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带外序列化

Fory 支持兼容 pickle5 的带外缓冲区序列化，可通过零拷贝高效处理大型数据结构。

概述​

带外序列化将元数据与实际数据缓冲区分离，可以实现：

  • 使用 memoryview 通过网络或 IPC 发送数据时进行零拷贝传输
  • 提升大型数据集的性能
  • 使用 pickle.PickleBuffer 实现 Pickle5 兼容
  • 灵活的流支持——可写入任意可写对象（文件、BytesIO、socket 等）

基础带外序列化​

import pyfory
import numpy as np

fory = pyfory.Fory(xlang=False, ref=False, strict=False)

# Large numpy array
array = np.arange(10000, dtype=np.float64)

# Serialize with out-of-band buffers
buffer_objects = []
serialized_data = fory.serialize(array, buffer_callback=buffer_objects.append)

# Convert buffer objects to memoryview for zero-copy transmission
# For contiguous buffers (bytes, numpy arrays), this is zero-copy
# For non-contiguous data, a copy may be created to ensure contiguity
buffers = [obj.getbuffer() for obj in buffer_objects]

# Deserialize with out-of-band buffers (accepts memoryview, bytes, or Buffer)
deserialized_array = fory.deserialize(serialized_data, buffers=buffers)

assert np.array_equal(array, deserialized_array)

Pandas DataFrame 带外序列化​

import pyfory
import pandas as pd
import numpy as np

fory = pyfory.Fory(xlang=False, ref=False, strict=False)

# Create a DataFrame with numeric columns
df = pd.DataFrame({
    'a': np.arange(1000, dtype=np.float64),
    'b': np.arange(1000, dtype=np.int64),
    'c': ['text'] * 1000
})

# Serialize with out-of-band buffers
buffer_objects = []
serialized_data = fory.serialize(df, buffer_callback=buffer_objects.append)
buffers = [obj.getbuffer() for obj in buffer_objects]

# Deserialize
deserialized_df = fory.deserialize(serialized_data, buffers=buffers)

assert df.equals(deserialized_df)

选择性带外序列化​

通过回调控制哪些缓冲区走带外路径：返回 True 将数据保留在带内，返回 False 则带外发送：

import pyfory
import numpy as np

fory = pyfory.Fory(xlang=False, ref=True, strict=False)

arr1 = np.arange(1000, dtype=np.float64)
arr2 = np.arange(2000, dtype=np.float64)
data = [arr1, arr2]

buffer_objects = []
counter = 0

def selective_callback(buffer_object):
    global counter
    counter += 1
    # Only send even-numbered buffers out-of-band
    if counter % 2 == 0:
        buffer_objects.append(buffer_object)
        return False  # Out-of-band
    return True  # In-band

serialized = fory.serialize(data, buffer_callback=selective_callback)
buffers = [obj.getbuffer() for obj in buffer_objects]
deserialized = fory.deserialize(serialized, buffers=buffers)

Pickle5 兼容性​

Fory 的带外序列化完全兼容 pickle 协议 5：

import pyfory
import pickle

fory = pyfory.Fory(xlang=False, ref=False, strict=False)

# PickleBuffer objects are automatically supported
data = b"Large binary data"
pickle_buffer = pickle.PickleBuffer(data)

# Serialize with buffer callback for out-of-band handling
buffer_objects = []
serialized = fory.serialize(pickle_buffer, buffer_callback=buffer_objects.append)
buffers = [obj.getbuffer() for obj in buffer_objects]

# Deserialize with buffers
deserialized = fory.deserialize(serialized, buffers=buffers)
assert bytes(deserialized.raw()) == data

将缓冲区写入不同的流​

BufferObject.write_to() 方法接受任意可写流对象：

import pyfory
import numpy as np
import io

fory = pyfory.Fory(xlang=False, ref=False, strict=False)

array = np.arange(1000, dtype=np.float64)

# Collect out-of-band buffers
buffer_objects = []
serialized = fory.serialize(array, buffer_callback=buffer_objects.append)

# Write to different stream types
for buffer_obj in buffer_objects:
    # Write to BytesIO (in-memory stream)
    bytes_stream = io.BytesIO()
    buffer_obj.write_to(bytes_stream)

    # Write to file
    with open('/tmp/buffer_data.bin', 'wb') as f:
        buffer_obj.write_to(f)

    # Get zero-copy memoryview (for contiguous buffers)
    mv = buffer_obj.getbuffer()
    assert isinstance(mv, memoryview)

注意：对于连续内存缓冲区（如 bytes、numpy 数组），getbuffer() 返回零拷贝 memoryview。对于不连续数据，可能会创建副本以保证连续性。

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