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Lodash _.memoize


The lodash .memoize method is a function that allows you to cache the results of a particular function call. When the function is called again with the same parameters, rather than recalculating the result, it will simply return the cached value.

This can be extremely useful for functions that are called frequently or that execute complex operations, as it saves time and resources during subsequent function calls.

To use the memoize function, you simply pass your original function as the first argument. This function should take one or more arguments as input and return a result.

const memoizedFunction = _.memoize(originalFunction);

Once you have memoized your function, you can simply call it in the same way that you would call the original function:

const result = memoizedFunction(argument1, argument2, ...);

Under the hood, the memoize method uses an object to maintain a cache of the results of the function calls. When you call the memoized function with a set of arguments, it checks to see if a cache entry already exists for those arguments. If it does, it returns the cached value. If it does not, it calls the original function, stores the result in the cache, and then returns the result.

One thing to note is that the memoized function will only cache results for the exact set of arguments that were passed to it. If you call the function again with different arguments, it will not return the cached value and will instead recalculate the result. Additionally, memoize only supports functions with string, number, symbol or boolean parameters. Object parameter are not supported.

The lodash .memoize method can help optimize the performance of your application by reducing the number of unnecessary function calculations. By caching the results of a function call, you can save time and resources during subsequent function calls, leading to a faster and more efficient application.

Overall, the lodash .memoize method is an essential tool for any developer who wishes to optimize their application's performance by reducing unnecessary function calculations.