44281ca81d
## What changes were proposed in this pull request? The `keyword_only` decorator in PySpark is not thread-safe. It writes kwargs to a static class variable in the decorator, which is then retrieved later in the class method as `_input_kwargs`. If multiple threads are constructing the same class with different kwargs, it becomes a race condition to read from the static class variable before it's overwritten. See [SPARK-19348](https://issues.apache.org/jira/browse/SPARK-19348) for reproduction code. This change will write the kwargs to a member variable so that multiple threads can operate on separate instances without the race condition. It does not protect against multiple threads operating on a single instance, but that is better left to the user to synchronize. ## How was this patch tested? Added new unit tests for using the keyword_only decorator and a regression test that verifies `_input_kwargs` can be overwritten from different class instances. Author: Bryan Cutler <cutlerb@gmail.com> Closes #16782 from BryanCutler/pyspark-keyword_only-threadsafe-SPARK-19348.
116 lines
4 KiB
Python
116 lines
4 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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PySpark is the Python API for Spark.
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Public classes:
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- :class:`SparkContext`:
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Main entry point for Spark functionality.
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- :class:`RDD`:
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A Resilient Distributed Dataset (RDD), the basic abstraction in Spark.
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- :class:`Broadcast`:
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A broadcast variable that gets reused across tasks.
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- :class:`Accumulator`:
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An "add-only" shared variable that tasks can only add values to.
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- :class:`SparkConf`:
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For configuring Spark.
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- :class:`SparkFiles`:
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Access files shipped with jobs.
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- :class:`StorageLevel`:
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Finer-grained cache persistence levels.
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- :class:`TaskContext`:
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Information about the current running task, avaialble on the workers and experimental.
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"""
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from functools import wraps
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import types
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from pyspark.conf import SparkConf
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from pyspark.context import SparkContext
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from pyspark.rdd import RDD
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from pyspark.files import SparkFiles
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from pyspark.storagelevel import StorageLevel
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from pyspark.accumulators import Accumulator, AccumulatorParam
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from pyspark.broadcast import Broadcast
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from pyspark.serializers import MarshalSerializer, PickleSerializer
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from pyspark.status import *
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from pyspark.taskcontext import TaskContext
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from pyspark.profiler import Profiler, BasicProfiler
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from pyspark.version import __version__
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def since(version):
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"""
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A decorator that annotates a function to append the version of Spark the function was added.
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"""
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import re
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indent_p = re.compile(r'\n( +)')
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def deco(f):
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indents = indent_p.findall(f.__doc__)
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indent = ' ' * (min(len(m) for m in indents) if indents else 0)
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f.__doc__ = f.__doc__.rstrip() + "\n\n%s.. versionadded:: %s" % (indent, version)
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return f
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return deco
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def copy_func(f, name=None, sinceversion=None, doc=None):
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"""
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Returns a function with same code, globals, defaults, closure, and
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name (or provide a new name).
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"""
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# See
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# http://stackoverflow.com/questions/6527633/how-can-i-make-a-deepcopy-of-a-function-in-python
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fn = types.FunctionType(f.__code__, f.__globals__, name or f.__name__, f.__defaults__,
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f.__closure__)
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# in case f was given attrs (note this dict is a shallow copy):
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fn.__dict__.update(f.__dict__)
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if doc is not None:
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fn.__doc__ = doc
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if sinceversion is not None:
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fn = since(sinceversion)(fn)
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return fn
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def keyword_only(func):
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"""
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A decorator that forces keyword arguments in the wrapped method
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and saves actual input keyword arguments in `_input_kwargs`.
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.. note:: Should only be used to wrap a method where first arg is `self`
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"""
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@wraps(func)
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def wrapper(self, *args, **kwargs):
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if len(args) > 0:
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raise TypeError("Method %s forces keyword arguments." % func.__name__)
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self._input_kwargs = kwargs
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return func(self, **kwargs)
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return wrapper
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# for back compatibility
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from pyspark.sql import SQLContext, HiveContext, Row
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__all__ = [
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"SparkConf", "SparkContext", "SparkFiles", "RDD", "StorageLevel", "Broadcast",
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"Accumulator", "AccumulatorParam", "MarshalSerializer", "PickleSerializer",
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"StatusTracker", "SparkJobInfo", "SparkStageInfo", "Profiler", "BasicProfiler", "TaskContext",
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]
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