Pyhon Lru Cache with time expiration In this tutorial, you'll learn: How to expand the functionality of the @lru_cache decorator and make it expire after a specific time Python Development
Python Decorators Guide 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. Python Development
Fabulous Python Decorators @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. Python Development
Python Decorators Cheat Sheet 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: Python Development
Python decorator to parallelize any 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. Python Development
Python - How To Refresh an JWT Access Token Using Decorators 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. Python Security Development
How To Refresh an Access Token Using Decorators 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. Python Development Networking
How use Python retry decorator function with API 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. Python Development Networking
