-
Eric Liang authored
## What changes were proposed in this pull request? For large objects, pickle does not raise useful error messages. However, we can wrap them to be slightly more user friendly: Example 1: ``` def run(): import numpy.random as nr b = nr.bytes(8 * 1000000000) sc.parallelize(range(1000), 1000).map(lambda x: len(b)).count() run() ``` Before: ``` error: 'i' format requires -2147483648 <= number <= 2147483647 ``` After: ``` pickle.PicklingError: Object too large to serialize: 'i' format requires -2147483648 <= number <= 2147483647 ``` Example 2: ``` def run(): import numpy.random as nr b = sc.broadcast(nr.bytes(8 * 1000000000)) sc.parallelize(range(1000), 1000).map(lambda x: len(b.value)).count() run() ``` Before: ``` SystemError: error return without exception set ``` After: ``` cPickle.PicklingError: Could not serialize broadcast: SystemError: error return without exception set ``` ## How was this patch tested? Manually tried out these cases cc davies Author: Eric Liang <ekl@databricks.com> Closes #15026 from ericl/spark-17472.
Eric Liang authored## What changes were proposed in this pull request? For large objects, pickle does not raise useful error messages. However, we can wrap them to be slightly more user friendly: Example 1: ``` def run(): import numpy.random as nr b = nr.bytes(8 * 1000000000) sc.parallelize(range(1000), 1000).map(lambda x: len(b)).count() run() ``` Before: ``` error: 'i' format requires -2147483648 <= number <= 2147483647 ``` After: ``` pickle.PicklingError: Object too large to serialize: 'i' format requires -2147483648 <= number <= 2147483647 ``` Example 2: ``` def run(): import numpy.random as nr b = sc.broadcast(nr.bytes(8 * 1000000000)) sc.parallelize(range(1000), 1000).map(lambda x: len(b.value)).count() run() ``` Before: ``` SystemError: error return without exception set ``` After: ``` cPickle.PicklingError: Could not serialize broadcast: SystemError: error return without exception set ``` ## How was this patch tested? Manually tried out these cases cc davies Author: Eric Liang <ekl@databricks.com> Closes #15026 from ericl/spark-17472.
broadcast.py 4.52 KiB
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import os
import sys
import gc
from tempfile import NamedTemporaryFile
from pyspark.cloudpickle import print_exec
if sys.version < '3':
import cPickle as pickle
else:
import pickle
unicode = str
__all__ = ['Broadcast']
# Holds broadcasted data received from Java, keyed by its id.
_broadcastRegistry = {}
def _from_id(bid):
from pyspark.broadcast import _broadcastRegistry
if bid not in _broadcastRegistry:
raise Exception("Broadcast variable '%s' not loaded!" % bid)
return _broadcastRegistry[bid]
class Broadcast(object):
"""
A broadcast variable created with L{SparkContext.broadcast()}.
Access its value through C{.value}.
Examples:
>>> from pyspark.context import SparkContext
>>> sc = SparkContext('local', 'test')
>>> b = sc.broadcast([1, 2, 3, 4, 5])
>>> b.value
[1, 2, 3, 4, 5]
>>> sc.parallelize([0, 0]).flatMap(lambda x: b.value).collect()
[1, 2, 3, 4, 5, 1, 2, 3, 4, 5]
>>> b.unpersist()
>>> large_broadcast = sc.broadcast(range(10000))
"""
def __init__(self, sc=None, value=None, pickle_registry=None, path=None):
"""
Should not be called directly by users -- use L{SparkContext.broadcast()}
instead.
"""
if sc is not None:
f = NamedTemporaryFile(delete=False, dir=sc._temp_dir)
self._path = self.dump(value, f)
self._jbroadcast = sc._jvm.PythonRDD.readBroadcastFromFile(sc._jsc, self._path)
self._pickle_registry = pickle_registry
else:
self._jbroadcast = None
self._path = path
def dump(self, value, f):
try:
pickle.dump(value, f, 2)
except pickle.PickleError:
raise
except Exception as e:
msg = "Could not serialize broadcast: " + e.__class__.__name__ + ": " + e.message
print_exec(sys.stderr)
raise pickle.PicklingError(msg)
f.close()
return f.name
def load(self, path):
with open(path, 'rb', 1 << 20) as f:
# pickle.load() may create lots of objects, disable GC
# temporary for better performance
gc.disable()
try:
return pickle.load(f)
finally:
gc.enable()
@property
def value(self):
""" Return the broadcasted value
"""
if not hasattr(self, "_value") and self._path is not None:
self._value = self.load(self._path)
return self._value
def unpersist(self, blocking=False):
"""
Delete cached copies of this broadcast on the executors. If the
broadcast is used after this is called, it will need to be
re-sent to each executor.
:param blocking: Whether to block until unpersisting has completed
"""
if self._jbroadcast is None:
raise Exception("Broadcast can only be unpersisted in driver")
self._jbroadcast.unpersist(blocking)
def destroy(self):
"""
Destroy all data and metadata related to this broadcast variable.
Use this with caution; once a broadcast variable has been destroyed,
it cannot be used again. This method blocks until destroy has
completed.
"""
if self._jbroadcast is None:
raise Exception("Broadcast can only be destroyed in driver")
self._jbroadcast.destroy()
os.unlink(self._path)
def __reduce__(self):
if self._jbroadcast is None:
raise Exception("Broadcast can only be serialized in driver")
self._pickle_registry.add(self)
return _from_id, (self._jbroadcast.id(),)
if __name__ == "__main__":
import doctest
(failure_count, test_count) = doctest.testmod()
if failure_count:
exit(-1)