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Python Interview Questions: Python Cache

Can you explain how you would use decorators in Python to add caching functionality to a specific function in a large application, and how you would handle cache invalidation?
Python Interview Questions: Python Cache 

Can you explain how you would use decorators in Python to add caching functionality to a specific function in a large application, and how you would handle cache invalidation?

Yes, I can explain how to use decorators in Python to add caching functionality to a specific function in a large application and how to handle cache invalidation.

First, I would create a decorator function called "cache" that takes in the function to be decorated as an argument. Inside the decorator function, I would define a dictionary to store the function's results, with the function's arguments as the keys and the results as the values.

Next, I would create a nested function called "wrapper" which would check if the function's arguments existed in the dictionary. If they do, it will return the cached result. If they don't, it would call the original function, store the result in the dictionary, and then return the result.

The decorator would then return the wrapper function. To use the decorator, I would simply add the "@cache" annotation before the function definition.

To handle cache invalidation, I would add a method to the decorator function that allows the user to clear the cache, and I would also include logic to periodically clear the cache or set a max cache size.

Example:


This is a basic example of how you could use decorators to add caching functionality to a specific function in a large application. In a real-world scenario, you would want to make sure that the cache is thread-safe, and you might want to use a more robust caching library such as functools.lru_cache or cachetools instead of rolling your own cache.

Similar Questions for Advanced Python Programmers:

  • Can you explain how you would use Python's built-in concurrent.futures library to parallelize a computation-heavy task?
  • How would you go about profiling and optimizing the performance of a large Python application?
  • Can you walk us through how you would use Python's multiprocessing library to parallelize a data processing task?
  • How would you handle missing data in a Pandas DataFrame and what techniques would you use to impute missing values?
  • Can you explain the difference between a tuple and a list in Python, and give an example of when you would use each?
  • Can you explain how you would use Python's asyncio library to handle a high number of concurrent connections?
  • How would you implement a custom iterator in Python?
  • Can you explain how you would use Python's pickle module to serialize and deserialize an object?
  • Can you explain the difference between a shallow copy and a deep copy in Python, and give an example of when you would use each?
  • Can you explain the difference between a class variable and an instance variable in Python, and give an example of when you would use each?


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