#
# 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.
#
import sys
from typing import (
Any,
Callable,
List,
Optional,
Union,
no_type_check,
overload,
TYPE_CHECKING,
)
from warnings import warn
from pyspark.errors.exceptions.captured import unwrap_spark_exception
from pyspark.rdd import _load_from_socket
from pyspark.sql.pandas.serializers import ArrowCollectSerializer
from pyspark.sql.pandas.types import _dedup_names
from pyspark.sql.types import ArrayType, MapType, TimestampType, StructType, DataType, _create_row
from pyspark.sql.utils import is_timestamp_ntz_preferred
from pyspark.traceback_utils import SCCallSiteSync
from pyspark.errors import PySparkTypeError
if TYPE_CHECKING:
import numpy as np
import pyarrow as pa
from py4j.java_gateway import JavaObject
from pyspark.sql.pandas._typing import DataFrameLike as PandasDataFrameLike
from pyspark.sql import DataFrame
class PandasConversionMixin:
"""
Mix-in for the conversion from Spark to pandas. Currently, only :class:`DataFrame`
can use this class.
"""
def toPandas(self) -> "PandasDataFrameLike":
"""
Returns the contents of this :class:`DataFrame` as Pandas ``pandas.DataFrame``.
This is only available if Pandas is installed and available.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Notes
-----
This method should only be used if the resulting Pandas ``pandas.DataFrame`` is
expected to be small, as all the data is loaded into the driver's memory.
Usage with ``spark.sql.execution.arrow.pyspark.enabled=True`` is experimental.
Examples
--------
>>> 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.types import _create_converter_to_pandas
from pyspark.sql.pandas.utils import require_minimum_pandas_version
require_minimum_pandas_version()
import pandas as pd
jconf = self.sparkSession._jconf
if jconf.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 jconf.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." % str(e)
)
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" % str(e)
)
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:
import pyarrow
self_destruct = jconf.arrowPySparkSelfDestructEnabled()
batches = self._collect_as_arrow(split_batches=self_destruct)
if len(batches) > 0:
table = pyarrow.Table.from_batches(batches)
# Ensure only the table has a reference to the batches, so that
# self_destruct (if enabled) is effective
del 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.
pandas_options = {"date_as_object": True}
if self_destruct:
# Configure PyArrow to use as little memory as possible:
# self_destruct - free columns as they are converted
# split_blocks - create a separate Pandas block for each column
# use_threads - convert one column at a time
pandas_options.update(
{
"self_destruct": True,
"split_blocks": True,
"use_threads": False,
}
)
# Rename columns to avoid duplicated column names.
pdf = table.rename_columns(
[f"col_{i}" for i in range(table.num_columns)]
).to_pandas(**pandas_options)
# Rename back to the original column names.
pdf.columns = self.columns
else:
pdf = pd.DataFrame(columns=self.columns)
if len(pdf.columns) > 0:
timezone = jconf.sessionLocalTimeZone()
struct_in_pandas = jconf.pandasStructHandlingMode()
error_on_duplicated_field_names = False
if struct_in_pandas == "legacy":
error_on_duplicated_field_names = True
struct_in_pandas = "dict"
return pd.concat(
[
_create_converter_to_pandas(
field.dataType,
field.nullable,
timezone=timezone,
struct_in_pandas=struct_in_pandas,
error_on_duplicated_field_names=error_on_duplicated_field_names,
)(pser)
for (_, pser), field in zip(pdf.items(), self.schema.fields)
],
axis="columns",
)
else:
return pdf
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" % str(e)
)
warn(msg)
raise
# Below is toPandas without Arrow optimization.
rows = self.collect()
if len(rows) > 0:
pdf = pd.DataFrame.from_records(
rows, index=range(len(rows)), columns=self.columns # type: ignore[arg-type]
)
else:
pdf = pd.DataFrame(columns=self.columns)
if len(pdf.columns) > 0:
timezone = jconf.sessionLocalTimeZone()
struct_in_pandas = jconf.pandasStructHandlingMode()
return pd.concat(
[
_create_converter_to_pandas(
field.dataType,
field.nullable,
timezone=timezone,
struct_in_pandas=(
"row" if struct_in_pandas == "legacy" else struct_in_pandas
),
error_on_duplicated_field_names=False,
timestamp_utc_localized=False,
)(pser)
for (_, pser), field in zip(pdf.items(), self.schema.fields)
],
axis="columns",
)
else:
return pdf
def _collect_as_arrow(self, split_batches: bool = False) -> List["pa.RecordBatch"]:
"""
Returns all records as a list of ArrowRecordBatches, pyarrow must be installed
and available on driver and worker Python environments.
This is an experimental feature.
:param split_batches: split batches such that each column is in its own allocation, so
that the selfDestruct optimization is effective; default False.
.. 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:
batch_stream = _load_from_socket((port, auth_secret), ArrowCollectSerializer())
if split_batches:
# When spark.sql.execution.arrow.pyspark.selfDestruct.enabled, ensure
# each column in each record batch is contained in its own allocation.
# Otherwise, selfDestruct does nothing; it frees each column as its
# converted, but each column will actually be a list of slices of record
# batches, and so no memory is actually freed until all columns are
# converted.
import pyarrow as pa
results = []
for batch_or_indices in batch_stream:
if isinstance(batch_or_indices, pa.RecordBatch):
batch_or_indices = pa.RecordBatch.from_arrays(
[
# This call actually reallocates the array
pa.concat_arrays([array])
for array in batch_or_indices
],
schema=batch_or_indices.schema,
)
results.append(batch_or_indices)
else:
results = list(batch_stream)
finally:
with unwrap_spark_exception():
# 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:
"""
Min-in for the conversion from pandas to Spark. Currently, only :class:`SparkSession`
can use this class.
"""
_jsparkSession: "JavaObject"
@overload
def createDataFrame(
self, data: "PandasDataFrameLike", samplingRatio: Optional[float] = ...
) -> "DataFrame":
...
@overload
def createDataFrame(
self,
data: "PandasDataFrameLike",
schema: Union[StructType, str],
verifySchema: bool = ...,
) -> "DataFrame":
...
def createDataFrame( # type: ignore[misc]
self,
data: "PandasDataFrameLike",
schema: Optional[Union[StructType, List[str]]] = None,
samplingRatio: Optional[float] = None,
verifySchema: bool = True,
) -> "DataFrame":
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._jconf.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, str) else x for x in data.columns]
if self._jconf.arrowPySparkEnabled() and len(data) > 0:
try:
return self._create_from_pandas_with_arrow(data, schema, timezone)
except Exception as e:
if self._jconf.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." % str(e)
)
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" % str(e)
)
warn(msg)
raise
converted_data = self._convert_from_pandas(data, schema, timezone)
return self._create_dataframe(converted_data, schema, samplingRatio, verifySchema)
def _convert_from_pandas(
self, pdf: "PandasDataFrameLike", schema: Union[StructType, str, List[str]], timezone: str
) -> List:
"""
Convert a pandas.DataFrame to list of records that can be used to make a DataFrame
Returns
-------
list
list of records
"""
import pandas as pd
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,
_get_local_timezone,
)
from pandas.core.dtypes.common import is_datetime64tz_dtype, is_timedelta64_dtype
copied = False
if isinstance(schema, StructType):
def _create_converter(data_type: DataType) -> Callable[[pd.Series], pd.Series]:
if isinstance(data_type, TimestampType):
def correct_timestamp(pser: pd.Series) -> pd.Series:
return _check_series_convert_timestamps_tz_local(pser, timezone)
return correct_timestamp
def _converter(dt: DataType) -> Optional[Callable[[Any], Any]]:
if isinstance(dt, ArrayType):
element_conv = _converter(dt.elementType) or (lambda x: x)
def convert_array(value: Any) -> Any:
if value is None:
return None
else:
return [element_conv(v) for v in value]
return convert_array
elif isinstance(dt, MapType):
key_conv = _converter(dt.keyType) or (lambda x: x)
value_conv = _converter(dt.valueType) or (lambda x: x)
def convert_map(value: Any) -> Any:
if value is None:
return None
else:
return {key_conv(k): value_conv(v) for k, v in value.items()}
return convert_map
elif isinstance(dt, StructType):
field_names = dt.names
dedup_field_names = _dedup_names(field_names)
field_convs = [
_converter(f.dataType) or (lambda x: x) for f in dt.fields
]
def convert_struct(value: Any) -> Any:
if value is None:
return None
elif isinstance(value, dict):
_values = [
field_convs[i](value.get(name, None))
for i, name in enumerate(dedup_field_names)
]
return _create_row(field_names, _values)
else:
_values = [
field_convs[i](value[i]) for i, name in enumerate(value)
]
return _create_row(field_names, _values)
return convert_struct
elif isinstance(dt, TimestampType):
def convert_timestamp(value: Any) -> Any:
if value is None:
return None
else:
return (
pd.Timestamp(value)
.tz_localize(timezone, ambiguous=False) # type: ignore
.tz_convert(_get_local_timezone())
.tz_localize(None)
.to_pydatetime()
)
return convert_timestamp
else:
return None
conv = _converter(data_type)
if conv is not None:
return lambda pser: pser.apply(conv) # type: ignore[return-value]
else:
return lambda pser: pser
if len(pdf.columns) > 0:
pdf = pd.concat(
[
_create_converter(field.dataType)(pser)
for (_, pser), field in zip(pdf.items(), schema.fields)
],
axis="columns",
)
copied = True
else:
should_localize = not is_timestamp_ntz_preferred()
for column, series in pdf.items():
s = series
if should_localize and is_datetime64tz_dtype(s.dtype) and s.dt.tz is not None:
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
for column, series in pdf.items():
if is_timedelta64_dtype(series):
if not copied:
pdf = pdf.copy()
copied = True
# Explicitly set the timedelta as object so the output of numpy records can
# hold the timedelta instances as are. Otherwise, it converts to the internal
# numeric values.
ser = pdf[column]
pdf[column] = pd.Series(
ser.dt.to_pytimedelta(), index=ser.index, dtype="object", name=ser.name
)
# Convert pandas.DataFrame to list of numpy records
np_records = pdf.set_axis(
[f"col_{i}" for i in range(len(pdf.columns))], axis="columns" # type: ignore[arg-type]
).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: "np.recarray") -> Optional["np.dtype"]:
"""
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.
Parameters
----------
rec : numpy.record
a numpy record to check field dtypes
Returns
-------
numpy.dtype
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 range(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: "PandasDataFrameLike", schema: Union[StructType, List[str]], timezone: str
) -> "DataFrame":
"""
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,
_deduplicate_field_names,
)
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 ( # type: ignore[attr-defined]
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()
prefer_timestamp_ntz = is_timestamp_ntz_preferred()
for name, field in zip(schema, arrow_schema):
struct.add(
name, from_arrow_type(field.type, prefer_timestamp_ntz), nullable=field.nullable
)
schema = struct
# Determine arrow types to coerce data when creating batches
if isinstance(schema, StructType):
spark_types = [_deduplicate_field_names(f.dataType) for f in schema.fields]
elif isinstance(schema, DataType):
raise PySparkTypeError(
error_class="UNSUPPORTED_DATA_TYPE_FOR_ARROW",
message_parameters={"data_type": str(schema)},
)
else:
# Any timestamps must be coerced to be compatible with Spark
spark_types = [
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 = self._jconf.arrowMaxRecordsPerBatch()
step = step if step > 0 else len(pdf)
pdf_slices = (pdf.iloc[start : start + step] for start in range(0, len(pdf), step))
# Create list of Arrow (columns, arrow_type, spark_type) for serializer dump_stream
arrow_data = [
[
(c, to_arrow_type(t) if t is not None else None, t)
for (_, c), t in zip(pdf_slice.items(), spark_types)
]
for pdf_slice in pdf_slices
]
jsparkSession = self._jsparkSession
safecheck = self._jconf.arrowSafeTypeConversion()
ser = ArrowStreamPandasSerializer(timezone, safecheck)
@no_type_check
def reader_func(temp_filename):
return self._jvm.PythonSQLUtils.readArrowStreamFromFile(temp_filename)
@no_type_check
def create_iter_server():
return self._jvm.ArrowIteratorServer()
# Create Spark DataFrame from Arrow stream file, using one batch per partition
jiter = self._sc._serialize_to_jvm(arrow_data, ser, reader_func, create_iter_server)
assert self._jvm is not None
jdf = self._jvm.PythonSQLUtils.toDataFrame(jiter, schema.json(), jsparkSession)
df = DataFrame(jdf, self)
df._schema = schema
return df
def _test() -> None:
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()