What are the performance differences between Map, Reduce, and Filter?

What are the performance differences between Map, Reduce, and Filter?

Map, Reduce, and Filter are all higher-order functions used in functional programming, but they have different performance characteristics depending on the context.

Can you explain how each function works?

Sure! The Map function applies a given function to each item in a list, returning a new list. Filter, on the other hand, creates a new list containing only the items that pass a certain condition. Reduce combines all items in a list into a single value.

In what scenarios would one be preferred over the others?

Map is great for transforming data, Filter is ideal for removing unwanted items, and Reduce is best for aggregating data. The choice depends on the specific task at hand.

How do they perform with large datasets?

With large datasets, performance can vary. Map and Filter can be parallelized easily, while Reduce may become a bottleneck if not managed properly, especially in distributed systems.