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apache / spark / master / . / python / pyspark / worker.py
blob: 466bb79246587536c5daceedeb803d58fcd95651 [file] [log] [blame]
#
# 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.
#
"""
Worker that receives input from Piped RDD.
"""
import os
import sys
import dataclasses
import time
import inspect
import itertools
import json
from typing import Any, Callable, Iterable, Iterator, Optional, Tuple
import faulthandler
from pyspark.accumulators import (
SpecialAccumulatorIds,
_accumulatorRegistry,
_deserialize_accumulator,
)
from pyspark.sql.streaming.stateful_processor_api_client import StatefulProcessorApiClient
from pyspark.sql.streaming.stateful_processor_util import TransformWithStateInPandasFuncMode
from pyspark.taskcontext import BarrierTaskContext, TaskContext
from pyspark.resource import ResourceInformation
from pyspark.util import PythonEvalType, local_connect_and_auth
from pyspark.serializers import (
write_int,
read_long,
read_bool,
write_long,
read_int,
SpecialLengths,
CPickleSerializer,
BatchedSerializer,
)
from pyspark.sql.conversion import LocalDataToArrowConversion, ArrowTableToRowsConversion
from pyspark.sql.functions import SkipRestOfInputTableException
from pyspark.sql.pandas.serializers import (
ArrowStreamPandasUDFSerializer,
ArrowStreamPandasUDTFSerializer,
CogroupArrowUDFSerializer,
CogroupPandasUDFSerializer,
ArrowStreamUDFSerializer,
ArrowStreamGroupUDFSerializer,
ApplyInPandasWithStateSerializer,
TransformWithStateInPandasSerializer,
TransformWithStateInPandasInitStateSerializer,
TransformWithStateInPySparkRowSerializer,
TransformWithStateInPySparkRowInitStateSerializer,
ArrowStreamArrowUDFSerializer,
ArrowBatchUDFSerializer,
ArrowStreamUDTFSerializer,
ArrowStreamArrowUDTFSerializer,
)
from pyspark.sql.pandas.types import to_arrow_type
from pyspark.sql.types import (
ArrayType,
BinaryType,
DataType,
MapType,
Row,
StringType,
StructType,
_create_row,
_parse_datatype_json_string,
)
from pyspark.util import fail_on_stopiteration, handle_worker_exception
from pyspark import shuffle
from pyspark.errors import PySparkRuntimeError, PySparkTypeError, PySparkValueError
from pyspark.worker_util import (
check_python_version,
read_command,
pickleSer,
send_accumulator_updates,
setup_broadcasts,
setup_memory_limits,
setup_spark_files,
utf8_deserializer,
)
try:
import memory_profiler # noqa: F401
has_memory_profiler = True
except Exception:
has_memory_profiler = False
def report_times(outfile, boot, init, finish):
write_int(SpecialLengths.TIMING_DATA, outfile)
write_long(int(1000 * boot), outfile)
write_long(int(1000 * init), outfile)
write_long(int(1000 * finish), outfile)
def chain(f, g):
"""chain two functions together"""
return lambda *a: g(f(*a))
def wrap_udf(f, args_offsets, kwargs_offsets, return_type):
func, args_kwargs_offsets = wrap_kwargs_support(f, args_offsets, kwargs_offsets)
if return_type.needConversion():
toInternal = return_type.toInternal
return args_kwargs_offsets, lambda *a: toInternal(func(*a))
else:
return args_kwargs_offsets, lambda *a: func(*a)
def wrap_scalar_pandas_udf(f, args_offsets, kwargs_offsets, return_type, runner_conf):
func, args_kwargs_offsets = wrap_kwargs_support(f, args_offsets, kwargs_offsets)
arrow_return_type = to_arrow_type(
return_type, prefers_large_types=use_large_var_types(runner_conf)
)
def verify_result_type(result):
if not hasattr(result, "__len__"):
pd_type = "pandas.DataFrame" if type(return_type) == StructType else "pandas.Series"
raise PySparkTypeError(
errorClass="UDF_RETURN_TYPE",
messageParameters={
"expected": pd_type,
"actual": type(result).__name__,
},
)
return result
def verify_result_length(result, length):
if len(result) != length:
raise PySparkRuntimeError(
errorClass="SCHEMA_MISMATCH_FOR_PANDAS_UDF",
messageParameters={
"udf_type": "pandas_udf",
"expected": str(length),
"actual": str(len(result)),
},
)
return result
return (
args_kwargs_offsets,
lambda *a: (
verify_result_length(verify_result_type(func(*a)), len(a[0])),
arrow_return_type,
),
)
def wrap_scalar_arrow_udf(f, args_offsets, kwargs_offsets, return_type, runner_conf):
func, args_kwargs_offsets = wrap_kwargs_support(f, args_offsets, kwargs_offsets)
arrow_return_type = to_arrow_type(
return_type, prefers_large_types=use_large_var_types(runner_conf)
)
def verify_result_type(result):
if not hasattr(result, "__len__"):
pd_type = "pyarrow.Array"
raise PySparkTypeError(
errorClass="UDF_RETURN_TYPE",
messageParameters={
"expected": pd_type,
"actual": type(result).__name__,
},
)
return result
def verify_result_length(result, length):
if len(result) != length:
raise PySparkRuntimeError(
errorClass="SCHEMA_MISMATCH_FOR_PANDAS_UDF",
messageParameters={
"udf_type": "arrow_udf",
"expected": str(length),
"actual": str(len(result)),
},
)
return result
return (
args_kwargs_offsets,
lambda *a: (
verify_result_length(verify_result_type(func(*a)), len(a[0])),
arrow_return_type,
),
)
def wrap_arrow_batch_udf(f, args_offsets, kwargs_offsets, return_type, runner_conf):
if use_legacy_pandas_udf_conversion(runner_conf):
return wrap_arrow_batch_udf_legacy(
f, args_offsets, kwargs_offsets, return_type, runner_conf
)
else:
return wrap_arrow_batch_udf_arrow(f, args_offsets, kwargs_offsets, return_type, runner_conf)
def wrap_arrow_batch_udf_arrow(f, args_offsets, kwargs_offsets, return_type, runner_conf):
from pyspark.sql.pandas.types import to_arrow_type
func, args_kwargs_offsets = wrap_kwargs_support(f, args_offsets, kwargs_offsets)
zero_arg_exec = False
if len(args_kwargs_offsets) == 0:
args_kwargs_offsets = (0,)
zero_arg_exec = True
arrow_return_type = to_arrow_type(
return_type, prefers_large_types=use_large_var_types(runner_conf)
)
if zero_arg_exec:
def get_args(*args: list):
return [() for _ in args[0]]
else:
def get_args(*args: list):
return zip(*args)
if "spark.sql.execution.pythonUDF.arrow.concurrency.level" in runner_conf:
from concurrent.futures import ThreadPoolExecutor
c = int(runner_conf["spark.sql.execution.pythonUDF.arrow.concurrency.level"])
@fail_on_stopiteration
def evaluate(*args):
with ThreadPoolExecutor(max_workers=c) as pool:
"""
Takes list of Python objects and returns tuple of
(results, arrow_return_type, return_type).
"""
return list(pool.map(lambda row: func(*row), get_args(*args)))
else:
@fail_on_stopiteration
def evaluate(*args):
"""
Takes list of Python objects and returns tuple of
(results, arrow_return_type, return_type).
"""
return [func(*row) for row in get_args(*args)]
def verify_result_length(result, length):
if len(result) != length:
raise PySparkRuntimeError(
errorClass="SCHEMA_MISMATCH_FOR_ARROW_PYTHON_UDF",
messageParameters={
"udf_type": "arrow_batch_udf",
"expected": str(length),
"actual": str(len(result)),
},
)
return result
return (
args_kwargs_offsets,
lambda *a: (verify_result_length(evaluate(*a), len(a[0])), arrow_return_type, return_type),
)
def wrap_arrow_batch_udf_legacy(f, args_offsets, kwargs_offsets, return_type, runner_conf):
import pandas as pd
func, args_kwargs_offsets = wrap_kwargs_support(f, args_offsets, kwargs_offsets)
zero_arg_exec = False
if len(args_kwargs_offsets) == 0:
args_kwargs_offsets = (0,) # Series([pyspark._NoValue, ...]) is used for 0-arg execution.
zero_arg_exec = True
arrow_return_type = to_arrow_type(
return_type, prefers_large_types=use_large_var_types(runner_conf)
)
# "result_func" ensures the result of a Python UDF to be consistent with/without Arrow
# optimization.
# Otherwise, an Arrow-optimized Python UDF raises "pyarrow.lib.ArrowTypeError: Expected a
# string or bytes dtype, got ..." whereas a non-Arrow-optimized Python UDF returns
# successfully.
result_func = lambda pdf: pdf # noqa: E731
if type(return_type) == StringType:
result_func = lambda r: str(r) if r is not None else r # noqa: E731
elif type(return_type) == BinaryType:
result_func = lambda r: bytes(r) if r is not None else r # noqa: E731
if zero_arg_exec:
def get_args(*args: pd.Series):
return [() for _ in args[0]]
else:
def get_args(*args: pd.Series):
return zip(*args)
if "spark.sql.execution.pythonUDF.arrow.concurrency.level" in runner_conf:
from concurrent.futures import ThreadPoolExecutor
c = int(runner_conf["spark.sql.execution.pythonUDF.arrow.concurrency.level"])
@fail_on_stopiteration
def evaluate(*args: pd.Series) -> pd.Series:
with ThreadPoolExecutor(max_workers=c) as pool:
return pd.Series(
list(pool.map(lambda row: result_func(func(*row)), get_args(*args)))
)
else:
@fail_on_stopiteration
def evaluate(*args: pd.Series) -> pd.Series:
return pd.Series([result_func(func(*row)) for row in get_args(*args)])
def verify_result_length(result, length):
if len(result) != length:
raise PySparkRuntimeError(
errorClass="SCHEMA_MISMATCH_FOR_PANDAS_UDF",
messageParameters={
"udf_type": "arrow_batch_udf",
"expected": str(length),
"actual": str(len(result)),
},
)
return result
return (
args_kwargs_offsets,
lambda *a: (verify_result_length(evaluate(*a), len(a[0])), arrow_return_type, return_type),
)
def wrap_pandas_batch_iter_udf(f, return_type, runner_conf):
arrow_return_type = to_arrow_type(
return_type, prefers_large_types=use_large_var_types(runner_conf)
)
iter_type_label = "pandas.DataFrame" if type(return_type) == StructType else "pandas.Series"
def verify_result(result):
if not isinstance(result, Iterator) and not hasattr(result, "__iter__"):
raise PySparkTypeError(
errorClass="UDF_RETURN_TYPE",
messageParameters={
"expected": "iterator of {}".format(iter_type_label),
"actual": type(result).__name__,
},
)
return result
def verify_element(elem):
import pandas as pd
if not isinstance(elem, pd.DataFrame if type(return_type) == StructType else pd.Series):
raise PySparkTypeError(
errorClass="UDF_RETURN_TYPE",
messageParameters={
"expected": "iterator of {}".format(iter_type_label),
"actual": "iterator of {}".format(type(elem).__name__),
},
)
verify_pandas_result(
elem, return_type, assign_cols_by_name=True, truncate_return_schema=True
)
return elem
return lambda *iterator: map(
lambda res: (res, arrow_return_type), map(verify_element, verify_result(f(*iterator)))
)
def verify_pandas_result(result, return_type, assign_cols_by_name, truncate_return_schema):
import pandas as pd
if type(return_type) == StructType:
if not isinstance(result, pd.DataFrame):
raise PySparkTypeError(
errorClass="UDF_RETURN_TYPE",
messageParameters={
"expected": "pandas.DataFrame",
"actual": type(result).__name__,
},
)
# check the schema of the result only if it is not empty or has columns
if not result.empty or len(result.columns) != 0:
# if any column name of the result is a string
# the column names of the result have to match the return type
# see create_array in pyspark.sql.pandas.serializers.ArrowStreamPandasSerializer
field_names = set([field.name for field in return_type.fields])
# only the first len(field_names) result columns are considered
# when truncating the return schema
result_columns = (
result.columns[: len(field_names)] if truncate_return_schema else result.columns
)
column_names = set(result_columns)
if (
assign_cols_by_name
and any(isinstance(name, str) for name in result.columns)
and column_names != field_names
):
missing = sorted(list(field_names.difference(column_names)))
missing = f" Missing: {', '.join(missing)}." if missing else ""
extra = sorted(list(column_names.difference(field_names)))
extra = f" Unexpected: {', '.join(extra)}." if extra else ""
raise PySparkRuntimeError(
errorClass="RESULT_COLUMNS_MISMATCH_FOR_PANDAS_UDF",
messageParameters={
"missing": missing,
"extra": extra,
},
)
# otherwise the number of columns of result have to match the return type
elif len(result_columns) != len(return_type):
raise PySparkRuntimeError(
errorClass="RESULT_LENGTH_MISMATCH_FOR_PANDAS_UDF",
messageParameters={
"expected": str(len(return_type)),
"actual": str(len(result.columns)),
},
)
else:
if not isinstance(result, pd.Series):
raise PySparkTypeError(
errorClass="UDF_RETURN_TYPE",
messageParameters={"expected": "pandas.Series", "actual": type(result).__name__},
)
def wrap_arrow_array_iter_udf(f, return_type, runner_conf):
arrow_return_type = to_arrow_type(
return_type, prefers_large_types=use_large_var_types(runner_conf)
)
def verify_result(result):
if not isinstance(result, Iterator) and not hasattr(result, "__iter__"):
raise PySparkTypeError(
errorClass="UDF_RETURN_TYPE",
messageParameters={
"expected": "iterator of pyarrow.Array",
"actual": type(result).__name__,
},
)
return result
def verify_element(elem):
import pyarrow as pa
if not isinstance(elem, pa.Array):
raise PySparkTypeError(
errorClass="UDF_RETURN_TYPE",
messageParameters={
"expected": "iterator of pyarrow.Array",
"actual": "iterator of {}".format(type(elem).__name__),
},
)
return elem
return lambda *iterator: map(
lambda res: (res, arrow_return_type), map(verify_element, verify_result(f(*iterator)))
)
def wrap_arrow_batch_iter_udf(f, return_type, runner_conf):
arrow_return_type = to_arrow_type(
return_type, prefers_large_types=use_large_var_types(runner_conf)
)
def verify_result(result):
if not isinstance(result, Iterator) and not hasattr(result, "__iter__"):
raise PySparkTypeError(
errorClass="UDF_RETURN_TYPE",
messageParameters={
"expected": "iterator of pyarrow.RecordBatch",
"actual": type(result).__name__,
},
)
return result
def verify_element(elem):
import pyarrow as pa
if not isinstance(elem, pa.RecordBatch):
raise PySparkTypeError(
errorClass="UDF_RETURN_TYPE",
messageParameters={
"expected": "iterator of pyarrow.RecordBatch",
"actual": "iterator of {}".format(type(elem).__name__),
},
)
return elem
return lambda *iterator: map(
lambda res: (res, arrow_return_type), map(verify_element, verify_result(f(*iterator)))
)
def wrap_cogrouped_map_arrow_udf(f, return_type, argspec, runner_conf):
_assign_cols_by_name = assign_cols_by_name(runner_conf)
if _assign_cols_by_name:
expected_cols_and_types = {
col.name: to_arrow_type(col.dataType) for col in return_type.fields
}
else:
expected_cols_and_types = [
(col.name, to_arrow_type(col.dataType)) for col in return_type.fields
]
def wrapped(left_key_table, left_value_table, right_key_table, right_value_table):
if len(argspec.args) == 2:
result = f(left_value_table, right_value_table)
elif len(argspec.args) == 3:
key_table = left_key_table if left_key_table.num_rows > 0 else right_key_table
key = tuple(c[0] for c in key_table.columns)
result = f(key, left_value_table, right_value_table)
verify_arrow_result(result, _assign_cols_by_name, expected_cols_and_types)
return result.to_batches()
return lambda kl, vl, kr, vr: (wrapped(kl, vl, kr, vr), to_arrow_type(return_type))
def wrap_cogrouped_map_pandas_udf(f, return_type, argspec, runner_conf):
_use_large_var_types = use_large_var_types(runner_conf)
_assign_cols_by_name = assign_cols_by_name(runner_conf)
def wrapped(left_key_series, left_value_series, right_key_series, right_value_series):
import pandas as pd
left_df = pd.concat(left_value_series, axis=1)
right_df = pd.concat(right_value_series, axis=1)
if len(argspec.args) == 2:
result = f(left_df, right_df)
elif len(argspec.args) == 3:
key_series = left_key_series if not left_df.empty else right_key_series
key = tuple(s[0] for s in key_series)
result = f(key, left_df, right_df)
verify_pandas_result(
result, return_type, _assign_cols_by_name, truncate_return_schema=False
)
return result
arrow_return_type = to_arrow_type(return_type, _use_large_var_types)
return lambda kl, vl, kr, vr: [(wrapped(kl, vl, kr, vr), arrow_return_type)]
def verify_arrow_result(table, assign_cols_by_name, expected_cols_and_types):
import pyarrow as pa
if not isinstance(table, pa.Table):
raise PySparkTypeError(
errorClass="UDF_RETURN_TYPE",
messageParameters={
"expected": "pyarrow.Table",
"actual": type(table).__name__,
},
)
# the types of the fields have to be identical to return type
# an empty table can have no columns; if there are columns, they have to match
if table.num_columns != 0 or table.num_rows != 0:
# columns are either mapped by name or position
if assign_cols_by_name:
actual_cols_and_types = {
name: dataType for name, dataType in zip(table.schema.names, table.schema.types)
}
missing = sorted(
list(set(expected_cols_and_types.keys()).difference(actual_cols_and_types.keys()))
)
extra = sorted(
list(set(actual_cols_and_types.keys()).difference(expected_cols_and_types.keys()))
)
if missing or extra:
missing = f" Missing: {', '.join(missing)}." if missing else ""
extra = f" Unexpected: {', '.join(extra)}." if extra else ""
raise PySparkRuntimeError(
errorClass="RESULT_COLUMNS_MISMATCH_FOR_ARROW_UDF",
messageParameters={
"missing": missing,
"extra": extra,
},
)
column_types = [
(name, expected_cols_and_types[name], actual_cols_and_types[name])
for name in sorted(expected_cols_and_types.keys())
]
else:
actual_cols_and_types = [
(name, dataType) for name, dataType in zip(table.schema.names, table.schema.types)
]
column_types = [
(expected_name, expected_type, actual_type)
for (expected_name, expected_type), (actual_name, actual_type) in zip(
expected_cols_and_types, actual_cols_and_types
)
]
type_mismatch = [
(name, expected, actual)
for name, expected, actual in column_types
if actual != expected
]
if type_mismatch:
raise PySparkRuntimeError(
errorClass="RESULT_TYPE_MISMATCH_FOR_ARROW_UDF",
messageParameters={
"mismatch": ", ".join(
"column '{}' (expected {}, actual {})".format(name, expected, actual)
for name, expected, actual in type_mismatch
)
},
)
def wrap_grouped_map_arrow_udf(f, return_type, argspec, runner_conf):
_assign_cols_by_name = assign_cols_by_name(runner_conf)
if _assign_cols_by_name:
expected_cols_and_types = {
col.name: to_arrow_type(col.dataType) for col in return_type.fields
}
else:
expected_cols_and_types = [
(col.name, to_arrow_type(col.dataType)) for col in return_type.fields
]
def wrapped(key_table, value_table):
if len(argspec.args) == 1:
result = f(value_table)
elif len(argspec.args) == 2:
key = tuple(c[0] for c in key_table.columns)
result = f(key, value_table)
verify_arrow_result(result, _assign_cols_by_name, expected_cols_and_types)
return result.to_batches()
arrow_return_type = to_arrow_type(return_type, use_large_var_types(runner_conf))
return lambda k, v: (wrapped(k, v), arrow_return_type)
def wrap_grouped_map_pandas_udf(f, return_type, argspec, runner_conf):
_use_large_var_types = use_large_var_types(runner_conf)
_assign_cols_by_name = assign_cols_by_name(runner_conf)
def wrapped(key_series, value_series):
import pandas as pd
if len(argspec.args) == 1:
result = f(pd.concat(value_series, axis=1))
elif len(argspec.args) == 2:
key = tuple(s[0] for s in key_series)
result = f(key, pd.concat(value_series, axis=1))
verify_pandas_result(
result, return_type, _assign_cols_by_name, truncate_return_schema=False
)
return result
arrow_return_type = to_arrow_type(return_type, _use_large_var_types)
return lambda k, v: [(wrapped(k, v), arrow_return_type)]
def wrap_grouped_transform_with_state_pandas_udf(f, return_type, runner_conf):
def wrapped(stateful_processor_api_client, mode, key, value_series_gen):
result_iter = f(stateful_processor_api_client, mode, key, value_series_gen)
# TODO(SPARK-49100): add verification that elements in result_iter are
# indeed of type pd.DataFrame and confirm to assigned cols
return result_iter
arrow_return_type = to_arrow_type(return_type, use_large_var_types(runner_conf))
return lambda p, m, k, v: [(wrapped(p, m, k, v), arrow_return_type)]
def wrap_grouped_transform_with_state_pandas_init_state_udf(f, return_type, runner_conf):
def wrapped(stateful_processor_api_client, mode, key, value_series_gen):
import pandas as pd
state_values_gen, init_states_gen = itertools.tee(value_series_gen, 2)
state_values = (df for x, _ in state_values_gen if not (df := pd.concat(x, axis=1)).empty)
init_states = (df for _, x in init_states_gen if not (df := pd.concat(x, axis=1)).empty)
result_iter = f(stateful_processor_api_client, mode, key, state_values, init_states)
# TODO(SPARK-49100): add verification that elements in result_iter are
# indeed of type pd.DataFrame and confirm to assigned cols
return result_iter
arrow_return_type = to_arrow_type(return_type, use_large_var_types(runner_conf))
return lambda p, m, k, v: [(wrapped(p, m, k, v), arrow_return_type)]
def wrap_grouped_transform_with_state_udf(f, return_type, runner_conf):
def wrapped(stateful_processor_api_client, mode, key, values):
result_iter = f(stateful_processor_api_client, mode, key, values)
# TODO(SPARK-XXXXX): add verification that elements in result_iter are
# indeed of type Row and confirm to assigned cols
return result_iter
arrow_return_type = to_arrow_type(return_type, use_large_var_types(runner_conf))
return lambda p, m, k, v: [(wrapped(p, m, k, v), arrow_return_type)]
def wrap_grouped_transform_with_state_init_state_udf(f, return_type, runner_conf):
def wrapped(stateful_processor_api_client, mode, key, values):
if mode == TransformWithStateInPandasFuncMode.PROCESS_DATA:
values_gen = values[0]
init_states_gen = values[1]
else:
values_gen = iter([])
init_states_gen = iter([])
result_iter = f(stateful_processor_api_client, mode, key, values_gen, init_states_gen)
# TODO(SPARK-XXXXX): add verification that elements in result_iter are
# indeed of type pd.DataFrame and confirm to assigned cols
return result_iter
arrow_return_type = to_arrow_type(return_type, use_large_var_types(runner_conf))
return lambda p, m, k, v: [(wrapped(p, m, k, v), arrow_return_type)]
def wrap_grouped_map_pandas_udf_with_state(f, return_type, runner_conf):
"""
Provides a new lambda instance wrapping user function of applyInPandasWithState.
The lambda instance receives (key series, iterator of value series, state) and performs
some conversion to be adapted with the signature of user function.
See the function doc of inner function `wrapped` for more details on what adapter does.
See the function doc of `mapper` function for
`eval_type == PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF_WITH_STATE` for more details on
the input parameters of lambda function.
Along with the returned iterator, the lambda instance will also produce the return_type as
converted to the arrow schema.
"""
_use_large_var_types = use_large_var_types(runner_conf)
def wrapped(key_series, value_series_gen, state):
"""
Provide an adapter of the user function performing below:
- Extract the first value of all columns in key series and produce as a tuple.
- If the state has timed out, call the user function with empty pandas DataFrame.
- If not, construct a new generator which converts each element of value series to
pandas DataFrame (lazy evaluation), and call the user function with the generator
- Verify each element of returned iterator to check the schema of pandas DataFrame.
"""
import pandas as pd
key = tuple(s[0] for s in key_series)
if state.hasTimedOut:
# Timeout processing pass empty iterator. Here we return an empty DataFrame instead.
values = [
pd.DataFrame(columns=pd.concat(next(value_series_gen), axis=1).columns),
]
else:
values = (pd.concat(x, axis=1) for x in value_series_gen)
result_iter = f(key, values, state)
def verify_element(result):
if not isinstance(result, pd.DataFrame):
raise PySparkTypeError(
errorClass="UDF_RETURN_TYPE",
messageParameters={
"expected": "iterator of pandas.DataFrame",
"actual": "iterator of {}".format(type(result).__name__),
},
)
# the number of columns of result have to match the return type
# but it is fine for result to have no columns at all if it is empty
if not (
len(result.columns) == len(return_type)
or (len(result.columns) == 0 and result.empty)
):
raise PySparkRuntimeError(
errorClass="RESULT_LENGTH_MISMATCH_FOR_PANDAS_UDF",
messageParameters={
"expected": str(len(return_type)),
"actual": str(len(result.columns)),
},
)
return result
if isinstance(result_iter, pd.DataFrame):
raise PySparkTypeError(
errorClass="UDF_RETURN_TYPE",
messageParameters={
"expected": "iterable of pandas.DataFrame",
"actual": type(result_iter).__name__,
},
)
try:
iter(result_iter)
except TypeError:
raise PySparkTypeError(
errorClass="UDF_RETURN_TYPE",
messageParameters={"expected": "iterable", "actual": type(result_iter).__name__},
)
result_iter_with_validation = (verify_element(x) for x in result_iter)
return (
result_iter_with_validation,
state,
)
arrow_return_type = to_arrow_type(return_type, _use_large_var_types)
return lambda k, v, s: [(wrapped(k, v, s), arrow_return_type)]
def wrap_grouped_agg_pandas_udf(f, args_offsets, kwargs_offsets, return_type, runner_conf):
func, args_kwargs_offsets = wrap_kwargs_support(f, args_offsets, kwargs_offsets)
arrow_return_type = to_arrow_type(
return_type, prefers_large_types=use_large_var_types(runner_conf)
)
def wrapped(*series):
import pandas as pd
result = func(*series)
return pd.Series([result])
return (
args_kwargs_offsets,
lambda *a: (wrapped(*a), arrow_return_type),
)
def wrap_grouped_agg_arrow_udf(f, args_offsets, kwargs_offsets, return_type, runner_conf):
func, args_kwargs_offsets = wrap_kwargs_support(f, args_offsets, kwargs_offsets)
arrow_return_type = to_arrow_type(
return_type, prefers_large_types=use_large_var_types(runner_conf)
)
def wrapped(*series):
import pyarrow as pa
result = func(*series)
return pa.array([result])
return (
args_kwargs_offsets,
lambda *a: (wrapped(*a), arrow_return_type),
)
def wrap_window_agg_pandas_udf(
f, args_offsets, kwargs_offsets, return_type, runner_conf, udf_index
):
window_bound_types_str = runner_conf.get("window_bound_types")
window_bound_type = [t.strip().lower() for t in window_bound_types_str.split(",")][udf_index]
if window_bound_type == "bounded":
return wrap_bounded_window_agg_pandas_udf(
f, args_offsets, kwargs_offsets, return_type, runner_conf
)
elif window_bound_type == "unbounded":
return wrap_unbounded_window_agg_pandas_udf(
f, args_offsets, kwargs_offsets, return_type, runner_conf
)
else:
raise PySparkRuntimeError(
errorClass="INVALID_WINDOW_BOUND_TYPE",
messageParameters={
"window_bound_type": window_bound_type,
},
)
def wrap_window_agg_arrow_udf(f, args_offsets, kwargs_offsets, return_type, runner_conf, udf_index):
window_bound_types_str = runner_conf.get("window_bound_types")
window_bound_type = [t.strip().lower() for t in window_bound_types_str.split(",")][udf_index]
if window_bound_type == "bounded":
return wrap_bounded_window_agg_arrow_udf(
f, args_offsets, kwargs_offsets, return_type, runner_conf
)
elif window_bound_type == "unbounded":
return wrap_unbounded_window_agg_arrow_udf(
f, args_offsets, kwargs_offsets, return_type, runner_conf
)
else:
raise PySparkRuntimeError(
errorClass="INVALID_WINDOW_BOUND_TYPE",
messageParameters={
"window_bound_type": window_bound_type,
},
)
def wrap_unbounded_window_agg_pandas_udf(f, args_offsets, kwargs_offsets, return_type, runner_conf):
func, args_kwargs_offsets = wrap_kwargs_support(f, args_offsets, kwargs_offsets)
# This is similar to grouped_agg_pandas_udf, the only difference
# is that window_agg_pandas_udf needs to repeat the return value
# to match window length, where grouped_agg_pandas_udf just returns
# the scalar value.
arrow_return_type = to_arrow_type(
return_type, prefers_large_types=use_large_var_types(runner_conf)
)
def wrapped(*series):
import pandas as pd
result = func(*series)
return pd.Series([result]).repeat(len(series[0]))
return (
args_kwargs_offsets,
lambda *a: (wrapped(*a), arrow_return_type),
)
def wrap_unbounded_window_agg_arrow_udf(f, args_offsets, kwargs_offsets, return_type, runner_conf):
func, args_kwargs_offsets = wrap_kwargs_support(f, args_offsets, kwargs_offsets)
# This is similar to wrap_unbounded_window_agg_pandas_udf, the only difference
# is that this function is for arrow udf.
arrow_return_type = to_arrow_type(
return_type, prefers_large_types=use_large_var_types(runner_conf)
)
def wrapped(*series):
import pyarrow as pa
result = func(*series)
return pa.repeat(result, len(series[0]))
return (
args_kwargs_offsets,
lambda *a: (wrapped(*a), arrow_return_type),
)
def wrap_bounded_window_agg_pandas_udf(f, args_offsets, kwargs_offsets, return_type, runner_conf):
# args_offsets should have at least 2 for begin_index, end_index.
assert len(args_offsets) >= 2, len(args_offsets)
func, args_kwargs_offsets = wrap_kwargs_support(f, args_offsets[2:], kwargs_offsets)
arrow_return_type = to_arrow_type(
return_type, prefers_large_types=use_large_var_types(runner_conf)
)
def wrapped(begin_index, end_index, *series):
import pandas as pd
result = []
# Index operation is faster on np.ndarray,
# So we turn the index series into np array
# here for performance
begin_array = begin_index.values
end_array = end_index.values
for i in range(len(begin_array)):
# Note: Create a slice from a series for each window is
# actually pretty expensive. However, there
# is no easy way to reduce cost here.
# Note: s.iloc[i : j] is about 30% faster than s[i: j], with
# the caveat that the created slices shares the same
# memory with s. Therefore, user are not allowed to
# change the value of input series inside the window
# function. It is rare that user needs to modify the
# input series in the window function, and therefore,
# it is be a reasonable restriction.
# Note: Calling reset_index on the slices will increase the cost
# of creating slices by about 100%. Therefore, for performance
# reasons we don't do it here.
series_slices = [s.iloc[begin_array[i] : end_array[i]] for s in series]
result.append(func(*series_slices))
return pd.Series(result)
return (
args_offsets[:2] + args_kwargs_offsets,
lambda *a: (wrapped(*a), arrow_return_type),
)
def wrap_bounded_window_agg_arrow_udf(f, args_offsets, kwargs_offsets, return_type, runner_conf):
# args_offsets should have at least 2 for begin_index, end_index.
assert len(args_offsets) >= 2, len(args_offsets)
func, args_kwargs_offsets = wrap_kwargs_support(f, args_offsets[2:], kwargs_offsets)
arrow_return_type = to_arrow_type(
return_type, prefers_large_types=use_large_var_types(runner_conf)
)
def wrapped(begin_index, end_index, *series):
import pyarrow as pa
assert isinstance(begin_index, pa.Int32Array), type(begin_index)
assert isinstance(end_index, pa.Int32Array), type(end_index)
result = []
for i in range(len(begin_index)):
offset = begin_index[i].as_py()
length = end_index[i].as_py() - offset
series_slices = [s.slice(offset=offset, length=length) for s in series]
result.append(func(*series_slices))
return pa.array(result)
return (
args_offsets[:2] + args_kwargs_offsets,
lambda *a: (wrapped(*a), arrow_return_type),
)
def wrap_kwargs_support(f, args_offsets, kwargs_offsets):
if len(kwargs_offsets):
keys = list(kwargs_offsets.keys())
len_args_offsets = len(args_offsets)
if len_args_offsets > 0:
def func(*args):
return f(*args[:len_args_offsets], **dict(zip(keys, args[len_args_offsets:])))
else:
def func(*args):
return f(**dict(zip(keys, args)))
return func, args_offsets + [kwargs_offsets[key] for key in keys]
else:
return f, args_offsets
def _supports_profiler(eval_type: int) -> bool:
return eval_type not in (
PythonEvalType.SQL_SCALAR_PANDAS_ITER_UDF,
PythonEvalType.SQL_SCALAR_ARROW_ITER_UDF,
PythonEvalType.SQL_MAP_PANDAS_ITER_UDF,
PythonEvalType.SQL_MAP_ARROW_ITER_UDF,
PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF_WITH_STATE,
)
def wrap_perf_profiler(f, result_id):
import cProfile
import pstats
from pyspark.sql.profiler import ProfileResultsParam
accumulator = _deserialize_accumulator(
SpecialAccumulatorIds.SQL_UDF_PROFIER, None, ProfileResultsParam
)
def profiling_func(*args, **kwargs):
with cProfile.Profile() as pr:
ret = f(*args, **kwargs)
st = pstats.Stats(pr)
st.stream = None # make it picklable
st.strip_dirs()
accumulator.add({result_id: (st, None)})
return ret
return profiling_func
def wrap_memory_profiler(f, result_id):
from pyspark.sql.profiler import ProfileResultsParam
from pyspark.profiler import UDFLineProfilerV2
accumulator = _deserialize_accumulator(
SpecialAccumulatorIds.SQL_UDF_PROFIER, None, ProfileResultsParam
)
def profiling_func(*args, **kwargs):
profiler = UDFLineProfilerV2()
wrapped = profiler(f)
ret = wrapped(*args, **kwargs)
codemap_dict = {
filename: list(line_iterator) for filename, line_iterator in profiler.code_map.items()
}
accumulator.add({result_id: (None, codemap_dict)})
return ret
return profiling_func
def read_single_udf(pickleSer, infile, eval_type, runner_conf, udf_index, profiler):
num_arg = read_int(infile)
if eval_type in (
PythonEvalType.SQL_BATCHED_UDF,
PythonEvalType.SQL_ARROW_BATCHED_UDF,
PythonEvalType.SQL_SCALAR_PANDAS_UDF,
PythonEvalType.SQL_SCALAR_ARROW_UDF,
PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF,
PythonEvalType.SQL_WINDOW_AGG_PANDAS_UDF,
PythonEvalType.SQL_WINDOW_AGG_ARROW_UDF,
# The below doesn't support named argument, but shares the same protocol.
PythonEvalType.SQL_SCALAR_PANDAS_ITER_UDF,
PythonEvalType.SQL_SCALAR_ARROW_ITER_UDF,
PythonEvalType.SQL_GROUPED_AGG_ARROW_UDF,
):
args_offsets = []
kwargs_offsets = {}
for _ in range(num_arg):
offset = read_int(infile)
if read_bool(infile):
name = utf8_deserializer.loads(infile)
kwargs_offsets[name] = offset
else:
args_offsets.append(offset)
else:
args_offsets = [read_int(infile) for i in range(num_arg)]
kwargs_offsets = {}
chained_func = None
for i in range(read_int(infile)):
f, return_type = read_command(pickleSer, infile)
if chained_func is None:
chained_func = f
else:
chained_func = chain(chained_func, f)
if profiler == "perf":
result_id = read_long(infile)
if _supports_profiler(eval_type):
profiling_func = wrap_perf_profiler(chained_func, result_id)
else:
profiling_func = chained_func
elif profiler == "memory":
result_id = read_long(infile)
if _supports_profiler(eval_type) and has_memory_profiler:
profiling_func = wrap_memory_profiler(chained_func, result_id)
else:
profiling_func = chained_func
else:
profiling_func = chained_func
if eval_type in (
PythonEvalType.SQL_SCALAR_PANDAS_ITER_UDF,
PythonEvalType.SQL_ARROW_BATCHED_UDF,
):
func = profiling_func
else:
# make sure StopIteration's raised in the user code are not ignored
# when they are processed in a for loop, raise them as RuntimeError's instead
func = fail_on_stopiteration(profiling_func)
# the last returnType will be the return type of UDF
if eval_type == PythonEvalType.SQL_SCALAR_PANDAS_UDF:
return wrap_scalar_pandas_udf(func, args_offsets, kwargs_offsets, return_type, runner_conf)
elif eval_type == PythonEvalType.SQL_SCALAR_ARROW_UDF:
return wrap_scalar_arrow_udf(func, args_offsets, kwargs_offsets, return_type, runner_conf)
elif eval_type == PythonEvalType.SQL_ARROW_BATCHED_UDF:
return wrap_arrow_batch_udf(func, args_offsets, kwargs_offsets, return_type, runner_conf)
elif eval_type == PythonEvalType.SQL_SCALAR_PANDAS_ITER_UDF:
return args_offsets, wrap_pandas_batch_iter_udf(func, return_type, runner_conf)
elif eval_type == PythonEvalType.SQL_SCALAR_ARROW_ITER_UDF:
return args_offsets, wrap_arrow_array_iter_udf(func, return_type, runner_conf)
elif eval_type == PythonEvalType.SQL_MAP_PANDAS_ITER_UDF:
return args_offsets, wrap_pandas_batch_iter_udf(func, return_type, runner_conf)
elif eval_type == PythonEvalType.SQL_MAP_ARROW_ITER_UDF:
return args_offsets, wrap_arrow_batch_iter_udf(func, return_type, runner_conf)
elif eval_type == PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF:
argspec = inspect.getfullargspec(chained_func) # signature was lost when wrapping it
return args_offsets, wrap_grouped_map_pandas_udf(func, return_type, argspec, runner_conf)
elif eval_type == PythonEvalType.SQL_GROUPED_MAP_ARROW_UDF:
argspec = inspect.getfullargspec(chained_func) # signature was lost when wrapping it
return args_offsets, wrap_grouped_map_arrow_udf(func, return_type, argspec, runner_conf)
elif eval_type == PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF_WITH_STATE:
return args_offsets, wrap_grouped_map_pandas_udf_with_state(func, return_type, runner_conf)
elif eval_type == PythonEvalType.SQL_TRANSFORM_WITH_STATE_PANDAS_UDF:
return args_offsets, wrap_grouped_transform_with_state_pandas_udf(
func, return_type, runner_conf
)
elif eval_type == PythonEvalType.SQL_TRANSFORM_WITH_STATE_PANDAS_INIT_STATE_UDF:
return args_offsets, wrap_grouped_transform_with_state_pandas_init_state_udf(
func, return_type, runner_conf
)
elif eval_type == PythonEvalType.SQL_TRANSFORM_WITH_STATE_PYTHON_ROW_UDF:
return args_offsets, wrap_grouped_transform_with_state_udf(func, return_type, runner_conf)
elif eval_type == PythonEvalType.SQL_TRANSFORM_WITH_STATE_PYTHON_ROW_INIT_STATE_UDF:
return args_offsets, wrap_grouped_transform_with_state_init_state_udf(
func, return_type, runner_conf
)
elif eval_type == PythonEvalType.SQL_COGROUPED_MAP_PANDAS_UDF:
argspec = inspect.getfullargspec(chained_func) # signature was lost when wrapping it
return args_offsets, wrap_cogrouped_map_pandas_udf(func, return_type, argspec, runner_conf)
elif eval_type == PythonEvalType.SQL_COGROUPED_MAP_ARROW_UDF:
argspec = inspect.getfullargspec(chained_func) # signature was lost when wrapping it
return args_offsets, wrap_cogrouped_map_arrow_udf(func, return_type, argspec, runner_conf)
elif eval_type == PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF:
return wrap_grouped_agg_pandas_udf(
func, args_offsets, kwargs_offsets, return_type, runner_conf
)
elif eval_type == PythonEvalType.SQL_GROUPED_AGG_ARROW_UDF:
return wrap_grouped_agg_arrow_udf(
func, args_offsets, kwargs_offsets, return_type, runner_conf
)
elif eval_type == PythonEvalType.SQL_WINDOW_AGG_PANDAS_UDF:
return wrap_window_agg_pandas_udf(
func, args_offsets, kwargs_offsets, return_type, runner_conf, udf_index
)
elif eval_type == PythonEvalType.SQL_WINDOW_AGG_ARROW_UDF:
return wrap_window_agg_arrow_udf(
func, args_offsets, kwargs_offsets, return_type, runner_conf, udf_index
)
elif eval_type == PythonEvalType.SQL_BATCHED_UDF:
return wrap_udf(func, args_offsets, kwargs_offsets, return_type)
else:
raise ValueError("Unknown eval type: {}".format(eval_type))
# Used by SQL_GROUPED_MAP_PANDAS_UDF, SQL_GROUPED_MAP_ARROW_UDF,
# SQL_COGROUPED_MAP_PANDAS_UDF, SQL_COGROUPED_MAP_ARROW_UDF,
# SQL_GROUPED_MAP_PANDAS_UDF_WITH_STATE,
# SQL_SCALAR_PANDAS_UDF and SQL_ARROW_BATCHED_UDF when
# returning StructType
def assign_cols_by_name(runner_conf):
return (
runner_conf.get(
"spark.sql.legacy.execution.pandas.groupedMap.assignColumnsByName", "true"
).lower()
== "true"
)
def use_large_var_types(runner_conf):
return runner_conf.get("spark.sql.execution.arrow.useLargeVarTypes", "false").lower() == "true"
def use_legacy_pandas_udf_conversion(runner_conf):
return (
runner_conf.get(
"spark.sql.legacy.execution.pythonUDF.pandas.conversion.enabled", "false"
).lower()
== "true"
)
# Read and process a serialized user-defined table function (UDTF) from a socket.
# It expects the UDTF to be in a specific format and performs various checks to
# ensure the UDTF is valid. This function also prepares a mapper function for applying
# the UDTF logic to input rows.
def read_udtf(pickleSer, infile, eval_type):
prefers_large_var_types = False
legacy_pandas_conversion = False
if eval_type == PythonEvalType.SQL_ARROW_TABLE_UDF:
runner_conf = {}
# Load conf used for arrow evaluation.
num_conf = read_int(infile)
for i in range(num_conf):
k = utf8_deserializer.loads(infile)
v = utf8_deserializer.loads(infile)
runner_conf[k] = v
prefers_large_var_types = use_large_var_types(runner_conf)
legacy_pandas_conversion = (
runner_conf.get(
"spark.sql.legacy.execution.pythonUDTF.pandas.conversion.enabled", "false"
).lower()
== "true"
)
input_types = [
field.dataType for field in _parse_datatype_json_string(utf8_deserializer.loads(infile))
]
if legacy_pandas_conversion:
# NOTE: if timezone is set here, that implies respectSessionTimeZone is True
safecheck = (
runner_conf.get(
"spark.sql.execution.pandas.convertToArrowArraySafely", "false"
).lower()
== "true"
)
int_to_decimal_coercion_enabled = (
runner_conf.get(
"spark.sql.execution.pythonUDF.pandas.intToDecimalCoercionEnabled", "false"
).lower()
== "true"
)
timezone = runner_conf.get("spark.sql.session.timeZone", None)
ser = ArrowStreamPandasUDTFSerializer(
timezone,
safecheck,
input_types=input_types,
int_to_decimal_coercion_enabled=int_to_decimal_coercion_enabled,
)
else:
ser = ArrowStreamUDTFSerializer()
elif eval_type == PythonEvalType.SQL_ARROW_UDTF:
runner_conf = {}
num_conf = read_int(infile)
for i in range(num_conf):
k = utf8_deserializer.loads(infile)
v = utf8_deserializer.loads(infile)
runner_conf[k] = v
prefers_large_var_types = use_large_var_types(runner_conf)
# Read the table argument offsets
num_table_arg_offsets = read_int(infile)
table_arg_offsets = [read_int(infile) for _ in range(num_table_arg_offsets)]
# Use PyArrow-native serializer for Arrow UDTFs with potential UDT support
ser = ArrowStreamArrowUDTFSerializer(table_arg_offsets=table_arg_offsets)
else:
# Each row is a group so do not batch but send one by one.
ser = BatchedSerializer(CPickleSerializer(), 1)
# See 'PythonUDTFRunner.PythonUDFWriterThread.writeCommand'
num_arg = read_int(infile)
args_offsets = []
kwargs_offsets = {}
for _ in range(num_arg):
offset = read_int(infile)
if read_bool(infile):
name = utf8_deserializer.loads(infile)
kwargs_offsets[name] = offset
else:
args_offsets.append(offset)
num_partition_child_indexes = read_int(infile)
partition_child_indexes = [read_int(infile) for i in range(num_partition_child_indexes)]
has_pickled_analyze_result = read_bool(infile)
if has_pickled_analyze_result:
pickled_analyze_result = pickleSer._read_with_length(infile)
else:
pickled_analyze_result = None
# Initially we assume that the UDTF __init__ method accepts the pickled AnalyzeResult,
# although we may set this to false later if we find otherwise.
handler = read_command(pickleSer, infile)
if not isinstance(handler, type):
raise PySparkRuntimeError(
f"Invalid UDTF handler type. Expected a class (type 'type'), but "
f"got an instance of {type(handler).__name__}."
)
return_type = _parse_datatype_json_string(utf8_deserializer.loads(infile))
if not isinstance(return_type, StructType):
raise PySparkRuntimeError(
f"The return type of a UDTF must be a struct type, but got {type(return_type)}."
)
udtf_name = utf8_deserializer.loads(infile)
# Update the handler that creates a new UDTF instance to first try calling the UDTF constructor
# with one argument containing the previous AnalyzeResult. If that fails, then try a constructor
# with no arguments. In this way each UDTF class instance can decide if it wants to inspect the
# AnalyzeResult.
udtf_init_args = inspect.getfullargspec(handler)
if has_pickled_analyze_result:
if len(udtf_init_args.args) > 2:
raise PySparkRuntimeError(
errorClass="UDTF_CONSTRUCTOR_INVALID_IMPLEMENTS_ANALYZE_METHOD",
messageParameters={"name": udtf_name},
)
elif len(udtf_init_args.args) == 2:
prev_handler = handler
def construct_udtf():
# Here we pass the AnalyzeResult to the UDTF's __init__ method.
return prev_handler(dataclasses.replace(pickled_analyze_result))
handler = construct_udtf
elif len(udtf_init_args.args) > 1:
raise PySparkRuntimeError(
errorClass="UDTF_CONSTRUCTOR_INVALID_NO_ANALYZE_METHOD",
messageParameters={"name": udtf_name},
)
class UDTFWithPartitions:
"""
This implements the logic of a UDTF that accepts an input TABLE argument with one or more
PARTITION BY expressions.
For example, let's assume we have a table like:
CREATE TABLE t (c1 INT, c2 INT) USING delta;
Then for the following queries:
SELECT * FROM my_udtf(TABLE (t) PARTITION BY c1, c2);
The partition_child_indexes will be: 0, 1.
SELECT * FROM my_udtf(TABLE (t) PARTITION BY c1, c2 + 4);
The partition_child_indexes will be: 0, 2 (where we add a projection for "c2 + 4").
"""
def __init__(self, create_udtf: Callable, partition_child_indexes: list):
"""
Creates a new instance of this class to wrap the provided UDTF with another one that
checks the values of projected partitioning expressions on consecutive rows to figure
out when the partition boundaries change.
Parameters
----------
create_udtf: function
Function to create a new instance of the UDTF to be invoked.
partition_child_indexes: list
List of integers identifying zero-based indexes of the columns of the input table
that contain projected partitioning expressions. This class will inspect these
values for each pair of consecutive input rows. When they change, this indicates
the boundary between two partitions, and we will invoke the 'terminate' method on
the UDTF class instance and then destroy it and create a new one to implement the
desired partitioning semantics.
"""
self._create_udtf: Callable = create_udtf
self._udtf = create_udtf()
self._prev_arguments: list = list()
self._partition_child_indexes: list = partition_child_indexes
self._eval_raised_skip_rest_of_input_table: bool = False
def eval(self, *args, **kwargs) -> Iterator:
changed_partitions = self._check_partition_boundaries(
list(args) + list(kwargs.values())
)
if changed_partitions:
if hasattr(self._udtf, "terminate"):
result = self._udtf.terminate()
if result is not None:
for row in result:
yield row
self._udtf = self._create_udtf()
self._eval_raised_skip_rest_of_input_table = False
if self._udtf.eval is not None and not self._eval_raised_skip_rest_of_input_table:
# Filter the arguments to exclude projected PARTITION BY values added by Catalyst.
filtered_args = [self._remove_partition_by_exprs(arg) for arg in args]
filtered_kwargs = {
key: self._remove_partition_by_exprs(value) for (key, value) in kwargs.items()
}
try:
result = self._udtf.eval(*filtered_args, **filtered_kwargs)
if result is not None:
for row in result:
yield row
except SkipRestOfInputTableException:
# If the 'eval' method raised this exception, then we should skip the rest of
# the rows in the current partition. Set this field to True here and then for
# each subsequent row in the partition, we will skip calling the 'eval' method
# until we see a change in the partition boundaries.
self._eval_raised_skip_rest_of_input_table = True
def terminate(self) -> Iterator:
if hasattr(self._udtf, "terminate"):
return self._udtf.terminate()
return iter(())
def cleanup(self) -> None:
if hasattr(self._udtf, "cleanup"):
self._udtf.cleanup()
def _check_partition_boundaries(self, arguments: list) -> bool:
result = False
if len(self._prev_arguments) > 0:
cur_table_arg = self._get_table_arg(arguments)
prev_table_arg = self._get_table_arg(self._prev_arguments)
cur_partitions_args = []
prev_partitions_args = []
for i in self._partition_child_indexes:
cur_partitions_args.append(cur_table_arg[i])
prev_partitions_args.append(prev_table_arg[i])
result = any(k != v for k, v in zip(cur_partitions_args, prev_partitions_args))
self._prev_arguments = arguments
return result
def _get_table_arg(self, inputs: list) -> Row:
return [x for x in inputs if type(x) is Row][0]
def _remove_partition_by_exprs(self, arg: Any) -> Any:
if isinstance(arg, Row):
new_row_keys = []
new_row_values = []
for i, (key, value) in enumerate(zip(arg.__fields__, arg)):
if i not in self._partition_child_indexes:
new_row_keys.append(key)
new_row_values.append(value)
return _create_row(new_row_keys, new_row_values)
else:
return arg
class ArrowUDTFWithPartition:
"""
Implements logic for an Arrow UDTF (SQL_ARROW_UDTF) that accepts a TABLE argument
with one or more PARTITION BY expressions.
Arrow UDTFs receive data as PyArrow RecordBatch objects instead of individual Row
objects. This wrapper ensures the UDTF's eval() method is called separately for each
unique partition key value combination.
How Catalyst handles PARTITION BY and ORDER BY:
------------------------------------------------
When a UDTF is called with PARTITION BY and/or ORDER BY clauses, Catalyst adds
operations to the physical plan to ensure correct data organization:
Example SQL:
SELECT * FROM my_udtf(TABLE(t) PARTITION BY key1, key2 ORDER BY value DESC)
Physical Plan generated by Catalyst:
1. Project: Adds partition_by_0 = key1, partition_by_1 = key2 columns
2. Exchange: hashpartitioning(partition_by_0, partition_by_1, 200)
- Shuffles data so rows with same partition keys go to same worker
3. Sort: [partition_by_0 ASC, partition_by_1 ASC, value DESC], local=true
- First sorts by partition keys to group them together
- Then sorts by ORDER BY expressions within each partition
- Local sort (not global) within each worker's data
4. Project: Creates struct with all columns including partition_by_* columns
5. ArrowEvalPythonUDTF: Executes this Python UDTF wrapper
Key guarantee: After the Sort operation, all rows with the same partition key
values are contiguous within each RecordBatch, allowing efficient boundary detection.
Example queries:
SELECT * FROM my_udtf(TABLE (t) PARTITION BY c1);
partition_child_indexes: [2] (refers to partition_by_0 column at index 2)
SELECT * FROM my_udtf(TABLE (t) PARTITION BY c1, c2);
partition_child_indexes: [2, 3] (partition_by_0 and partition_by_1 columns)
SELECT * FROM my_udtf(TABLE (t) PARTITION BY c1, c2 + 4);
partition_child_indexes: 0, 2 (adds a projection for "c2 + 4").
"""
def __init__(self, create_udtf: Callable, partition_child_indexes: list):
"""
Create a new instance that wraps the provided Arrow UDTF with partitioning
logic.
Parameters
----------
create_udtf: function
Function that creates a new instance of the Arrow UDTF to invoke.
partition_child_indexes: list
Zero-based indexes of input-table columns that contain projected
partitioning expressions.
"""
self._create_udtf: Callable = create_udtf
self._udtf = create_udtf()
self._partition_child_indexes: list = partition_child_indexes
# Track last partition key from previous batch
self._last_partition_key: Optional[Tuple[Any, ...]] = None
self._eval_raised_skip_rest_of_input_table: bool = False
def eval(self, *args, **kwargs) -> Iterator:
"""Handle partitioning logic for Arrow UDTFs that receive RecordBatch objects."""
import pyarrow as pa
# Get the original batch with partition columns
original_batch = self._get_table_arg(list(args) + list(kwargs.values()))
if not isinstance(original_batch, pa.RecordBatch):
# Arrow UDTFs with PARTITION BY must have a TABLE argument that
# results in a PyArrow RecordBatch
raise PySparkRuntimeError(
errorClass="INVALID_ARROW_UDTF_TABLE_ARGUMENT",
messageParameters={
"actual_type": str(type(original_batch))
if original_batch is not None
else "None"
},
)
# Remove partition columns to get the filtered arguments
filtered_args = [self._remove_partition_by_exprs(arg) for arg in args]
filtered_kwargs = {
key: self._remove_partition_by_exprs(value) for (key, value) in kwargs.items()
}
# Get the filtered RecordBatch (without partition columns)
filtered_batch = self._get_table_arg(filtered_args + list(filtered_kwargs.values()))
# Process the RecordBatch by partitions
yield from self._process_arrow_batch_by_partitions(
original_batch, filtered_batch, filtered_args, filtered_kwargs
)
def _process_arrow_batch_by_partitions(
self, original_batch, filtered_batch, filtered_args, filtered_kwargs
) -> Iterator:
"""Process an Arrow RecordBatch that may contain multiple partition key values.
When using PARTITION BY with Arrow UDTFs, a single RecordBatch from Spark may contain
rows with different partition key values. For example, with 10 distinct partition keys
and 2 workers, each worker might receive a batch containing 5 different partition key
values.
According to UDTF PARTITION BY semantics, the UDTF's eval() method must be called
separately for each unique partition key value, not for the entire batch. This method
handles splitting the batch by partition boundaries and calling the UDTF appropriately.
The implementation leverages two key properties:
1. Catalyst guarantees rows with the same partition key are contiguous (pre-sorted)
2. Arrow's columnar format allows efficient boundary detection
Parameters:
-----------
original_batch : pa.RecordBatch
The original batch including partition columns, used for detecting boundaries
filtered_batch : pa.RecordBatch
The batch with partition columns removed, to be passed to the UDTF
filtered_args : list
Arguments with partition columns filtered out
filtered_kwargs : dict
Keyword arguments with partition columns filtered out
Yields:
-------
Iterator of pa.Table objects returned by the UDTF's eval() method
"""
import pyarrow as pa
# This class should only be used when partition_child_indexes is non-empty
assert self._partition_child_indexes, (
"ArrowUDTFWithPartition should only be instantiated when "
"len(partition_child_indexes) > 0"
)
# Detect partition boundaries.
boundaries = self._detect_partition_boundaries(original_batch)
# Process each contiguous partition
for i in range(len(boundaries) - 1):
start_idx = boundaries[i]
end_idx = boundaries[i + 1]
# Get the partition key for this segment
partition_key = tuple(
original_batch.column(idx)[start_idx].as_py()
for idx in self._partition_child_indexes
)
# Check if this is a continuation of the previous batch's partition
# TODO: This check is only necessary for the first boundary in each batch.
# The following boundaries are always for new partitions within the same batch.
# This could be optimized by only checking i == 0.
is_new_partition = (
self._last_partition_key is not None
and partition_key != self._last_partition_key
)
if is_new_partition:
# Previous partition ended, call terminate
if hasattr(self._udtf, "terminate"):
terminate_result = self._udtf.terminate()
if terminate_result is not None:
yield from terminate_result
# Create new UDTF instance for new partition
self._udtf = self._create_udtf()
self._eval_raised_skip_rest_of_input_table = False
# Slice the filtered batch for this partition
partition_batch = filtered_batch.slice(start_idx, end_idx - start_idx)
# Update the last partition key
self._last_partition_key = partition_key
# Update filtered args to use the partition batch
partition_filtered_args = []
for arg in filtered_args:
if isinstance(arg, pa.RecordBatch):
partition_filtered_args.append(partition_batch)
else:
partition_filtered_args.append(arg)
partition_filtered_kwargs = {}
for key, value in filtered_kwargs.items():
if isinstance(value, pa.RecordBatch):
partition_filtered_kwargs[key] = partition_batch
else:
partition_filtered_kwargs[key] = value
# Call the UDTF with this partition's data
if not self._eval_raised_skip_rest_of_input_table:
try:
result = self._udtf.eval(
*partition_filtered_args, **partition_filtered_kwargs
)
if result is not None:
yield from result
except SkipRestOfInputTableException:
# Skip remaining rows in this partition
self._eval_raised_skip_rest_of_input_table = True
# Don't terminate here - let the next batch or final terminate handle it
def terminate(self) -> Iterator:
if hasattr(self._udtf, "terminate"):
return self._udtf.terminate()
return iter(())
def cleanup(self) -> None:
if hasattr(self._udtf, "cleanup"):
self._udtf.cleanup()
def _get_table_arg(self, inputs: list):
"""Get the table argument (RecordBatch) from the inputs list.
For Arrow UDTFs with TABLE arguments, we can guarantee the table argument
will be a pa.RecordBatch, not a Row.
"""
import pyarrow as pa
# Find all RecordBatch arguments
batches = [arg for arg in inputs if isinstance(arg, pa.RecordBatch)]
if len(batches) == 0:
# No RecordBatch found - this shouldn't happen for Arrow UDTFs with TABLE arguments
return None
elif len(batches) == 1:
return batches[0]
else:
# Multiple RecordBatch arguments found - this is unexpected
raise RuntimeError(
f"Expected exactly one pa.RecordBatch argument for TABLE parameter, "
f"but found {len(batches)}. Received types: "
f"{[type(arg).__name__ for arg in inputs]}"
)
def _detect_partition_boundaries(self, batch) -> list:
"""
Efficiently detect partition boundaries in a batch with contiguous partitions.
Since Catalyst ensures rows with the same partition key are contiguous,
we only need to find where partition values change.
Returns:
List of indices where each partition starts, plus the total row count.
For example: [0, 3, 8, 10] means partitions are rows [0:3), [3:8), [8:10)
"""
boundaries = [0] # First partition starts at index 0
if batch.num_rows <= 1:
boundaries.append(batch.num_rows)
return boundaries
# Get partition column arrays
partition_arrays = [batch.column(i) for i in self._partition_child_indexes]
# Find boundaries by comparing consecutive rows
for row_idx in range(1, batch.num_rows):
# Check if any partition column changed from previous row
partition_changed = False
for col_array in partition_arrays:
if col_array[row_idx].as_py() != col_array[row_idx - 1].as_py():
partition_changed = True
break
if partition_changed:
boundaries.append(row_idx)
boundaries.append(batch.num_rows) # Last boundary at end
return boundaries
def _remove_partition_by_exprs(self, arg: Any) -> Any:
"""
Remove partition columns from the RecordBatch argument.
Why this is needed:
When a UDTF is called with TABLE(t) PARTITION BY expressions, Catalyst transforms
the data:
1. Adds complex partition expressions as new columns
(e.g., "c2 + 4" becomes a new column)
2. Repartitions data by partition columns using hash partitioning
3. Sends ALL columns (including partition columns) to the Python worker
Partition columns serve two purposes:
- Routing: decide which worker processes which partition
- Boundary detection: know when one partition ends and another begins
However, the user's UDTF should only receive the actual table data, not the
partition columns. This method filters out partition columns before passing
data to the user's UDTF eval() method.
Example:
- User writes: SELECT * FROM udtf(TABLE(t) PARTITION BY c1, c2)
- Catalyst sends: RecordBatch with [c1, c2, c3, c4],
partition_child_indexes=[0, 1]
- This method removes columns at indexes 0, 1 if they are pure partition columns
- UDTF.eval() receives: RecordBatch with only the non-partition columns
"""
import pyarrow as pa
if isinstance(arg, pa.RecordBatch):
# Remove partition columns from the RecordBatch
keep_indices = [
i
for i in range(len(arg.schema.names))
if i not in self._partition_child_indexes
]
if keep_indices:
# Select only the columns we want to keep
keep_arrays = [arg.column(i) for i in keep_indices]
keep_names = [arg.schema.names[i] for i in keep_indices]
return pa.RecordBatch.from_arrays(keep_arrays, names=keep_names)
else:
# If no columns remain, return an empty RecordBatch with the same number of rows
return pa.RecordBatch.from_arrays(
[], schema=pa.schema([]), num_rows=arg.num_rows
)
# For non-RecordBatch arguments (like scalar pa.Arrays), return unchanged
return arg
# Instantiate the UDTF class.
try:
if len(partition_child_indexes) > 0:
# Determine if this is an Arrow UDTF
is_arrow_udtf = eval_type == PythonEvalType.SQL_ARROW_UDTF
if is_arrow_udtf:
udtf = ArrowUDTFWithPartition(handler, partition_child_indexes)
else:
udtf = UDTFWithPartitions(handler, partition_child_indexes)
else:
udtf = handler()
except Exception as e:
raise PySparkRuntimeError(
errorClass="UDTF_EXEC_ERROR",
messageParameters={"method_name": "__init__", "error": str(e)},
)
# Validate the UDTF
if not hasattr(udtf, "eval"):
raise PySparkRuntimeError(
"Failed to execute the user defined table function because it has not "
"implemented the 'eval' method. Please add the 'eval' method and try "
"the query again."
)
# Check that the arguments provided to the UDTF call match the expected parameters defined
# in the 'eval' method signature.
try:
inspect.signature(udtf.eval).bind(*args_offsets, **kwargs_offsets)
except TypeError as e:
raise PySparkRuntimeError(
errorClass="UDTF_EVAL_METHOD_ARGUMENTS_DO_NOT_MATCH_SIGNATURE",
messageParameters={"name": udtf_name, "reason": str(e)},
) from None
def build_null_checker(return_type: StructType) -> Optional[Callable[[Any], None]]:
def raise_(result_column_index):
raise PySparkRuntimeError(
errorClass="UDTF_EXEC_ERROR",
messageParameters={
"method_name": "eval' or 'terminate",
"error": f"Column {result_column_index} within a returned row had a "
+ "value of None, either directly or within array/struct/map "
+ "subfields, but the corresponding column type was declared as "
+ "non-nullable; please update the UDTF to return a non-None value at "
+ "this location or otherwise declare the column type as nullable.",
},
)
def checker(data_type: DataType, result_column_index: int):
if isinstance(data_type, ArrayType):
element_checker = checker(data_type.elementType, result_column_index)
contains_null = data_type.containsNull
if element_checker is None and contains_null:
return None
def check_array(arr):
if isinstance(arr, list):
for e in arr:
if e is None:
if not contains_null:
raise_(result_column_index)
elif element_checker is not None:
element_checker(e)
return check_array
elif isinstance(data_type, MapType):
key_checker = checker(data_type.keyType, result_column_index)
value_checker = checker(data_type.valueType, result_column_index)
value_contains_null = data_type.valueContainsNull
if value_checker is None and value_contains_null:
def check_map(map):
if isinstance(map, dict):
for k, v in map.items():
if k is None:
raise_(result_column_index)
elif key_checker is not None:
key_checker(k)
else:
def check_map(map):
if isinstance(map, dict):
for k, v in map.items():
if k is None:
raise_(result_column_index)
elif key_checker is not None:
key_checker(k)
if v is None:
if not value_contains_null:
raise_(result_column_index)
elif value_checker is not None:
value_checker(v)
return check_map
elif isinstance(data_type, StructType):
field_checkers = [checker(f.dataType, result_column_index) for f in data_type]
nullables = [f.nullable for f in data_type]
if all(c is None for c in field_checkers) and all(nullables):
return None
def check_struct(struct):
if isinstance(struct, tuple):
for value, checker, nullable in zip(struct, field_checkers, nullables):
if value is None:
if not nullable:
raise_(result_column_index)
elif checker is not None:
checker(value)
return check_struct
else:
return None
field_checkers = [
checker(f.dataType, result_column_index=i) for i, f in enumerate(return_type)
]
nullables = [f.nullable for f in return_type]
if all(c is None for c in field_checkers) and all(nullables):
return None
def check(row):
if isinstance(row, tuple):
for i, (value, checker, nullable) in enumerate(zip(row, field_checkers, nullables)):
if value is None:
if not nullable:
raise_(i)
elif checker is not None:
checker(value)
return check
check_output_row_against_schema = build_null_checker(return_type)
if eval_type == PythonEvalType.SQL_ARROW_TABLE_UDF and legacy_pandas_conversion:
def wrap_arrow_udtf(f, return_type):
import pandas as pd
arrow_return_type = to_arrow_type(
return_type, prefers_large_types=prefers_large_var_types
)
return_type_size = len(return_type)
def verify_result(result):
if not isinstance(result, pd.DataFrame):
raise PySparkTypeError(
errorClass="INVALID_ARROW_UDTF_RETURN_TYPE",
messageParameters={
"return_type": type(result).__name__,
"value": str(result),
"func": f.__name__,
},
)
# Validate the output schema when the result dataframe has either output
# rows or columns. Note that we avoid using `df.empty` here because the
# result dataframe may contain an empty row. For example, when a UDTF is
# defined as follows: def eval(self): yield tuple().
if len(result) > 0 or len(result.columns) > 0:
if len(result.columns) != return_type_size:
raise PySparkRuntimeError(
errorClass="UDTF_RETURN_SCHEMA_MISMATCH",
messageParameters={
"expected": str(return_type_size),
"actual": str(len(result.columns)),
"func": f.__name__,
},
)
# Verify the type and the schema of the result.
verify_pandas_result(
result, return_type, assign_cols_by_name=False, truncate_return_schema=False
)
return result
# Wrap the exception thrown from the UDTF in a PySparkRuntimeError.
def func(*a: Any) -> Any:
try:
return f(*a)
except SkipRestOfInputTableException:
raise
except Exception as e:
raise PySparkRuntimeError(
errorClass="UDTF_EXEC_ERROR",
messageParameters={"method_name": f.__name__, "error": str(e)},
)
def check_return_value(res):
# Check whether the result of an arrow UDTF is iterable before
# using it to construct a pandas DataFrame.
if res is not None:
if not isinstance(res, Iterable):
raise PySparkRuntimeError(
errorClass="UDTF_RETURN_NOT_ITERABLE",
messageParameters={
"type": type(res).__name__,
"func": f.__name__,
},
)
if check_output_row_against_schema is not None:
for row in res:
if row is not None:
check_output_row_against_schema(row)
yield row
else:
yield from res
def evaluate(*args: pd.Series, num_rows=1):
if len(args) == 0:
for _ in range(num_rows):
yield (
verify_result(pd.DataFrame(check_return_value(func()))),
arrow_return_type,
return_type,
)
else:
# Create tuples from the input pandas Series, each tuple
# represents a row across all Series.
row_tuples = zip(*args)
for row in row_tuples:
yield (
verify_result(pd.DataFrame(check_return_value(func(*row)))),
arrow_return_type,
return_type,
)
return evaluate
eval_func_kwargs_support, args_kwargs_offsets = wrap_kwargs_support(
getattr(udtf, "eval"), args_offsets, kwargs_offsets
)
eval = wrap_arrow_udtf(eval_func_kwargs_support, return_type)
if hasattr(udtf, "terminate"):
terminate = wrap_arrow_udtf(getattr(udtf, "terminate"), return_type)
else:
terminate = None
cleanup = getattr(udtf, "cleanup") if hasattr(udtf, "cleanup") else None
def mapper(_, it):
try:
for a in it:
# The eval function yields an iterator. Each element produced by this
# iterator is a tuple in the form of (pandas.DataFrame, arrow_return_type).
yield from eval(*[a[o] for o in args_kwargs_offsets], num_rows=len(a[0]))
if terminate is not None:
yield from terminate()
except SkipRestOfInputTableException:
if terminate is not None:
yield from terminate()
finally:
if cleanup is not None:
cleanup()
return mapper, None, ser, ser
elif eval_type == PythonEvalType.SQL_ARROW_TABLE_UDF and not legacy_pandas_conversion:
def wrap_arrow_udtf(f, return_type):
import pyarrow as pa
arrow_return_type = to_arrow_type(
return_type, prefers_large_types=prefers_large_var_types
)
return_type_size = len(return_type)
def verify_result(result):
if not isinstance(result, pa.Table):
raise PySparkTypeError(
errorClass="INVALID_ARROW_UDTF_RETURN_TYPE",
messageParameters={
"return_type": type(result).__name__,
"value": str(result),
"func": f.__name__,
},
)
# Validate the output schema when the result dataframe has either output
# rows or columns. Note that we avoid using `df.empty` here because the
# result dataframe may contain an empty row. For example, when a UDTF is
# defined as follows: def eval(self): yield tuple().
if result.num_rows > 0 or result.num_columns > 0:
if result.num_columns != return_type_size:
raise PySparkRuntimeError(
errorClass="UDTF_RETURN_SCHEMA_MISMATCH",
messageParameters={
"expected": str(return_type_size),
"actual": str(result.num_columns),
"func": f.__name__,
},
)
# Verify the type and the schema of the result.
verify_arrow_result(
result,
assign_cols_by_name=False,
expected_cols_and_types=[
(field.name, field.type) for field in arrow_return_type
],
)
return result
# Wrap the exception thrown from the UDTF in a PySparkRuntimeError.
def func(*a: Any) -> Any:
try:
return f(*a)
except SkipRestOfInputTableException:
raise
except Exception as e:
raise PySparkRuntimeError(
errorClass="UDTF_EXEC_ERROR",
messageParameters={"method_name": f.__name__, "error": str(e)},
)
def check_return_value(res):
# Check whether the result of an arrow UDTF is iterable before
# using it to construct a pandas DataFrame.
if res is not None:
if not isinstance(res, Iterable):
raise PySparkRuntimeError(
errorClass="UDTF_RETURN_NOT_ITERABLE",
messageParameters={
"type": type(res).__name__,
"func": f.__name__,
},
)
for row in res:
if not isinstance(row, tuple) and return_type_size == 1:
row = (row,)
if check_output_row_against_schema is not None:
if row is not None:
check_output_row_against_schema(row)
yield row
def convert_to_arrow(data: Iterable):
data = list(check_return_value(data))
if len(data) == 0:
# Return one empty RecordBatch to match the left side of the lateral join
return [
pa.RecordBatch.from_pylist(data, schema=pa.schema(list(arrow_return_type)))
]
def raise_conversion_error(original_exception):
raise PySparkRuntimeError(
errorClass="UDTF_ARROW_TYPE_CONVERSION_ERROR",
messageParameters={
"data": str(data),
"schema": return_type.simpleString(),
"arrow_schema": str(arrow_return_type),
},
) from original_exception
try:
table = LocalDataToArrowConversion.convert(
data, return_type, prefers_large_var_types
)
except PySparkValueError as e:
if e.getErrorClass() == "AXIS_LENGTH_MISMATCH":
raise PySparkRuntimeError(
errorClass="UDTF_RETURN_SCHEMA_MISMATCH",
messageParameters={
"expected": e.getMessageParameters()[
"expected_length"
], # type: ignore[index]
"actual": e.getMessageParameters()[
"actual_length"
], # type: ignore[index]
"func": f.__name__,
},
) from e
# Fall through to general conversion error
raise_conversion_error(e)
except Exception as e:
raise_conversion_error(e)
return verify_result(table).to_batches()
def evaluate(*args: list, num_rows=1):
if len(args) == 0:
for _ in range(num_rows):
for batch in convert_to_arrow(func()):
yield batch, arrow_return_type
else:
for row in zip(*args):
for batch in convert_to_arrow(func(*row)):
yield batch, arrow_return_type
return evaluate
eval_func_kwargs_support, args_kwargs_offsets = wrap_kwargs_support(
getattr(udtf, "eval"), args_offsets, kwargs_offsets
)
eval = wrap_arrow_udtf(eval_func_kwargs_support, return_type)
if hasattr(udtf, "terminate"):
terminate = wrap_arrow_udtf(getattr(udtf, "terminate"), return_type)
else:
terminate = None
cleanup = getattr(udtf, "cleanup") if hasattr(udtf, "cleanup") else None
def mapper(_, it):
try:
converters = [
ArrowTableToRowsConversion._create_converter(dt, none_on_identity=True)
for dt in input_types
]
for a in it:
pylist = [
[conv(v) for v in column.to_pylist()]
if conv is not None
else column.to_pylist()
for column, conv in zip(a.columns, converters)
]
# The eval function yields an iterator. Each element produced by this
# iterator is a tuple in the form of (pyarrow.RecordBatch, arrow_return_type).
yield from eval(*[pylist[o] for o in args_kwargs_offsets], num_rows=a.num_rows)
if terminate is not None:
yield from terminate()
except SkipRestOfInputTableException:
if terminate is not None:
yield from terminate()
finally:
if cleanup is not None:
cleanup()
return mapper, None, ser, ser
elif eval_type == PythonEvalType.SQL_ARROW_UDTF:
def wrap_pyarrow_udtf(f, return_type):
import pyarrow as pa
arrow_return_type = to_arrow_type(
return_type, prefers_large_types=use_large_var_types(runner_conf)
)
return_type_size = len(return_type)
def verify_result(result):
# Validate the output schema when the result has columns
if result.num_columns != return_type_size:
raise PySparkRuntimeError(
errorClass="UDTF_RETURN_SCHEMA_MISMATCH",
messageParameters={
"expected": str(return_type_size),
"actual": str(result.num_columns),
"func": f.__name__,
},
)
# We verify the type of the result and do type corerion
# in the serializer
return result
# Wrap the exception thrown from the UDTF in a PySparkRuntimeError.
def func(*a: Any) -> Any:
try:
return f(*a)
except SkipRestOfInputTableException:
raise
except Exception as e:
raise PySparkRuntimeError(
errorClass="UDTF_EXEC_ERROR",
messageParameters={"method_name": f.__name__, "error": str(e)},
)
def check_return_value(res):
# Check whether the result of a PyArrow UDTF is iterable before processing
if res is not None:
if not isinstance(res, Iterable):
raise PySparkRuntimeError(
errorClass="UDTF_RETURN_NOT_ITERABLE",
messageParameters={
"type": type(res).__name__,
"func": f.__name__,
},
)
return res
else:
return iter([])
def convert_to_arrow(data: Iterable):
data_iter = check_return_value(data)
# Handle PyArrow Tables/RecordBatches directly
is_empty = True
for item in data_iter:
is_empty = False
if isinstance(item, pa.Table):
yield from item.to_batches()
elif isinstance(item, pa.RecordBatch):
yield item
else:
# Arrow UDTF should only return Arrow types (RecordBatch/Table)
raise PySparkRuntimeError(
errorClass="UDTF_ARROW_TYPE_CONVERSION_ERROR",
messageParameters={},
)
if is_empty:
yield pa.RecordBatch.from_pylist([], schema=pa.schema(list(arrow_return_type)))
def evaluate(*args: pa.RecordBatch):
# For Arrow UDTFs, unpack the RecordBatches and pass them to the function
for batch in convert_to_arrow(func(*args)):
yield verify_result(batch), arrow_return_type
return evaluate
eval_func_kwargs_support, args_kwargs_offsets = wrap_kwargs_support(
getattr(udtf, "eval"), args_offsets, kwargs_offsets
)
eval = wrap_pyarrow_udtf(eval_func_kwargs_support, return_type)
if hasattr(udtf, "terminate"):
terminate = wrap_pyarrow_udtf(getattr(udtf, "terminate"), return_type)
else:
terminate = None
cleanup = getattr(udtf, "cleanup") if hasattr(udtf, "cleanup") else None
def mapper(_, it):
try:
for a in it:
# For PyArrow UDTFs, pass RecordBatches directly (no row conversion needed)
yield from eval(*[a[o] for o in args_kwargs_offsets])
if terminate is not None:
yield from terminate()
except SkipRestOfInputTableException:
if terminate is not None:
yield from terminate()
finally:
if cleanup is not None:
cleanup()
return mapper, None, ser, ser
else:
def wrap_udtf(f, return_type):
assert return_type.needConversion()
toInternal = return_type.toInternal
return_type_size = len(return_type)
def verify_and_convert_result(result):
if result is not None:
if hasattr(result, "__UDT__"):
# UDT object should not be returned directly.
raise PySparkRuntimeError(
errorClass="UDTF_INVALID_OUTPUT_ROW_TYPE",
messageParameters={
"type": type(result).__name__,
"func": f.__name__,
},
)
if hasattr(result, "__len__") and len(result) != return_type_size:
raise PySparkRuntimeError(
errorClass="UDTF_RETURN_SCHEMA_MISMATCH",
messageParameters={
"expected": str(return_type_size),
"actual": str(len(result)),
"func": f.__name__,
},
)
if not (isinstance(result, (list, dict, tuple)) or hasattr(result, "__dict__")):
raise PySparkRuntimeError(
errorClass="UDTF_INVALID_OUTPUT_ROW_TYPE",
messageParameters={
"type": type(result).__name__,
"func": f.__name__,
},
)
if check_output_row_against_schema is not None:
check_output_row_against_schema(result)
return toInternal(result)
# Evaluate the function and return a tuple back to the executor.
def evaluate(*a) -> tuple:
try:
res = f(*a)
except SkipRestOfInputTableException:
raise
except Exception as e:
raise PySparkRuntimeError(
errorClass="UDTF_EXEC_ERROR",
messageParameters={"method_name": f.__name__, "error": str(e)},
)
if res is None:
# If the function returns None or does not have an explicit return statement,
# an empty tuple is returned to the executor.
# This is because directly constructing tuple(None) results in an exception.
return tuple()
if not isinstance(res, Iterable):
raise PySparkRuntimeError(
errorClass="UDTF_RETURN_NOT_ITERABLE",
messageParameters={
"type": type(res).__name__,
"func": f.__name__,
},
)
# If the function returns a result, we map it to the internal representation and
# returns the results as a tuple.
return tuple(map(verify_and_convert_result, res))
return evaluate
eval_func_kwargs_support, args_kwargs_offsets = wrap_kwargs_support(
getattr(udtf, "eval"), args_offsets, kwargs_offsets
)
eval = wrap_udtf(eval_func_kwargs_support, return_type)
if hasattr(udtf, "terminate"):
terminate = wrap_udtf(getattr(udtf, "terminate"), return_type)
else:
terminate = None
cleanup = getattr(udtf, "cleanup") if hasattr(udtf, "cleanup") else None
# Return an iterator of iterators.
def mapper(_, it):
try:
for a in it:
yield eval(*[a[o] for o in args_kwargs_offsets])
if terminate is not None:
yield terminate()
except SkipRestOfInputTableException:
if terminate is not None:
yield terminate()
finally:
if cleanup is not None:
cleanup()
return mapper, None, ser, ser
def read_udfs(pickleSer, infile, eval_type):
runner_conf = {}
state_server_port = None
key_schema = None
if eval_type in (
PythonEvalType.SQL_ARROW_BATCHED_UDF,
PythonEvalType.SQL_SCALAR_PANDAS_UDF,
PythonEvalType.SQL_SCALAR_ARROW_UDF,
PythonEvalType.SQL_COGROUPED_MAP_PANDAS_UDF,
PythonEvalType.SQL_SCALAR_PANDAS_ITER_UDF,
PythonEvalType.SQL_SCALAR_ARROW_ITER_UDF,
PythonEvalType.SQL_MAP_PANDAS_ITER_UDF,
PythonEvalType.SQL_MAP_ARROW_ITER_UDF,
PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF,
PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF,
PythonEvalType.SQL_GROUPED_AGG_ARROW_UDF,
PythonEvalType.SQL_WINDOW_AGG_PANDAS_UDF,
PythonEvalType.SQL_WINDOW_AGG_ARROW_UDF,
PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF_WITH_STATE,
PythonEvalType.SQL_GROUPED_MAP_ARROW_UDF,
PythonEvalType.SQL_COGROUPED_MAP_ARROW_UDF,
PythonEvalType.SQL_TRANSFORM_WITH_STATE_PANDAS_UDF,
PythonEvalType.SQL_TRANSFORM_WITH_STATE_PANDAS_INIT_STATE_UDF,
PythonEvalType.SQL_TRANSFORM_WITH_STATE_PYTHON_ROW_UDF,
PythonEvalType.SQL_TRANSFORM_WITH_STATE_PYTHON_ROW_INIT_STATE_UDF,
):
# Load conf used for pandas_udf evaluation
num_conf = read_int(infile)
for i in range(num_conf):
k = utf8_deserializer.loads(infile)
v = utf8_deserializer.loads(infile)
runner_conf[k] = v
state_object_schema = None
if eval_type == PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF_WITH_STATE:
state_object_schema = StructType.fromJson(json.loads(utf8_deserializer.loads(infile)))
elif (
eval_type == PythonEvalType.SQL_TRANSFORM_WITH_STATE_PANDAS_UDF
or eval_type == PythonEvalType.SQL_TRANSFORM_WITH_STATE_PANDAS_INIT_STATE_UDF
or eval_type == PythonEvalType.SQL_TRANSFORM_WITH_STATE_PYTHON_ROW_UDF
or eval_type == PythonEvalType.SQL_TRANSFORM_WITH_STATE_PYTHON_ROW_INIT_STATE_UDF
):
state_server_port = read_int(infile)
if state_server_port == -1:
state_server_port = utf8_deserializer.loads(infile)
key_schema = StructType.fromJson(json.loads(utf8_deserializer.loads(infile)))
# NOTE: if timezone is set here, that implies respectSessionTimeZone is True
timezone = runner_conf.get("spark.sql.session.timeZone", None)
prefers_large_var_types = use_large_var_types(runner_conf)
safecheck = (
runner_conf.get("spark.sql.execution.pandas.convertToArrowArraySafely", "false").lower()
== "true"
)
int_to_decimal_coercion_enabled = (
runner_conf.get(
"spark.sql.execution.pythonUDF.pandas.intToDecimalCoercionEnabled", "false"
).lower()
== "true"
)
_assign_cols_by_name = assign_cols_by_name(runner_conf)
if eval_type == PythonEvalType.SQL_COGROUPED_MAP_ARROW_UDF:
ser = CogroupArrowUDFSerializer(_assign_cols_by_name)
elif eval_type == PythonEvalType.SQL_COGROUPED_MAP_PANDAS_UDF:
ser = CogroupPandasUDFSerializer(
timezone,
safecheck,
_assign_cols_by_name,
int_to_decimal_coercion_enabled=int_to_decimal_coercion_enabled,
arrow_cast=True,
)
elif eval_type == PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF_WITH_STATE:
arrow_max_records_per_batch = runner_conf.get(
"spark.sql.execution.arrow.maxRecordsPerBatch", 10000
)
arrow_max_records_per_batch = int(arrow_max_records_per_batch)
ser = ApplyInPandasWithStateSerializer(
timezone,
safecheck,
_assign_cols_by_name,
state_object_schema,
arrow_max_records_per_batch,
prefers_large_var_types,
int_to_decimal_coercion_enabled=int_to_decimal_coercion_enabled,
)
elif eval_type == PythonEvalType.SQL_TRANSFORM_WITH_STATE_PANDAS_UDF:
arrow_max_records_per_batch = runner_conf.get(
"spark.sql.execution.arrow.maxRecordsPerBatch", 10000
)
arrow_max_records_per_batch = int(arrow_max_records_per_batch)
ser = TransformWithStateInPandasSerializer(
timezone,
safecheck,
_assign_cols_by_name,
arrow_max_records_per_batch,
int_to_decimal_coercion_enabled=int_to_decimal_coercion_enabled,
)
elif eval_type == PythonEvalType.SQL_TRANSFORM_WITH_STATE_PANDAS_INIT_STATE_UDF:
arrow_max_records_per_batch = runner_conf.get(
"spark.sql.execution.arrow.maxRecordsPerBatch", 10000
)
arrow_max_records_per_batch = int(arrow_max_records_per_batch)
ser = TransformWithStateInPandasInitStateSerializer(
timezone,
safecheck,
_assign_cols_by_name,
arrow_max_records_per_batch,
int_to_decimal_coercion_enabled=int_to_decimal_coercion_enabled,
)
elif eval_type == PythonEvalType.SQL_TRANSFORM_WITH_STATE_PYTHON_ROW_UDF:
arrow_max_records_per_batch = runner_conf.get(
"spark.sql.execution.arrow.maxRecordsPerBatch", 10000
)
arrow_max_records_per_batch = int(arrow_max_records_per_batch)
ser = TransformWithStateInPySparkRowSerializer(arrow_max_records_per_batch)
elif eval_type == PythonEvalType.SQL_TRANSFORM_WITH_STATE_PYTHON_ROW_INIT_STATE_UDF:
arrow_max_records_per_batch = runner_conf.get(
"spark.sql.execution.arrow.maxRecordsPerBatch", 10000
)
arrow_max_records_per_batch = int(arrow_max_records_per_batch)
ser = TransformWithStateInPySparkRowInitStateSerializer(arrow_max_records_per_batch)
elif eval_type == PythonEvalType.SQL_MAP_ARROW_ITER_UDF:
ser = ArrowStreamUDFSerializer()
elif eval_type == PythonEvalType.SQL_GROUPED_MAP_ARROW_UDF:
ser = ArrowStreamGroupUDFSerializer(_assign_cols_by_name)
elif eval_type in (
PythonEvalType.SQL_SCALAR_ARROW_UDF,
PythonEvalType.SQL_SCALAR_ARROW_ITER_UDF,
PythonEvalType.SQL_GROUPED_AGG_ARROW_UDF,
PythonEvalType.SQL_WINDOW_AGG_ARROW_UDF,
):
# Arrow cast and safe check are always enabled
ser = ArrowStreamArrowUDFSerializer(timezone, True, _assign_cols_by_name, True)
elif (
eval_type == PythonEvalType.SQL_ARROW_BATCHED_UDF
and not use_legacy_pandas_udf_conversion(runner_conf)
):
input_types = [
f.dataType for f in _parse_datatype_json_string(utf8_deserializer.loads(infile))
]
ser = ArrowBatchUDFSerializer(
timezone, safecheck, input_types, int_to_decimal_coercion_enabled
)
else:
# Scalar Pandas UDF handles struct type arguments as pandas DataFrames instead of
# pandas Series. See SPARK-27240.
df_for_struct = (
eval_type == PythonEvalType.SQL_SCALAR_PANDAS_UDF
or eval_type == PythonEvalType.SQL_SCALAR_PANDAS_ITER_UDF
or eval_type == PythonEvalType.SQL_MAP_PANDAS_ITER_UDF
)
# Arrow-optimized Python UDF takes a struct type argument as a Row
struct_in_pandas = (
"row" if eval_type == PythonEvalType.SQL_ARROW_BATCHED_UDF else "dict"
)
ndarray_as_list = eval_type == PythonEvalType.SQL_ARROW_BATCHED_UDF
# Arrow-optimized Python UDF takes input types
input_types = (
[f.dataType for f in _parse_datatype_json_string(utf8_deserializer.loads(infile))]
if eval_type == PythonEvalType.SQL_ARROW_BATCHED_UDF
else None
)
ser = ArrowStreamPandasUDFSerializer(
timezone,
safecheck,
_assign_cols_by_name,
df_for_struct,
struct_in_pandas,
ndarray_as_list,
True,
input_types,
int_to_decimal_coercion_enabled=int_to_decimal_coercion_enabled,
)
else:
batch_size = int(os.environ.get("PYTHON_UDF_BATCH_SIZE", "100"))
ser = BatchedSerializer(CPickleSerializer(), batch_size)
is_profiling = read_bool(infile)
if is_profiling:
profiler = utf8_deserializer.loads(infile)
else:
profiler = None
num_udfs = read_int(infile)
is_scalar_iter = eval_type in (
PythonEvalType.SQL_SCALAR_PANDAS_ITER_UDF,
PythonEvalType.SQL_SCALAR_ARROW_ITER_UDF,
)
is_map_pandas_iter = eval_type == PythonEvalType.SQL_MAP_PANDAS_ITER_UDF
is_map_arrow_iter = eval_type == PythonEvalType.SQL_MAP_ARROW_ITER_UDF
if is_scalar_iter or is_map_pandas_iter or is_map_arrow_iter:
# TODO: Better error message for num_udfs != 1
if is_scalar_iter:
assert num_udfs == 1, "One SCALAR_ITER UDF expected here."
if is_map_pandas_iter:
assert num_udfs == 1, "One MAP_PANDAS_ITER UDF expected here."
if is_map_arrow_iter:
assert num_udfs == 1, "One MAP_ARROW_ITER UDF expected here."
arg_offsets, udf = read_single_udf(
pickleSer, infile, eval_type, runner_conf, udf_index=0, profiler=profiler
)
def func(_, iterator):
num_input_rows = 0
def map_batch(batch):
nonlocal num_input_rows
udf_args = [batch[offset] for offset in arg_offsets]
num_input_rows += len(udf_args[0])
if len(udf_args) == 1:
return udf_args[0]
else:
return tuple(udf_args)
iterator = map(map_batch, iterator)
result_iter = udf(iterator)
num_output_rows = 0
for result_batch, result_type in result_iter:
num_output_rows += len(result_batch)
# This check is for Scalar Iterator UDF to fail fast.
# The length of the entire input can only be explicitly known
# by consuming the input iterator in user side. Therefore,
# it's very unlikely the output length is higher than
# input length.
if is_scalar_iter and num_output_rows > num_input_rows:
raise PySparkRuntimeError(
errorClass="PANDAS_UDF_OUTPUT_EXCEEDS_INPUT_ROWS", messageParameters={}
)
yield (result_batch, result_type)
if is_scalar_iter:
try:
next(iterator)
except StopIteration:
pass
else:
raise PySparkRuntimeError(
errorClass="STOP_ITERATION_OCCURRED_FROM_SCALAR_ITER_PANDAS_UDF",
messageParameters={},
)
if num_output_rows != num_input_rows:
raise PySparkRuntimeError(
errorClass="RESULT_LENGTH_MISMATCH_FOR_SCALAR_ITER_PANDAS_UDF",
messageParameters={
"output_length": str(num_output_rows),
"input_length": str(num_input_rows),
},
)
# profiling is not supported for UDF
return func, None, ser, ser
def extract_key_value_indexes(grouped_arg_offsets):
"""
Helper function to extract the key and value indexes from arg_offsets for the grouped and
cogrouped pandas udfs. See BasePandasGroupExec.resolveArgOffsets for equivalent scala code.
Parameters
----------
grouped_arg_offsets: list
List containing the key and value indexes of columns of the
DataFrames to be passed to the udf. It consists of n repeating groups where n is the
number of DataFrames. Each group has the following format:
group[0]: length of group
group[1]: length of key indexes
group[2.. group[1] +2]: key attributes
group[group[1] +3 group[0]]: value attributes
"""
parsed = []
idx = 0
while idx < len(grouped_arg_offsets):
offsets_len = grouped_arg_offsets[idx]
idx += 1
offsets = grouped_arg_offsets[idx : idx + offsets_len]
split_index = offsets[0] + 1
offset_keys = offsets[1:split_index]
offset_values = offsets[split_index:]
parsed.append([offset_keys, offset_values])
idx += offsets_len
return parsed
if eval_type == PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF:
# We assume there is only one UDF here because grouped map doesn't
# support combining multiple UDFs.
assert num_udfs == 1
# See FlatMapGroupsInPandasExec for how arg_offsets are used to
# distinguish between grouping attributes and data attributes
arg_offsets, f = read_single_udf(
pickleSer, infile, eval_type, runner_conf, udf_index=0, profiler=profiler
)
parsed_offsets = extract_key_value_indexes(arg_offsets)
# Create function like this:
# mapper a: f([a[0]], [a[0], a[1]])
def mapper(a):
keys = [a[o] for o in parsed_offsets[0][0]]
vals = [a[o] for o in parsed_offsets[0][1]]
return f(keys, vals)
elif eval_type == PythonEvalType.SQL_TRANSFORM_WITH_STATE_PANDAS_UDF:
# We assume there is only one UDF here because grouped map doesn't
# support combining multiple UDFs.
assert num_udfs == 1
# See TransformWithStateInPandasExec for how arg_offsets are used to
# distinguish between grouping attributes and data attributes
arg_offsets, f = read_single_udf(
pickleSer, infile, eval_type, runner_conf, udf_index=0, profiler=profiler
)
parsed_offsets = extract_key_value_indexes(arg_offsets)
ser.key_offsets = parsed_offsets[0][0]
stateful_processor_api_client = StatefulProcessorApiClient(state_server_port, key_schema)
def mapper(a):
mode = a[0]
if mode == TransformWithStateInPandasFuncMode.PROCESS_DATA:
key = a[1]
def values_gen():
for x in a[2]:
retVal = x[1].iloc[:, parsed_offsets[0][1]]
yield retVal
# This must be generator comprehension - do not materialize.
return f(stateful_processor_api_client, mode, key, values_gen())
else:
# mode == PROCESS_TIMER or mode == COMPLETE
return f(stateful_processor_api_client, mode, None, iter([]))
elif eval_type == PythonEvalType.SQL_TRANSFORM_WITH_STATE_PANDAS_INIT_STATE_UDF:
# We assume there is only one UDF here because grouped map doesn't
# support combining multiple UDFs.
assert num_udfs == 1
# See TransformWithStateInPandasExec for how arg_offsets are used to
# distinguish between grouping attributes and data attributes
arg_offsets, f = read_single_udf(
pickleSer, infile, eval_type, runner_conf, udf_index=0, profiler=profiler
)
# parsed offsets:
# [
# [groupingKeyOffsets, dedupDataOffsets],
# [initStateGroupingOffsets, dedupInitDataOffsets]
# ]
parsed_offsets = extract_key_value_indexes(arg_offsets)
ser.key_offsets = parsed_offsets[0][0]
ser.init_key_offsets = parsed_offsets[1][0]
stateful_processor_api_client = StatefulProcessorApiClient(state_server_port, key_schema)
def mapper(a):
mode = a[0]
if mode == TransformWithStateInPandasFuncMode.PROCESS_DATA:
key = a[1]
def values_gen():
for x in a[2]:
retVal = [x[1][o] for o in parsed_offsets[0][1]]
initVal = [x[2][o] for o in parsed_offsets[1][1]]
yield retVal, initVal
# This must be generator comprehension - do not materialize.
return f(stateful_processor_api_client, mode, key, values_gen())
else:
# mode == PROCESS_TIMER or mode == COMPLETE
return f(stateful_processor_api_client, mode, None, iter([]))
elif eval_type == PythonEvalType.SQL_TRANSFORM_WITH_STATE_PYTHON_ROW_UDF:
# We assume there is only one UDF here because grouped map doesn't
# support combining multiple UDFs.
assert num_udfs == 1
# See TransformWithStateInPySparkExec for how arg_offsets are used to
# distinguish between grouping attributes and data attributes
arg_offsets, f = read_single_udf(
pickleSer, infile, eval_type, runner_conf, udf_index=0, profiler=profiler
)
parsed_offsets = extract_key_value_indexes(arg_offsets)
ser.key_offsets = parsed_offsets[0][0]
stateful_processor_api_client = StatefulProcessorApiClient(state_server_port, key_schema)
def mapper(a):
mode = a[0]
if mode == TransformWithStateInPandasFuncMode.PROCESS_DATA:
key = a[1]
values = a[2]
# This must be generator comprehension - do not materialize.
return f(stateful_processor_api_client, mode, key, values)
else:
# mode == PROCESS_TIMER or mode == COMPLETE
return f(stateful_processor_api_client, mode, None, iter([]))
elif eval_type == PythonEvalType.SQL_TRANSFORM_WITH_STATE_PYTHON_ROW_INIT_STATE_UDF:
# We assume there is only one UDF here because grouped map doesn't
# support combining multiple UDFs.
assert num_udfs == 1
# See TransformWithStateInPandasExec for how arg_offsets are used to
# distinguish between grouping attributes and data attributes
arg_offsets, f = read_single_udf(
pickleSer, infile, eval_type, runner_conf, udf_index=0, profiler=profiler
)
# parsed offsets:
# [
# [groupingKeyOffsets, dedupDataOffsets],
# [initStateGroupingOffsets, dedupInitDataOffsets]
# ]
parsed_offsets = extract_key_value_indexes(arg_offsets)
ser.key_offsets = parsed_offsets[0][0]
ser.init_key_offsets = parsed_offsets[1][0]
stateful_processor_api_client = StatefulProcessorApiClient(state_server_port, key_schema)
def mapper(a):
mode = a[0]
if mode == TransformWithStateInPandasFuncMode.PROCESS_DATA:
key = a[1]
values = a[2]
# This must be generator comprehension - do not materialize.
return f(stateful_processor_api_client, mode, key, values)
else:
# mode == PROCESS_TIMER or mode == COMPLETE
return f(stateful_processor_api_client, mode, None, iter([]))
elif eval_type == PythonEvalType.SQL_GROUPED_MAP_ARROW_UDF:
import pyarrow as pa
# We assume there is only one UDF here because grouped map doesn't
# support combining multiple UDFs.
assert num_udfs == 1
# See FlatMapGroupsInPandasExec for how arg_offsets are used to
# distinguish between grouping attributes and data attributes
arg_offsets, f = read_single_udf(
pickleSer, infile, eval_type, runner_conf, udf_index=0, profiler=profiler
)
parsed_offsets = extract_key_value_indexes(arg_offsets)
def batch_from_offset(batch, offsets):
return pa.RecordBatch.from_arrays(
arrays=[batch.columns[o] for o in offsets],
names=[batch.schema.names[o] for o in offsets],
)
def table_from_batches(batches, offsets):
return pa.Table.from_batches([batch_from_offset(batch, offsets) for batch in batches])
def mapper(a):
keys = table_from_batches(a, parsed_offsets[0][0])
vals = table_from_batches(a, parsed_offsets[0][1])
return f(keys, vals)
elif eval_type == PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF_WITH_STATE:
# We assume there is only one UDF here because grouped map doesn't
# support combining multiple UDFs.
assert num_udfs == 1
# See FlatMapGroupsInPandas(WithState)Exec for how arg_offsets are used to
# distinguish between grouping attributes and data attributes
arg_offsets, f = read_single_udf(
pickleSer, infile, eval_type, runner_conf, udf_index=0, profiler=profiler
)
parsed_offsets = extract_key_value_indexes(arg_offsets)
def mapper(a):
"""
The function receives (iterator of data, state) and performs extraction of key and
value from the data, with retaining lazy evaluation.
See `load_stream` in `ApplyInPandasWithStateSerializer` for more details on the input
and see `wrap_grouped_map_pandas_udf_with_state` for more details on how output will
be used.
"""
from itertools import tee
state = a[1]
data_gen = (x[0] for x in a[0])
# We know there should be at least one item in the iterator/generator.
# We want to peek the first element to construct the key, hence applying
# tee to construct the key while we retain another iterator/generator
# for values.
keys_gen, values_gen = tee(data_gen)
keys_elem = next(keys_gen)
keys = [keys_elem[o] for o in parsed_offsets[0][0]]
# This must be generator comprehension - do not materialize.
vals = ([x[o] for o in parsed_offsets[0][1]] for x in values_gen)
return f(keys, vals, state)
elif eval_type == PythonEvalType.SQL_COGROUPED_MAP_PANDAS_UDF:
# We assume there is only one UDF here because cogrouped map doesn't
# support combining multiple UDFs.
assert num_udfs == 1
arg_offsets, f = read_single_udf(
pickleSer, infile, eval_type, runner_conf, udf_index=0, profiler=profiler
)
parsed_offsets = extract_key_value_indexes(arg_offsets)
def mapper(a):
df1_keys = [a[0][o] for o in parsed_offsets[0][0]]
df1_vals = [a[0][o] for o in parsed_offsets[0][1]]
df2_keys = [a[1][o] for o in parsed_offsets[1][0]]
df2_vals = [a[1][o] for o in parsed_offsets[1][1]]
return f(df1_keys, df1_vals, df2_keys, df2_vals)
elif eval_type == PythonEvalType.SQL_COGROUPED_MAP_ARROW_UDF:
import pyarrow as pa
# We assume there is only one UDF here because cogrouped map doesn't
# support combining multiple UDFs.
assert num_udfs == 1
arg_offsets, f = read_single_udf(
pickleSer, infile, eval_type, runner_conf, udf_index=0, profiler=profiler
)
parsed_offsets = extract_key_value_indexes(arg_offsets)
def batch_from_offset(batch, offsets):
return pa.RecordBatch.from_arrays(
arrays=[batch.columns[o] for o in offsets],
names=[batch.schema.names[o] for o in offsets],
)
def table_from_batches(batches, offsets):
return pa.Table.from_batches([batch_from_offset(batch, offsets) for batch in batches])
def mapper(a):
df1_keys = table_from_batches(a[0], parsed_offsets[0][0])
df1_vals = table_from_batches(a[0], parsed_offsets[0][1])
df2_keys = table_from_batches(a[1], parsed_offsets[1][0])
df2_vals = table_from_batches(a[1], parsed_offsets[1][1])
return f(df1_keys, df1_vals, df2_keys, df2_vals)
else:
udfs = []
for i in range(num_udfs):
udfs.append(
read_single_udf(
pickleSer, infile, eval_type, runner_conf, udf_index=i, profiler=profiler
)
)
def mapper(a):
result = tuple(f(*[a[o] for o in arg_offsets]) for arg_offsets, f in udfs)
# In the special case of a single UDF this will return a single result rather
# than a tuple of results; this is the format that the JVM side expects.
if len(result) == 1:
return result[0]
else:
return result
def func(_, it):
return map(mapper, it)
# profiling is not supported for UDF
return func, None, ser, ser
def main(infile, outfile):
faulthandler_log_path = os.environ.get("PYTHON_FAULTHANDLER_DIR", None)
tracebackDumpIntervalSeconds = os.environ.get("PYTHON_TRACEBACK_DUMP_INTERVAL_SECONDS", None)
try:
if faulthandler_log_path:
faulthandler_log_path = os.path.join(faulthandler_log_path, str(os.getpid()))
faulthandler_log_file = open(faulthandler_log_path, "w")
faulthandler.enable(file=faulthandler_log_file)
boot_time = time.time()
split_index = read_int(infile)
if split_index == -1: # for unit tests
sys.exit(-1)
if tracebackDumpIntervalSeconds is not None and int(tracebackDumpIntervalSeconds) > 0:
faulthandler.dump_traceback_later(int(tracebackDumpIntervalSeconds), repeat=True)
check_python_version(infile)
# read inputs only for a barrier task
isBarrier = read_bool(infile)
memory_limit_mb = int(os.environ.get("PYSPARK_EXECUTOR_MEMORY_MB", "-1"))
setup_memory_limits(memory_limit_mb)
# initialize global state
taskContext = None
if isBarrier:
boundPort = read_int(infile)
secret = None
if boundPort == -1:
boundPort = utf8_deserializer.loads(infile)
else:
secret = utf8_deserializer.loads(infile)
taskContext = BarrierTaskContext._getOrCreate()
BarrierTaskContext._initialize(boundPort, secret)
# Set the task context instance here, so we can get it by TaskContext.get for
# both TaskContext and BarrierTaskContext
TaskContext._setTaskContext(taskContext)
else:
taskContext = TaskContext._getOrCreate()
# read inputs for TaskContext info
taskContext._stageId = read_int(infile)
taskContext._partitionId = read_int(infile)
taskContext._attemptNumber = read_int(infile)
taskContext._taskAttemptId = read_long(infile)
taskContext._cpus = read_int(infile)
taskContext._resources = {}
for r in range(read_int(infile)):
key = utf8_deserializer.loads(infile)
name = utf8_deserializer.loads(infile)
addresses = []
taskContext._resources = {}
for a in range(read_int(infile)):
addresses.append(utf8_deserializer.loads(infile))
taskContext._resources[key] = ResourceInformation(name, addresses)
taskContext._localProperties = dict()
for i in range(read_int(infile)):
k = utf8_deserializer.loads(infile)
v = utf8_deserializer.loads(infile)
taskContext._localProperties[k] = v
shuffle.MemoryBytesSpilled = 0
shuffle.DiskBytesSpilled = 0
_accumulatorRegistry.clear()
setup_spark_files(infile)
setup_broadcasts(infile)
_accumulatorRegistry.clear()
eval_type = read_int(infile)
if eval_type == PythonEvalType.NON_UDF:
func, profiler, deserializer, serializer = read_command(pickleSer, infile)
elif eval_type in (
PythonEvalType.SQL_TABLE_UDF,
PythonEvalType.SQL_ARROW_TABLE_UDF,
PythonEvalType.SQL_ARROW_UDTF,
):
func, profiler, deserializer, serializer = read_udtf(pickleSer, infile, eval_type)
else:
func, profiler, deserializer, serializer = read_udfs(pickleSer, infile, eval_type)
init_time = time.time()
def process():
iterator = deserializer.load_stream(infile)
out_iter = func(split_index, iterator)
try:
serializer.dump_stream(out_iter, outfile)
finally:
if hasattr(out_iter, "close"):
out_iter.close()
if profiler:
profiler.profile(process)
else:
process()
# Reset task context to None. This is a guard code to avoid residual context when worker
# reuse.
TaskContext._setTaskContext(None)
BarrierTaskContext._setTaskContext(None)
except BaseException as e:
handle_worker_exception(e, outfile)
sys.exit(-1)
finally:
if faulthandler_log_path:
faulthandler.disable()
faulthandler_log_file.close()
os.remove(faulthandler_log_path)
finish_time = time.time()
report_times(outfile, boot_time, init_time, finish_time)
write_long(shuffle.MemoryBytesSpilled, outfile)
write_long(shuffle.DiskBytesSpilled, outfile)
# Mark the beginning of the accumulators section of the output
write_int(SpecialLengths.END_OF_DATA_SECTION, outfile)
send_accumulator_updates(outfile)
# check end of stream
if read_int(infile) == SpecialLengths.END_OF_STREAM:
write_int(SpecialLengths.END_OF_STREAM, outfile)
else:
# write a different value to tell JVM to not reuse this worker
write_int(SpecialLengths.END_OF_DATA_SECTION, outfile)
sys.exit(-1)
# Force to cancel dump_traceback_later
faulthandler.cancel_dump_traceback_later()
if __name__ == "__main__":
# Read information about how to connect back to the JVM from the environment.
conn_info = os.environ.get(
"PYTHON_WORKER_FACTORY_SOCK_PATH", int(os.environ.get("PYTHON_WORKER_FACTORY_PORT", -1))
)
auth_secret = os.environ.get("PYTHON_WORKER_FACTORY_SECRET")
(sock_file, _) = local_connect_and_auth(conn_info, auth_secret)
# TODO: Remove the following two lines and use `Process.pid()` when we drop JDK 8.
write_int(os.getpid(), sock_file)
sock_file.flush()
main(sock_file, sock_file)