Source code for pyspark.serializers

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PySpark supports custom serializers for transferring data; this can improve

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

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()

PySpark serializes objects in batches; by default, the batch size is chosen based
on the size of objects and is also configurable by SparkContext's C{batchSize}

>>> 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]]
>>> int(rdd._jrdd.count())
>>> sc.stop()

import sys
from itertools import chain, product
import marshal
import struct
import types
import collections
import zlib
import itertools

if sys.version < '3':
    import cPickle as pickle
    protocol = 2
    from itertools import izip as zip, imap as map
    import pickle
    protocol = 3
    xrange = range

from pyspark import cloudpickle

__all__ = ["PickleSerializer", "MarshalSerializer", "UTF8Deserializer"]

class SpecialLengths(object):
    TIMING_DATA = -3
    END_OF_STREAM = -4
    NULL = -5

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 an iterator of deserialized batches (iterable) of objects from the input stream.
        If the serializer does not operate on batches the default implementation returns an
        iterator of single element lists.
        return map(lambda x: [x], 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__) and self.__dict__ == other.__dict__

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

    def __repr__(self):
        return "%s()" % self.__class__.__name__

    def __hash__(self):
        return hash(str(self))

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 __init__(self):
        # On Python 2.6, we can't write bytearrays to streams, so we need to convert them
        # to strings first. Check if the version number is that old.
        self._only_write_strings = sys.version_info[0:2] <= (2, 6)

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

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

    def _write_with_length(self, obj, stream):
        serialized = self.dumps(obj)
        if serialized is None:
            raise ValueError("serialized value should not be None")
        if len(serialized) > (1 << 31):
            raise ValueError("can not serialize object larger than 2G")
        write_int(len(serialized), stream)
        if self._only_write_strings:

    def _read_with_length(self, stream):
        length = read_int(stream)
        if length == SpecialLengths.END_OF_DATA_SECTION:
            raise EOFError
        elif length == SpecialLengths.NULL:
            return None
        obj =
        if len(obj) < length:
            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 ArrowSerializer(FramedSerializer):
    Serializes bytes as Arrow data with the Arrow file format.

    def dumps(self, batch):
        import pyarrow as pa
        import io
        sink = io.BytesIO()
        writer = pa.RecordBatchFileWriter(sink, batch.schema)
        return sink.getvalue()

    def loads(self, obj):
        import pyarrow as pa
        reader = pa.RecordBatchFileReader(pa.BufferReader(obj))
        return reader.read_all()

    def __repr__(self):
        return "ArrowSerializer"

def _create_batch(series, timezone):
    Create an Arrow record batch from the given pandas.Series or list of Series, with optional type.

    :param series: A single pandas.Series, list of Series, or list of (series, arrow_type)
    :param timezone: A timezone to respect when handling timestamp values
    :return: Arrow RecordBatch
    import decimal
    from distutils.version import LooseVersion
    import pyarrow as pa
    from pyspark.sql.types import _check_series_convert_timestamps_internal
    # Make input conform to [(series1, type1), (series2, type2), ...]
    if not isinstance(series, (list, tuple)) or \
            (len(series) == 2 and isinstance(series[1], pa.DataType)):
        series = [series]
    series = ((s, None) if not isinstance(s, (list, tuple)) else s for s in series)

    def create_array(s, t):
        mask = s.isnull()
        # Ensure timestamp series are in expected form for Spark internal representation
        if t is not None and pa.types.is_timestamp(t):
            s = _check_series_convert_timestamps_internal(s.fillna(0), timezone)
            # TODO: need cast after Arrow conversion, ns values cause error with pandas 0.19.2
            return pa.Array.from_pandas(s, mask=mask).cast(t, safe=False)
        elif t is not None and pa.types.is_string(t) and sys.version < '3':
            # TODO: need decode before converting to Arrow in Python 2
            return pa.Array.from_pandas(s.apply(
                lambda v: v.decode("utf-8") if isinstance(v, str) else v), mask=mask, type=t)
        elif t is not None and pa.types.is_decimal(t) and \
                LooseVersion("0.9.0") <= LooseVersion(pa.__version__) < LooseVersion("0.10.0"):
            # TODO: see ARROW-2432. Remove when the minimum PyArrow version becomes 0.10.0.
            return pa.Array.from_pandas(s.apply(
                lambda v: decimal.Decimal('NaN') if v is None else v), mask=mask, type=t)
        return pa.Array.from_pandas(s, mask=mask, type=t)

    arrs = [create_array(s, t) for s, t in series]
    return pa.RecordBatch.from_arrays(arrs, ["_%d" % i for i in xrange(len(arrs))])

class ArrowStreamPandasSerializer(Serializer):
    Serializes Pandas.Series as Arrow data with Arrow streaming format.

    def __init__(self, timezone):
        super(ArrowStreamPandasSerializer, self).__init__()
        self._timezone = timezone

    def dump_stream(self, iterator, stream):
        Make ArrowRecordBatches from Pandas Series and serialize. Input is a single series or
        a list of series accompanied by an optional pyarrow type to coerce the data to.
        import pyarrow as pa
        writer = None
            for series in iterator:
                batch = _create_batch(series, self._timezone)
                if writer is None:
                    write_int(SpecialLengths.START_ARROW_STREAM, stream)
                    writer = pa.RecordBatchStreamWriter(stream, batch.schema)
            if writer is not None:

    def load_stream(self, stream):
        Deserialize ArrowRecordBatches to an Arrow table and return as a list of pandas.Series.
        from pyspark.sql.types import from_arrow_schema, _check_dataframe_convert_date, \
        import pyarrow as pa
        reader = pa.open_stream(stream)
        schema = from_arrow_schema(reader.schema)
        for batch in reader:
            pdf = batch.to_pandas()
            pdf = _check_dataframe_convert_date(pdf, schema)
            pdf = _check_dataframe_localize_timestamps(pdf, self._timezone)
            yield [c for _, c in pdf.iteritems()]

    def __repr__(self):
        return "ArrowStreamPandasSerializer"

class BatchedSerializer(Serializer):

    Serializes a stream of objects in batches by calling its wrapped
    Serializer with streams of objects.


    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)
        elif hasattr(iterator, "__len__") and hasattr(iterator, "__getslice__"):
            n = len(iterator)
            for i in xrange(0, n, self.batchSize):
                yield iterator[i: i + self.batchSize]
            items = []
            count = 0
            for item in iterator:
                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 __repr__(self):
        return "BatchedSerializer(%s, %d)" % (str(self.serializer), self.batchSize)

class FlattenedValuesSerializer(BatchedSerializer):

    Serializes a stream of list of pairs, split the list of values
    which contain more than a certain number of objects to make them
    have similar sizes.
    def __init__(self, serializer, batchSize=10):
        BatchedSerializer.__init__(self, serializer, batchSize)

    def _batched(self, iterator):
        n = self.batchSize
        for key, values in iterator:
            for i in range(0, len(values), n):
                yield key, values[i:i + n]

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

    def __repr__(self):
        return "FlattenedValuesSerializer(%s, %d)" % (self.serializer, self.batchSize)

class AutoBatchedSerializer(BatchedSerializer):
    Choose the size of batch automatically based on the size of object

    def __init__(self, serializer, bestSize=1 << 16):
        BatchedSerializer.__init__(self, serializer, self.UNKNOWN_BATCH_SIZE)
        self.bestSize = bestSize

    def dump_stream(self, iterator, stream):
        batch, best = 1, self.bestSize
        iterator = iter(iterator)
        while True:
            vs = list(itertools.islice(iterator, batch))
            if not vs:

            bytes = self.serializer.dumps(vs)
            write_int(len(bytes), stream)

            size = len(bytes)
            if size < best:
                batch *= 2
            elif size > best * 10 and batch > 1:
                batch //= 2

    def __repr__(self):
        return "AutoBatchedSerializer(%s)" % self.serializer

class CartesianDeserializer(Serializer):

    Deserializes the JavaRDD cartesian() of two PythonRDDs.
    Due to pyspark batching we cannot simply use the result of the Java RDD cartesian,
    we additionally need to do the cartesian within each pair of batches.

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

    def _load_stream_without_unbatching(self, stream):
        key_batch_stream = self.key_ser._load_stream_without_unbatching(stream)
        val_batch_stream = self.val_ser._load_stream_without_unbatching(stream)
        for (key_batch, val_batch) in zip(key_batch_stream, val_batch_stream):
            # for correctness with repeated cartesian/zip this must be returned as one batch
            yield product(key_batch, val_batch)

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

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

class PairDeserializer(Serializer):

    Deserializes the JavaRDD zip() of two PythonRDDs.
    Due to pyspark batching we cannot simply use the result of the Java RDD zip,
    we additionally need to do the zip within each pair of batches.

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

    def _load_stream_without_unbatching(self, stream):
        key_batch_stream = self.key_ser._load_stream_without_unbatching(stream)
        val_batch_stream = self.val_ser._load_stream_without_unbatching(stream)
        for (key_batch, val_batch) in zip(key_batch_stream, val_batch_stream):
            # For double-zipped RDDs, the batches can be iterators from other PairDeserializer,
            # instead of lists. We need to convert them to lists if needed.
            key_batch = key_batch if hasattr(key_batch, '__len__') else list(key_batch)
            val_batch = val_batch if hasattr(val_batch, '__len__') else list(val_batch)
            if len(key_batch) != len(val_batch):
                raise ValueError("Can not deserialize PairRDD with different number of items"
                                 " in batches: (%d, %d)" % (len(key_batch), len(val_batch)))
            # for correctness with repeated cartesian/zip this must be returned as one batch
            yield zip(key_batch, val_batch)

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

    def __repr__(self):
        return "PairDeserializer(%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

# Hack namedtuple, make it picklable

__cls = {}

def _restore(name, fields, value):
    """ Restore an object of namedtuple"""
    k = (name, fields)
    cls = __cls.get(k)
    if cls is None:
        cls = collections.namedtuple(name, fields)
        __cls[k] = cls
    return cls(*value)

def _hack_namedtuple(cls):
    """ Make class generated by namedtuple picklable """
    name = cls.__name__
    fields = cls._fields

    def __reduce__(self):
        return (_restore, (name, fields, tuple(self)))
    cls.__reduce__ = __reduce__
    cls._is_namedtuple_ = True
    return cls

def _hijack_namedtuple():
    """ Hack namedtuple() to make it picklable """
    # hijack only one time
    if hasattr(collections.namedtuple, "__hijack"):

    global _old_namedtuple  # or it will put in closure
    global _old_namedtuple_kwdefaults  # or it will put in closure too

    def _copy_func(f):
        return types.FunctionType(f.__code__, f.__globals__, f.__name__,
                                  f.__defaults__, f.__closure__)

    def _kwdefaults(f):
        # __kwdefaults__ contains the default values of keyword-only arguments which are
        # introduced from Python 3. The possible cases for __kwdefaults__ in namedtuple
        # are as below:
        # - Does not exist in Python 2.
        # - Returns None in <= Python 3.5.x.
        # - Returns a dictionary containing the default values to the keys from Python 3.6.x
        #    (See
        kargs = getattr(f, "__kwdefaults__", None)
        if kargs is None:
            return {}
            return kargs

    _old_namedtuple = _copy_func(collections.namedtuple)
    _old_namedtuple_kwdefaults = _kwdefaults(collections.namedtuple)

    def namedtuple(*args, **kwargs):
        for k, v in _old_namedtuple_kwdefaults.items():
            kwargs[k] = kwargs.get(k, v)
        cls = _old_namedtuple(*args, **kwargs)
        return _hack_namedtuple(cls)

    # replace namedtuple with the new one
    collections.namedtuple.__globals__["_old_namedtuple_kwdefaults"] = _old_namedtuple_kwdefaults
    collections.namedtuple.__globals__["_old_namedtuple"] = _old_namedtuple
    collections.namedtuple.__globals__["_hack_namedtuple"] = _hack_namedtuple
    collections.namedtuple.__code__ = namedtuple.__code__
    collections.namedtuple.__hijack = 1

    # hack the cls already generated by namedtuple.
    # Those created in other modules can be pickled as normal,
    # so only hack those in __main__ module
    for n, o in sys.modules["__main__"].__dict__.items():
        if (type(o) is type and o.__base__ is tuple
                and hasattr(o, "_fields")
                and "__reduce__" not in o.__dict__):
            _hack_namedtuple(o)  # hack inplace


[docs]class PickleSerializer(FramedSerializer): """ Serializes objects using Python's pickle serializer: This serializer supports nearly any Python object, but may not be as fast as more specialized serializers. """
[docs] def dumps(self, obj): return pickle.dumps(obj, protocol)
if sys.version >= '3': def loads(self, obj, encoding="bytes"): return pickle.loads(obj, encoding=encoding) else:
[docs] def loads(self, obj, encoding=None): return pickle.loads(obj)
class CloudPickleSerializer(PickleSerializer): def dumps(self, obj): return cloudpickle.dumps(obj, 2)
[docs]class MarshalSerializer(FramedSerializer): """ Serializes objects using Python's Marshal serializer: This serializer is faster than PickleSerializer but supports fewer datatypes. """
[docs] def dumps(self, obj): return marshal.dumps(obj)
[docs] def loads(self, obj): return marshal.loads(obj)
class AutoSerializer(FramedSerializer): """ Choose marshal or pickle as serialization protocol automatically """ def __init__(self): FramedSerializer.__init__(self) self._type = None def dumps(self, obj): if self._type is not None: return b'P' + pickle.dumps(obj, -1) try: return b'M' + marshal.dumps(obj) except Exception: self._type = b'P' return b'P' + pickle.dumps(obj, -1) 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 serialization 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 = 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 = 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 = if not length: raise EOFError return struct.unpack("!i", length)[0] def write_int(value, stream): stream.write(struct.pack("!i", value)) def read_bool(stream): length = if not length: raise EOFError return struct.unpack("!?", length)[0] def write_with_length(obj, stream): write_int(len(obj), stream) stream.write(obj) class ChunkedStream(object): """ This file-like object takes a stream of data, of unknown length, and breaks it into fixed length frames. The intended use case is serializing large data and sending it immediately over a socket -- we do not want to buffer the entire data before sending it, but the receiving end needs to know whether or not there is more data coming. It works by buffering the incoming data in some fixed-size chunks. If the buffer is full, it first sends the buffer size, then the data. This repeats as long as there is more data to send. When this is closed, it sends the length of whatever data is in the buffer, then that data, and finally a "length" of -1 to indicate the stream has completed. """ def __init__(self, wrapped, buffer_size): self.buffer_size = buffer_size self.buffer = bytearray(buffer_size) self.current_pos = 0 self.wrapped = wrapped def write(self, bytes): byte_pos = 0 byte_remaining = len(bytes) while byte_remaining > 0: new_pos = byte_remaining + self.current_pos if new_pos < self.buffer_size: # just put it in our buffer self.buffer[self.current_pos:new_pos] = bytes[byte_pos:] self.current_pos = new_pos byte_remaining = 0 else: # fill the buffer, send the length then the contents, and start filling again space_left = self.buffer_size - self.current_pos new_byte_pos = byte_pos + space_left self.buffer[self.current_pos:self.buffer_size] = bytes[byte_pos:new_byte_pos] write_int(self.buffer_size, self.wrapped) self.wrapped.write(self.buffer) byte_remaining -= space_left byte_pos = new_byte_pos self.current_pos = 0 def close(self): # if there is anything left in the buffer, write it out first if self.current_pos > 0: write_int(self.current_pos, self.wrapped) self.wrapped.write(self.buffer[:self.current_pos]) # -1 length indicates to the receiving end that we're done. write_int(-1, self.wrapped) self.wrapped.close() if __name__ == '__main__': import doctest (failure_count, test_count) = doctest.testmod() if failure_count: exit(-1)