d03aebbe65
## What changes were proposed in this pull request? Integrate Apache Arrow with Spark to increase performance of `DataFrame.toPandas`. This has been done by using Arrow to convert data partitions on the executor JVM to Arrow payload byte arrays where they are then served to the Python process. The Python DataFrame can then collect the Arrow payloads where they are combined and converted to a Pandas DataFrame. Data types except complex, date, timestamp, and decimal are currently supported, otherwise an `UnsupportedOperation` exception is thrown. Additions to Spark include a Scala package private method `Dataset.toArrowPayload` that will convert data partitions in the executor JVM to `ArrowPayload`s as byte arrays so they can be easily served. A package private class/object `ArrowConverters` that provide data type mappings and conversion routines. In Python, a private method `DataFrame._collectAsArrow` is added to collect Arrow payloads and a SQLConf "spark.sql.execution.arrow.enable" can be used in `toPandas()` to enable using Arrow (uses the old conversion by default). ## How was this patch tested? Added a new test suite `ArrowConvertersSuite` that will run tests on conversion of Datasets to Arrow payloads for supported types. The suite will generate a Dataset and matching Arrow JSON data, then the dataset is converted to an Arrow payload and finally validated against the JSON data. This will ensure that the schema and data has been converted correctly. Added PySpark tests to verify the `toPandas` method is producing equal DataFrames with and without pyarrow. A roundtrip test to ensure the pandas DataFrame produced by pyspark is equal to a one made directly with pandas. Author: Bryan Cutler <cutlerb@gmail.com> Author: Li Jin <ice.xelloss@gmail.com> Author: Li Jin <li.jin@twosigma.com> Author: Wes McKinney <wes.mckinney@twosigma.com> Closes #18459 from BryanCutler/toPandas_with_arrow-SPARK-13534.
612 lines
18 KiB
Python
612 lines
18 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, imap as map
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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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"""
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Return an iterator of deserialized batches (lists) of objects from the input stream.
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if the serializer does not operate on batches the default implementation returns an
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iterator of single element lists.
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"""
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return map(lambda x: [x], 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 ArrowSerializer(FramedSerializer):
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"""
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Serializes an Arrow stream.
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"""
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def dumps(self, obj):
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raise NotImplementedError
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def loads(self, obj):
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import pyarrow as pa
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reader = pa.RecordBatchFileReader(pa.BufferReader(obj))
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return reader.read_all()
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def __repr__(self):
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return "ArrowSerializer"
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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(Serializer):
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"""
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Deserializes the JavaRDD cartesian() of two PythonRDDs.
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Due to pyspark batching we cannot simply use the result of the Java RDD cartesian,
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we additionally need to do the cartesian within each pair of batches.
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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_without_unbatching(self, stream):
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key_batch_stream = self.key_ser._load_stream_without_unbatching(stream)
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val_batch_stream = self.val_ser._load_stream_without_unbatching(stream)
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for (key_batch, val_batch) in zip(key_batch_stream, val_batch_stream):
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# for correctness with repeated cartesian/zip this must be returned as one batch
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yield product(key_batch, val_batch)
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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 __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(Serializer):
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"""
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Deserializes the JavaRDD zip() of two PythonRDDs.
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Due to pyspark batching we cannot simply use the result of the Java RDD zip,
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we additionally need to do the zip within each pair of batches.
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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_without_unbatching(self, stream):
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key_batch_stream = self.key_ser._load_stream_without_unbatching(stream)
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val_batch_stream = self.val_ser._load_stream_without_unbatching(stream)
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for (key_batch, val_batch) in zip(key_batch_stream, val_batch_stream):
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if len(key_batch) != len(val_batch):
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raise ValueError("Can not deserialize PairRDD with different number of items"
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" in batches: (%d, %d)" % (len(key_batch), len(val_batch)))
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# for correctness with repeated cartesian/zip this must be returned as one batch
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yield zip(key_batch, val_batch)
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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 __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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global _old_namedtuple_kwdefaults # or it will put in closure too
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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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def _kwdefaults(f):
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# __kwdefaults__ contains the default values of keyword-only arguments which are
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# introduced from Python 3. The possible cases for __kwdefaults__ in namedtuple
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# are as below:
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#
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# - Does not exist in Python 2.
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# - Returns None in <= Python 3.5.x.
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# - Returns a dictionary containing the default values to the keys from Python 3.6.x
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# (See https://bugs.python.org/issue25628).
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kargs = getattr(f, "__kwdefaults__", None)
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if kargs is None:
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return {}
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else:
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return kargs
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_old_namedtuple = _copy_func(collections.namedtuple)
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_old_namedtuple_kwdefaults = _kwdefaults(collections.namedtuple)
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def namedtuple(*args, **kwargs):
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for k, v in _old_namedtuple_kwdefaults.items():
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kwargs[k] = kwargs.get(k, v)
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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_kwdefaults"] = _old_namedtuple_kwdefaults
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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):
|
|
_type = obj[0]
|
|
if _type == b'M':
|
|
return marshal.loads(obj[1:])
|
|
elif _type == b'P':
|
|
return pickle.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)
|
|
assert isinstance(serializer, FramedSerializer), "serializer must be a FramedSerializer"
|
|
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))
|
|
|
|
def __repr__(self):
|
|
return "CompressedSerializer(%s)" % self.serializer
|
|
|
|
|
|
class UTF8Deserializer(Serializer):
|
|
|
|
"""
|
|
Deserializes streams written by String.getBytes.
|
|
"""
|
|
|
|
def __init__(self, use_unicode=True):
|
|
self.use_unicode = use_unicode
|
|
|
|
def loads(self, stream):
|
|
length = read_int(stream)
|
|
if length == SpecialLengths.END_OF_DATA_SECTION:
|
|
raise EOFError
|
|
elif length == SpecialLengths.NULL:
|
|
return None
|
|
s = stream.read(length)
|
|
return s.decode("utf-8") if self.use_unicode else s
|
|
|
|
def load_stream(self, stream):
|
|
try:
|
|
while True:
|
|
yield self.loads(stream)
|
|
except struct.error:
|
|
return
|
|
except EOFError:
|
|
return
|
|
|
|
def __repr__(self):
|
|
return "UTF8Deserializer(%s)" % self.use_unicode
|
|
|
|
|
|
def read_long(stream):
|
|
length = stream.read(8)
|
|
if not 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 not 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)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
import doctest
|
|
(failure_count, test_count) = doctest.testmod()
|
|
if failure_count:
|
|
exit(-1)
|