七个好用的装饰器

1、个好dispach

Python 天然支持多态，装饰但使用 dispatch 可以让你的个好代码更加容易阅读。

安装：

pip install multipledispatch

使用：

>>> from multipledispatch import dispatch

>>> @dispatch(int,装饰 int)

... def add(x, y):

... return x + y

>>> @dispatch(object, object)

... def add(x, y):

... return "%s + %s" % (x, y)

>>> add(1, 2)

3

>>> add(1, hello)

1 + hello

2、click

click 可以很方便地让你实现命令行工具。个好

安装：

pip install click

使用：demo2.py :

import click

@click.command()

@click.option(--count,装饰 default=1, help=Number of greetings.)

@click.option(--name, prompt=Your name,

help=The person to greet.)

def hello(count, name):

"""Simple program that greets NAME for a total of COUNT times."""

for x in range(count):

click.echo(f"Hello { name}!")

if __name__ == __main__:

hello()

运行结果：

❯ python demo2.py --count=3 --name=joih

Hello joih!

Hello joih!

Hello joih!

❯ python demo2.py --count=3

Your name: somenzz

Hello somenzz!

Hello somenzz!

Hello somenzz!

3、celery

分布式的个好任务队列，非 Celery 莫属。亿华云计算装饰

from celery import Celery

app = Celery(tasks,个好 broker=pyamqp://guest@localhost//)

@app.task

def add(x, y):

return x + y

4、deprecated

这个相信大家在使用别的装饰包时都遇到过，当要下线一个老版本的个好函数的时候就可以使用这个装饰器。

安装：

pip install Deprecated

使用：demo4.py

from deprecated import deprecated

@deprecated ("This function is 装饰deprecated, please do not use it")

def func1():

pass

func1()

运行效果如下：

❯ python demo4.py

demo4.py:6: DeprecationWarning: Call to deprecated function (or staticmethod) func1. (This function is deprecated, please do not use it)

func1()

5、deco.concurrent

安装：

pip install deco

使用 DECO 就像在 Python 程序中查找或创建两个函数一样简单。个好我们可以用 @concurrent 装饰需要并行运行的源码下载装饰函数，用 @synchronized 装饰调用并行函数的个好函数，使用举例：

from deco import concurrent, synchronized

@concurrent # We add this for the concurrent function

def process_url(url, data):

#Does some work which takes a while

return result

@synchronized # And we add this for the function which calls the concurrent function

def process_data_set(data):

results = { }

for url in urls:

results[url] = process_url(url, data)

return results

6、cachetools

缓存工具

安装：

pip install cachetools

使用：

from cachetools import cached, LRUCache, TTLCache

# speed up calculating Fibonacci numbers with dynamic programming

@cached(cache={ })

def fib(n):

return n if n < 2 else fib(n - 1) + fib(n - 2)

# cache least recently used Python Enhancement Proposals

@cached(cache=LRUCache(maxsize=32))

def get_pep(num):

url = http://www.python.org/dev/peps/pep-%04d/ % num

with urllib.request.urlopen(url) as s:

return s.read()

# cache weather data for no longer than ten minutes

@cached(cache=TTLCache(maxsize=1024, ttl=600))

def get_weather(place):

return owm.weather_at_place(place).get_weather()

 7、retry

重试装饰器，支持各种各样的重试需求。

安装：

pip install tenacity

使用：

import random

from tenacity import retry

@retry

def do_something_unreliable():

if random.randint(0, 10) > 1:

raise IOError("Broken sauce, everything is hosed!!!111one")

else:

return "Awesome sauce!"

@retry(stop=stop_after_attempt(7))

def stop_after_7_attempts():

print("Stopping after 7 attempts")

raise Exception

@retry(stop=stop_after_delay(10))

def stop_after_10_s():

print("Stopping after 10 seconds")

raise Exception

@retry(stop=(stop_after_delay(10) | stop_after_attempt(5)))

def stop_after_10_s_or_5_retries():

print("Stopping after 10 seconds or 5 retries")

raise Exception高防服务器