While SOLID principles have been widely used in Object-Oriented Programming (OOP), functional programming (FP) has developed its own guiding principles for writing clean, maintainable, and scalable code. These principles stem from mathematical foundations and practical patterns used in FP-heavy languages such as Haskell, Clojure, and Elm.
In this article, we explore key FP principles, their connections to existing literature, and practical Clojure examples demonstrating their application.
1. DRY (Don’t Repeat Yourself)
Principle: Avoid redundancy by abstracting common patterns.
Literature Reference: The DRY principle was first introduced in The Pragmatic Programmer by Andrew Hunt and David Thomas and is reinforced in Structure and Interpretation of Computer Programs (SICP) by Harold Abelson and Gerald Jay Sussman, which emphasizes abstraction as a fundamental concept in programming.
How FP Achieves It:
- FP eliminates redundancy through higher-order functions and function composition.
- Instead of duplicating logic, FP abstracts reusable behavior.
Example:
(defn apply-discount [price discount-fn]
(discount-fn price))
(defn ten-percent-discount [price] (* price 0.9))
(defn twenty-percent-discount [price] (* price 0.8))
(println (apply-discount 100 ten-percent-discount)) ; 90.0
(println (apply-discount 100 twenty-percent-discount)) ; 80.0By passing functions as arguments, we eliminate code duplication and follow DRY.
2. Functional Core, Imperative Shell
Principle: Keep business logic pure and push side effects to the boundaries.
Literature Reference: This principle is discussed in Functional Programming in Scala by Paul Chiusano and Runar Bjarnason and is also reflected in Software Design for Flexibility by Chris Hanson and Gerald Jay Sussman, which highlights designing software that remains adaptable over time.
How FP Achieves It:
- Core logic is implemented using pure functions.
- Side effects (I/O, database calls) are isolated in a separate layer.
Example:
(defn process-data [data]
(assoc data :processed true))
(defn log-to-console [message]
(println "LOG:" message))
(defn main [data]
(let [result (process-data data)]
(log-to-console "Processing complete")
result))
(main {:id 1 :name "Alice"})Processing logic remains pure, while logging is handled separately.
3. Immutability & Referential Transparency
Principle: Data should not be mutated after creation, and functions should produce the same output for the same input.
Literature Reference: Referential transparency is a key concept in Haskell: The Craft of Functional Programming by Simon Thompson and is foundational in SICP, which introduces the importance of using expressions rather than stateful computations.
How FP Achieves It:
- FP languages encourage immutable data structures.
- Eliminating mutation improves concurrency and reasoning.
Example:
(defn update-score [player new-score]
(assoc player :score new-score))
(def player {:name "Alice" :score 10})
(println (update-score player 20))
(println player) ; Original remains unchangedSince update-score returns a new map instead of modifying the original, immutability is preserved.
4. Composition Over Inheritance
Principle: Instead of class hierarchies, compose behavior using small functions.
Literature Reference: Discussed extensively in Functional Programming in JavaScript by Luis Atencio and Software Design for Flexibility, which explores using composable functions to avoid rigid class structures.
How FP Achieves It:
- Behavior is composed using small functions instead of extending classes.
- Reduces tight coupling and improves reusability.
Example:
(defn greet [name] (str "Hello, " name "!"))
(defn exclaim [sentence] (str sentence "!!!"))
(def greet-and-exclaim (comp exclaim greet))
(println (greet-and-exclaim "Alice"))Functions are combined using comp instead of creating a class hierarchy.
5. Algebraic Data Types (ADTs) and Type-Driven Development
Principle: Use sum and product types to model domain logic safely.
Literature Reference: Algebraic Data Types (ADTs) are central to Programming in Haskell by Graham Hutton and are indirectly covered in SICP, where structured data types are used to create composable abstractions.
How FP Achieves It:
- Sealed types or tagged unions represent well-defined structures.
- Pattern matching simplifies branching logic.
Example:
(defmulti handle-event :type)
(defmethod handle-event :login [event]
(println "User logged in:" (:user event)))
(defmethod handle-event :logout [event]
(println "User logged out:" (:user event)))
(handle-event {:type :login :user "Alice"})
(handle-event {:type :logout :user "Bob"})By defining event types explicitly, we prevent invalid states.
Conclusion
While SOLID principles were developed for OOP, functional programming has its own guiding principles. These principles—rooted in immutability, function composition, and algebraic data types—help developers build maintainable, scalable systems. Drawing from literature such as Structure and Interpretation of Computer Programs, Software Design for Flexibility, and Domain-Driven Design, we see how FP fosters clear, composable code.
By embracing these FP principles, developers can create robust software without the complexities of OOP patterns. Would you like to see more examples or a deep dive into a specific principle?