path: root/python/pyspark/serializers.py

#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

"""
PySpark supports custom serializers for transferring data; this can improve
performance.

By default, PySpark uses L{PickleSerializer} to serialize objects using Python's
C{cPickle} serializer, which can serialize nearly any Python object.
Other serializers, like L{MarshalSerializer}, support fewer datatypes but can be
faster.

The serializer is chosen when creating L{SparkContext}:

>>> from pyspark.context import SparkContext
>>> from pyspark.serializers import MarshalSerializer
>>> sc = SparkContext('local', 'test', serializer=MarshalSerializer())
>>> sc.parallelize(list(range(1000))).map(lambda x: 2 * x).take(10)
[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]
>>> sc.stop()

By default, PySpark serialize objects in batches; the batch size can be
controlled through SparkContext's C{batchSize} parameter
(the default size is 1024 objects):

>>> sc = SparkContext('local', 'test', batchSize=2)
>>> rdd = sc.parallelize(range(16), 4).map(lambda x: x)

Behind the scenes, this creates a JavaRDD with four partitions, each of
which contains two batches of two objects:

>>> rdd.glom().collect()
[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]]
>>> rdd._jrdd.count()
8L
>>> sc.stop()

A batch size of -1 uses an unlimited batch size, and a size of 1 disables
batching:

>>> sc = SparkContext('local', 'test', batchSize=1)
>>> rdd = sc.parallelize(range(16), 4).map(lambda x: x)
>>> rdd.glom().collect()
[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]]
>>> rdd._jrdd.count()
16L
"""

import cPickle
from itertools import chain, izip, product
import marshal
import struct
from pyspark import cloudpickle


__all__ = ["PickleSerializer", "MarshalSerializer"]


class SpecialLengths(object):
    END_OF_DATA_SECTION = -1
    PYTHON_EXCEPTION_THROWN = -2
    TIMING_DATA = -3


class Serializer(object):

    def dump_stream(self, iterator, stream):
        """
        Serialize an iterator of objects to the output stream.
        """
        raise NotImplementedError

    def load_stream(self, stream):
        """
        Return an iterator of deserialized objects from the input stream.
        """
        raise NotImplementedError


    def _load_stream_without_unbatching(self, stream):
        return self.load_stream(stream)

    # Note: our notion of "equality" is that output generated by
    # equal serializers can be deserialized using the same serializer.

    # This default implementation handles the simple cases;
    # subclasses should override __eq__ as appropriate.

    def __eq__(self, other):
        return isinstance(other, self.__class__)

    def __ne__(self, other):
        return not self.__eq__(other)


class FramedSerializer(Serializer):
    """
    Serializer that writes objects as a stream of (length, data) pairs,
    where C{length} is a 32-bit integer and data is C{length} bytes.
    """

    def dump_stream(self, iterator, stream):
        for obj in iterator:
            self._write_with_length(obj, stream)

    def load_stream(self, stream):
        while True:
            try:
                yield self._read_with_length(stream)
            except EOFError:
                return

    def _write_with_length(self, obj, stream):
        serialized = self.dumps(obj)
        write_int(len(serialized), stream)
        stream.write(serialized)

    def _read_with_length(self, stream):
        length = read_int(stream)
        obj = stream.read(length)
        if obj == "":
            raise EOFError
        return self.loads(obj)

    def dumps(self, obj):
        """
        Serialize an object into a byte array.
        When batching is used, this will be called with an array of objects.
        """
        raise NotImplementedError

    def loads(self, obj):
        """
        Deserialize an object from a byte array.
        """
        raise NotImplementedError


class BatchedSerializer(Serializer):
    """
    Serializes a stream of objects in batches by calling its wrapped
    Serializer with streams of objects.
    """

    UNLIMITED_BATCH_SIZE = -1

    def __init__(self, serializer, batchSize=UNLIMITED_BATCH_SIZE):
        self.serializer = serializer
        self.batchSize = batchSize

    def _batched(self, iterator):
        if self.batchSize == self.UNLIMITED_BATCH_SIZE:
            yield list(iterator)
        else:
            items = []
            count = 0
            for item in iterator:
                items.append(item)
                count += 1
                if count == self.batchSize:
                    yield items
                    items = []
                    count = 0
            if items:
                yield items

    def dump_stream(self, iterator, stream):
        self.serializer.dump_stream(self._batched(iterator), stream)

    def load_stream(self, stream):
        return chain.from_iterable(self._load_stream_without_unbatching(stream))

    def _load_stream_without_unbatching(self, stream):
            return self.serializer.load_stream(stream)

    def __eq__(self, other):
        return isinstance(other, BatchedSerializer) and \
               other.serializer == self.serializer

    def __str__(self):
        return "BatchedSerializer<%s>" % str(self.serializer)


class CartesianDeserializer(FramedSerializer):
    """
    Deserializes the JavaRDD cartesian() of two PythonRDDs.
    """

    def __init__(self, key_ser, val_ser):
        self.key_ser = key_ser
        self.val_ser = val_ser

    def load_stream(self, stream):
        key_stream = self.key_ser._load_stream_without_unbatching(stream)
        val_stream = self.val_ser._load_stream_without_unbatching(stream)
        key_is_batched = isinstance(self.key_ser, BatchedSerializer)
        val_is_batched = isinstance(self.val_ser, BatchedSerializer)
        for (keys, vals) in izip(key_stream, val_stream):
            keys = keys if key_is_batched else [keys]
            vals = vals if val_is_batched else [vals]
            for pair in product(keys, vals):
                yield pair

    def __eq__(self, other):
        return isinstance(other, CartesianDeserializer) and \
               self.key_ser == other.key_ser and self.val_ser == other.val_ser

    def __str__(self):
        return "CartesianDeserializer<%s, %s>" % \
               (str(self.key_ser), str(self.val_ser))


class NoOpSerializer(FramedSerializer):

    def loads(self, obj): return obj
    def dumps(self, obj): return obj


class PickleSerializer(FramedSerializer):
    """
    Serializes objects using Python's cPickle serializer:

        http://docs.python.org/2/library/pickle.html

    This serializer supports nearly any Python object, but may
    not be as fast as more specialized serializers.
    """

    def dumps(self, obj): return cPickle.dumps(obj, 2)
    loads = cPickle.loads

class CloudPickleSerializer(PickleSerializer):

    def dumps(self, obj): return cloudpickle.dumps(obj, 2)


class MarshalSerializer(FramedSerializer):
    """
    Serializes objects using Python's Marshal serializer:

        http://docs.python.org/2/library/marshal.html

    This serializer is faster than PickleSerializer but supports fewer datatypes.
    """

    dumps = marshal.dumps
    loads = marshal.loads


class MUTF8Deserializer(Serializer):
    """
    Deserializes streams written by Java's DataOutputStream.writeUTF().
    """

    def loads(self, stream):
        length = struct.unpack('>H', stream.read(2))[0]
        return stream.read(length).decode('utf8')

    def load_stream(self, stream):
        while True:
            try:
                yield self.loads(stream)
            except struct.error:
                return
            except EOFError:
                return


def read_long(stream):
    length = stream.read(8)
    if length == "":
        raise EOFError
    return struct.unpack("!q", length)[0]


def write_long(value, stream):
    stream.write(struct.pack("!q", value))


def pack_long(value):
    return struct.pack("!q", value)


def read_int(stream):
    length = stream.read(4)
    if length == "":
        raise EOFError
    return struct.unpack("!i", length)[0]


def write_int(value, stream):
    stream.write(struct.pack("!i", value))


def write_with_length(obj, stream):
    write_int(len(obj), stream)
    stream.write(obj)