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Use built-in caching mechanisms to improve efficiency

Caching is a method of storing quantitative data for subsequent acquisition needs, designed to speed up data acquisition.

The data generation process may require operations such as computation, regularization, and remote retrieval. If the same data needs to be used multiple times, each regeneration will waste time.

Therefore, if data obtained through operations such as computation or remote request is cached, subsequent data acquisition requirements will be accelerated.

Python 3.2+ provides us with an easy-to-implement mechanism to achieve this without requiring you to write such logic code.

This mechanism is implemented in the lru_cache decorator of the functool module.

Parameter explanation:

maxsize: Maximum number of results that can be cached for this function call. If None, there is no limit. When set to a power of 2, performance is best.

Type: If True, calls of different parameter types are cached separately.

For example:

The output is as follows, and you can see that the second call does not execute the function body, but returns the result directly to the cache:

calculating: 1 + 233 calculating: 2 + 35

The following is a classic Fibonacci sequence, where there is a lot of duplication when you specify a larger n

The timeit described in point 6 can now be used to test how much efficiency can be improved.

If lru_cache is not used, the runtime is 31 seconds:

With lru_cache, it runs too fast, so I adjusted the value of n from 30 to 500, but even then the runtime is only 0.0004 seconds. The increase in speed is very significant.

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