43d9c7e7e5
### What changes were proposed in this pull request? Prevent unnecessary copies of data during conversion from Arrow to Pandas. ### Why are the changes needed? During conversion of pyarrow data to Pandas, columns are checked for timestamp types and then modified to correct for local timezone. If the data contains no timestamp types, then unnecessary copies of the data can be made. This is most prevalent when checking columns of a pandas DataFrame where each series is assigned back to the DataFrame, regardless if it had timestamps. See https://www.mail-archive.com/devarrow.apache.org/msg17008.html and ARROW-7596 for discussion. ### Does this PR introduce any user-facing change? No ### How was this patch tested? Existing tests Closes #27358 from BryanCutler/pyspark-pandas-timestamp-copy-fix-SPARK-30640. Authored-by: Bryan Cutler <cutlerb@gmail.com> Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
430 lines
19 KiB
Python
430 lines
19 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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import sys
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import warnings
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if sys.version >= '3':
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basestring = unicode = str
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xrange = range
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else:
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from itertools import izip as zip
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from pyspark import since
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from pyspark.rdd import _load_from_socket
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from pyspark.sql.pandas.serializers import ArrowCollectSerializer
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from pyspark.sql.types import IntegralType
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from pyspark.sql.types import *
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from pyspark.traceback_utils import SCCallSiteSync
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from pyspark.util import _exception_message
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class PandasConversionMixin(object):
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"""
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Min-in for the conversion from Spark to pandas. Currently, only :class:`DataFrame`
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can use this class.
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"""
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@since(1.3)
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def toPandas(self):
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"""
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Returns the contents of this :class:`DataFrame` as Pandas ``pandas.DataFrame``.
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This is only available if Pandas is installed and available.
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.. note:: This method should only be used if the resulting Pandas's :class:`DataFrame` is
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expected to be small, as all the data is loaded into the driver's memory.
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.. note:: Usage with spark.sql.execution.arrow.pyspark.enabled=True is experimental.
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>>> df.toPandas() # doctest: +SKIP
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age name
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0 2 Alice
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1 5 Bob
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"""
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from pyspark.sql.dataframe import DataFrame
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assert isinstance(self, DataFrame)
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from pyspark.sql.pandas.utils import require_minimum_pandas_version
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require_minimum_pandas_version()
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import numpy as np
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import pandas as pd
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timezone = self.sql_ctx._conf.sessionLocalTimeZone()
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if self.sql_ctx._conf.arrowPySparkEnabled():
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use_arrow = True
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try:
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from pyspark.sql.pandas.types import to_arrow_schema
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from pyspark.sql.pandas.utils import require_minimum_pyarrow_version
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require_minimum_pyarrow_version()
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to_arrow_schema(self.schema)
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except Exception as e:
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if self.sql_ctx._conf.arrowPySparkFallbackEnabled():
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msg = (
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"toPandas attempted Arrow optimization because "
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"'spark.sql.execution.arrow.pyspark.enabled' is set to true; however, "
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"failed by the reason below:\n %s\n"
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"Attempting non-optimization as "
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"'spark.sql.execution.arrow.pyspark.fallback.enabled' is set to "
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"true." % _exception_message(e))
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warnings.warn(msg)
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use_arrow = False
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else:
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msg = (
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"toPandas attempted Arrow optimization because "
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"'spark.sql.execution.arrow.pyspark.enabled' is set to true, but has "
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"reached the error below and will not continue because automatic fallback "
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"with 'spark.sql.execution.arrow.pyspark.fallback.enabled' has been set to "
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"false.\n %s" % _exception_message(e))
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warnings.warn(msg)
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raise
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# Try to use Arrow optimization when the schema is supported and the required version
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# of PyArrow is found, if 'spark.sql.execution.arrow.pyspark.enabled' is enabled.
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if use_arrow:
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try:
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from pyspark.sql.pandas.types import _check_series_localize_timestamps
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import pyarrow
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batches = self._collect_as_arrow()
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if len(batches) > 0:
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table = pyarrow.Table.from_batches(batches)
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# Pandas DataFrame created from PyArrow uses datetime64[ns] for date type
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# values, but we should use datetime.date to match the behavior with when
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# Arrow optimization is disabled.
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pdf = table.to_pandas(date_as_object=True)
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for field in self.schema:
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if isinstance(field.dataType, TimestampType):
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pdf[field.name] = \
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_check_series_localize_timestamps(pdf[field.name], timezone)
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return pdf
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else:
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return pd.DataFrame.from_records([], columns=self.columns)
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except Exception as e:
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# We might have to allow fallback here as well but multiple Spark jobs can
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# be executed. So, simply fail in this case for now.
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msg = (
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"toPandas attempted Arrow optimization because "
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"'spark.sql.execution.arrow.pyspark.enabled' is set to true, but has "
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"reached the error below and can not continue. Note that "
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"'spark.sql.execution.arrow.pyspark.fallback.enabled' does not have an "
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"effect on failures in the middle of "
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"computation.\n %s" % _exception_message(e))
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warnings.warn(msg)
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raise
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# Below is toPandas without Arrow optimization.
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pdf = pd.DataFrame.from_records(self.collect(), columns=self.columns)
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dtype = {}
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for field in self.schema:
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pandas_type = PandasConversionMixin._to_corrected_pandas_type(field.dataType)
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# SPARK-21766: if an integer field is nullable and has null values, it can be
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# inferred by pandas as float column. Once we convert the column with NaN back
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# to integer type e.g., np.int16, we will hit exception. So we use the inferred
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# float type, not the corrected type from the schema in this case.
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if pandas_type is not None and \
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not(isinstance(field.dataType, IntegralType) and field.nullable and
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pdf[field.name].isnull().any()):
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dtype[field.name] = pandas_type
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# Ensure we fall back to nullable numpy types, even when whole column is null:
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if isinstance(field.dataType, IntegralType) and pdf[field.name].isnull().any():
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dtype[field.name] = np.float64
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if isinstance(field.dataType, BooleanType) and pdf[field.name].isnull().any():
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dtype[field.name] = np.object
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for f, t in dtype.items():
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pdf[f] = pdf[f].astype(t, copy=False)
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if timezone is None:
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return pdf
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else:
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from pyspark.sql.pandas.types import _check_series_convert_timestamps_local_tz
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for field in self.schema:
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# TODO: handle nested timestamps, such as ArrayType(TimestampType())?
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if isinstance(field.dataType, TimestampType):
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pdf[field.name] = \
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_check_series_convert_timestamps_local_tz(pdf[field.name], timezone)
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return pdf
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@staticmethod
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def _to_corrected_pandas_type(dt):
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"""
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When converting Spark SQL records to Pandas :class:`DataFrame`, the inferred data type
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may be wrong. This method gets the corrected data type for Pandas if that type may be
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inferred incorrectly.
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"""
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import numpy as np
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if type(dt) == ByteType:
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return np.int8
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elif type(dt) == ShortType:
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return np.int16
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elif type(dt) == IntegerType:
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return np.int32
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elif type(dt) == LongType:
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return np.int64
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elif type(dt) == FloatType:
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return np.float32
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elif type(dt) == DoubleType:
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return np.float64
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elif type(dt) == BooleanType:
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return np.bool
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elif type(dt) == TimestampType:
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return np.datetime64
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else:
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return None
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def _collect_as_arrow(self):
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"""
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Returns all records as a list of ArrowRecordBatches, pyarrow must be installed
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and available on driver and worker Python environments.
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.. note:: Experimental.
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"""
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from pyspark.sql.dataframe import DataFrame
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assert isinstance(self, DataFrame)
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with SCCallSiteSync(self._sc):
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port, auth_secret, jsocket_auth_server = self._jdf.collectAsArrowToPython()
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# Collect list of un-ordered batches where last element is a list of correct order indices
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try:
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results = list(_load_from_socket((port, auth_secret), ArrowCollectSerializer()))
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finally:
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# Join serving thread and raise any exceptions from collectAsArrowToPython
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jsocket_auth_server.getResult()
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# Separate RecordBatches from batch order indices in results
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batches = results[:-1]
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batch_order = results[-1]
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# Re-order the batch list using the correct order
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return [batches[i] for i in batch_order]
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class SparkConversionMixin(object):
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"""
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Min-in for the conversion from pandas to Spark. Currently, only :class:`SparkSession`
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can use this class.
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"""
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def createDataFrame(self, data, schema=None, samplingRatio=None, verifySchema=True):
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from pyspark.sql import SparkSession
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assert isinstance(self, SparkSession)
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from pyspark.sql.pandas.utils import require_minimum_pandas_version
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require_minimum_pandas_version()
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timezone = self._wrapped._conf.sessionLocalTimeZone()
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# If no schema supplied by user then get the names of columns only
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if schema is None:
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schema = [str(x) if not isinstance(x, basestring) else
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(x.encode('utf-8') if not isinstance(x, str) else x)
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for x in data.columns]
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if self._wrapped._conf.arrowPySparkEnabled() and len(data) > 0:
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try:
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return self._create_from_pandas_with_arrow(data, schema, timezone)
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except Exception as e:
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from pyspark.util import _exception_message
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if self._wrapped._conf.arrowPySparkFallbackEnabled():
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msg = (
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"createDataFrame attempted Arrow optimization because "
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"'spark.sql.execution.arrow.pyspark.enabled' is set to true; however, "
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"failed by the reason below:\n %s\n"
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"Attempting non-optimization as "
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"'spark.sql.execution.arrow.pyspark.fallback.enabled' is set to "
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"true." % _exception_message(e))
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warnings.warn(msg)
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else:
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msg = (
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"createDataFrame attempted Arrow optimization because "
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"'spark.sql.execution.arrow.pyspark.enabled' is set to true, but has "
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"reached the error below and will not continue because automatic "
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"fallback with 'spark.sql.execution.arrow.pyspark.fallback.enabled' "
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"has been set to false.\n %s" % _exception_message(e))
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warnings.warn(msg)
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raise
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data = self._convert_from_pandas(data, schema, timezone)
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return self._create_dataframe(data, schema, samplingRatio, verifySchema)
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def _convert_from_pandas(self, pdf, schema, timezone):
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"""
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Convert a pandas.DataFrame to list of records that can be used to make a DataFrame
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:return list of records
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"""
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from pyspark.sql import SparkSession
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assert isinstance(self, SparkSession)
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if timezone is not None:
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from pyspark.sql.pandas.types import _check_series_convert_timestamps_tz_local
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copied = False
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if isinstance(schema, StructType):
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for field in schema:
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# TODO: handle nested timestamps, such as ArrayType(TimestampType())?
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if isinstance(field.dataType, TimestampType):
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s = _check_series_convert_timestamps_tz_local(pdf[field.name], timezone)
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if s is not pdf[field.name]:
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if not copied:
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# Copy once if the series is modified to prevent the original
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# Pandas DataFrame from being updated
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pdf = pdf.copy()
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copied = True
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pdf[field.name] = s
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else:
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for column, series in pdf.iteritems():
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s = _check_series_convert_timestamps_tz_local(series, timezone)
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if s is not series:
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if not copied:
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# Copy once if the series is modified to prevent the original
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# Pandas DataFrame from being updated
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pdf = pdf.copy()
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copied = True
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pdf[column] = s
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# Convert pandas.DataFrame to list of numpy records
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np_records = pdf.to_records(index=False)
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# Check if any columns need to be fixed for Spark to infer properly
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if len(np_records) > 0:
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record_dtype = self._get_numpy_record_dtype(np_records[0])
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if record_dtype is not None:
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return [r.astype(record_dtype).tolist() for r in np_records]
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# Convert list of numpy records to python lists
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return [r.tolist() for r in np_records]
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def _get_numpy_record_dtype(self, rec):
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"""
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Used when converting a pandas.DataFrame to Spark using to_records(), this will correct
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the dtypes of fields in a record so they can be properly loaded into Spark.
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:param rec: a numpy record to check field dtypes
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:return corrected dtype for a numpy.record or None if no correction needed
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"""
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import numpy as np
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cur_dtypes = rec.dtype
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col_names = cur_dtypes.names
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record_type_list = []
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has_rec_fix = False
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for i in xrange(len(cur_dtypes)):
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curr_type = cur_dtypes[i]
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# If type is a datetime64 timestamp, convert to microseconds
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# NOTE: if dtype is datetime[ns] then np.record.tolist() will output values as longs,
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# conversion from [us] or lower will lead to py datetime objects, see SPARK-22417
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if curr_type == np.dtype('datetime64[ns]'):
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curr_type = 'datetime64[us]'
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has_rec_fix = True
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record_type_list.append((str(col_names[i]), curr_type))
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return np.dtype(record_type_list) if has_rec_fix else None
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def _create_from_pandas_with_arrow(self, pdf, schema, timezone):
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"""
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Create a DataFrame from a given pandas.DataFrame by slicing it into partitions, converting
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to Arrow data, then sending to the JVM to parallelize. If a schema is passed in, the
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data types will be used to coerce the data in Pandas to Arrow conversion.
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"""
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from pyspark.sql import SparkSession
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from pyspark.sql.dataframe import DataFrame
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assert isinstance(self, SparkSession)
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from pyspark.sql.pandas.serializers import ArrowStreamPandasSerializer
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from pyspark.sql.types import TimestampType
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from pyspark.sql.pandas.types import from_arrow_type, to_arrow_type
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from pyspark.sql.pandas.utils import require_minimum_pandas_version, \
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require_minimum_pyarrow_version
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require_minimum_pandas_version()
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require_minimum_pyarrow_version()
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from pandas.api.types import is_datetime64_dtype, is_datetime64tz_dtype
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import pyarrow as pa
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# Create the Spark schema from list of names passed in with Arrow types
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if isinstance(schema, (list, tuple)):
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arrow_schema = pa.Schema.from_pandas(pdf, preserve_index=False)
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struct = StructType()
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for name, field in zip(schema, arrow_schema):
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struct.add(name, from_arrow_type(field.type), nullable=field.nullable)
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schema = struct
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# Determine arrow types to coerce data when creating batches
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if isinstance(schema, StructType):
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arrow_types = [to_arrow_type(f.dataType) for f in schema.fields]
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elif isinstance(schema, DataType):
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raise ValueError("Single data type %s is not supported with Arrow" % str(schema))
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else:
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# Any timestamps must be coerced to be compatible with Spark
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arrow_types = [to_arrow_type(TimestampType())
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if is_datetime64_dtype(t) or is_datetime64tz_dtype(t) else None
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for t in pdf.dtypes]
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# Slice the DataFrame to be batched
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step = -(-len(pdf) // self.sparkContext.defaultParallelism) # round int up
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pdf_slices = (pdf[start:start + step] for start in xrange(0, len(pdf), step))
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# Create list of Arrow (columns, type) for serializer dump_stream
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arrow_data = [[(c, t) for (_, c), t in zip(pdf_slice.iteritems(), arrow_types)]
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for pdf_slice in pdf_slices]
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jsqlContext = self._wrapped._jsqlContext
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safecheck = self._wrapped._conf.arrowSafeTypeConversion()
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col_by_name = True # col by name only applies to StructType columns, can't happen here
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ser = ArrowStreamPandasSerializer(timezone, safecheck, col_by_name)
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def reader_func(temp_filename):
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return self._jvm.PythonSQLUtils.readArrowStreamFromFile(jsqlContext, temp_filename)
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def create_RDD_server():
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return self._jvm.ArrowRDDServer(jsqlContext)
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# Create Spark DataFrame from Arrow stream file, using one batch per partition
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jrdd = self._sc._serialize_to_jvm(arrow_data, ser, reader_func, create_RDD_server)
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jdf = self._jvm.PythonSQLUtils.toDataFrame(jrdd, schema.json(), jsqlContext)
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df = DataFrame(jdf, self._wrapped)
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df._schema = schema
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return df
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def _test():
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import doctest
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from pyspark.sql import SparkSession
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import pyspark.sql.pandas.conversion
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globs = pyspark.sql.pandas.conversion.__dict__.copy()
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spark = SparkSession.builder\
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.master("local[4]")\
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.appName("sql.pandas.conversion tests")\
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.getOrCreate()
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globs['spark'] = spark
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(failure_count, test_count) = doctest.testmod(
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pyspark.sql.pandas.conversion, globs=globs,
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optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF)
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spark.stop()
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if failure_count:
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sys.exit(-1)
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if __name__ == "__main__":
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_test()
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