The Power of Python Decorators At their core, Python’s decorators allow you to extend and modify the behavior of a callable (functions, methods, and classes) without permanently modifying the callable itself. Any sufficiently generic functionality you can tack on to an existing class or function’s behavior makes a great use case for decoration. This includes the following: logging enforcing access control and authentication instrumentation and timing functions rate-limiting caching and more Sure, decorators are relatively complicated to wrap your head around for the first time, but they’re a highly useful feature that you’ll often encounter in third-party frameworks and the Python standard library.
@cache @functools.cache(user_function) Simple lightweight unbounded function cache. Sometimes called "memoize" . Returns the same as lru_cache(maxsize=None), creating a thin wrapper around a dictionary lookup for the function arguments. Because it never needs to evict (remove) old values, this is smaller and faster than lru_cache() with a size limit. example: from functools import cache @cache def factorial(n): return n * factorial(n-1) if n else 1 >>> factorial(10) # no previously cached result, makes 11 recursive calls 3628800 >>> factorial.
Using decorators The normal way of using a decorator is by specifying it just before the definition of the function you want to decorate: @decorator def f(arg_1, arg_2): ... If you want to decorate an already existing function you can use the following syntax: f = decorator(f) Decorator not changing the decorated function If you don’t want to change the decorated function, a decorator is simply a function taking in and returning a function:
Wouldn’t it be cool if you can speed up your program by just adding a decorator to the function? Wouldn’t it be cool if you don’t have to worry about running the data in a list as a parallelly? Today we are going to write a python decorator which exactly does these automatically for you, so that you can concentrate more on the logics of your code than worrying about multi-threading issues.
When you create a big python application, you can began to run into challenges with longer scripts that extended beyond the expiration of a single JWT. To elegantly solve this, You can use decorators to check the token’s expiration and request a new token if necessary. This article goes over the framework I set up so that you can apply a similar mechanism in your own scripts. Setting the Scene import time import requests class myAPI(): host = None key = None secret = None access_token = None access_token_expiration = None def __init__(self,host,key,secret): # the function that is executed when # an instance of the class is created pass def getAccessToken(self): # the function that is # used to request the JWT pass class Decorators(): @staticmethod def refreshToken(decorated): # the function that is used to check # the JWT and refresh if necessary pass Our class will work by requiring the fields necessary to request the JWT.
When I was creating a one script, which uses JWT for authentication, I began to run into challenges with longer scripts that extended beyond the expiration of a single JWT. To elegantly solve this, I used decorators to check the token’s expiration and request a new token if necessary. This article goes over the framework I set up so that you can apply a similar mechanism in your own scripts.
An ever-increasing proportion of a typical company’s infrastructure is moving to the cloud. More companies are shifting towards a micro-service approach. These paradigm shifts away from local to cloud-based means that you probably also have faced a situation where you had to pull data from somewhere or write data somewhere that is not your local computer. On a small scale, there rarely are problems around that. If some extraction or writeback fails, you would typically notice that and would be able to remedy the mistake.