"pickle" --- Python object serialization
****************************************

**Source code:** Lib/pickle.py

======================================================================

The "pickle" module implements binary protocols for serializing and
de-serializing a Python object structure.  *"Pickling"* is the process
whereby a Python object hierarchy is converted into a byte stream, and
*"unpickling"* is the inverse operation, whereby a byte stream (from a
*binary file* or *bytes-like object*) is converted back into an object
hierarchy.  Pickling (and unpickling) is alternatively known as
"serialization", "marshalling," [1] or "flattening"; however, to avoid
confusion, the terms used here are "pickling" and "unpickling".

警告: "pickle" 模块**并不安全**。你只应该对你信任的数据进行unpickle
  操作。 构建恶意的 pickle 数据来**在解封时执行任意代码**是可能的。绝
  对不要对 不信任来源的数据和可能被篡改过的数据进行解封。请考虑使用
  "hmac" 来对 数据进行签名，确保数据没有被篡改。在你处理不信任数据时，
  更安全的序列 化格式如 "json" 可能更为适合。参见  Comparison with
  json 。


Relationship to other Python modules
====================================


Comparison with "marshal"
-------------------------

Python has a more primitive serialization module called "marshal", but
in general "pickle" should always be the preferred way to serialize
Python objects.  "marshal" exists primarily to support Python's ".pyc"
files.

The "pickle" module differs from "marshal" in several significant
ways:

* The "pickle" module keeps track of the objects it has already
  serialized, so that later references to the same object won't be
  serialized again. "marshal" doesn't do this.

  This has implications both for recursive objects and object sharing.
  Recursive objects are objects that contain references to themselves.
  These are not handled by marshal, and in fact, attempting to marshal
  recursive objects will crash your Python interpreter.  Object
  sharing happens when there are multiple references to the same
  object in different places in the object hierarchy being serialized.
  "pickle" stores such objects only once, and ensures that all other
  references point to the master copy.  Shared objects remain shared,
  which can be very important for mutable objects.

* "marshal" cannot be used to serialize user-defined classes and
  their instances.  "pickle" can save and restore class instances
  transparently, however the class definition must be importable and
  live in the same module as when the object was stored.

* 同样用于序列化的 "marshal" 格式不保证数据能移植到不同的 Python 版
  本 中。因为它的主要任务是支持 ".pyc" 文件，必要时会以破坏向后兼容的
  方式 更改这种序列化格式，为此 Python 的实现者保留了更改格式的权利。
  "pickle" 序列化格式可以在不同版本的 Python 中实现向后兼容，前提是选
  择了合适的 pickle 协议。如果你的数据要在 Python 2 与 Python 3 之间跨
  越传递，封存和解封的代码在 2 和 3 之间也是不同的。


Comparison with "json"
----------------------

There are fundamental differences between the pickle protocols and
JSON (JavaScript Object Notation):

* JSON is a text serialization format (it outputs unicode text,
  although most of the time it is then encoded to "utf-8"), while
  pickle is a binary serialization format;

* JSON is human-readable, while pickle is not;

* JSON is interoperable and widely used outside of the Python
  ecosystem, while pickle is Python-specific;

* 默认情况下，JSON 只能表示 Python 内置类型的子集，不能表示自定义的
  类 ；但 pickle 可以表示大量的 Python 数据类型（可以合理使用 Python
  的对 象内省功能自动地表示大多数类型，复杂情况可以通过实现 specific
  object APIs 来解决）。

* 不像pickle，对一个不信任的JSON进行反序列化的操作本身不会造成任意代
  码 执行漏洞。

也參考: The "json" module: a standard library module allowing JSON
  serialization and deserialization.


Data stream format
==================

The data format used by "pickle" is Python-specific.  This has the
advantage that there are no restrictions imposed by external standards
such as JSON or XDR (which can't represent pointer sharing); however
it means that non-Python programs may not be able to reconstruct
pickled Python objects.

By default, the "pickle" data format uses a relatively compact binary
representation.  If you need optimal size characteristics, you can
efficiently compress pickled data.

The module "pickletools" contains tools for analyzing data streams
generated by "pickle".  "pickletools" source code has extensive
comments about opcodes used by pickle protocols.

当前共有 6 种不同的协议可用于封存操作。 使用的协议版本越高，读取所生成
pickle 对象所需的 Python 版本就要越新。

* Protocol version 0 is the original "human-readable" protocol and
  is backwards compatible with earlier versions of Python.

* Protocol version 1 is an old binary format which is also
  compatible with earlier versions of Python.

* Protocol version 2 was introduced in Python 2.3.  It provides much
  more efficient pickling of *new-style class*es.  Refer to **PEP
  307** for information about improvements brought by protocol 2.

* v3 版协议是在 Python 3.0 中引入的。 它显式地支持 "bytes" 字节对象
  ， 不能使用 Python 2.x 解封。这是 Python 3.0-3.7 的默认协议。

* v4 版协议添加于 Python 3.4。它支持存储非常大的对象，能存储更多种类
  的 对象，还包括一些针对数据格式的优化。它是Python 3.8使用的默认协议
  。有 关第 4 版协议带来改进的信息，请参阅 **PEP 3154**。

* 第 5 版协议是在 Python 3.8 中加入的。 它增加了对带外数据的支持，并
  可 加速带内数据处理。 请参阅 **PEP 574** 了解第 5 版协议所带来的改进
  的 详情。

備註: Serialization is a more primitive notion than persistence;
  although "pickle" reads and writes file objects, it does not handle
  the issue of naming persistent objects, nor the (even more
  complicated) issue of concurrent access to persistent objects.  The
  "pickle" module can transform a complex object into a byte stream
  and it can transform the byte stream into an object with the same
  internal structure. Perhaps the most obvious thing to do with these
  byte streams is to write them onto a file, but it is also
  conceivable to send them across a network or store them in a
  database.  The "shelve" module provides a simple interface to pickle
  and unpickle objects on DBM- style database files.


Module Interface
================

To serialize an object hierarchy, you simply call the "dumps()"
function. Similarly, to de-serialize a data stream, you call the
"loads()" function. However, if you want more control over
serialization and de-serialization, you can create a "Pickler" or an
"Unpickler" object, respectively.

The "pickle" module provides the following constants:

pickle.HIGHEST_PROTOCOL

   An integer, the highest protocol version available.  This value can
   be passed as a *protocol* value to functions "dump()" and "dumps()"
   as well as the "Pickler" constructor.

pickle.DEFAULT_PROTOCOL

   整数，用于 pickle 数据的默认 协议版本。它可能小于
   "HIGHEST_PROTOCOL"。当前默认协议是 v4，它在 Python 3.4 中首次引入，
   与之前的版本不兼容。

   3.0 版更變: 默认协议版本是 3。

   3.8 版更變: 默认协议版本是 4。

The "pickle" module provides the following functions to make the
pickling process more convenient:

pickle.dump(obj, file, protocol=None, *, fix_imports=True, buffer_callback=None)

   将对象 *obj* 封存以后的对象写入已打开的 *file object* *file*。它等
   同于 "Pickler(file, protocol).dump(obj)"。

   参数 *file*、*protocol*、*fix_imports* 和 *buffer_callback* 的含义
   与它们在 "Pickler" 的构造函数中的含义相同。

   3.8 版更變: 加入了 *buffer_callback* 参数。

pickle.dumps(obj, protocol=None, *, fix_imports=True, buffer_callback=None)

   将 *obj* 封存以后的对象作为 "bytes" 类型直接返回，而不是将其写入到
   文件。

   参数 *protocol*、*fix_imports* 和 *buffer_callback* 的含义与它们在
   "Pickler" 的构造函数中的含义相同。

   3.8 版更變: 加入了 *buffer_callback* 参数。

pickle.load(file, *, fix_imports=True, encoding="ASCII", errors="strict", buffers=None)

   从已打开的 *file object* *文件* 中读取封存后的对象，重建其中特定对
   象的层次结构并返回。它相当于 "Unpickler(file).load()"。

   Pickle 协议版本是自动检测出来的，所以不需要参数来指定协议。封存对象
   以外的其他字节将被忽略。

   参数 *file*、*fix_imports*、*encoding*、*errors*、*strict* 和
   *buffers* 的含义与它们在 "Unpickler" 的构造函数中的含义相同。

   3.8 版更變: 加入了 *buffers* 参数。

pickle.loads(bytes_object, *, fix_imports=True, encoding="ASCII", errors="strict", buffers=None)

   对于封存生成的对象 *bytes_object*，还原出原对象的结构并返回。

   Pickle 协议版本是自动检测出来的，所以不需要参数来指定协议。封存对象
   以外的其他字节将被忽略。

   参数 *file*、*fix_imports*、*encoding*、*errors*、*strict* 和
   *buffers* 的含义与它们在 "Unpickler" 的构造函数中的含义相同。

   3.8 版更變: 加入了 *buffers* 参数。

The "pickle" module defines three exceptions:

exception pickle.PickleError

   Common base class for the other pickling exceptions.  It inherits
   "Exception".

exception pickle.PicklingError

   Error raised when an unpicklable object is encountered by
   "Pickler". It inherits "PickleError".

   Refer to What can be pickled and unpickled? to learn what kinds of
   objects can be pickled.

exception pickle.UnpicklingError

   Error raised when there is a problem unpickling an object, such as
   a data corruption or a security violation.  It inherits
   "PickleError".

   Note that other exceptions may also be raised during unpickling,
   including (but not necessarily limited to) AttributeError,
   EOFError, ImportError, and IndexError.

"pickle" 模块包含了 3 个类，"Pickler"、"Unpickler" 和 "PickleBuffer"：

class pickle.Pickler(file, protocol=None, *, fix_imports=True, buffer_callback=None)

   This takes a binary file for writing a pickle data stream.

   The optional *protocol* argument, an integer, tells the pickler to
   use the given protocol; supported protocols are 0 to
   "HIGHEST_PROTOCOL". If not specified, the default is
   "DEFAULT_PROTOCOL".  If a negative number is specified,
   "HIGHEST_PROTOCOL" is selected.

   The *file* argument must have a write() method that accepts a
   single bytes argument.  It can thus be an on-disk file opened for
   binary writing, an "io.BytesIO" instance, or any other custom
   object that meets this interface.

   If *fix_imports* is true and *protocol* is less than 3, pickle will
   try to map the new Python 3 names to the old module names used in
   Python 2, so that the pickle data stream is readable with Python 2.

   如果 *buffer_callback* 为 None（默认情况），缓冲区视图（buffer view
   ）将会作为 pickle 流的一部分被序列化到 *file* 中。

   如果 *buffer_callback* 不为 None，那它可以用缓冲区视图调用任意次。
   如果某次调用返回了 False 值（例如 None），则给定的缓冲区是 带外的，
   否则缓冲区是带内的（例如保存在了 pickle 流里面）。

   如果 *buffer_callback* 不是 None 且 *protocol* 是 None 或小于 5，就
   会出错。

   3.8 版更變: 加入了 *buffer_callback* 参数。

   dump(obj)

      将 *obj* 封存后的内容写入已打开的文件对象，该文件对象已经在构造
      函数中指定。

   persistent_id(obj)

      Do nothing by default.  This exists so a subclass can override
      it.

      If "persistent_id()" returns "None", *obj* is pickled as usual.
      Any other value causes "Pickler" to emit the returned value as a
      persistent ID for *obj*.  The meaning of this persistent ID
      should be defined by "Unpickler.persistent_load()".  Note that
      the value returned by "persistent_id()" cannot itself have a
      persistent ID.

      See Persistence of External Objects for details and examples of
      uses.

   dispatch_table

      A pickler object's dispatch table is a registry of *reduction
      functions* of the kind which can be declared using
      "copyreg.pickle()".  It is a mapping whose keys are classes and
      whose values are reduction functions.  A reduction function
      takes a single argument of the associated class and should
      conform to the same interface as a "__reduce__()" method.

      By default, a pickler object will not have a "dispatch_table"
      attribute, and it will instead use the global dispatch table
      managed by the "copyreg" module. However, to customize the
      pickling for a specific pickler object one can set the
      "dispatch_table" attribute to a dict-like object.
      Alternatively, if a subclass of "Pickler" has a "dispatch_table"
      attribute then this will be used as the default dispatch table
      for instances of that class.

      See Dispatch Tables for usage examples.

      3.3 版新加入.

   reducer_override(self, obj)

      可以在 "Pickler" 的子类中定义的特殊 reducer。此方法的优先级高于
      "dispatch_table" 中的任何 reducer。它应该与 "__reduce__()" 方法
      遵循相同的接口，它也可以返回 "NotImplemented"，这将使用
      "dispatch_table" 里注册的 reducer 来封存 "obj"。

      参阅 类型，函数和其他对象的自定义归约 获取详细的示例。

      3.8 版新加入.

   fast

      Deprecated. Enable fast mode if set to a true value.  The fast
      mode disables the usage of memo, therefore speeding the pickling
      process by not generating superfluous PUT opcodes.  It should
      not be used with self-referential objects, doing otherwise will
      cause "Pickler" to recurse infinitely.

      Use "pickletools.optimize()" if you need more compact pickles.

class pickle.Unpickler(file, *, fix_imports=True, encoding="ASCII", errors="strict", buffers=None)

   This takes a binary file for reading a pickle data stream.

   The protocol version of the pickle is detected automatically, so no
   protocol argument is needed.

   参数 *file* 必须有三个方法，read() 方法接受一个整数参数，readinto()
   方法接受一个缓冲区作为参数，readline() 方法不需要参数，这与
   "io.BufferedIOBase" 里定义的接口是相同的。因此 *file* 可以是一个磁
   盘上用于二进制读取的文件，也可以是一个 "io.BytesIO" 实例，也可以是
   满足这一接口的其他任何自定义对象。

   可选的参数是 *fix_imports*, *encoding* 和 *errors*，用于控制由
   Python 2 生成的 pickle 流的兼容性。如果 *fix_imports* 为 True，则
   pickle 将尝试将旧的 Python 2 名称映射到 Python 3 中对应的新名称。
   *encoding* 和 *errors* 参数告诉 pickle 如何解码 Python 2 存储的 8
   位字符串实例；这两个参数默认分别为 'ASCII' 和 'strict'。*encoding*
   参数可置为 'bytes' 来将这些 8 位字符串实例读取为字节对象。读取
   NumPy array 和 Python 2 存储的 "datetime"、"date" 和 "time" 实例时
   ，请使用 "encoding='latin1'"。

   如果 *buffers* 为 None（默认值），则反序列化所需的所有数据都必须包
   含在 pickle 流中。这意味着在实例化 "Pickler" 时（或调用 "dump()" 或
   "dumps()" 时），参数 *buffer_callback* 为 None。

   如果 *buffers* 不为 None，则每次 pickle 流引用 带外 缓冲区视图时，
   消耗的对象都应该是可迭代的启用缓冲区的对象。这样的缓冲区应该按顺序
   地提供给 Pickler 对象的 *buffer_callback* 方法。

   3.8 版更變: 加入了 *buffers* 参数。

   load()

      从构造函数中指定的文件对象里读取封存好的对象，重建其中特定对象的
      层次结构并返回。封存对象以外的其他字节将被忽略。

   persistent_load(pid)

      Raise an "UnpicklingError" by default.

      If defined, "persistent_load()" should return the object
      specified by the persistent ID *pid*.  If an invalid persistent
      ID is encountered, an "UnpicklingError" should be raised.

      See Persistence of External Objects for details and examples of
      uses.

   find_class(module, name)

      Import *module* if necessary and return the object called *name*
      from it, where the *module* and *name* arguments are "str"
      objects.  Note, unlike its name suggests, "find_class()" is also
      used for finding functions.

      Subclasses may override this to gain control over what type of
      objects and how they can be loaded, potentially reducing
      security risks. Refer to Restricting Globals for details.

      引发一个 审核事件 "pickle.find_class" 附带参数 "module"、"name"
      。

class pickle.PickleBuffer(buffer)

   缓冲区的包装器 (wrapper)，缓冲区中包含着可封存的数据。*buffer* 必须
   是一个 buffer-providing 对象，比如 *bytes-like object* 或多维数组。

   "PickleBuffer" 本身就可以生成缓冲区对象，因此可以将其传递给需要缓冲
   区生成器的其他 API，比如 "memoryview"。

   "PickleBuffer" 对象只能用 pickle 版本 5 及以上协议进行序列化。它们
   符合 带外序列化 的条件。

   3.8 版新加入.

   raw()

      返回该缓冲区底层内存区域的 "memoryview"。 返回的对象是一维的、C
      连续布局的 memoryview，格式为 "B" (无符号字节)。 如果缓冲区既不
      是 C 连续布局也不是 Fortran 连续布局的，则抛出 "BufferError" 异
      常。

   release()

      释放由 PickleBuffer 占用的底层缓冲区。


What can be pickled and unpickled?
==================================

The following types can be pickled:

* "None", "True", and "False"

* integers, floating point numbers, complex numbers

* strings, bytes, bytearrays

* tuples, lists, sets, and dictionaries containing only picklable
  objects

* functions defined at the top level of a module (using "def", not
  "lambda")

* built-in functions defined at the top level of a module

* classes that are defined at the top level of a module

* instances of such classes whose "__dict__" or the result of
  calling "__getstate__()" is picklable  (see section Pickling Class
  Instances for details).

Attempts to pickle unpicklable objects will raise the "PicklingError"
exception; when this happens, an unspecified number of bytes may have
already been written to the underlying file.  Trying to pickle a
highly recursive data structure may exceed the maximum recursion
depth, a "RecursionError" will be raised in this case.  You can
carefully raise this limit with "sys.setrecursionlimit()".

Note that functions (built-in and user-defined) are pickled by "fully
qualified" name reference, not by value. [2]  This means that only the
function name is pickled, along with the name of the module the
function is defined in.  Neither the function's code, nor any of its
function attributes are pickled.  Thus the defining module must be
importable in the unpickling environment, and the module must contain
the named object, otherwise an exception will be raised. [3]

Similarly, classes are pickled by named reference, so the same
restrictions in the unpickling environment apply.  Note that none of
the class's code or data is pickled, so in the following example the
class attribute "attr" is not restored in the unpickling environment:

   class Foo:
       attr = 'A class attribute'

   picklestring = pickle.dumps(Foo)

These restrictions are why picklable functions and classes must be
defined in the top level of a module.

Similarly, when class instances are pickled, their class's code and
data are not pickled along with them.  Only the instance data are
pickled.  This is done on purpose, so you can fix bugs in a class or
add methods to the class and still load objects that were created with
an earlier version of the class.  If you plan to have long-lived
objects that will see many versions of a class, it may be worthwhile
to put a version number in the objects so that suitable conversions
can be made by the class's "__setstate__()" method.


Pickling Class Instances
========================

In this section, we describe the general mechanisms available to you
to define, customize, and control how class instances are pickled and
unpickled.

In most cases, no additional code is needed to make instances
picklable.  By default, pickle will retrieve the class and the
attributes of an instance via introspection. When a class instance is
unpickled, its "__init__()" method is usually *not* invoked.  The
default behaviour first creates an uninitialized instance and then
restores the saved attributes.  The following code shows an
implementation of this behaviour:

   def save(obj):
       return (obj.__class__, obj.__dict__)

   def load(cls, attributes):
       obj = cls.__new__(cls)
       obj.__dict__.update(attributes)
       return obj

Classes can alter the default behaviour by providing one or several
special methods:

object.__getnewargs_ex__()

   In protocols 2 and newer, classes that implements the
   "__getnewargs_ex__()" method can dictate the values passed to the
   "__new__()" method upon unpickling.  The method must return a pair
   "(args, kwargs)" where *args* is a tuple of positional arguments
   and *kwargs* a dictionary of named arguments for constructing the
   object.  Those will be passed to the "__new__()" method upon
   unpickling.

   You should implement this method if the "__new__()" method of your
   class requires keyword-only arguments.  Otherwise, it is
   recommended for compatibility to implement "__getnewargs__()".

   3.6 版更變: "__getnewargs_ex__()" is now used in protocols 2 and 3.

object.__getnewargs__()

   This method serves a similar purpose as "__getnewargs_ex__()", but
   supports only positional arguments.  It must return a tuple of
   arguments "args" which will be passed to the "__new__()" method
   upon unpickling.

   "__getnewargs__()" will not be called if "__getnewargs_ex__()" is
   defined.

   3.6 版更變: Before Python 3.6, "__getnewargs__()" was called
   instead of "__getnewargs_ex__()" in protocols 2 and 3.

object.__getstate__()

   Classes can further influence how their instances are pickled; if
   the class defines the method "__getstate__()", it is called and the
   returned object is pickled as the contents for the instance,
   instead of the contents of the instance's dictionary.  If the
   "__getstate__()" method is absent, the instance's "__dict__" is
   pickled as usual.

object.__setstate__(state)

   Upon unpickling, if the class defines "__setstate__()", it is
   called with the unpickled state.  In that case, there is no
   requirement for the state object to be a dictionary.  Otherwise,
   the pickled state must be a dictionary and its items are assigned
   to the new instance's dictionary.

   備註: If "__getstate__()" returns a false value, the
     "__setstate__()" method will not be called upon unpickling.

Refer to the section Handling Stateful Objects for more information
about how to use the methods "__getstate__()" and "__setstate__()".

備註: At unpickling time, some methods like "__getattr__()",
  "__getattribute__()", or "__setattr__()" may be called upon the
  instance.  In case those methods rely on some internal invariant
  being true, the type should implement "__getnewargs__()" or
  "__getnewargs_ex__()" to establish such an invariant; otherwise,
  neither "__new__()" nor "__init__()" will be called.

As we shall see, pickle does not use directly the methods described
above.  In fact, these methods are part of the copy protocol which
implements the "__reduce__()" special method.  The copy protocol
provides a unified interface for retrieving the data necessary for
pickling and copying objects. [4]

Although powerful, implementing "__reduce__()" directly in your
classes is error prone.  For this reason, class designers should use
the high-level interface (i.e., "__getnewargs_ex__()",
"__getstate__()" and "__setstate__()") whenever possible.  We will
show, however, cases where using "__reduce__()" is the only option or
leads to more efficient pickling or both.

object.__reduce__()

   The interface is currently defined as follows.  The "__reduce__()"
   method takes no argument and shall return either a string or
   preferably a tuple (the returned object is often referred to as the
   "reduce value").

   If a string is returned, the string should be interpreted as the
   name of a global variable.  It should be the object's local name
   relative to its module; the pickle module searches the module
   namespace to determine the object's module.  This behaviour is
   typically useful for singletons.

   如果返回的是元组，则应当包含 2 到 6 个元素，可选元素可以省略或设置
   为 "None"。每个元素代表的意义如下：

   * A callable object that will be called to create the initial
     version of the object.

   * A tuple of arguments for the callable object.  An empty tuple
     must be given if the callable does not accept any argument.

   * Optionally, the object's state, which will be passed to the
     object's "__setstate__()" method as previously described.  If the
     object has no such method then, the value must be a dictionary
     and it will be added to the object's "__dict__" attribute.

   * Optionally, an iterator (and not a sequence) yielding
     successive items. These items will be appended to the object
     either using "obj.append(item)" or, in batch, using
     "obj.extend(list_of_items)". This is primarily used for list
     subclasses, but may be used by other classes as long as they have
     "append()" and "extend()" methods with the appropriate signature.
     (Whether "append()" or "extend()" is used depends on which pickle
     protocol version is used as well as the number of items to
     append, so both must be supported.)

   * Optionally, an iterator (not a sequence) yielding successive
     key- value pairs.  These items will be stored to the object using
     "obj[key] = value".  This is primarily used for dictionary
     subclasses, but may be used by other classes as long as they
     implement "__setitem__()".

   * 可选元素，一个带有 "(obj, state)" 签名的可调用对象。该可调用对
     象 允许用户以编程方式控制特定对象的状态更新行为，而不是使用 "obj"
     的 静态 "__setstate__()" 方法。如果此处不是 "None"，则此可调用对
     象的 优先级高于 "obj" 的 "__setstate__()"。

     3.8 版新加入: 新增了元组的第 6 项，可选元素 "(obj, state)"。

object.__reduce_ex__(protocol)

   Alternatively, a "__reduce_ex__()" method may be defined.  The only
   difference is this method should take a single integer argument,
   the protocol version.  When defined, pickle will prefer it over the
   "__reduce__()" method.  In addition, "__reduce__()" automatically
   becomes a synonym for the extended version.  The main use for this
   method is to provide backwards-compatible reduce values for older
   Python releases.


Persistence of External Objects
-------------------------------

For the benefit of object persistence, the "pickle" module supports
the notion of a reference to an object outside the pickled data
stream.  Such objects are referenced by a persistent ID, which should
be either a string of alphanumeric characters (for protocol 0) [5] or
just an arbitrary object (for any newer protocol).

"pickle" 模块不提供对持久化 ID 的解析工作，它将解析工作分配给用户定义
的方法，分别是 pickler 中的 "persistent_id()" 方法和 unpickler 中的
"persistent_load()" 方法。

要通过持久化 ID 将外部对象封存，必须在 pickler 中实现
"persistent_id()" 方法，该方法接受需要被封存的对象作为参数，返回一个
"None" 或返回该对象的持久化 ID。如果返回 "None"，该对象会被按照默认方
式封存为数据流。如果返回字符串形式的持久化 ID，则会封存这个字符串并加
上一个标记，这样 unpickler 才能将其识别为持久化 ID。

To unpickle external objects, the unpickler must have a custom
"persistent_load()" method that takes a persistent ID object and
returns the referenced object.

Here is a comprehensive example presenting how persistent ID can be
used to pickle external objects by reference.

   # Simple example presenting how persistent ID can be used to pickle
   # external objects by reference.

   import pickle
   import sqlite3
   from collections import namedtuple

   # Simple class representing a record in our database.
   MemoRecord = namedtuple("MemoRecord", "key, task")

   class DBPickler(pickle.Pickler):

       def persistent_id(self, obj):
           # Instead of pickling MemoRecord as a regular class instance, we emit a
           # persistent ID.
           if isinstance(obj, MemoRecord):
               # Here, our persistent ID is simply a tuple, containing a tag and a
               # key, which refers to a specific record in the database.
               return ("MemoRecord", obj.key)
           else:
               # If obj does not have a persistent ID, return None. This means obj
               # needs to be pickled as usual.
               return None


   class DBUnpickler(pickle.Unpickler):

       def __init__(self, file, connection):
           super().__init__(file)
           self.connection = connection

       def persistent_load(self, pid):
           # This method is invoked whenever a persistent ID is encountered.
           # Here, pid is the tuple returned by DBPickler.
           cursor = self.connection.cursor()
           type_tag, key_id = pid
           if type_tag == "MemoRecord":
               # Fetch the referenced record from the database and return it.
               cursor.execute("SELECT * FROM memos WHERE key=?", (str(key_id),))
               key, task = cursor.fetchone()
               return MemoRecord(key, task)
           else:
               # Always raises an error if you cannot return the correct object.
               # Otherwise, the unpickler will think None is the object referenced
               # by the persistent ID.
               raise pickle.UnpicklingError("unsupported persistent object")


   def main():
       import io
       import pprint

       # Initialize and populate our database.
       conn = sqlite3.connect(":memory:")
       cursor = conn.cursor()
       cursor.execute("CREATE TABLE memos(key INTEGER PRIMARY KEY, task TEXT)")
       tasks = (
           'give food to fish',
           'prepare group meeting',
           'fight with a zebra',
           )
       for task in tasks:
           cursor.execute("INSERT INTO memos VALUES(NULL, ?)", (task,))

       # Fetch the records to be pickled.
       cursor.execute("SELECT * FROM memos")
       memos = [MemoRecord(key, task) for key, task in cursor]
       # Save the records using our custom DBPickler.
       file = io.BytesIO()
       DBPickler(file).dump(memos)

       print("Pickled records:")
       pprint.pprint(memos)

       # Update a record, just for good measure.
       cursor.execute("UPDATE memos SET task='learn italian' WHERE key=1")

       # Load the records from the pickle data stream.
       file.seek(0)
       memos = DBUnpickler(file, conn).load()

       print("Unpickled records:")
       pprint.pprint(memos)


   if __name__ == '__main__':
       main()


Dispatch Tables
---------------

If one wants to customize pickling of some classes without disturbing
any other code which depends on pickling, then one can create a
pickler with a private dispatch table.

The global dispatch table managed by the "copyreg" module is available
as "copyreg.dispatch_table".  Therefore, one may choose to use a
modified copy of "copyreg.dispatch_table" as a private dispatch table.

For example

   f = io.BytesIO()
   p = pickle.Pickler(f)
   p.dispatch_table = copyreg.dispatch_table.copy()
   p.dispatch_table[SomeClass] = reduce_SomeClass

creates an instance of "pickle.Pickler" with a private dispatch table
which handles the "SomeClass" class specially.  Alternatively, the
code

   class MyPickler(pickle.Pickler):
       dispatch_table = copyreg.dispatch_table.copy()
       dispatch_table[SomeClass] = reduce_SomeClass
   f = io.BytesIO()
   p = MyPickler(f)

does the same, but all instances of "MyPickler" will by default share
the same dispatch table.  The equivalent code using the "copyreg"
module is

   copyreg.pickle(SomeClass, reduce_SomeClass)
   f = io.BytesIO()
   p = pickle.Pickler(f)


Handling Stateful Objects
-------------------------

Here's an example that shows how to modify pickling behavior for a
class. The "TextReader" class opens a text file, and returns the line
number and line contents each time its "readline()" method is called.
If a "TextReader" instance is pickled, all attributes *except* the
file object member are saved. When the instance is unpickled, the file
is reopened, and reading resumes from the last location. The
"__setstate__()" and "__getstate__()" methods are used to implement
this behavior.

   class TextReader:
       """Print and number lines in a text file."""

       def __init__(self, filename):
           self.filename = filename
           self.file = open(filename)
           self.lineno = 0

       def readline(self):
           self.lineno += 1
           line = self.file.readline()
           if not line:
               return None
           if line.endswith('\n'):
               line = line[:-1]
           return "%i: %s" % (self.lineno, line)

       def __getstate__(self):
           # Copy the object's state from self.__dict__ which contains
           # all our instance attributes. Always use the dict.copy()
           # method to avoid modifying the original state.
           state = self.__dict__.copy()
           # Remove the unpicklable entries.
           del state['file']
           return state

       def __setstate__(self, state):
           # Restore instance attributes (i.e., filename and lineno).
           self.__dict__.update(state)
           # Restore the previously opened file's state. To do so, we need to
           # reopen it and read from it until the line count is restored.
           file = open(self.filename)
           for _ in range(self.lineno):
               file.readline()
           # Finally, save the file.
           self.file = file

A sample usage might be something like this:

   >>> reader = TextReader("hello.txt")
   >>> reader.readline()
   '1: Hello world!'
   >>> reader.readline()
   '2: I am line number two.'
   >>> new_reader = pickle.loads(pickle.dumps(reader))
   >>> new_reader.readline()
   '3: Goodbye!'


类型，函数和其他对象的自定义归约
================================

3.8 版新加入.

有时，"dispatch_table" 可能不够灵活。 特别是当我们想要基于对象类型以外
的其他规则来对封存进行定制，或是当我们想要对函数和类的封存进行定制的时
候。

对于那些情况，可能要基于 "Pickler" 类进行子类化并实现
"reducer_override()" 方法。 此方法可返回任意的归约元组 (参见
"__reduce__()")。 它也可以选择返回 "NotImplemented" 来回退到传统行为。

如果同时定义了 "dispatch_table" 和 "reducer_override()"，则
"reducer_override()" 方法具有优先权。

備註: 出于性能理由，可能不会为以下对象调用 "reducer_override()":
  "None", "True", "False", 以及 "int", "float", "bytes", "str",
  "dict", "set", "frozenset", "list" 和 "tuple" 的具体实例。

以下是一个简单的例子，其中我们允许封存并重新构建一个给定的类:

   import io
   import pickle

   class MyClass:
       my_attribute = 1

   class MyPickler(pickle.Pickler):
       def reducer_override(self, obj):
           """Custom reducer for MyClass."""
           if getattr(obj, "__name__", None) == "MyClass":
               return type, (obj.__name__, obj.__bases__,
                             {'my_attribute': obj.my_attribute})
           else:
               # For any other object, fallback to usual reduction
               return NotImplemented

   f = io.BytesIO()
   p = MyPickler(f)
   p.dump(MyClass)

   del MyClass

   unpickled_class = pickle.loads(f.getvalue())

   assert isinstance(unpickled_class, type)
   assert unpickled_class.__name__ == "MyClass"
   assert unpickled_class.my_attribute == 1


外部缓冲区
==========

3.8 版新加入.

在某些场景中，"pickle" 模块会被用来传输海量的数据。 因此，最小化内存复
制次数以保证性能和节省资源是很重要的。 但是 "pickle" 模块的正常运作会
将图类对象结构转换为字节序列流，因此在本质上就要从封存流中来回复制数据
。

如果 *provider* (待传输对象类型的实现) 和 *consumer* (通信系统的实现)
都支持 pickle 第 5 版或更高版本所提供的外部传输功能，则此约束可以被撤
销。


提供方 API
----------

大的待封存数据对象必须实现协议 5 及以上版本专属的 "__reduce_ex__()" 方
法，该方法将为任意大的数据返回一个 "PickleBuffer" 实例（而不是 "bytes"
对象等）。

"PickleBuffer" 对象会 *表明* 底层缓冲区可被用于外部数据传输。 那些对象
仍将保持与 "pickle" 模块的正常用法兼容。 但是，使用方也可以选择告知
"pickle" 它们将自行处理那些缓冲区。


使用方 API
----------

当序列化一个对象图时，通信系统可以启用对所生成 "PickleBuffer" 对象的定
制处理。

发送端需要传递 *buffer_callback* 参数到 "Pickler" (或是到 "dump()" 或
"dumps()" 函数)，该回调函数将在封存对象图时附带每个所生成的
"PickleBuffer" 被调用。 由 *buffer_callback* 所累积的缓冲区的数据将不
会被拷贝到 pickle 流，而是仅插入一个简单的标记。

接收端需要传递 *buffers* 参数到 "Unpickler" (或是到 "load()" 或
"loads()" 函数)，其值是一个由缓冲区组成的可迭代对象，它会被传递给
*buffer_callback*。 该可迭代对象应当按其被传递给 *buffer_callback* 时
的顺序产生缓冲区。 这些缓冲区将提供对象重构造器所期望的数据，对这些数
据的封存产生了原本的 "PickleBuffer" 对象。

在发送端和接受端之间，通信系统可以自由地实现它自己用于外部缓冲区的传输
机制。 潜在的优化包括使用共享内存或基于特定数据类型的压缩等。


示例
----

下面是一个小例子，在其中我们实现了一个 "bytearray" 的子类，能够用于外
部缓冲区封存:

   class ZeroCopyByteArray(bytearray):

       def __reduce_ex__(self, protocol):
           if protocol >= 5:
               return type(self)._reconstruct, (PickleBuffer(self),), None
           else:
               # PickleBuffer is forbidden with pickle protocols <= 4.
               return type(self)._reconstruct, (bytearray(self),)

       @classmethod
       def _reconstruct(cls, obj):
           with memoryview(obj) as m:
               # Get a handle over the original buffer object
               obj = m.obj
               if type(obj) is cls:
                   # Original buffer object is a ZeroCopyByteArray, return it
                   # as-is.
                   return obj
               else:
                   return cls(obj)

重构造器 ("_reconstruct" 类方法) 会在缓冲区的提供对象具有正确类型时返
回该对象。 在此小示例中这是模拟零拷贝行为的便捷方式。

在使用方，我们可以按通常方式封存那些对象，它们在反序列化时将提供原始对
象的一个副本:

   b = ZeroCopyByteArray(b"abc")
   data = pickle.dumps(b, protocol=5)
   new_b = pickle.loads(data)
   print(b == new_b)  # True
   print(b is new_b)  # False: a copy was made

但是如果我们传入 *buffer_callback* 然后在反序列化时给回累积的缓冲区，
我们就能够取回原始对象:

   b = ZeroCopyByteArray(b"abc")
   buffers = []
   data = pickle.dumps(b, protocol=5, buffer_callback=buffers.append)
   new_b = pickle.loads(data, buffers=buffers)
   print(b == new_b)  # True
   print(b is new_b)  # True: no copy was made

这个例子受限于 "bytearray" 会自行分配内存这一事实：你无法基于另一个对
象的内存创建 "bytearray" 的实例。 但是，第三方数据类型例如 NumPy 数组
则没有这种限制，允许在单独进程或系统间传输时使用零拷贝的封存（或是尽可
能少地拷贝） 。

也參考: **PEP 574** -- 带有外部数据缓冲区的 pickle 协议 5


Restricting Globals
===================

By default, unpickling will import any class or function that it finds
in the pickle data.  For many applications, this behaviour is
unacceptable as it permits the unpickler to import and invoke
arbitrary code.  Just consider what this hand-crafted pickle data
stream does when loaded:

   >>> import pickle
   >>> pickle.loads(b"cos\nsystem\n(S'echo hello world'\ntR.")
   hello world
   0

In this example, the unpickler imports the "os.system()" function and
then apply the string argument "echo hello world".  Although this
example is inoffensive, it is not difficult to imagine one that could
damage your system.

For this reason, you may want to control what gets unpickled by
customizing "Unpickler.find_class()".  Unlike its name suggests,
"Unpickler.find_class()" is called whenever a global (i.e., a class or
a function) is requested.  Thus it is possible to either completely
forbid globals or restrict them to a safe subset.

Here is an example of an unpickler allowing only few safe classes from
the "builtins" module to be loaded:

   import builtins
   import io
   import pickle

   safe_builtins = {
       'range',
       'complex',
       'set',
       'frozenset',
       'slice',
   }

   class RestrictedUnpickler(pickle.Unpickler):

       def find_class(self, module, name):
           # Only allow safe classes from builtins.
           if module == "builtins" and name in safe_builtins:
               return getattr(builtins, name)
           # Forbid everything else.
           raise pickle.UnpicklingError("global '%s.%s' is forbidden" %
                                        (module, name))

   def restricted_loads(s):
       """Helper function analogous to pickle.loads()."""
       return RestrictedUnpickler(io.BytesIO(s)).load()

A sample usage of our unpickler working has intended:

   >>> restricted_loads(pickle.dumps([1, 2, range(15)]))
   [1, 2, range(0, 15)]
   >>> restricted_loads(b"cos\nsystem\n(S'echo hello world'\ntR.")
   Traceback (most recent call last):
     ...
   pickle.UnpicklingError: global 'os.system' is forbidden
   >>> restricted_loads(b'cbuiltins\neval\n'
   ...                  b'(S\'getattr(__import__("os"), "system")'
   ...                  b'("echo hello world")\'\ntR.')
   Traceback (most recent call last):
     ...
   pickle.UnpicklingError: global 'builtins.eval' is forbidden

As our examples shows, you have to be careful with what you allow to
be unpickled.  Therefore if security is a concern, you may want to
consider alternatives such as the marshalling API in "xmlrpc.client"
or third-party solutions.


Performance
===========

Recent versions of the pickle protocol (from protocol 2 and upwards)
feature efficient binary encodings for several common features and
built-in types. Also, the "pickle" module has a transparent optimizer
written in C.


Examples
========

For the simplest code, use the "dump()" and "load()" functions.

   import pickle

   # An arbitrary collection of objects supported by pickle.
   data = {
       'a': [1, 2.0, 3, 4+6j],
       'b': ("character string", b"byte string"),
       'c': {None, True, False}
   }

   with open('data.pickle', 'wb') as f:
       # Pickle the 'data' dictionary using the highest protocol available.
       pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)

The following example reads the resulting pickled data.

   import pickle

   with open('data.pickle', 'rb') as f:
       # The protocol version used is detected automatically, so we do not
       # have to specify it.
       data = pickle.load(f)

也參考:

  Module "copyreg"
     Pickle interface constructor registration for extension types.

  Module "pickletools"
     Tools for working with and analyzing pickled data.

  Module "shelve"
     Indexed databases of objects; uses "pickle".

  Module "copy"
     Shallow and deep object copying.

  Module "marshal"
     High-performance serialization of built-in types.

-[ 註解 ]-

[1] Don't confuse this with the "marshal" module

[2] 这就是为什么 "lambda" 函数不可以被封存：所有的匿名函数都有同一
    个名 字："<lambda>"。

[3] The exception raised will likely be an "ImportError" or an
    "AttributeError" but it could be something else.

[4] The "copy" module uses this protocol for shallow and deep
    copying operations.

[5] The limitation on alphanumeric characters is due to the fact
    the persistent IDs, in protocol 0, are delimited by the newline
    character.  Therefore if any kind of newline characters occurs in
    persistent IDs, the resulting pickle will become unreadable.
