Functional programming has been in existence for the last six decades, but so far, it hasn’t ceased to overcome the general use of object oriented programming. With the explosive growth of machine learning and big data, functional programming has been growing in popularity because of the simplicity at which pure functions can be parallelized. Code for data analysis tasks and workflows are also easier to follow, test, and maintain using the functional paradigm lending towards its growing use in the future.
Even object oriented programming languages like Java and Python have adopted enormous concepts from functional programming like built in map, filter, and reduce functionality. They also have the ability to declare pure in-line lambda functions and creation of first-class and higher-order functions to pass in functions as parameters.
Simply put, functional programming entails building pure functions for fixed variables and changing state through their responses. In contrast, other programming languages have the ability to change application state by mutating variable references from within a program. You can learn about more of the differences of functional programming vs object oriented program in this article.
Because of its pure nature, functional programming is impressive for coveted tasks like machine learning and data analysis. However, that doesn’t mean you should ditch other programming languages and go completely functional as each programming languages has tasks it does better than others. But, it is vital to learn the basic principles so that you can apply them to your advantage when necessary. It’s for these reasons functional programming is termed the future, but maybe more the future of machine learning and big data then all of software development.
With that in mind, let’s look at why software developers are falling in love with functional programming making it an integral part of computer science in the future. Let’s also consider some of the features of objected oriented programming to see if functional programming can replace it completely. Spoiler: the two will likely co-exist in software development and even in the same application.
Use of Pure Functions
Pure functions repeatedly generate the same result and have no outside values influencing the end-result. Due to this feature of pure functions, algorithms created using functional programming are effortless to identify and correct errors.
Pure functions typically adopt the mathematics solving methodologies. For instance, if you key in 3+3 in a calculator, the answer will be 6. The same applies to pure functions . There are no external elements involved, therefore rendering the functional programs pure. An example of this would be an inline lambda function that takes n as input and returns n + n.
Debugging is a chronic issue for both the programmers and the developers, and that’s why they are switching to functional programming because its programs are easy to debug due to the use of pure functions.
The utilization of pure functions makes functional programming completely transparent. The notable distinction between pure and impure functions in functional programming languages remarkably improves the level of transparency accomplished by these programs.
Besides, pure functions work exclusively without factoring in external elements. That means they only work with the users’ entries. In contrast, non-functional programming languages factor in extra inputs and return outputs that are somewhat different from the user’s information.
Since there are no external elements involved in functional programming, the algorithm doesn’t deviate from the user’s input. Therefore, regardless of the output, it conforms with the information and has no sinister aspect.
Functional programming offers a variety of benefits, including improved readability of values. The use of pure values means their state remains unchanged till the end. The values also enhance program readability, making it a no-brainer for the developer to grasp the code.
Since a vast majority of functional programming languages are high level and the users are taking every function as a value, it is effortless for developers to recall the functional program and ultimately incorporate those values into other various functions.
In a layman’s language, ‘a variable’ is something that keeps changing from state to state. However, this is the opposite of functional programming.
Here, the programmer/developer cannot doctor the variables immediately; it has been instigated. One may be of the perception that constancy of variables coupled with recursion could result in a decline in performance.
However, it is necessary to comprehend that this feature ensures the program is unaltered till the end. Along with the valuable protection that functional programming renders by using pure functions, this methodology makes the functional programming languages more superior than their counterparts on matters of security.
Security being a key element in software development has prompted the developers to switch to functional programming to better their system security.
Seamless Parallel Programming
Functional programming is typically reckoned for its capacity to develop complete parallel programs. It’s undeniable that developing applications is one of the most reckoned use scenarios, especially among the developers who have adopted functional programming.
The main factor behind this is the static application of variables in various functional programming languages. Because pure functions don’t modify the variables and work only on the user inputs, it becomes less complex to handle parallelism.
Moreover, considering the programs’ improved dependability, the room for error is greatly reduced, thereby enhancing the program’s efficiency.
Validating Functional Signatures
Signature validation is a critical aspect of software development. Functional programming is highly rated for its ability to make functional signatures extremely valid and worthwhile when contrasted to other ways that exclude functional programs. The pure variables of functional programming language ensure the signatures outline all the information about a function’s working alongside their argument details and any other necessary details.
Furthermore, this is another practical reason why functional programming is the future of software development.
As a functional programming language, Scala allows developers to write code in both OOP and FP (functional programming). Scala mainly focuses on writing software using exclusive pure functions and constancy values. This language is in the process of becoming fully functional.
Why functional programming won’t deprecate object-oriented programming
Object-oriented programming (OOP) has become a critical component of software development. Despite the introduction of numerous programming languages such as C++ and Java, developers looking to develop mobile software must grasp an object-oriented approach. The same applies to intricate web development, considering the popularity of OOP languages such as PHP and Python.
Some IT professionals feel that object-oriented programming is obsolete, and thus, grasping its concepts is a complete waste of time. They find it illogical to use objects instead of the top-down methodology of traditional programming in languages such as Visual Basic.
If you have coded this type of software, you’re possibly accustomed to breaking complex problems into less-complex problems and solving them separately in units of code. If you have a grasp of functional programming, which views code elements as proper mathematical functions and prohibits them from doctoring other aspects, i.e., the output reflects the user’s input.
Despite the rise in popularity of functional programming, object-oriented programming is still in use. Some of the reasons why functional programming has not deprecated OOP include:
Reuse of code through inheritance
Suppose that one guy wants a metallic table object in addition to your table object, and another one requires a glass object. Everyone builds their objects singly but realizes similarities amongst them. Every object is simply a different type of table. This is where the inheritance method proves to save time. Here you need to create one generic class (table) and then define the subclass (Metallic and Glass) to adopt the generic glass features.
Certainly, Metallic and glass tables have their distinctive qualities and functions. Since there are similarities between the two, the two inheriting classes can reuse the prevailing code rather than writing these functions once more.
If, for instance, you want to make changes to all the table objects, irrespective of the type, all you need is to make adjustments to your table class, and all the table objects will adopt the new code. That’s why some developers still prefer OOP over functional programming.
Flexibility via polymorphism
Based on the above example, you don’t need a lot of functions to get started. For instance, metallic and glass objects share some commonalities, but there are other unique traits in each of them.
This is where polymorphism comes into play. This is because a single function can inherit the traits in the parent class. For instance, the parent Table class called ‘maintenance’ would work with MetallicTableMaintenance and GlassTableMaintenance. This is where polymorphism seems pivotal in software development.
Effective Problem Solving
A programming language like C has a decent reputation in the programming world, but developing software in a top-down language is highly discouraged unless you’re comfortable with it. This is because there are higher prospects of project collapse due to complexities. On the other hand, writing a program in a functional programming language such as Haskell or ML can be routine.
Object-oriented programming is typically the most natural and sensible approach once you get to use it. OOP languages allow developers to split your software into medium-sized problems that can be solved independently.
Data Abstraction doesn’t display the trivial elements to the user; instead, it only shows essential details. For instance, a car is considered a vehicle instead of its parts.
Data Abstraction is termed as the process of pinpointing only the indispensable traits of an object overlooking the irrelevant details. The features and properties of an object set it apart from other objects sharing similarities to aid in classifying/grouping objects.
Take the example of a person operating a computer. He only knows that pressing the power button will turn on/off the computer; he or she doesn’t know the PC internal mechanisms that power on and off the PC. This is all abstraction is all about.
In OOP programming languages, abstraction is realized through interfaces and abstract classes. It is possible to achieve 100% abstraction via the use of interfaces.
Functional programming, though preferred over object-oriented programming for immutable code and concurrency, is far away from deprecating OOP. As seen above, code reusability, data abstraction, effective problem solving, and flexibility in polymorphism are only achieved via object-oriented programming. In regards to system security, functional programming outwits object-oriented programming, and it’s for that reason functional programming is the future. Especially as more large companies depend on machine learning and artificial intelligence in their business applications. Functional programming is especially important in writing code to train ml models that can be trained on large networks of computer hosts.