introduction
Today we will talk about a very popular but often "disruptive" topic:Overoptimized. Many developers, especially those who are new to Python, are often fascinated by "advanced skills", which makes their programs neither concise nor easy to maintain. Have you also worked hard for a cool-looking feature for a whole day, but found that there was no substantial improvement at all?
So, let’s take a look with you today. What are the “advanced techniques” in Python development?Overoptimized, it should be avoided as much as possible!
1. Unnecessary metaprogramming
This question should be considered a classic "over-optimization" in Python. As we all know, Python has powerful metaprogramming capabilities, such as decorators, reflections, dynamic creation classes, etc. However, sometimes, when we write code, we unconsciously use these features in order to "look great". As a result, the program looks not concise and others will have a headache when they see it.
For example, like this:
class Meta(type):
def __new__(cls, name, bases, dct):
dct['new_method'] = lambda self: "Hello, I am a dynamically added method!"
return super().__new__(cls, name, bases, dct)
class MyClass(metaclass=Meta):
pass
obj = MyClass()
print(obj.new_method())
The code looks quite "advanced", right? Actually, this is just usedMetaclassTo add methods dynamically, the purpose is simple:MyClassThere is one classnew_methodmethod. But is it really necessary to use metaclasses for this purpose? The answer is:uncertain. In most cases, this technique does not make the code clearer, but instead increases the cost of understanding.
My advice is:If there is no special requirement, try to avoid metaprogramming. Python is already flexible enough that many times you can implement functions through ordinary inheritance or combination without making the code too complicated.
2. Premature optimization: Accelerate ahead of time rather than on-demand optimization
As developers, we always wonder, how can programs be written faster? Therefore, many people like to optimize the code in advance and use various techniques to speed up. In fact, this "premature optimization" way of thinking is wrong.
For example:
You just wrote a simple program, such as reading a file:
with open('', 'r') as file:
lines = ()
Then you start to worry about the file being too large and the reading speed is slow, so you add complex optimization methods such as parallelism and caching to this code. As a result, your program adds countless complexity with a few lines of code, andNot much performance improvement。
My advice is:If your program does not have obvious performance bottlenecks, try to avoid premature optimization. First write a version that works normally, then find the performance bottleneck through analysis and then do optimization. Remember, the optimization should beOn demandof.
3. Overuse of multi-threading and multi-processing
Python has multi-threaded and multi-process support, and many developers are used to using them from the beginning of the program, believing that this can improve concurrency performance.But in fact, the overhead of multi-threading and multi-processing is very large, and Python's global interpreter lock (GIL) will also reduce the performance of multi-threading.
Let's give a simple example:
If you have a simple task to handle, like looping through a list:
def process_item(item):
return item * 2
data = [1, 2, 3, 4, 5]
result = [process_item(item) for item in data]
This code has no performance problems at all. If you force multiple threads to speed up processing, it may lead to higher overhead and the code will become complicated. In case like this,No need at allUse multi-threading or multi-processing.
My advice is:Multithreading or multiprocessing is considered only when tasks are time-consuming enough and can be processed in parallel. For simple computing tasks, a single thread is enough.
4. Unnecessary functional programming skills
Python supports functional programming, everyone knows this. But sometimes,Overusepicturemap、filter、lambdaFunctional programming techniques such as this will make the code more obscure and affect readability. Especially for some beginners,lambdaNo matter how much you write, you won’t understand what the whole program is doing.
For example:
data = [1, 2, 3, 4, 5] result = list(map(lambda x: x * 2, filter(lambda x: x % 2 == 0, data)))
This code is usedmap、filterandlambda, it does look more "cool", but it may not be easy for other developers (or their future self). You might as well write it directly with a normal for loop, but it will be clearer:
data = [1, 2, 3, 4, 5]
result = []
for item in data:
if item % 2 == 0:
(item * 2)
My advice is:If you want the code to be readable, try to avoid too much functional programming. Python's list comprehension and regular loops are already powerful enough to meet most needs.
5. Overuse inheritance
Python is an object-oriented programming language, and inheritance is one of the most important features. But many times, developers may overuse inheritance to "extend" the functions of the class, resulting in complex code levels and difficult to understand.
For example, such nested inheritance:
class Animal:
def speak(self):
print("Animal speaks")
class Dog(Animal):
def speak(self):
print("Dog barks")
class Labrador(Dog):
def speak(self):
print("Labrador barks happily")
This code can be solved completely through combination. The increase in inheritance level not only makes the code more complex, but also makes it easy to appearProblems caused by multiple inheritance, such as diamond inheritance.
My advice is:When designing classes, if the inheritance level is not deep, try to use it as much as possiblecombinationNot inheritance. You can make the code more modular and easier to understand and extend by separating the behavior into different classes.
6. Abuse of magic methods
Python has many magic methods, such as__getattr__、__setattr__、__call__etc. These methods are indeed powerful, but they are also very easy to abuse. If you don't have a legitimate reason to use them,Try to avoid using magic methods。
For example:
class MyClass:
def __getattr__(self, name):
return f"Accessed nonexistent attribute: {name}"
obj = MyClass()
print(obj.some_nonexistent_attribute)
While this code looks cool, does it really bring any benefits? No, many times these magic methods make the code more difficult to understand and debug, especially when the project becomes more complex.
My advice is:Avoid magic methods if not very necessary. They make the behavior of the code less intuitive and are usually not suitable for most applications.
7. Unnecessarily complex design patterns
Sometimes, in order to be "high-end", people want to introduce various design patterns into their projects, such as singletons, observers, factories, etc. These design patterns are indeed very useful in some specific scenarios, butNot applicable to every project。
For example:
When you are just writing a simple configuration loader, you don't need to write code specifically to implement "singleton mode":
class Config:
_instance = None
def __new__(cls):
if not cls._instance:
cls._instance = super().__new__(cls)
return cls._instance
The introduction of this design pattern actually makes the code more complex and may not necessarily bring better results.
My advice is:When introducing design patterns,Consider the simplicity and maintainability of the code first, do not design patterns for the sake of design patterns.
Conclusion
Okay, that’s all for today’s content! Through these practical examples, I hope everyone can realize that not every seemingly "advanced" technique is suitable for use in their own code. Over-optimization will not only not make the code better, but may cause more trouble.
So, next time you write code, remember to treat "advanced skills" rationally and solve problems in the simplest way is the smartest choice.
The above are the detailed contents of 7 common scenarios for avoiding over-optimization in Python development. For more information on avoiding over-optimization in Python, please pay attention to my other related articles!