0b85516781
The reason it wasn't working was passing a bytearray to stream.write(), which is not supported in Python 2.6 but is in 2.7. (This array came from NumPy when we converted data to send it over to Java). Now we just convert those bytearrays to strings of bytes, which preserves nonprintable characters as well. Author: Matei Zaharia <matei@databricks.com> Closes #335 from mateiz/mllib-python-2.6 and squashes the following commits: f26c59f [Matei Zaharia] Update docs to no longer say we need Python 2.7 a84d6af [Matei Zaharia] SPARK-1421. Make MLlib work on Python 2.6
348 lines
10 KiB
Python
348 lines
10 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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By default, PySpark serialize objects in batches; the batch size can be
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controlled through SparkContext's C{batchSize} parameter
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(the default size is 1024 objects):
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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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A batch size of -1 uses an unlimited batch size, and a size of 1 disables
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batching:
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>>> sc = SparkContext('local', 'test', batchSize=1)
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>>> rdd = sc.parallelize(range(16), 4).map(lambda x: x)
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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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16L
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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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from pyspark import cloudpickle
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__all__ = ["PickleSerializer", "MarshalSerializer"]
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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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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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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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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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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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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
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def __str__(self):
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return "BatchedSerializer<%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 __str__(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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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 __str__(self):
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return "PairDeserializer<%s, %s>" % \
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(str(self.key_ser), str(self.val_ser))
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class NoOpSerializer(FramedSerializer):
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def loads(self, obj): return obj
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def dumps(self, obj): return obj
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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): return cPickle.dumps(obj, 2)
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loads = cPickle.loads
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class CloudPickleSerializer(PickleSerializer):
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def dumps(self, obj): 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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dumps = marshal.dumps
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loads = marshal.loads
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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 loads(self, stream):
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length = read_int(stream)
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return stream.read(length).decode('utf8')
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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.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 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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