In this tutorial, you will learn about how to cache frequently needed data in Python for making the code faster.
Caching is a programming technique to store frequently required data in a temporary location for faster access rather than requesting it from the main source every time.
Every Python programmer should be familiar with the concept of caching. In this tutorial, we will learn how to implement caching in a Python program using the cachetools Python library. cachetools includes a number of classes that implement caches using various cache algorithms derived from Cache class which, in turn, is derived from the collections.MutableMapping.
TTLCache is an implementation of LRU cache with a per-item time-to-live (TTL) value. Each cached item has a time-to-live value and the item will no longer be accessible after the time-to-live value expires.
from cachetools import TTLCache from datetime import datetime, timedelta #Creating cache where each item will be accessbile for 1 hour cache_data = TTLCache(maxsize=50000, ttl=timedelta(hours=1), timer=datetime.now) data_item1 = "http://example1.com" data_item2 = "http://example2.com" data_item3 = "http://example3.com" data_item4 = "http://example4.com" data_item5 = "http://example5.com" #Storing data in cache cache_data[hash(data_item1)] = data_item1 cache_data[hash(data_item2)] = data_item2 cache_data[hash(data_item3)] = data_item3 cache_data[hash(data_item4)] = data_item4 cache_data[hash(data_item5)] = data_item5 #Accessing data from cache item = cache_data.get(hash(data_item2), None) print("Getting from cache = ", item)
The output of the above example will look like this: