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#
# 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
import zlib
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):
if len(keys) != len(vals):
raise ValueError("Can not deserialize RDD with different number of items"
" in pair: (%d, %d)" % (len(keys), len(vals)))
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(*args, **kwargs):
cls = _old_namedtuple(*args, **kwargs)
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 CompressedSerializer(FramedSerializer):
"""
compress the serialized data
"""
def __init__(self, serializer):
FramedSerializer.__init__(self)
self.serializer = serializer
def dumps(self, obj):
return zlib.compress(self.serializer.dumps(obj), 1)
def loads(self, obj):
return self.serializer.loads(zlib.decompress(obj))
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)
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