column: value
+ for column, value in zip(self._columns, values)})
+
+ def combine_batches(self, batches: List[pd.DataFrame]):
+ return pd.concat(batches)
+
+ def estimate_byte_size(self, batch: pd.DataFrame):
+ return batch.memory_usage().sum()
+
+ def get_length(self, batch: pd.DataFrame):
+ return len(batch)
+
+
+class DataFrameBatchConverterDropIndex(DataFrameBatchConverter):
+ """A DataFrameBatchConverter that assumes the DataFrame index has no meaning.
+
+ When producing a DataFrame from Rows, a meaningless index will be generated.
+ When exploding a DataFrame into Rows, the index will be dropped.
+ """
+ def _get_series(self, batch: pd.DataFrame):
+ return [batch[column] for column in batch.columns]
+
+ def produce_batch(self, elements):
+ # Note from_records has an index= parameter
+ batch = pd.DataFrame.from_records(elements, columns=self._columns)
+
+ for column, typehint in self._element_type._fields:
+ batch[column] = batch[column].astype(dtype_from_typehint(typehint))
+
+ return batch
+
+
+class DataFrameBatchConverterKeepIndex(DataFrameBatchConverter):
+ """A DataFrameBatchConverter that preserves the DataFrame index.
+
+ This is tracked via options on the Beam schema. Each field in the schema that
+ should map to the index is tagged in an option with name 'dataframe:index'.
+ """
+ def __init__(self, element_type: RowTypeConstraint, index_columns: List[Any]):
+ super().__init__(element_type)
+ self._index_columns = index_columns
+
+ def _get_series(self, batch: pd.DataFrame):
+ assert list(batch.index.names) == self._index_columns
+ return [
+ batch.index.get_level_values(i) for i in range(len(batch.index.names))
+ ] + [batch[column] for column in batch.columns]
+
+ def produce_batch(self, elements):
+ # Note from_records has an index= parameter
+ batch = pd.DataFrame.from_records(elements, columns=self._columns)
+
+ for column, typehint in self._element_type._fields:
+ batch[column] = batch[column].astype(dtype_from_typehint(typehint))
+
+ return batch.set_index(self._index_columns)
+
+
+class SeriesBatchConverter(BatchConverter):
+ def __init__(
+ self,
+ element_type: type,
+ dtype,
+ ):
+ super().__init__(pd.DataFrame, element_type)
+ self._dtype = dtype
+
+ if is_nullable(element_type):
+
+ def unbatch(series):
+ for isnull, value in zip(pd.isnull(series), series):
+ yield None if isnull else value
+ else:
+
+ def unbatch(series):
+ yield from series
+
+ self.explode_batch = unbatch
+
+ @staticmethod
+ def from_typehints(element_type,
+ batch_type) -> Optional['SeriesBatchConverter']:
+ if not batch_type == pd.Series:
+ return None
+
+ dtype = dtype_from_typehint(element_type)
+
+ return SeriesBatchConverter(element_type, dtype)
+
+ def produce_batch(self, elements: List[Any]) -> pd.Series:
+ return pd.Series(elements, dtype=self._dtype)
+
+ def explode_batch(self, batch: pd.Series):
+ raise NotImplementedError(
+ "explode_batch should be generated in SeriesBatchConverter.__init__")
Review Comment:
We branch on `is_nullable` one time in `__init__` and assign explode_batch with a null-checking or non-null-checking alternative.
##########
sdks/python/apache_beam/typehints/pandas_type_compatibility_test.py:
##########
@@ -0,0 +1,191 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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.
+#
+
+"""Unit tests for pandas batched type converters."""
+
+import unittest
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+from parameterized import parameterized
+from parameterized import parameterized_class
+
+from apache_beam.typehints import row_type
+from apache_beam.typehints import typehints
+from apache_beam.typehints.batch import BatchConverter
+
+
+@parameterized_class([
+ {
+ 'batch_typehint': pd.DataFrame,
+ 'element_typehint': row_type.RowTypeConstraint.from_fields([
+ ('foo', int),
+ ('bar', float),
+ ('baz', str),
+ ]),
+ 'match_index': False,
+ 'batch': pd.DataFrame({
+ 'foo': pd.Series(range(100), dtype='int64'),
+ 'bar': pd.Series([i / 100 for i in range(100)], dtype='float64'),
+ 'baz': pd.Series([str(i) for i in range(100)],
+ dtype=pd.StringDtype()),
+ }),
+ },
+ {
+ 'batch_typehint': pd.DataFrame,
+ 'element_typehint': row_type.RowTypeConstraint.from_fields(
+ [
+ ('an_index', int),
+ ('foo', int),
+ ('bar', float),
+ ('baz', str),
+ ],
+ field_options={'an_index': [('beam:dataframe:index', None)]},
+ ),
+ 'match_index': True,
+ 'batch': pd.DataFrame({
+ 'foo': pd.Series(range(100), dtype='int64'),
+ 'bar': pd.Series([i / 100 for i in range(100)], dtype='float64'),
+ 'baz': pd.Series([str(i) for i in range(100)],
+ dtype=pd.StringDtype()),
+ }).set_index(pd.Int64Index(range(123, 223), name='an_index')),
+ },
+ {
+ 'batch_typehint': pd.DataFrame,
+ 'element_typehint': row_type.RowTypeConstraint.from_fields(
+ [
+ ('an_index', int),
+ ('another_index', int),
+ ('foo', int),
+ ('bar', float),
+ ('baz', str),
+ ],
+ field_options={
+ 'an_index': [('beam:dataframe:index', None)],
+ 'another_index': [('beam:dataframe:index', None)],
+ }),
+ 'match_index': True,
+ 'batch': pd.DataFrame({
+ 'foo': pd.Series(range(100), dtype='int64'),
+ 'bar': pd.Series([i / 100 for i in range(100)], dtype='float64'),
+ 'baz': pd.Series([str(i) for i in range(100)],
+ dtype=pd.StringDtype()),
+ }).set_index([
+ pd.Int64Index(range(123, 223), name='an_index'),
+ pd.Int64Index(range(475, 575), name='another_index'),
+ ]),
+ },
+ {
+ 'batch_typehint': pd.Series,
+ 'element_typehint': int,
+ 'match_index': False,
+ 'batch': pd.Series(range(500)),
+ },
+ {
+ 'batch_typehint': pd.Series,
+ 'element_typehint': str,
+ 'batch': pd.Series(['foo', 'bar', 'baz', 'def', 'ghi', 'abc'] * 10,
+ dtype=pd.StringDtype()),
+ },
+ {
+ 'batch_typehint': pd.Series,
+ 'element_typehint': Optional[np.int64],
+ 'batch': pd.Series((i if i % 11 else None for i in range(500)),
+ dtype=pd.Int64Dtype()),
+ },
+ {
+ 'batch_typehint': pd.Series,
+ 'element_typehint': Optional[str],
+ 'batch': pd.Series(['foo', None, 'bar', 'baz', None, 'def', 'ghi'] * 10,
+ dtype=pd.StringDtype()),
+ },
+])
+class DataFrameBatchConverterTest(unittest.TestCase):
+ def create_batch_converter(self):
+ return BatchConverter.from_typehints(
+ element_type=self.element_typehint, batch_type=self.batch_typehint)
+
+ def setUp(self):
+ self.converter = self.create_batch_converter()
+ self.normalized_batch_typehint = typehints.normalize(self.batch_typehint)
+ self.normalized_element_typehint = typehints.normalize(
+ self.element_typehint)
+
+ def equality_check(self, left, right):
+ if isinstance(left, pd.DataFrame):
+ if self.match_index:
+ pd.testing.assert_frame_equal(left.sort_index(), right.sort_index())
+ else:
+ pd.testing.assert_frame_equal(
+ left.sort_values(by=list(left.columns)).reset_index(drop=True),
+ right.sort_values(by=list(right.columns)).reset_index(drop=True))
+ elif isinstance(left, pd.Series):
+ pd.testing.assert_series_equal(
+ left.sort_values().reset_index(drop=True),
+ right.sort_values().reset_index(drop=True))
+ else:
+ raise TypeError(f"Encountered unexpected type, left is a {type(left)!r}")
Review Comment:
`assert_series_equal` or `assert_frame_equal` will raise if `right` isn't the appropriate type.
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