Python Function Caching
Caching means storing the data in a place from where it can be served faster. In the case of data that has been frequently used, the computer assigns a cache memory, so it does not have to load it again and again from the main memory. The purpose of the cache is to make the tasks more efficient and quicker. The same is true for web browsers; the pages we load again and again are stored in the cache for faster retrieval. In Python, however, we have to do it all manually, as the program will not store anything in the cache itself.
How to use function caching in Python?
Function caching is a way to improve code's performance by storing the function's return values. Before the 3.2 updates of Python, we had to create a cache ourselves by storing the value in a variable or by other such means. But in Python 3.2, there is a new update in the functools module of Python. To use this module, we have to import it first.
We have been facilitated with the help of a decorator known as lru_cache. Decorators are an essential part of Python. Decorators in Python can be used for a variety of different purposes.
We have to pass maxsize as a parameter with the decorator. maxsize is defined to tell the program how many values we want to store in the cache. It automatically stores the values for the latest number of calls.
We set the maxsize equal to 4, and the program uses the same call five times. Then, the program will only be able to retrieve the data faster for the last five calls because caching is only storing data for them. It is important to define the maxsize as per our requirements because it takes up memory accordingly, so for a better program, it should be precisely according to our needs.