Lately I’ve been doing a lot of swapping between Java and Clojure. In doing so I’ve started to notice many similarities between the two, but also lots of differences. One of the main differences being how they handle the flow of data. Functional programming (FP) emphasizes the flow of data through pure functions—functions that have no side effects and return the same output given the same input. This makes understanding and managing data flow central to writing effective FP code.

What is Data Flow?

In FP, data flow refers to how data moves through a series of transformations. Instead of modifying data in place (as is common in imperative programming), FP encourages creating new data structures based on the transformations applied.

For example, consider a simple process where you have a collection of numbers, and you want to transform it by filtering out odd numbers, squaring the even numbers, and summing the results. In FP, you might approach it like this:

(->> [1 2 3 4 5 6]
     (filter even?)
     (map #(* % %))
     (reduce +))

This pipeline demonstrates the flow of data: the initial collection [1 2 3 4 5 6] flows through each transformation—filtering, mapping, and reducing—without mutating the original list.

Benefits of Functional Data Flow

  • Readability: Each step in a pipeline is self-contained and clearly shows what happens to the data, making it easier to understand and maintain.
  • Testability: Pure functions, which FP promotes, are easy to test because they only depend on their inputs and outputs.
  • Concurrency: With immutability, data cannot be changed by multiple threads, making FP well-suited for concurrent and parallel processing.

By treating data as immutable and transformations as pure functions, FP creates predictable, scalable, and cleanly structured programs. Whether it’s a simple list operation or a complex application, data flow in FP always revolves around transforming and passing data through a chain of pure functions.