OSCL-LXR spark-3.0.0/​python/​pyspark/​sql/​pandas/​conversion.py	Source navigation Diff markup Identifier search General search
	Version: spark-3.0.0 hadoop-3.2.0-src hadoop-3.3.0-src spark-2.4.7 linux-4.15.13 Revert   Change

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

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