pickle —— Python 對象序列化 · Python3.7.3官方文檔簡體中文

### 導航 - [索引](../genindex.xhtml "總目錄") - [模塊](../py-modindex.xhtml "Python 模塊索引") | - [下一頁](copyreg.xhtml "copyreg --- Register pickle support functions") | - [上一頁](persistence.xhtml "數據持久化") | - ![](https://box.kancloud.cn/a721fc7ec672275e257bbbfde49a4d4e_16x16.png) - [Python](https://www.python.org/) ? - zh\_CN 3.7.3 [文檔](../index.xhtml) ? - [Python 標準庫](index.xhtml) ? - [數據持久化](persistence.xhtml) ? - $('.inline-search').show(0); | # [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") —— Python 對象序列化 **源代碼：** [Lib/pickle.py](https://github.com/python/cpython/tree/3.7/Lib/pickle.py) \[https://github.com/python/cpython/tree/3.7/Lib/pickle.py\] - - - - - - 模塊 [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") 實現了對一個 Python 對象結構的二進制序列化和反序列化。 *"Pickling"* 是將 Python 對象和所擁有的層次結構被轉化為一個字節流的過程，而 *"unpickling"* 是相反的操作，會將（來自一個 [binary file](../glossary.xhtml#term-binary-file) 或者 [bytes-like object](../glossary.xhtml#term-bytes-like-object) 的）字節流轉化回一個對象層次結構。Pickling（和 unpickling）也被稱為“序列化”, “編組” [1](#id6) 或者 “平面化”。而為了避免混亂，此處采用術語 “pickling” 和 “unpickling”。警告 [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") 模塊在接受被錯誤地構造或者被惡意地構造的數據時不安全。永遠不要 unpickle 來自于不受信任的或者未經驗證的來源的數據。 ## 與其他 Python 模塊間的關系 ### 與 `marshal` 間的關系 Python 有一個更原始的序列化模塊稱為 [`marshal`](marshal.xhtml#module-marshal "marshal: Convert Python objects to streams of bytes and back (with different constraints).")，但一般地 [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") 應該是序列化 Python 對象時的首選。[`marshal`](marshal.xhtml#module-marshal "marshal: Convert Python objects to streams of bytes and back (with different constraints).") 存在主要是為了支持 Python 的 `.pyc` 文件. [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") 模塊與 [`marshal`](marshal.xhtml#module-marshal "marshal: Convert Python objects to streams of bytes and back (with different constraints).") 在如下幾方面顯著地不同： - [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") 模塊會跟蹤已被序列化的對象，所以該對象之后再次被引用時不會再次被序列化。[`marshal`](marshal.xhtml#module-marshal "marshal: Convert Python objects to streams of bytes and back (with different constraints).") 不會這么做。這隱含了遞歸對象和共享對象。遞歸對象指包含對自己的引用的對象。這種對象并不會被 marshal 接受，并且實際上嘗試 marshal 遞歸對象會讓你的 Python 解釋器崩潰。對象共享發生在對象層級中存在多處引用同一對象時。[`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") 只會存儲這些對象一次，并確保其他的引用指向同一個主副本。共享對象將保持共享，這可能對可變對象非常重要。 - [`marshal`](marshal.xhtml#module-marshal "marshal: Convert Python objects to streams of bytes and back (with different constraints).") 不能被用于序列化用戶定義類及其實例。[`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") 能夠透明地存儲并保存類實例，然而此時類定義必須能夠從與被存儲時相同的模塊被引入。 - The [`marshal`](marshal.xhtml#module-marshal "marshal: Convert Python objects to streams of bytes and back (with different constraints).") serialization format is not guaranteed to be portable across Python versions. Because its primary job in life is to support `.pyc` files, the Python implementers reserve the right to change the serialization format in non-backwards compatible ways should the need arise. The [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") serialization format is guaranteed to be backwards compatible across Python releases provided a compatible pickle protocol is chosen and pickling and unpickling code deals with Python 2 to Python 3 type differences if your data is crossing that unique breaking change language boundary. ### 與 `json` 模塊的比較 Pickle 協議和 [JSON (JavaScript Object Notation)](http://json.org) \[http://json.org\] 間有著本質的不同： - JSON 是一個文本序列化格式（它輸出 unicode 文本，盡管在大多數時候它會接著以 `utf-8` 編碼），而 pickle 是一個二進制序列化格式； - JSON is human-readable, while pickle is not; - JSON is interoperable and widely used outside of the Python ecosystem, while pickle is Python-specific; - JSON, by default, can only represent a subset of the Python built-in types, and no custom classes; pickle can represent an extremely large number of Python types (many of them automatically, by clever usage of Python's introspection facilities; complex cases can be tackled by implementing [specific object APIs](#pickle-inst)). 參見 The [`json`](json.xhtml#module-json "json: Encode and decode the JSON format.") module: a standard library module allowing JSON serialization and deserialization. ## Data stream format The data format used by [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") 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`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") data format uses a relatively compact binary representation. If you need optimal size characteristics, you can efficiently [compress](archiving.xhtml) pickled data. The module [`pickletools`](pickletools.xhtml#module-pickletools "pickletools: Contains extensive comments about the pickle protocols and pickle-machine opcodes, as well as some useful functions.") contains tools for analyzing data streams generated by [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back."). [`pickletools`](pickletools.xhtml#module-pickletools "pickletools: Contains extensive comments about the pickle protocols and pickle-machine opcodes, as well as some useful functions.") source code has extensive comments about opcodes used by pickle protocols. There are currently 5 different protocols which can be used for pickling. The higher the protocol used, the more recent the version of Python needed to read the pickle produced. - 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](../glossary.xhtml#term-new-style-class)es. Refer to [**PEP 307**](https://www.python.org/dev/peps/pep-0307) \[https://www.python.org/dev/peps/pep-0307\] for information about improvements brought by protocol 2. - Protocol version 3 was added in Python 3.0. It has explicit support for [`bytes`](stdtypes.xhtml#bytes "bytes") objects and cannot be unpickled by Python 2.x. This is the default protocol, and the recommended protocol when compatibility with other Python 3 versions is required. - Protocol version 4 was added in Python 3.4. It adds support for very large objects, pickling more kinds of objects, and some data format optimizations. Refer to [**PEP 3154**](https://www.python.org/dev/peps/pep-3154) \[https://www.python.org/dev/peps/pep-3154\] for information about improvements brought by protocol 4. 注解 Serialization is a more primitive notion than persistence; although [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") 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-pickle "pickle: Convert Python objects to streams of bytes and back.") 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`](shelve.xhtml#module-shelve "shelve: Python object persistence.")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()`](#pickle.dumps "pickle.dumps") function. Similarly, to de-serialize a data stream, you call the [`loads()`](#pickle.loads "pickle.loads") function. However, if you want more control over serialization and de-serialization, you can create a [`Pickler`](#pickle.Pickler "pickle.Pickler") or an [`Unpickler`](#pickle.Unpickler "pickle.Unpickler") object, respectively. The [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") module provides the following constants: `pickle.``HIGHEST_PROTOCOL`An integer, the highest [protocol version](#pickle-protocols)available. This value can be passed as a *protocol* value to functions [`dump()`](#pickle.dump "pickle.dump") and [`dumps()`](#pickle.dumps "pickle.dumps") as well as the [`Pickler`](#pickle.Pickler "pickle.Pickler")constructor. `pickle.``DEFAULT_PROTOCOL`An integer, the default [protocol version](#pickle-protocols) used for pickling. May be less than [`HIGHEST_PROTOCOL`](#pickle.HIGHEST_PROTOCOL "pickle.HIGHEST_PROTOCOL"). Currently the default protocol is 3, a new protocol designed for Python 3. The [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") module provides the following functions to make the pickling process more convenient: `pickle.``dump`(*obj*, *file*, *protocol=None*, *\**, *fix\_imports=True*)Write a pickled representation of *obj* to the open [file object](../glossary.xhtml#term-file-object) *file*. This is equivalent to `Pickler(file, protocol).dump(obj)`. The optional *protocol* argument, an integer, tells the pickler to use the given protocol; supported protocols are 0 to [`HIGHEST_PROTOCOL`](#pickle.HIGHEST_PROTOCOL "pickle.HIGHEST_PROTOCOL"). If not specified, the default is [`DEFAULT_PROTOCOL`](#pickle.DEFAULT_PROTOCOL "pickle.DEFAULT_PROTOCOL"). If a negative number is specified, [`HIGHEST_PROTOCOL`](#pickle.HIGHEST_PROTOCOL "pickle.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`](io.xhtml#io.BytesIO "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. `pickle.``dumps`(*obj*, *protocol=None*, *\**, *fix\_imports=True*)Return the pickled representation of the object as a [`bytes`](stdtypes.xhtml#bytes "bytes") object, instead of writing it to a file. Arguments *protocol* and *fix\_imports* have the same meaning as in [`dump()`](#pickle.dump "pickle.dump"). `pickle.``load`(*file*, *\**, *fix\_imports=True*, *encoding="ASCII"*, *errors="strict"*)Read a pickled object representation from the open [file object](../glossary.xhtml#term-file-object)*file* and return the reconstituted object hierarchy specified therein. This is equivalent to `Unpickler(file).load()`. The protocol version of the pickle is detected automatically, so no protocol argument is needed. Bytes past the pickled object's representation are ignored. The argument *file* must have two methods, a read() method that takes an integer argument, and a readline() method that requires no arguments. Both methods should return bytes. Thus *file* can be an on-disk file opened for binary reading, an [`io.BytesIO`](io.xhtml#io.BytesIO "io.BytesIO") object, or any other custom object that meets this interface. Optional keyword arguments are *fix\_imports*, *encoding* and *errors*, which are used to control compatibility support for pickle stream generated by Python 2. If *fix\_imports* is true, pickle will try to map the old Python 2 names to the new names used in Python 3. The *encoding* and *errors* tell pickle how to decode 8-bit string instances pickled by Python 2; these default to 'ASCII' and 'strict', respectively. The *encoding* can be 'bytes' to read these 8-bit string instances as bytes objects. Using `encoding='latin1'` is required for unpickling NumPy arrays and instances of [`datetime`](datetime.xhtml#datetime.datetime "datetime.datetime"), [`date`](datetime.xhtml#datetime.date "datetime.date") and [`time`](datetime.xhtml#datetime.time "datetime.time") pickled by Python 2. `pickle.``loads`(*bytes\_object*, *\**, *fix\_imports=True*, *encoding="ASCII"*, *errors="strict"*)Read a pickled object hierarchy from a [`bytes`](stdtypes.xhtml#bytes "bytes") object and return the reconstituted object hierarchy specified therein. The protocol version of the pickle is detected automatically, so no protocol argument is needed. Bytes past the pickled object's representation are ignored. Optional keyword arguments are *fix\_imports*, *encoding* and *errors*, which are used to control compatibility support for pickle stream generated by Python 2. If *fix\_imports* is true, pickle will try to map the old Python 2 names to the new names used in Python 3. The *encoding* and *errors* tell pickle how to decode 8-bit string instances pickled by Python 2; these default to 'ASCII' and 'strict', respectively. The *encoding* can be 'bytes' to read these 8-bit string instances as bytes objects. Using `encoding='latin1'` is required for unpickling NumPy arrays and instances of [`datetime`](datetime.xhtml#datetime.datetime "datetime.datetime"), [`date`](datetime.xhtml#datetime.date "datetime.date") and [`time`](datetime.xhtml#datetime.time "datetime.time") pickled by Python 2. The [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") module defines three exceptions: *exception* `pickle.``PickleError`Common base class for the other pickling exceptions. It inherits [`Exception`](exceptions.xhtml#Exception "Exception"). *exception* `pickle.``PicklingError`Error raised when an unpicklable object is encountered by [`Pickler`](#pickle.Pickler "pickle.Pickler"). It inherits [`PickleError`](#pickle.PickleError "pickle.PickleError"). Refer to [What can be pickled and unpickled?](#pickle-picklable) 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`](#pickle.PickleError "pickle.PickleError"). Note that other exceptions may also be raised during unpickling, including (but not necessarily limited to) AttributeError, EOFError, ImportError, and IndexError. The [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.") module exports two classes, [`Pickler`](#pickle.Pickler "pickle.Pickler") and [`Unpickler`](#pickle.Unpickler "pickle.Unpickler"): *class* `pickle.``Pickler`(*file*, *protocol=None*, *\**, *fix\_imports=True*)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`](#pickle.HIGHEST_PROTOCOL "pickle.HIGHEST_PROTOCOL"). If not specified, the default is [`DEFAULT_PROTOCOL`](#pickle.DEFAULT_PROTOCOL "pickle.DEFAULT_PROTOCOL"). If a negative number is specified, [`HIGHEST_PROTOCOL`](#pickle.HIGHEST_PROTOCOL "pickle.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`](io.xhtml#io.BytesIO "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. `dump`(*obj*)Write a pickled representation of *obj* to the open file object given in the constructor. `persistent_id`(*obj*)Do nothing by default. This exists so a subclass can override it. If [`persistent_id()`](#pickle.Pickler.persistent_id "pickle.Pickler.persistent_id") returns `None`, *obj* is pickled as usual. Any other value causes [`Pickler`](#pickle.Pickler "pickle.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()`](#pickle.Unpickler.persistent_load "pickle.Unpickler.persistent_load"). Note that the value returned by [`persistent_id()`](#pickle.Pickler.persistent_id "pickle.Pickler.persistent_id") cannot itself have a persistent ID. See [Persistence of External Objects](#pickle-persistent) 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()`](copyreg.xhtml#copyreg.pickle "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__()`](#object.__reduce__ "object.__reduce__")method. By default, a pickler object will not have a [`dispatch_table`](#pickle.Pickler.dispatch_table "pickle.Pickler.dispatch_table") attribute, and it will instead use the global dispatch table managed by the [`copyreg`](copyreg.xhtml#module-copyreg "copyreg: Register pickle support functions.") module. However, to customize the pickling for a specific pickler object one can set the [`dispatch_table`](#pickle.Pickler.dispatch_table "pickle.Pickler.dispatch_table") attribute to a dict-like object. Alternatively, if a subclass of [`Pickler`](#pickle.Pickler "pickle.Pickler") has a [`dispatch_table`](#pickle.Pickler.dispatch_table "pickle.Pickler.dispatch_table") attribute then this will be used as the default dispatch table for instances of that class. See [Dispatch Tables](#pickle-dispatch) for usage examples. 3\.3 新版功能. `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`](#pickle.Pickler "pickle.Pickler") to recurse infinitely. Use [`pickletools.optimize()`](pickletools.xhtml#pickletools.optimize "pickletools.optimize") if you need more compact pickles. *class* `pickle.``Unpickler`(*file*, *\**, *fix\_imports=True*, *encoding="ASCII"*, *errors="strict"*)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. The argument *file* must have two methods, a read() method that takes an integer argument, and a readline() method that requires no arguments. Both methods should return bytes. Thus *file* can be an on-disk file object opened for binary reading, an [`io.BytesIO`](io.xhtml#io.BytesIO "io.BytesIO") object, or any other custom object that meets this interface. Optional keyword arguments are *fix\_imports*, *encoding* and *errors*, which are used to control compatibility support for pickle stream generated by Python 2. If *fix\_imports* is true, pickle will try to map the old Python 2 names to the new names used in Python 3. The *encoding* and *errors* tell pickle how to decode 8-bit string instances pickled by Python 2; these default to 'ASCII' and 'strict', respectively. The *encoding* can be 'bytes' to read these 8-bit string instances as bytes objects. `load`()Read a pickled object representation from the open file object given in the constructor, and return the reconstituted object hierarchy specified therein. Bytes past the pickled object's representation are ignored. `persistent_load`(*pid*)Raise an [`UnpicklingError`](#pickle.UnpicklingError "pickle.UnpicklingError") by default. If defined, [`persistent_load()`](#pickle.Unpickler.persistent_load "pickle.Unpickler.persistent_load") should return the object specified by the persistent ID *pid*. If an invalid persistent ID is encountered, an [`UnpicklingError`](#pickle.UnpicklingError "pickle.UnpicklingError") should be raised. See [Persistence of External Objects](#pickle-persistent) 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`](stdtypes.xhtml#str "str") objects. Note, unlike its name suggests, [`find_class()`](#pickle.Unpickler.find_class "pickle.Unpickler.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](#pickle-restrict) for details. ## 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`](../reference/compound_stmts.xhtml#def), not [`lambda`](../reference/expressions.xhtml#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__`](stdtypes.xhtml#object.__dict__ "object.__dict__") or the result of calling [`__getstate__()`](#object.__getstate__ "object.__getstate__") is picklable (see section [Pickling Class Instances](#pickle-inst) for details). Attempts to pickle unpicklable objects will raise the [`PicklingError`](#pickle.PicklingError "pickle.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`](exceptions.xhtml#RecursionError "RecursionError") will be raised in this case. You can carefully raise this limit with [`sys.setrecursionlimit()`](sys.xhtml#sys.setrecursionlimit "sys.setrecursionlimit"). Note that functions (built-in and user-defined) are pickled by "fully qualified" name reference, not by value. [2](#id7) 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](#id8) 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__()`](#object.__setstate__ "object.__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__()`](../reference/datamodel.xhtml#object.__init__ "object.__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__()`](#object.__getnewargs_ex__ "object.__getnewargs_ex__") method can dictate the values passed to the [`__new__()`](../reference/datamodel.xhtml#object.__new__ "object.__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__()`](../reference/datamodel.xhtml#object.__new__ "object.__new__") method upon unpickling. You should implement this method if the [`__new__()`](../reference/datamodel.xhtml#object.__new__ "object.__new__") method of your class requires keyword-only arguments. Otherwise, it is recommended for compatibility to implement [`__getnewargs__()`](#object.__getnewargs__ "object.__getnewargs__"). 在 3.6 版更改: [`__getnewargs_ex__()`](#object.__getnewargs_ex__ "object.__getnewargs_ex__") is now used in protocols 2 and 3. `object.``__getnewargs__`()This method serves a similar purpose as [`__getnewargs_ex__()`](#object.__getnewargs_ex__ "object.__getnewargs_ex__"), but supports only positional arguments. It must return a tuple of arguments `args` which will be passed to the [`__new__()`](../reference/datamodel.xhtml#object.__new__ "object.__new__") method upon unpickling. [`__getnewargs__()`](#object.__getnewargs__ "object.__getnewargs__") will not be called if [`__getnewargs_ex__()`](#object.__getnewargs_ex__ "object.__getnewargs_ex__") is defined. 在 3.6 版更改: Before Python 3.6, [`__getnewargs__()`](#object.__getnewargs__ "object.__getnewargs__") was called instead of [`__getnewargs_ex__()`](#object.__getnewargs_ex__ "object.__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__()`](#object.__getstate__ "object.__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__()`](#object.__getstate__ "object.__getstate__") method is absent, the instance's [`__dict__`](stdtypes.xhtml#object.__dict__ "object.__dict__") is pickled as usual. `object.``__setstate__`(*state*)Upon unpickling, if the class defines [`__setstate__()`](#object.__setstate__ "object.__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. 注解 If [`__getstate__()`](#object.__getstate__ "object.__getstate__") returns a false value, the [`__setstate__()`](#object.__setstate__ "object.__setstate__")method will not be called upon unpickling. Refer to the section [Handling Stateful Objects](#pickle-state) for more information about how to use the methods [`__getstate__()`](#object.__getstate__ "object.__getstate__") and [`__setstate__()`](#object.__setstate__ "object.__setstate__"). 注解 At unpickling time, some methods like [`__getattr__()`](../reference/datamodel.xhtml#object.__getattr__ "object.__getattr__"), [`__getattribute__()`](../reference/datamodel.xhtml#object.__getattribute__ "object.__getattribute__"), or [`__setattr__()`](../reference/datamodel.xhtml#object.__setattr__ "object.__setattr__") may be called upon the instance. In case those methods rely on some internal invariant being true, the type should implement [`__getnewargs__()`](#object.__getnewargs__ "object.__getnewargs__") or [`__getnewargs_ex__()`](#object.__getnewargs_ex__ "object.__getnewargs_ex__") to establish such an invariant; otherwise, neither [`__new__()`](../reference/datamodel.xhtml#object.__new__ "object.__new__") nor [`__init__()`](../reference/datamodel.xhtml#object.__init__ "object.__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__()`](#object.__reduce__ "object.__reduce__") special method. The copy protocol provides a unified interface for retrieving the data necessary for pickling and copying objects. [4](#id9) Although powerful, implementing [`__reduce__()`](#object.__reduce__ "object.__reduce__") directly in your classes is error prone. For this reason, class designers should use the high-level interface (i.e., [`__getnewargs_ex__()`](#object.__getnewargs_ex__ "object.__getnewargs_ex__"), [`__getstate__()`](#object.__getstate__ "object.__getstate__") and [`__setstate__()`](#object.__setstate__ "object.__setstate__")) whenever possible. We will show, however, cases where using [`__reduce__()`](#object.__reduce__ "object.__reduce__") is the only option or leads to more efficient pickling or both. `object.``__reduce__`()The interface is currently defined as follows. The [`__reduce__()`](#object.__reduce__ "object.__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. When a tuple is returned, it must be between two and five items long. Optional items can either be omitted, or `None` can be provided as their value. The semantics of each item are in order: - 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__()`](#object.__setstate__ "object.__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__`](stdtypes.xhtml#object.__dict__ "object.__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__()`](../reference/datamodel.xhtml#object.__setitem__ "object.__setitem__"). `object.``__reduce_ex__`(*protocol*)Alternatively, a [`__reduce_ex__()`](#object.__reduce_ex__ "object.__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__()`](#object.__reduce__ "object.__reduce__")method. In addition, [`__reduce__()`](#object.__reduce__ "object.__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-pickle "pickle: Convert Python objects to streams of bytes and back.") 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](#id10) or just an arbitrary object (for any newer protocol). The resolution of such persistent IDs is not defined by the [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back.")module; it will delegate this resolution to the user defined methods on the pickler and unpickler, [`persistent_id()`](#pickle.Pickler.persistent_id "pickle.Pickler.persistent_id") and [`persistent_load()`](#pickle.Unpickler.persistent_load "pickle.Unpickler.persistent_load") respectively. To pickle objects that have an external persistent id, the pickler must have a custom [`persistent_id()`](#pickle.Pickler.persistent_id "pickle.Pickler.persistent_id") method that takes an object as an argument and returns either `None` or the persistent id for that object. When `None` is returned, the pickler simply pickles the object as normal. When a persistent ID string is returned, the pickler will pickle that object, along with a marker so that the unpickler will recognize it as a persistent ID. To unpickle external objects, the unpickler must have a custom [`persistent_load()`](#pickle.Unpickler.persistent_load "pickle.Unpickler.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`](copyreg.xhtml#module-copyreg "copyreg: Register pickle support functions.") 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`](#pickle.Pickler "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`](copyreg.xhtml#module-copyreg "copyreg: Register pickle support functions.") 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__()`](#object.__setstate__ "object.__setstate__") and [`__getstate__()`](#object.__getstate__ "object.__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!' ``` ## 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()`](os.xhtml#os.system "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()`](#pickle.Unpickler.find_class "pickle.Unpickler.find_class"). Unlike its name suggests, [`Unpickler.find_class()`](#pickle.Unpickler.find_class "pickle.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`](builtins.xhtml#module-builtins "builtins: The module that provides the built-in namespace.") 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`](xmlrpc.client.xhtml#module-xmlrpc.client "xmlrpc.client: XML-RPC client access.") or third-party solutions. ## 性能 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-pickle "pickle: Convert Python objects to streams of bytes and back.") module has a transparent optimizer written in C. ## 示例 For the simplest code, use the [`dump()`](#pickle.dump "pickle.dump") and [`load()`](#pickle.load "pickle.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) ``` 參見 Module [`copyreg`](copyreg.xhtml#module-copyreg "copyreg: Register pickle support functions.")Pickle interface constructor registration for extension types. Module [`pickletools`](pickletools.xhtml#module-pickletools "pickletools: Contains extensive comments about the pickle protocols and pickle-machine opcodes, as well as some useful functions.")Tools for working with and analyzing pickled data. 模塊 [`shelve`](shelve.xhtml#module-shelve "shelve: Python object persistence.")Indexed databases of objects; uses [`pickle`](#module-pickle "pickle: Convert Python objects to streams of bytes and back."). Module [`copy`](copy.xhtml#module-copy "copy: Shallow and deep copy operations.")Shallow and deep object copying. Module [`marshal`](marshal.xhtml#module-marshal "marshal: Convert Python objects to streams of bytes and back (with different constraints).")High-performance serialization of built-in types. 腳注 [1](#id1)Don't confuse this with the [`marshal`](marshal.xhtml#module-marshal "marshal: Convert Python objects to streams of bytes and back (with different constraints).") module [2](#id2)This is why [`lambda`](../reference/expressions.xhtml#lambda) functions cannot be pickled: all `lambda` functions share the same name: `<lambda>`. [3](#id3)The exception raised will likely be an [`ImportError`](exceptions.xhtml#ImportError "ImportError") or an [`AttributeError`](exceptions.xhtml#AttributeError "AttributeError") but it could be something else. [4](#id4)The [`copy`](copy.xhtml#module-copy "copy: Shallow and deep copy operations.") module uses this protocol for shallow and deep copying operations. [5](#id5)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. ### 導航 - [索引](../genindex.xhtml "總目錄") - [模塊](../py-modindex.xhtml "Python 模塊索引") | - [下一頁](copyreg.xhtml "copyreg --- Register pickle support functions") | - [上一頁](persistence.xhtml "數據持久化") | - ![](https://box.kancloud.cn/a721fc7ec672275e257bbbfde49a4d4e_16x16.png) - [Python](https://www.python.org/) ? - zh\_CN 3.7.3 [文檔](../index.xhtml) ? - [Python 標準庫](index.xhtml) ? - [數據持久化](persistence.xhtml) ? - $('.inline-search').show(0); | ? [版權所有](../copyright.xhtml) 2001-2019, Python Software Foundation. Python 軟件基金會是一個非盈利組織。 [請捐助。](https://www.python.org/psf/donations/) 最后更新于 5月 21, 2019. [發現了問題](../bugs.xhtml)？使用[Sphinx](http://sphinx.pocoo.org/)1.8.4 創建。