The article discusses the use of the flexicache library for caching function results effectively. By employing different policies, such as time and modification time, it illustrates how functions can be cached for performance optimization. The examples demonstrate the functionality of caching, checking for cache validity, and handling cache invalidation based on time delays or file modifications. The combination of these mechanisms ensures that applications can benefit from both speed and data accuracy, ultimately enhancing the efficiency of callable functions in Python.
Using the flexicache decorator with time and mtime policies provides a robust caching mechanism that gracefully handles both time-based and file modification based cache invalidation.
By applying flexicache with time_policy and mtime_policy, we ensure not just performance through caching but also accuracy by invalidating caches when needed.
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