MapFilterReduce

MapFilterReduce is a programming paradigm and a set of operations commonly used in functional programming and data processing. It breaks down complex data manipulations into three distinct stages: map, filter, and reduce.

The map operation takes an input collection and applies a function to each element, producing a new collection of the same size. For example, if you have a list of numbers and you map the square function to it, you would get a new list where each number is the square of the corresponding number in the original list.

The filter operation takes a collection and a predicate function. It iterates through the collection, keeping only those elements for which the predicate function returns true. For instance, if you have a list of numbers and you filter for even numbers, you would get a new list containing only the even numbers from the original list.

The reduce operation, also known as fold or aggregate, takes a collection and a function that combines elements. It iterates through the collection, applying the function cumulatively to reduce the collection into a single value. For example, if you have a list of numbers and you reduce it by summing, you would get the total sum of all the numbers in the list.

These three operations can be chained together to perform sophisticated data transformations efficiently. The map operation transforms data, the filter operation selects relevant data, and the reduce operation summarizes or aggregates the processed data. This pattern is widely adopted in distributed computing frameworks like Hadoop and Spark for processing large datasets.