9fd82dbbcb
serializer is imported multiple times during doctests, so it's better to make _hijack_namedtuple() safe to be called multiple times. Author: Davies Liu <davies.liu@gmail.com> Closes #1771 from davies/fix and squashes the following commits: 1a9e336 [Davies Liu] fix unit tests
444 lines
13 KiB
Python
444 lines
13 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.
|
|
#
|
|
|
|
"""
|
|
PySpark supports custom serializers for transferring data; this can improve
|
|
performance.
|
|
|
|
By default, PySpark uses L{PickleSerializer} to serialize objects using Python's
|
|
C{cPickle} serializer, which can serialize nearly any Python object.
|
|
Other serializers, like L{MarshalSerializer}, support fewer datatypes but can be
|
|
faster.
|
|
|
|
The serializer is chosen when creating L{SparkContext}:
|
|
|
|
>>> from pyspark.context import SparkContext
|
|
>>> from pyspark.serializers import MarshalSerializer
|
|
>>> sc = SparkContext('local', 'test', serializer=MarshalSerializer())
|
|
>>> sc.parallelize(list(range(1000))).map(lambda x: 2 * x).take(10)
|
|
[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]
|
|
>>> sc.stop()
|
|
|
|
By default, PySpark serialize objects in batches; the batch size can be
|
|
controlled through SparkContext's C{batchSize} parameter
|
|
(the default size is 1024 objects):
|
|
|
|
>>> sc = SparkContext('local', 'test', batchSize=2)
|
|
>>> rdd = sc.parallelize(range(16), 4).map(lambda x: x)
|
|
|
|
Behind the scenes, this creates a JavaRDD with four partitions, each of
|
|
which contains two batches of two objects:
|
|
|
|
>>> rdd.glom().collect()
|
|
[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]]
|
|
>>> rdd._jrdd.count()
|
|
8L
|
|
>>> sc.stop()
|
|
|
|
A batch size of -1 uses an unlimited batch size, and a size of 1 disables
|
|
batching:
|
|
|
|
>>> sc = SparkContext('local', 'test', batchSize=1)
|
|
>>> rdd = sc.parallelize(range(16), 4).map(lambda x: x)
|
|
>>> rdd.glom().collect()
|
|
[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]]
|
|
>>> rdd._jrdd.count()
|
|
16L
|
|
"""
|
|
|
|
import cPickle
|
|
from itertools import chain, izip, product
|
|
import marshal
|
|
import struct
|
|
import sys
|
|
import types
|
|
import collections
|
|
|
|
from pyspark import cloudpickle
|
|
|
|
|
|
__all__ = ["PickleSerializer", "MarshalSerializer"]
|
|
|
|
|
|
class SpecialLengths(object):
|
|
END_OF_DATA_SECTION = -1
|
|
PYTHON_EXCEPTION_THROWN = -2
|
|
TIMING_DATA = -3
|
|
|
|
|
|
class Serializer(object):
|
|
|
|
def dump_stream(self, iterator, stream):
|
|
"""
|
|
Serialize an iterator of objects to the output stream.
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
def load_stream(self, stream):
|
|
"""
|
|
Return an iterator of deserialized objects from the input stream.
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
def _load_stream_without_unbatching(self, stream):
|
|
return self.load_stream(stream)
|
|
|
|
# Note: our notion of "equality" is that output generated by
|
|
# equal serializers can be deserialized using the same serializer.
|
|
|
|
# This default implementation handles the simple cases;
|
|
# subclasses should override __eq__ as appropriate.
|
|
|
|
def __eq__(self, other):
|
|
return isinstance(other, self.__class__)
|
|
|
|
def __ne__(self, other):
|
|
return not self.__eq__(other)
|
|
|
|
|
|
class FramedSerializer(Serializer):
|
|
"""
|
|
Serializer that writes objects as a stream of (length, data) pairs,
|
|
where C{length} is a 32-bit integer and data is C{length} bytes.
|
|
"""
|
|
|
|
def __init__(self):
|
|
# On Python 2.6, we can't write bytearrays to streams, so we need to convert them
|
|
# to strings first. Check if the version number is that old.
|
|
self._only_write_strings = sys.version_info[0:2] <= (2, 6)
|
|
|
|
def dump_stream(self, iterator, stream):
|
|
for obj in iterator:
|
|
self._write_with_length(obj, stream)
|
|
|
|
def load_stream(self, stream):
|
|
while True:
|
|
try:
|
|
yield self._read_with_length(stream)
|
|
except EOFError:
|
|
return
|
|
|
|
def _write_with_length(self, obj, stream):
|
|
serialized = self.dumps(obj)
|
|
write_int(len(serialized), stream)
|
|
if self._only_write_strings:
|
|
stream.write(str(serialized))
|
|
else:
|
|
stream.write(serialized)
|
|
|
|
def _read_with_length(self, stream):
|
|
length = read_int(stream)
|
|
obj = stream.read(length)
|
|
if obj == "":
|
|
raise EOFError
|
|
return self.loads(obj)
|
|
|
|
def dumps(self, obj):
|
|
"""
|
|
Serialize an object into a byte array.
|
|
When batching is used, this will be called with an array of objects.
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
def loads(self, obj):
|
|
"""
|
|
Deserialize an object from a byte array.
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
|
|
class BatchedSerializer(Serializer):
|
|
"""
|
|
Serializes a stream of objects in batches by calling its wrapped
|
|
Serializer with streams of objects.
|
|
"""
|
|
|
|
UNLIMITED_BATCH_SIZE = -1
|
|
|
|
def __init__(self, serializer, batchSize=UNLIMITED_BATCH_SIZE):
|
|
self.serializer = serializer
|
|
self.batchSize = batchSize
|
|
|
|
def _batched(self, iterator):
|
|
if self.batchSize == self.UNLIMITED_BATCH_SIZE:
|
|
yield list(iterator)
|
|
else:
|
|
items = []
|
|
count = 0
|
|
for item in iterator:
|
|
items.append(item)
|
|
count += 1
|
|
if count == self.batchSize:
|
|
yield items
|
|
items = []
|
|
count = 0
|
|
if items:
|
|
yield items
|
|
|
|
def dump_stream(self, iterator, stream):
|
|
self.serializer.dump_stream(self._batched(iterator), stream)
|
|
|
|
def load_stream(self, stream):
|
|
return chain.from_iterable(self._load_stream_without_unbatching(stream))
|
|
|
|
def _load_stream_without_unbatching(self, stream):
|
|
return self.serializer.load_stream(stream)
|
|
|
|
def __eq__(self, other):
|
|
return (isinstance(other, BatchedSerializer) and
|
|
other.serializer == self.serializer)
|
|
|
|
def __str__(self):
|
|
return "BatchedSerializer<%s>" % str(self.serializer)
|
|
|
|
|
|
class CartesianDeserializer(FramedSerializer):
|
|
"""
|
|
Deserializes the JavaRDD cartesian() of two PythonRDDs.
|
|
"""
|
|
|
|
def __init__(self, key_ser, val_ser):
|
|
self.key_ser = key_ser
|
|
self.val_ser = val_ser
|
|
|
|
def prepare_keys_values(self, stream):
|
|
key_stream = self.key_ser._load_stream_without_unbatching(stream)
|
|
val_stream = self.val_ser._load_stream_without_unbatching(stream)
|
|
key_is_batched = isinstance(self.key_ser, BatchedSerializer)
|
|
val_is_batched = isinstance(self.val_ser, BatchedSerializer)
|
|
for (keys, vals) in izip(key_stream, val_stream):
|
|
keys = keys if key_is_batched else [keys]
|
|
vals = vals if val_is_batched else [vals]
|
|
yield (keys, vals)
|
|
|
|
def load_stream(self, stream):
|
|
for (keys, vals) in self.prepare_keys_values(stream):
|
|
for pair in product(keys, vals):
|
|
yield pair
|
|
|
|
def __eq__(self, other):
|
|
return (isinstance(other, CartesianDeserializer) and
|
|
self.key_ser == other.key_ser and self.val_ser == other.val_ser)
|
|
|
|
def __str__(self):
|
|
return "CartesianDeserializer<%s, %s>" % \
|
|
(str(self.key_ser), str(self.val_ser))
|
|
|
|
|
|
class PairDeserializer(CartesianDeserializer):
|
|
"""
|
|
Deserializes the JavaRDD zip() of two PythonRDDs.
|
|
"""
|
|
|
|
def __init__(self, key_ser, val_ser):
|
|
self.key_ser = key_ser
|
|
self.val_ser = val_ser
|
|
|
|
def load_stream(self, stream):
|
|
for (keys, vals) in self.prepare_keys_values(stream):
|
|
for pair in izip(keys, vals):
|
|
yield pair
|
|
|
|
def __eq__(self, other):
|
|
return (isinstance(other, PairDeserializer) and
|
|
self.key_ser == other.key_ser and self.val_ser == other.val_ser)
|
|
|
|
def __str__(self):
|
|
return "PairDeserializer<%s, %s>" % (str(self.key_ser), str(self.val_ser))
|
|
|
|
|
|
class NoOpSerializer(FramedSerializer):
|
|
|
|
def loads(self, obj):
|
|
return obj
|
|
|
|
def dumps(self, obj):
|
|
return obj
|
|
|
|
|
|
# Hook namedtuple, make it picklable
|
|
|
|
__cls = {}
|
|
|
|
|
|
def _restore(name, fields, value):
|
|
""" Restore an object of namedtuple"""
|
|
k = (name, fields)
|
|
cls = __cls.get(k)
|
|
if cls is None:
|
|
cls = collections.namedtuple(name, fields)
|
|
__cls[k] = cls
|
|
return cls(*value)
|
|
|
|
|
|
def _hack_namedtuple(cls):
|
|
""" Make class generated by namedtuple picklable """
|
|
name = cls.__name__
|
|
fields = cls._fields
|
|
def __reduce__(self):
|
|
return (_restore, (name, fields, tuple(self)))
|
|
cls.__reduce__ = __reduce__
|
|
return cls
|
|
|
|
|
|
def _hijack_namedtuple():
|
|
""" Hack namedtuple() to make it picklable """
|
|
# hijack only one time
|
|
if hasattr(collections.namedtuple, "__hijack"):
|
|
return
|
|
|
|
global _old_namedtuple # or it will put in closure
|
|
def _copy_func(f):
|
|
return types.FunctionType(f.func_code, f.func_globals, f.func_name,
|
|
f.func_defaults, f.func_closure)
|
|
|
|
_old_namedtuple = _copy_func(collections.namedtuple)
|
|
|
|
def namedtuple(name, fields, verbose=False, rename=False):
|
|
cls = _old_namedtuple(name, fields, verbose, rename)
|
|
return _hack_namedtuple(cls)
|
|
|
|
# replace namedtuple with new one
|
|
collections.namedtuple.func_globals["_old_namedtuple"] = _old_namedtuple
|
|
collections.namedtuple.func_globals["_hack_namedtuple"] = _hack_namedtuple
|
|
collections.namedtuple.func_code = namedtuple.func_code
|
|
collections.namedtuple.__hijack = 1
|
|
|
|
# hack the cls already generated by namedtuple
|
|
# those created in other module can be pickled as normal,
|
|
# so only hack those in __main__ module
|
|
for n, o in sys.modules["__main__"].__dict__.iteritems():
|
|
if (type(o) is type and o.__base__ is tuple
|
|
and hasattr(o, "_fields")
|
|
and "__reduce__" not in o.__dict__):
|
|
_hack_namedtuple(o) # hack inplace
|
|
|
|
|
|
_hijack_namedtuple()
|
|
|
|
|
|
class PickleSerializer(FramedSerializer):
|
|
"""
|
|
Serializes objects using Python's cPickle serializer:
|
|
|
|
http://docs.python.org/2/library/pickle.html
|
|
|
|
This serializer supports nearly any Python object, but may
|
|
not be as fast as more specialized serializers.
|
|
"""
|
|
|
|
def dumps(self, obj):
|
|
return cPickle.dumps(obj, 2)
|
|
|
|
loads = cPickle.loads
|
|
|
|
|
|
class CloudPickleSerializer(PickleSerializer):
|
|
|
|
def dumps(self, obj):
|
|
return cloudpickle.dumps(obj, 2)
|
|
|
|
|
|
class MarshalSerializer(FramedSerializer):
|
|
"""
|
|
Serializes objects using Python's Marshal serializer:
|
|
|
|
http://docs.python.org/2/library/marshal.html
|
|
|
|
This serializer is faster than PickleSerializer but supports fewer datatypes.
|
|
"""
|
|
|
|
dumps = marshal.dumps
|
|
loads = marshal.loads
|
|
|
|
|
|
class AutoSerializer(FramedSerializer):
|
|
"""
|
|
Choose marshal or cPickle as serialization protocol autumatically
|
|
"""
|
|
def __init__(self):
|
|
FramedSerializer.__init__(self)
|
|
self._type = None
|
|
|
|
def dumps(self, obj):
|
|
if self._type is not None:
|
|
return 'P' + cPickle.dumps(obj, -1)
|
|
try:
|
|
return 'M' + marshal.dumps(obj)
|
|
except Exception:
|
|
self._type = 'P'
|
|
return 'P' + cPickle.dumps(obj, -1)
|
|
|
|
def loads(self, obj):
|
|
_type = obj[0]
|
|
if _type == 'M':
|
|
return marshal.loads(obj[1:])
|
|
elif _type == 'P':
|
|
return cPickle.loads(obj[1:])
|
|
else:
|
|
raise ValueError("invalid sevialization type: %s" % _type)
|
|
|
|
|
|
class UTF8Deserializer(Serializer):
|
|
"""
|
|
Deserializes streams written by String.getBytes.
|
|
"""
|
|
|
|
def loads(self, stream):
|
|
length = read_int(stream)
|
|
return stream.read(length).decode('utf8')
|
|
|
|
def load_stream(self, stream):
|
|
while True:
|
|
try:
|
|
yield self.loads(stream)
|
|
except struct.error:
|
|
return
|
|
except EOFError:
|
|
return
|
|
|
|
|
|
def read_long(stream):
|
|
length = stream.read(8)
|
|
if length == "":
|
|
raise EOFError
|
|
return struct.unpack("!q", length)[0]
|
|
|
|
|
|
def write_long(value, stream):
|
|
stream.write(struct.pack("!q", value))
|
|
|
|
|
|
def pack_long(value):
|
|
return struct.pack("!q", value)
|
|
|
|
|
|
def read_int(stream):
|
|
length = stream.read(4)
|
|
if length == "":
|
|
raise EOFError
|
|
return struct.unpack("!i", length)[0]
|
|
|
|
|
|
def write_int(value, stream):
|
|
stream.write(struct.pack("!i", value))
|
|
|
|
|
|
def write_with_length(obj, stream):
|
|
write_int(len(obj), stream)
|
|
stream.write(obj)
|