0812d6c17c
### What changes were proposed in this pull request? This improves error handling when a failure in conversion from Pandas to Arrow occurs. And fixes tests to be compatible with upcoming Arrow 2.0.0 release. ### Why are the changes needed? Current tests will fail with Arrow 2.0.0 because of a change in error message when the schema is invalid. For these cases, the current error message also includes information on disabling safe conversion config, which is mainly meant for floating point truncation and overflow. The tests have been updated to use a message that is show for past Arrow versions, and upcoming. If the user enters an invalid schema, the error produced by pyarrow is not consistent and either `TypeError` or `ArrowInvalid`, with the latter being caught, and raised as a `RuntimeError` with the extra info. The error handling is improved by: - narrowing the exception type to `TypeError`s, which `ArrowInvalid` is a subclass and what is raised on safe conversion failures. - The exception is only raised with additional information on disabling "spark.sql.execution.pandas.convertToArrowArraySafely" if it is enabled in the first place. - The original exception is chained to better show it to the user. ### Does this PR introduce _any_ user-facing change? Yes, the error re-raised changes from a RuntimeError to a ValueError, which better categorizes this type of error and in-line with the original Arrow error. ### How was this patch tested? Existing tests, using pyarrow 1.0.1 and 2.0.0-snapshot Closes #29951 from BryanCutler/arrow-better-handle-pandas-errors-SPARK-33073. Authored-by: Bryan Cutler <cutlerb@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
285 lines
12 KiB
Python
285 lines
12 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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Serializers for PyArrow and pandas conversions. See `pyspark.serializers` for more details.
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"""
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from pyspark.serializers import Serializer, read_int, write_int, UTF8Deserializer
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class SpecialLengths(object):
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END_OF_DATA_SECTION = -1
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PYTHON_EXCEPTION_THROWN = -2
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TIMING_DATA = -3
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END_OF_STREAM = -4
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NULL = -5
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START_ARROW_STREAM = -6
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class ArrowCollectSerializer(Serializer):
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"""
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Deserialize a stream of batches followed by batch order information. Used in
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PandasConversionMixin._collect_as_arrow() after invoking Dataset.collectAsArrowToPython()
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in the JVM.
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"""
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def __init__(self):
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self.serializer = ArrowStreamSerializer()
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def dump_stream(self, iterator, stream):
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return self.serializer.dump_stream(iterator, stream)
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def load_stream(self, stream):
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"""
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Load a stream of un-ordered Arrow RecordBatches, where the last iteration yields
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a list of indices that can be used to put the RecordBatches in the correct order.
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"""
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# load the batches
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for batch in self.serializer.load_stream(stream):
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yield batch
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# load the batch order indices or propagate any error that occurred in the JVM
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num = read_int(stream)
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if num == -1:
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error_msg = UTF8Deserializer().loads(stream)
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raise RuntimeError("An error occurred while calling "
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"ArrowCollectSerializer.load_stream: {}".format(error_msg))
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batch_order = []
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for i in range(num):
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index = read_int(stream)
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batch_order.append(index)
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yield batch_order
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def __repr__(self):
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return "ArrowCollectSerializer(%s)" % self.serializer
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class ArrowStreamSerializer(Serializer):
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"""
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Serializes Arrow record batches as a stream.
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"""
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def dump_stream(self, iterator, stream):
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import pyarrow as pa
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writer = None
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try:
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for batch in iterator:
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if writer is None:
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writer = pa.RecordBatchStreamWriter(stream, batch.schema)
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writer.write_batch(batch)
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finally:
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if writer is not None:
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writer.close()
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def load_stream(self, stream):
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import pyarrow as pa
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reader = pa.ipc.open_stream(stream)
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for batch in reader:
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yield batch
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def __repr__(self):
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return "ArrowStreamSerializer"
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class ArrowStreamPandasSerializer(ArrowStreamSerializer):
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"""
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Serializes Pandas.Series as Arrow data with Arrow streaming format.
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:param timezone: A timezone to respect when handling timestamp values
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:param safecheck: If True, conversion from Arrow to Pandas checks for overflow/truncation
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:param assign_cols_by_name: If True, then Pandas DataFrames will get columns by name
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"""
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def __init__(self, timezone, safecheck, assign_cols_by_name):
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super(ArrowStreamPandasSerializer, self).__init__()
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self._timezone = timezone
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self._safecheck = safecheck
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self._assign_cols_by_name = assign_cols_by_name
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def arrow_to_pandas(self, arrow_column):
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from pyspark.sql.pandas.types import _check_series_localize_timestamps
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import pyarrow
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# If the given column is a date type column, creates a series of datetime.date directly
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# instead of creating datetime64[ns] as intermediate data to avoid overflow caused by
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# datetime64[ns] type handling.
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s = arrow_column.to_pandas(date_as_object=True)
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if pyarrow.types.is_timestamp(arrow_column.type):
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return _check_series_localize_timestamps(s, self._timezone)
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else:
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return s
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def _create_batch(self, series):
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"""
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Create an Arrow record batch from the given pandas.Series or list of Series,
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with optional type.
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:param series: A single pandas.Series, list of Series, or list of (series, arrow_type)
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:return: Arrow RecordBatch
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"""
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import pandas as pd
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import pyarrow as pa
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from pyspark.sql.pandas.types import _check_series_convert_timestamps_internal
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from pandas.api.types import is_categorical
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# Make input conform to [(series1, type1), (series2, type2), ...]
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if not isinstance(series, (list, tuple)) or \
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(len(series) == 2 and isinstance(series[1], pa.DataType)):
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series = [series]
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series = ((s, None) if not isinstance(s, (list, tuple)) else s for s in series)
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def create_array(s, t):
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mask = s.isnull()
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# Ensure timestamp series are in expected form for Spark internal representation
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if t is not None and pa.types.is_timestamp(t):
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s = _check_series_convert_timestamps_internal(s, self._timezone)
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elif is_categorical(s.dtype):
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# Note: This can be removed once minimum pyarrow version is >= 0.16.1
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s = s.astype(s.dtypes.categories.dtype)
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try:
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array = pa.Array.from_pandas(s, mask=mask, type=t, safe=self._safecheck)
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except ValueError as e:
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if self._safecheck:
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error_msg = "Exception thrown when converting pandas.Series (%s) to " + \
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"Arrow Array (%s). It can be caused by overflows or other " + \
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"unsafe conversions warned by Arrow. Arrow safe type check " + \
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"can be disabled by using SQL config " + \
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"`spark.sql.execution.pandas.convertToArrowArraySafely`."
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raise ValueError(error_msg % (s.dtype, t)) from e
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else:
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raise e
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return array
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arrs = []
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for s, t in series:
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if t is not None and pa.types.is_struct(t):
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if not isinstance(s, pd.DataFrame):
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raise ValueError("A field of type StructType expects a pandas.DataFrame, "
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"but got: %s" % str(type(s)))
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# Input partition and result pandas.DataFrame empty, make empty Arrays with struct
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if len(s) == 0 and len(s.columns) == 0:
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arrs_names = [(pa.array([], type=field.type), field.name) for field in t]
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# Assign result columns by schema name if user labeled with strings
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elif self._assign_cols_by_name and any(isinstance(name, str)
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for name in s.columns):
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arrs_names = [(create_array(s[field.name], field.type), field.name)
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for field in t]
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# Assign result columns by position
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else:
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arrs_names = [(create_array(s[s.columns[i]], field.type), field.name)
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for i, field in enumerate(t)]
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struct_arrs, struct_names = zip(*arrs_names)
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arrs.append(pa.StructArray.from_arrays(struct_arrs, struct_names))
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else:
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arrs.append(create_array(s, t))
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return pa.RecordBatch.from_arrays(arrs, ["_%d" % i for i in range(len(arrs))])
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def dump_stream(self, iterator, stream):
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"""
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Make ArrowRecordBatches from Pandas Series and serialize. Input is a single series or
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a list of series accompanied by an optional pyarrow type to coerce the data to.
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"""
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batches = (self._create_batch(series) for series in iterator)
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super(ArrowStreamPandasSerializer, self).dump_stream(batches, stream)
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def load_stream(self, stream):
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"""
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Deserialize ArrowRecordBatches to an Arrow table and return as a list of pandas.Series.
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"""
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batches = super(ArrowStreamPandasSerializer, self).load_stream(stream)
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import pyarrow as pa
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for batch in batches:
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yield [self.arrow_to_pandas(c) for c in pa.Table.from_batches([batch]).itercolumns()]
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def __repr__(self):
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return "ArrowStreamPandasSerializer"
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class ArrowStreamPandasUDFSerializer(ArrowStreamPandasSerializer):
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"""
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Serializer used by Python worker to evaluate Pandas UDFs
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"""
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def __init__(self, timezone, safecheck, assign_cols_by_name, df_for_struct=False):
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super(ArrowStreamPandasUDFSerializer, self) \
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.__init__(timezone, safecheck, assign_cols_by_name)
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self._df_for_struct = df_for_struct
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def arrow_to_pandas(self, arrow_column):
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import pyarrow.types as types
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if self._df_for_struct and types.is_struct(arrow_column.type):
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import pandas as pd
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series = [super(ArrowStreamPandasUDFSerializer, self).arrow_to_pandas(column)
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.rename(field.name)
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for column, field in zip(arrow_column.flatten(), arrow_column.type)]
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s = pd.concat(series, axis=1)
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else:
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s = super(ArrowStreamPandasUDFSerializer, self).arrow_to_pandas(arrow_column)
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return s
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def dump_stream(self, iterator, stream):
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"""
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Override because Pandas UDFs require a START_ARROW_STREAM before the Arrow stream is sent.
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This should be sent after creating the first record batch so in case of an error, it can
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be sent back to the JVM before the Arrow stream starts.
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"""
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def init_stream_yield_batches():
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should_write_start_length = True
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for series in iterator:
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batch = self._create_batch(series)
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if should_write_start_length:
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write_int(SpecialLengths.START_ARROW_STREAM, stream)
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should_write_start_length = False
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yield batch
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return ArrowStreamSerializer.dump_stream(self, init_stream_yield_batches(), stream)
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def __repr__(self):
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return "ArrowStreamPandasUDFSerializer"
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class CogroupUDFSerializer(ArrowStreamPandasUDFSerializer):
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def load_stream(self, stream):
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"""
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Deserialize Cogrouped ArrowRecordBatches to a tuple of Arrow tables and yield as two
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lists of pandas.Series.
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"""
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import pyarrow as pa
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dataframes_in_group = None
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while dataframes_in_group is None or dataframes_in_group > 0:
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dataframes_in_group = read_int(stream)
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if dataframes_in_group == 2:
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batch1 = [batch for batch in ArrowStreamSerializer.load_stream(self, stream)]
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batch2 = [batch for batch in ArrowStreamSerializer.load_stream(self, stream)]
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yield (
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[self.arrow_to_pandas(c) for c in pa.Table.from_batches(batch1).itercolumns()],
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[self.arrow_to_pandas(c) for c in pa.Table.from_batches(batch2).itercolumns()]
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)
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elif dataframes_in_group != 0:
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raise ValueError(
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'Invalid number of pandas.DataFrames in group {0}'.format(dataframes_in_group))
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