563 lines
16 KiB
Python
563 lines
16 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 supports custom serializers for transferring data; this can improve
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performance.
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By default, PySpark uses L{PickleSerializer} to serialize objects using Python's
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C{cPickle} serializer, which can serialize nearly any Python object.
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Other serializers, like L{MarshalSerializer}, support fewer datatypes but can be
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faster.
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The serializer is chosen when creating L{SparkContext}:
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>>> from pyspark.context import SparkContext
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>>> from pyspark.serializers import MarshalSerializer
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>>> sc = SparkContext('local', 'test', serializer=MarshalSerializer())
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>>> sc.parallelize(list(range(1000))).map(lambda x: 2 * x).take(10)
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[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]
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>>> sc.stop()
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PySpark serialize objects in batches; By default, the batch size is chosen based
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on the size of objects, also configurable by SparkContext's C{batchSize} parameter:
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>>> sc = SparkContext('local', 'test', batchSize=2)
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>>> rdd = sc.parallelize(range(16), 4).map(lambda x: x)
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Behind the scenes, this creates a JavaRDD with four partitions, each of
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which contains two batches of two objects:
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>>> rdd.glom().collect()
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[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]]
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>>> int(rdd._jrdd.count())
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8
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>>> sc.stop()
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"""
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import sys
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from itertools import chain, product
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import marshal
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import struct
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import types
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import collections
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import zlib
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import itertools
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if sys.version < '3':
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import cPickle as pickle
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protocol = 2
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from itertools import izip as zip
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else:
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import pickle
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protocol = 3
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xrange = range
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from pyspark import cloudpickle
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__all__ = ["PickleSerializer", "MarshalSerializer", "UTF8Deserializer"]
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class SpecialLengths(object):
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END_OF_DATA_SECTION = -1
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PYTHON_EXCEPTION_THROWN = -2
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TIMING_DATA = -3
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END_OF_STREAM = -4
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NULL = -5
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class Serializer(object):
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def dump_stream(self, iterator, stream):
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"""
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Serialize an iterator of objects to the output stream.
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"""
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raise NotImplementedError
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def load_stream(self, stream):
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"""
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Return an iterator of deserialized objects from the input stream.
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"""
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raise NotImplementedError
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def _load_stream_without_unbatching(self, stream):
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return self.load_stream(stream)
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# Note: our notion of "equality" is that output generated by
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# equal serializers can be deserialized using the same serializer.
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# This default implementation handles the simple cases;
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# subclasses should override __eq__ as appropriate.
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def __eq__(self, other):
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return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
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def __ne__(self, other):
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return not self.__eq__(other)
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def __repr__(self):
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return "%s()" % self.__class__.__name__
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def __hash__(self):
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return hash(str(self))
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class FramedSerializer(Serializer):
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"""
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Serializer that writes objects as a stream of (length, data) pairs,
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where C{length} is a 32-bit integer and data is C{length} bytes.
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"""
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def __init__(self):
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# On Python 2.6, we can't write bytearrays to streams, so we need to convert them
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# to strings first. Check if the version number is that old.
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self._only_write_strings = sys.version_info[0:2] <= (2, 6)
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def dump_stream(self, iterator, stream):
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for obj in iterator:
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self._write_with_length(obj, stream)
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def load_stream(self, stream):
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while True:
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try:
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yield self._read_with_length(stream)
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except EOFError:
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return
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def _write_with_length(self, obj, stream):
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serialized = self.dumps(obj)
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if serialized is None:
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raise ValueError("serialized value should not be None")
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if len(serialized) > (1 << 31):
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raise ValueError("can not serialize object larger than 2G")
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write_int(len(serialized), stream)
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if self._only_write_strings:
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stream.write(str(serialized))
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else:
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stream.write(serialized)
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def _read_with_length(self, stream):
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length = read_int(stream)
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if length == SpecialLengths.END_OF_DATA_SECTION:
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raise EOFError
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elif length == SpecialLengths.NULL:
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return None
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obj = stream.read(length)
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if len(obj) < length:
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raise EOFError
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return self.loads(obj)
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def dumps(self, obj):
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"""
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Serialize an object into a byte array.
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When batching is used, this will be called with an array of objects.
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"""
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raise NotImplementedError
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def loads(self, obj):
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"""
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Deserialize an object from a byte array.
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"""
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raise NotImplementedError
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class BatchedSerializer(Serializer):
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"""
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Serializes a stream of objects in batches by calling its wrapped
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Serializer with streams of objects.
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"""
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UNLIMITED_BATCH_SIZE = -1
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UNKNOWN_BATCH_SIZE = 0
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def __init__(self, serializer, batchSize=UNLIMITED_BATCH_SIZE):
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self.serializer = serializer
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self.batchSize = batchSize
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def _batched(self, iterator):
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if self.batchSize == self.UNLIMITED_BATCH_SIZE:
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yield list(iterator)
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elif hasattr(iterator, "__len__") and hasattr(iterator, "__getslice__"):
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n = len(iterator)
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for i in xrange(0, n, self.batchSize):
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yield iterator[i: i + self.batchSize]
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else:
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items = []
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count = 0
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for item in iterator:
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items.append(item)
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count += 1
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if count == self.batchSize:
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yield items
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items = []
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count = 0
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if items:
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yield items
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def dump_stream(self, iterator, stream):
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self.serializer.dump_stream(self._batched(iterator), stream)
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def load_stream(self, stream):
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return chain.from_iterable(self._load_stream_without_unbatching(stream))
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def _load_stream_without_unbatching(self, stream):
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return self.serializer.load_stream(stream)
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def __repr__(self):
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return "BatchedSerializer(%s, %d)" % (str(self.serializer), self.batchSize)
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class FlattenedValuesSerializer(BatchedSerializer):
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"""
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Serializes a stream of list of pairs, split the list of values
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which contain more than a certain number of objects to make them
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have similar sizes.
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"""
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def __init__(self, serializer, batchSize=10):
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BatchedSerializer.__init__(self, serializer, batchSize)
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def _batched(self, iterator):
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n = self.batchSize
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for key, values in iterator:
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for i in range(0, len(values), n):
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yield key, values[i:i + n]
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def load_stream(self, stream):
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return self.serializer.load_stream(stream)
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def __repr__(self):
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return "FlattenedValuesSerializer(%s, %d)" % (self.serializer, self.batchSize)
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class AutoBatchedSerializer(BatchedSerializer):
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"""
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Choose the size of batch automatically based on the size of object
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"""
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def __init__(self, serializer, bestSize=1 << 16):
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BatchedSerializer.__init__(self, serializer, self.UNKNOWN_BATCH_SIZE)
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self.bestSize = bestSize
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def dump_stream(self, iterator, stream):
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batch, best = 1, self.bestSize
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iterator = iter(iterator)
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while True:
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vs = list(itertools.islice(iterator, batch))
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if not vs:
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break
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bytes = self.serializer.dumps(vs)
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write_int(len(bytes), stream)
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stream.write(bytes)
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size = len(bytes)
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if size < best:
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batch *= 2
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elif size > best * 10 and batch > 1:
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batch //= 2
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def __repr__(self):
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return "AutoBatchedSerializer(%s)" % self.serializer
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class CartesianDeserializer(FramedSerializer):
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"""
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Deserializes the JavaRDD cartesian() of two PythonRDDs.
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"""
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def __init__(self, key_ser, val_ser):
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FramedSerializer.__init__(self)
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self.key_ser = key_ser
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self.val_ser = val_ser
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def prepare_keys_values(self, stream):
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key_stream = self.key_ser._load_stream_without_unbatching(stream)
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val_stream = self.val_ser._load_stream_without_unbatching(stream)
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key_is_batched = isinstance(self.key_ser, BatchedSerializer)
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val_is_batched = isinstance(self.val_ser, BatchedSerializer)
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for (keys, vals) in zip(key_stream, val_stream):
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keys = keys if key_is_batched else [keys]
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vals = vals if val_is_batched else [vals]
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yield (keys, vals)
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def load_stream(self, stream):
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for (keys, vals) in self.prepare_keys_values(stream):
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for pair in product(keys, vals):
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yield pair
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def __repr__(self):
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return "CartesianDeserializer(%s, %s)" % \
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(str(self.key_ser), str(self.val_ser))
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class PairDeserializer(CartesianDeserializer):
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"""
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Deserializes the JavaRDD zip() of two PythonRDDs.
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"""
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def load_stream(self, stream):
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for (keys, vals) in self.prepare_keys_values(stream):
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if len(keys) != len(vals):
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raise ValueError("Can not deserialize RDD with different number of items"
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" in pair: (%d, %d)" % (len(keys), len(vals)))
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for pair in zip(keys, vals):
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yield pair
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def __repr__(self):
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return "PairDeserializer(%s, %s)" % (str(self.key_ser), str(self.val_ser))
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class NoOpSerializer(FramedSerializer):
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def loads(self, obj):
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return obj
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def dumps(self, obj):
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return obj
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# Hook namedtuple, make it picklable
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__cls = {}
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def _restore(name, fields, value):
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""" Restore an object of namedtuple"""
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k = (name, fields)
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cls = __cls.get(k)
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if cls is None:
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cls = collections.namedtuple(name, fields)
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__cls[k] = cls
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return cls(*value)
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def _hack_namedtuple(cls):
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""" Make class generated by namedtuple picklable """
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name = cls.__name__
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fields = cls._fields
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def __reduce__(self):
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return (_restore, (name, fields, tuple(self)))
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cls.__reduce__ = __reduce__
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cls._is_namedtuple_ = True
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return cls
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def _hijack_namedtuple():
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""" Hack namedtuple() to make it picklable """
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# hijack only one time
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if hasattr(collections.namedtuple, "__hijack"):
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return
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global _old_namedtuple # or it will put in closure
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def _copy_func(f):
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return types.FunctionType(f.__code__, f.__globals__, f.__name__,
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f.__defaults__, f.__closure__)
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_old_namedtuple = _copy_func(collections.namedtuple)
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def namedtuple(*args, **kwargs):
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cls = _old_namedtuple(*args, **kwargs)
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return _hack_namedtuple(cls)
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# replace namedtuple with new one
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collections.namedtuple.__globals__["_old_namedtuple"] = _old_namedtuple
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collections.namedtuple.__globals__["_hack_namedtuple"] = _hack_namedtuple
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collections.namedtuple.__code__ = namedtuple.__code__
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collections.namedtuple.__hijack = 1
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# hack the cls already generated by namedtuple
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# those created in other module can be pickled as normal,
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# so only hack those in __main__ module
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for n, o in sys.modules["__main__"].__dict__.items():
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if (type(o) is type and o.__base__ is tuple
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and hasattr(o, "_fields")
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and "__reduce__" not in o.__dict__):
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_hack_namedtuple(o) # hack inplace
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_hijack_namedtuple()
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class PickleSerializer(FramedSerializer):
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"""
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Serializes objects using Python's pickle serializer:
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http://docs.python.org/2/library/pickle.html
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This serializer supports nearly any Python object, but may
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not be as fast as more specialized serializers.
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"""
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def dumps(self, obj):
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return pickle.dumps(obj, protocol)
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if sys.version >= '3':
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def loads(self, obj, encoding="bytes"):
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return pickle.loads(obj, encoding=encoding)
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else:
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def loads(self, obj, encoding=None):
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return pickle.loads(obj)
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class CloudPickleSerializer(PickleSerializer):
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def dumps(self, obj):
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return cloudpickle.dumps(obj, 2)
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class MarshalSerializer(FramedSerializer):
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"""
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Serializes objects using Python's Marshal serializer:
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http://docs.python.org/2/library/marshal.html
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This serializer is faster than PickleSerializer but supports fewer datatypes.
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"""
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def dumps(self, obj):
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return marshal.dumps(obj)
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def loads(self, obj):
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return marshal.loads(obj)
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class AutoSerializer(FramedSerializer):
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"""
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Choose marshal or pickle as serialization protocol automatically
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"""
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def __init__(self):
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FramedSerializer.__init__(self)
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self._type = None
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def dumps(self, obj):
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if self._type is not None:
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return b'P' + pickle.dumps(obj, -1)
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try:
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return b'M' + marshal.dumps(obj)
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except Exception:
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self._type = b'P'
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return b'P' + pickle.dumps(obj, -1)
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def loads(self, obj):
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_type = obj[0]
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if _type == b'M':
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return marshal.loads(obj[1:])
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elif _type == b'P':
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return pickle.loads(obj[1:])
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else:
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raise ValueError("invalid sevialization type: %s" % _type)
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class CompressedSerializer(FramedSerializer):
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"""
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Compress the serialized data
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"""
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def __init__(self, serializer):
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FramedSerializer.__init__(self)
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assert isinstance(serializer, FramedSerializer), "serializer must be a FramedSerializer"
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self.serializer = serializer
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def dumps(self, obj):
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return zlib.compress(self.serializer.dumps(obj), 1)
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def loads(self, obj):
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return self.serializer.loads(zlib.decompress(obj))
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def __repr__(self):
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return "CompressedSerializer(%s)" % self.serializer
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class UTF8Deserializer(Serializer):
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"""
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Deserializes streams written by String.getBytes.
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"""
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def __init__(self, use_unicode=True):
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self.use_unicode = use_unicode
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def loads(self, stream):
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length = read_int(stream)
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if length == SpecialLengths.END_OF_DATA_SECTION:
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raise EOFError
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elif length == SpecialLengths.NULL:
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return None
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s = stream.read(length)
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return s.decode("utf-8") if self.use_unicode else s
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def load_stream(self, stream):
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try:
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while True:
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yield self.loads(stream)
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except struct.error:
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return
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except EOFError:
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return
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def __repr__(self):
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return "UTF8Deserializer(%s)" % self.use_unicode
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def read_long(stream):
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length = stream.read(8)
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if not length:
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raise EOFError
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return struct.unpack("!q", length)[0]
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def write_long(value, stream):
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stream.write(struct.pack("!q", value))
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def pack_long(value):
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return struct.pack("!q", value)
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def read_int(stream):
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length = stream.read(4)
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if not length:
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raise EOFError
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return struct.unpack("!i", length)[0]
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def write_int(value, stream):
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stream.write(struct.pack("!i", value))
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def write_with_length(obj, stream):
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write_int(len(obj), stream)
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stream.write(obj)
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if __name__ == '__main__':
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import doctest
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(failure_count, test_count) = doctest.testmod()
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if failure_count:
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exit(-1)
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