#
# 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 :class:`CloudPickleSerializer` to serialize objects using Python's
`cPickle` serializer, which can serialize nearly any Python object.
Other serializers, like :class:`MarshalSerializer`, support fewer datatypes but can be
faster.


Examples
--------
The serializer is chosen when creating :class:`SparkContext`:

>>> from pyspark.core.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 `batchSize`
parameter:

>>> 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())
8
>>> sc.stop()
"""

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

pickle_protocol = pickle.HIGHEST_PROTOCOL

from pyspark import cloudpickle


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


class SpecialLengths:
    END_OF_DATA_SECTION = -1
    PYTHON_EXCEPTION_THROWN = -2
    TIMING_DATA = -3
    END_OF_STREAM = -4
    NULL = -5
    START_ARROW_STREAM = -6


class Serializer:
    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 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 _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 `length` is a 32-bit integer and data is `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)
        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)
        stream.write(serialized)

    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 = stream.read(length)
        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 BatchedSerializer(Serializer):

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

    UNLIMITED_BATCH_SIZE = -1
    UNKNOWN_BATCH_SIZE = 0

    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 range(0, n, self.batchSize):
                yield iterator[i : i + self.batchSize]
        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 __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:
                break

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

            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


if os.environ.get("PYSPARK_ENABLE_NAMEDTUPLE_PATCH") == "1":
    # Hack namedtuple, make it picklable.
    # For Python 3.8+, we use CPickle-based cloudpickle.
    # SPARK-41189: There are still behaviour differences between regular pickle
    # and Cloudpickle e.g., bug fixes from the upstream. It's safer to have
    # a switch to turn on and off for the time being.

    __cls = {}  # type: ignore[var-annotated]

    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"):
            return

        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__
            )

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

        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

    _hijack_namedtuple()


class PickleSerializer(FramedSerializer):

    """
    Serializes objects using Python's pickle 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 pickle.dumps(obj, pickle_protocol)

    def loads(self, obj, encoding="bytes"):
        return pickle.loads(obj, encoding=encoding)


class CloudPickleSerializer(FramedSerializer):
    def dumps(self, obj):
        from pyspark.util import print_exec

        try:
            return cloudpickle.dumps(obj, pickle_protocol)
        except pickle.PickleError:
            raise
        except Exception as e:
            emsg = str(e)
            if "'i' format requires" in emsg:
                msg = "Object too large to serialize: %s" % emsg
            else:
                msg = "Could not serialize object: %s: %s" % (e.__class__.__name__, emsg)
            print_exec(sys.stderr)
            raise pickle.PicklingError(msg)

    def loads(self, obj, encoding="bytes"):
        return cloudpickle.loads(obj, encoding=encoding)


if os.environ.get("PYSPARK_ENABLE_NAMEDTUPLE_PATCH") == "1":
    CPickleSerializer = PickleSerializer
else:
    CPickleSerializer = CloudPickleSerializer  # type: ignore[misc, assignment]


class MarshalSerializer(FramedSerializer):

    """
    Serializes objects using Python's Marshal serializer:

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

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

    def dumps(self, obj):
        return marshal.dumps(obj)

    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 = 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 read_bool(stream):
    length = stream.read(1)
    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:

    """
    This is a 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()

    @property
    def closed(self):
        """
        Return True if the `wrapped` object has been closed.
        NOTE: this property is required by pyarrow to be used as a file-like object in
        pyarrow.RecordBatchStreamWriter from ArrowStreamSerializer
        """
        return self.wrapped.closed


if __name__ == "__main__":
    import doctest

    (failure_count, test_count) = doctest.testmod()
    if failure_count:
        sys.exit(-1)