<filename>python/pyspark/serializers.py
import struct
import cPickle


class Batch(object):
    """
    Used to store multiple RDD entries as a single Java object.

    This relieves us from having to explicitly track whether an RDD
    is stored as batches of objects and avoids problems when processing
    the union() of batched and unbatched RDDs (e.g. the union() of textFile()
    with another RDD).
    """
    def __init__(self, items):
        self.items = items


def batched(iterator, batchSize):
    if batchSize == -1: # unlimited batch size
        yield Batch(list(iterator))
    else:
        items = []
        count = 0
        for item in iterator:
            items.append(item)
            count += 1
            if count == batchSize:
                yield Batch(items)
                items = []
                count = 0
        if items:
            yield Batch(items)


def dump_pickle(obj):
    return cPickle.dumps(obj, 2)


load_pickle = cPickle.loads


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 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)


def read_with_length(stream):
    length = read_int(stream)
    obj = stream.read(length)
    if obj == "":
        raise EOFError
    return obj


def read_from_pickle_file(stream):
    try:
        while True:
            obj = load_pickle(read_with_length(stream))
            if type(obj) == Batch:  # We don't care about inheritance
                for item in obj.items:
                    yield item
            else:
                yield obj
    except EOFError:
        return
