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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2022/11/25 19:05:07 UTC
[GitHub] [spark] bjornjorgensen commented on a diff in pull request #38802: [WIP] Packaging for Spark Connect Preview
bjornjorgensen commented on code in PR #38802:
URL: https://github.com/apache/spark/pull/38802#discussion_r1032653384
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connector/connect/clients/python/pyspark-connect/pyspark/serializers.py:
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@@ -0,0 +1,681 @@
+#
+# 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.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
+from pyspark.util import print_exec
+
+
+__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 sys.version_info < (3, 8) or os.environ.get("PYSPARK_ENABLE_NAMEDTUPLE_PATCH") == "1":
+ # Hack namedtuple, make it picklable.
+ # For Python 3.8+, we use CPickle-based cloudpickle.
+ # For Python 3.7 and below, we use legacy build-in CPickle which
+ # requires namedtuple hack.
+ # The whole hack here should be removed once we drop Python 3.7.
+
+ __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):
+ 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 sys.version_info < (3, 8) or 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
Review Comment:
This is a file-like object that takes a stream
add that
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