spark-instrumented-optimizer/python/pyspark/__init__.pyi
HyukjinKwon 41af409b7b [SPARK-35303][PYTHON] Enable pinned thread mode by default
### What changes were proposed in this pull request?

PySpark added pinned thread mode at https://github.com/apache/spark/pull/24898 to sync Python thread to JVM thread. Previously, one JVM thread could be reused which ends up with messed inheritance hierarchy such as thread local especially when multiple jobs run in parallel. To completely fix this, we should enable this mode by default.

### Why are the changes needed?

To correctly support parallel job submission and management.

### Does this PR introduce _any_ user-facing change?

Yes, now Python thread is mapped to JVM thread one to one.

### How was this patch tested?

Existing tests should cover it.

Closes #32429 from HyukjinKwon/SPARK-35303.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
2021-06-18 12:02:29 +09:00

78 lines
2.7 KiB
Python

#
# 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.
from typing import Callable, Optional, TypeVar
from pyspark.accumulators import ( # noqa: F401
Accumulator as Accumulator,
AccumulatorParam as AccumulatorParam,
)
from pyspark.broadcast import Broadcast as Broadcast # noqa: F401
from pyspark.conf import SparkConf as SparkConf # noqa: F401
from pyspark.context import SparkContext as SparkContext # noqa: F401
from pyspark.files import SparkFiles as SparkFiles # noqa: F401
from pyspark.status import (
StatusTracker as StatusTracker,
SparkJobInfo as SparkJobInfo,
SparkStageInfo as SparkStageInfo,
) # noqa: F401
from pyspark.profiler import ( # noqa: F401
BasicProfiler as BasicProfiler,
Profiler as Profiler,
)
from pyspark.rdd import RDD as RDD, RDDBarrier as RDDBarrier # noqa: F401
from pyspark.serializers import ( # noqa: F401
MarshalSerializer as MarshalSerializer,
PickleSerializer as PickleSerializer,
)
from pyspark.status import ( # noqa: F401
SparkJobInfo as SparkJobInfo,
SparkStageInfo as SparkStageInfo,
StatusTracker as StatusTracker,
)
from pyspark.storagelevel import StorageLevel as StorageLevel # noqa: F401
from pyspark.taskcontext import ( # noqa: F401
BarrierTaskContext as BarrierTaskContext,
BarrierTaskInfo as BarrierTaskInfo,
TaskContext as TaskContext,
)
from pyspark.util import (
InheritableThread as InheritableThread, # noqa: F401
inheritable_thread_target as inheritable_thread_target, # noqa: F401
)
from pyspark.version import __version__ as __version__
# Compatibility imports
from pyspark.sql import ( # noqa: F401
SQLContext as SQLContext,
HiveContext as HiveContext,
Row as Row,
)
T = TypeVar("T")
F = TypeVar("F", bound=Callable)
def since(version: str) -> Callable[[T], T]: ...
def copy_func(
f: F,
name: Optional[str] = ...,
sinceversion: Optional[str] = ...,
doc: Optional[str] = ...,
) -> F: ...
def keyword_only(func: F) -> F: ...