


Serialization and deserialization of Python objects are key aspects of any non-trivial program. If you save something to a Python file, you do object serialization and deserialization if you read the configuration file, or if you respond to an HTTP request.
Serialization and deserialization are the most boring things in the world in a sense. Who cares about all these formats and protocols? You want to persist or stream some Python objects and retrieve them in full at a later time.
This is a great way to see the world on a conceptual level. However, on a practical level, the serialization scheme, format, or protocol you choose may determine how quickly the program runs, security, freedom of maintenance status, and the degree of interoperability with other systems.
There are so many options because different situations require different solutions. The "one-size-fits-all" approach doesn't work. In this two-part tutorial, I will:
- Overview of the advantages and disadvantages of the most successful serialization and deserialization schemes
- Show how to use them
- Provides guidelines for choosing between specific use cases
Running example
In the following section, we will serialize and deserialize the same Python object graph using different serializers. To avoid duplication, let's define these object graphs here.
Simple object diagram
A simple object graph is a dictionary containing a list of integers, strings, floating point numbers, boolean and datetime objects, as well as a user-defined class instance with dump, load, and dump() methods that can be serialized to an open file (file-like object).
-
The
load() method deserializes from an open file-like object.
-
TypeError: as follows: ``` Traceback (most recent call last):
File "serialize.py", line 49, in
print(json.dumps(complex)
File "/usr/lib/python3.8/json/init.py", line 231, in dumps
return _default_encoder.encode(obj)
File "/usr/lib/python3.8/json/encoder.py", line 199, in encode
chunks = self.iterencode(o, _one_shot=True)
File "/usr/lib/python3.8/json/encoder.py", line 257, in iterencode
return _iterencode(o, 0)
File "/usr/lib/python3.8/json/encoder.py", line 179, in default
raise TypeError(f'Object of type {o.class.name} '
TypeError: Object of type A is not JSON serializable<code> 哇!這看起來一點(diǎn)也不好。發(fā)生了什么?錯(cuò)誤消息是 JSONEncoder 類使用的 default() 方法在 JSON 編碼器遇到無法序列化的對(duì)象時(shí)調(diào)用的。 自定義編碼器的任務(wù)是將其轉(zhuǎn)換為 JSON 編碼器能夠編碼的 Python 對(duì)象圖。在本例中,我們有兩個(gè)需要特殊編碼的對(duì)象:A 類。以下編碼器可以完成這項(xiàng)工作。每個(gè)特殊對(duì)象都轉(zhuǎn)換為“\_\_A\_\_”和 pprint 函數(shù)的 load() 和 object_hook 參數(shù),允許您提供自定義函數(shù)來將字典轉(zhuǎn)換為對(duì)象。 </code>
def decode_object(o):
if 'A' in o:
a = A()
a.dict.update(o['A'])
return a
elif 'datetime' in o:
return datetime.strptime(o['datetime'], '%Y-%m-%dT%H:%M:%S')
return o<code> 讓我們使用 object_hook 參數(shù)進(jìn)行解碼。 </code>
deserialized = json.loads(serialized, object_hook=decode_object)
print(deserialized)
# prints: {'a': <main.a at="" object="">, 'when': datetime.datetime(2016, 3, 7, 0, 0)}
deserialized == complex
# evaluates to False
main.a><code> 結(jié)論 ---------- 在本教程的第一部分中,您學(xué)習(xí)了 Python 對(duì)象序列化和反序列化的通用概念,并探討了使用 Pickle 和 JSON 序列化 Python 對(duì)象的來龍去脈。 在第二部分中,您將學(xué)習(xí) YAML、性能和安全問題,以及對(duì)其他序列化方案的快速回顧。 *這篇文章已更新,并包含 Esther Vaati 的貢獻(xiàn)。Esther 是 Envato Tuts+ 的軟件開發(fā)人員和撰稿人。*</code>
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