c246b95dd2
UTF8Deserializer can not be used in BatchedSerializer, so always use PickleSerializer() when change batchSize in zip(). Also, if two RDD have the same batch size already, they did not need re-serialize any more. Author: Davies Liu <davies@databricks.com> Closes #3706 from davies/fix_4841 and squashes the following commits: 20ce3a3 [Davies Liu] fix bug in _reserialize() e3ebf7c [Davies Liu] add comment 379d2c8 [Davies Liu] fix zip with textFile()
533 lines
15 KiB
Python
533 lines
15 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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>>> rdd._jrdd.count()
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8L
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>>> sc.stop()
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"""
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import cPickle
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from itertools import chain, izip, product
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import marshal
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import struct
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import sys
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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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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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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__)
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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 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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obj = stream.read(length)
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if obj == "":
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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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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 __eq__(self, other):
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return (isinstance(other, BatchedSerializer) and
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other.serializer == self.serializer and other.batchSize == self.batchSize)
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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 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 __eq__(self, other):
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return (isinstance(other, AutoBatchedSerializer) and
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other.serializer == self.serializer and other.bestSize == self.bestSize)
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def __str__(self):
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return "AutoBatchedSerializer(%s)" % str(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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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 izip(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 __eq__(self, other):
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return (isinstance(other, CartesianDeserializer) and
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self.key_ser == other.key_ser and self.val_ser == other.val_ser)
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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 __init__(self, key_ser, val_ser):
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self.key_ser = key_ser
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self.val_ser = val_ser
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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 izip(keys, vals):
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yield pair
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def __eq__(self, other):
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return (isinstance(other, PairDeserializer) and
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self.key_ser == other.key_ser and self.val_ser == other.val_ser)
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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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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.func_code, f.func_globals, f.func_name,
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f.func_defaults, f.func_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.func_globals["_old_namedtuple"] = _old_namedtuple
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collections.namedtuple.func_globals["_hack_namedtuple"] = _hack_namedtuple
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collections.namedtuple.func_code = namedtuple.func_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__.iteritems():
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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 cPickle 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 cPickle.dumps(obj, 2)
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def loads(self, obj):
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return cPickle.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 cPickle 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 'P' + cPickle.dumps(obj, -1)
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try:
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return 'M' + marshal.dumps(obj)
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except Exception:
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self._type = 'P'
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return 'P' + cPickle.dumps(obj, -1)
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def loads(self, obj):
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_type = obj[0]
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if _type == 'M':
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return marshal.loads(obj[1:])
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elif _type == 'P':
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return cPickle.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 __eq__(self, other):
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return isinstance(other, CompressedSerializer) and self.serializer == other.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=False):
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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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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 __eq__(self, other):
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return isinstance(other, UTF8Deserializer) and self.use_unicode == other.use_unicode
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def read_long(stream):
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length = stream.read(8)
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if 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 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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doctest.testmod()
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