Have you heard of Functional Programming? Want to know what all the buzz is about? If you’re looking to learn more about FP, the best languages for functional programming, and FP interview questions, you’ve come to the right place — this article covers all this and more!
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In this article, we’ll cover:
- Define Functional Programming: What Does Functional Programming Mean?
- Functional Programming History
- Programming Languages That Support Functional Programming
- Basic Concepts of Functional Programming Terminology
- Characteristics of Functional Programming
- The Benefits of Functional Programming
- Limitations of Functional Programming
- Applications of Functional Programming
- Functional Programming vs. Object-Oriented Programming
- The Efficiency of a Program Code
- Importance of Functional Programming in Big Tech Companies and FP Interviews Questions
- Functional Programming FAQs
Define Functional Programming: What Does Functional Programming Mean?
Functional programming represents a programming paradigm that aims to bind all things in pure mathematical functions based on lambda calculus. In other words, FP is an approach to software construction based on creating pure functions. FP helps in solving problems where many different operations need to be performed on the same data set. Some common use cases of FP include AI applications like machine learning, speech and vision modeling, and language processing.
Here are some more interesting facts about FP:
- FP avoids concepts like mutable data and shared state that you’d find in Object-Oriented Programming.
- Unlike imperative programming, functional programming focuses on “what to solve” over “how to solve,” i.e., its emphasis is on declarations and expressions over the execution of statements.
- Using expressions instead of statements is also key to lambda calculus — a framework for studying computation using functions, the basis of almost every FP language. Lambda calculus is arguably the most concise programming language and represents a theoretical framework that deals with functions and their evaluation.
- Unlike other programming paradigms, in which value output depends on a local state or a global state, in FP, value output only depends on the parameters passed to the function.
Functional Programming History
- As mentioned above, Lambda Calculus is the foundation of Functional Programming. It was developed for functional definition, application, and recursion by Alonzo Church in the 1930s.
- The first functional programming language called LISP was created in 1958 and published in 1960 by John McCarthy of MIT.
- The functional programming language called Meta Language or ML was developed in 1973, originally by Robin Milner, at the University of Edinburgh, who won a Turing Award in 1991. ML was the first language that included Polymorphic Type Inference along with a type-safe exception-handling mechanism.
- In the 1970s, another small FP language was developed at the University of Edinburgh called Hope, derived from a language called NPL. Both Hope and NPL are known for being the first languages with call-by-pattern evaluation and algebraic data types. In the 1980s, Hope added algebraic data types for recursion along with equational reasoning.
- In 2004, the now popular FP language Scala was created by Martin Odersky.
Programming Languages That Support Functional Programming
There are some languages specially designed for functional programming, and some that aren’t but do support it. We’ll now learn more about these languages and learn what this means.
What Is a Functional Programming Language?
Functional programming languages are languages specially designed for functional programming and aim to mimic mathematical functions. FP languages are designed for handling list processing applications, symbolic computation, and support all characteristics associated with functional programming.
FP languages can be of two types:
- Pure functional languages like Haskell that support only functional paradigms
- Impure functional languages like LISP that support both functional and imperative programming styles
Best Functional Programming Languages
The following table lists some of the most notable and popular programming languages that support functional programming. Note that while all these languages support functional programming significantly, some are not considered FP languages.
Considered FP Language or not?
Basic Concepts of Functional Programming Terminology
- Pure functions: Pure functions are immutable, which means they consistently give the same output given the same parameters, regardless of any other factors. Pure functions are also deterministic, have no hidden I/O, and have no side effects. They either give output or modify the argument/global variables. The programs created using FP are easy to debug, and concurrent application writing is easier to manage.
- Recursion: Since there’s no iteration, no loops in FP, it relies on recursion for this purpose. Recursive functions repeatedly call themselves till the execution reaches the base case to implement recursion.
- Referential transparency: It refers to the concept that:
a. The same language expression always leads to the same output.
b. The initialized variables don’t get their values modified throughout program execution.
c. All operations occur as if they’re happening for the first time.
Referential transparency makes it easier to know what happened during execution and eliminates chances of any side-effects due to modification of variables during execution.
- Functions are First-Class and can be Higher-Order: When functions are treated as variables, they are called first-class functions. In FP, functions are first-class as they are treated like any other variable. This means that in FP, we can pass functions to other functions as parameters, return from them, and store them in data structures. When a function takes some other functions as parameters or returns functions, or both, we call it a higher-order function. Hence, we can also say that in FP, first-class functions can be higher-order functions.
- Immutable variables: In FP, data is immutable, and preference is given to easily creating new data structures instead of altering existing ones. This means that in FP, once we initialize a variable, it is impossible to modify it. While we can create a new variable in FP, we cannot alter an existing variable. This helps in the preservation of the state of the variable throughout program execution.
Characteristics of Functional Programming
In essence, here are some prominent characteristics of functional programming:
- FP focuses on:
a. The results over the process
b. The expressions over the statements
c. “What to solve/compute” over “how to solve/compute”
- FP supports lazy evaluation features, and some important concepts also present in OOP like Inheritance, Abstraction, Polymorphism, Encapsulation, and higher-order functions.
- FP does not support loop statements like for, do-while, etc. It also does not support conditional statements like if-else, switch, etc. In essence, FP doesn’t support iterations and flow controls; it uses function calls and functions directly.
- FP is based on lambda calculus, which involves leveraging mathematical functions that use conditional expressions and recursion to compute. This is achieved by splitting the problem into functions that handle specific parts of what needs to be solved.
- In FP, data is immutable, which literally means “data is unable to change.” This is because, in FP, objects and values are created by initialization, then used. Their state or value is never changed, and if needed, a new object is created, but the state of an existing object is never altered.
The Benefits of Functional Programming
- It helps you avoid errors, side effects, and confusion since FP does not allow state change after initialization.
- FP code is much easier to debug, test, and execute due to immutable values.
- It offers fault tolerance, concurrency, parallel processing, and hot code deployment.
- Offers functional Constructs like Lazy Maps & Lists, etc., and supports nested functions.
- Uses Lambda Calculus, offers succinct code and better modularity, which helps increase the productivity of the software engineer using FP.
- Supports lazy evaluation, so the value evaluated is stored only when needed.
- Allows treating functions as values that can be passed to other functions or returned from them.
- Since it is made of independent units that can run concurrently, FP code is very efficient.
Limitations of Functional Programming
- If recursion is used, the performance can degrade since the values are immutable, and using recursion instead of loops can become challenging.
- Pure functions can sometimes degrade code readability and while writing them is easy I/O operations and merging pure functions with the remaining application is complicated.
- FP can be difficult for beginners to grasp
- Objects may not accurately represent the problem, and since objects evolve during coding, this can be hard to maintain.
- Requires an extensive environmental setup, a large memory space.
- Needs a lot of mocking; re-use is complex and requires consistent refactoring. There’s a need to create new objects every time for computation.
Applications of Functional Programming
While FP languages are used more for academic than commercial software development purposes, many FP languages like Haskell, Clojure, Erlang, etc., are extensively used for industrial and commercial application development. Here are some examples:
- WhatsApp uses Erlang, which enables just above 100 WhatsApp employees to manage the data of over 1.5 billion users.
- Facebook uses Haskell for its anti-spam system.
- FP style is crucial to succeeding in domains like financial analysis and statistics, where languages like R support the FP style of coding.
- Some elements of FP are also used by domain-specific languages like SQL and Lex or Yacc for avoiding mutable values.
- FP comes in handy when applications require lots of mathematical calculations and require parallelism and concurrency.
Functional Programming vs. Object-oriented Programming
FP uses immutable data
OOP uses mutable data
FP follows the Declarative Programming Model
OOP follows the Imperative Programming Model
FP focuses on “what to solve”
OOP focuses on “how to solve”
FP supports Parallel Programming
OOP isn’t best for Parallel Programming
FP functions have no-side effects
OOP functions can cause significant side effects
Flow control is done using recursion and function calls instead of loops and conditionals
Flow control is done using loops and conditional
FP uses recursion to iterate
OOP uses loops to iterate
Order of execution of statements isn’t
Order of execution of statements is critical
Supports abstraction over
data as well as behavior
Supports abstraction only over data
Efficiency of a Program Code
An efficient program causes minimum overhead and risk due to decreased space and time resource requirements and is also not a serious risk for the operating environment.
A program’s efficiency depends on several factors and can be increased by optimizing for these factors. Here are some tips and factors to look out for that can increase the efficiency of your code. The trick is to optimize for algorithmic efficiency and execution speed in all ways possible:
- Make sure the speeds of the machine and the compiler are high enough to meet or exceed your requirements.
- Choose the optimal Operating System and programming language to fine-tune and optimize your performance.
- Organize data effectively in the program and use efficient data structures for storage.
- Use efficient algorithms to solve each sub-problem at each step.
- Ensure there is no unnecessary code or redundant steps processing: Create reusable components and use them wherever possible.
- Use optimal memory and non-volatile storage.
- Handle errors and exceptions in all program layers, ensure the code has data integrity, consistency, and is in line with the design logic and flow of the algorithm.
Importance of Functional Programming in Big Tech Companies and FP FAANG Interviews Questions
If you’re great at functional programming in a language relevant to a company, it can significantly increase your chances of getting hired. As mentioned above, Facebook uses Haskell and WhatsApp uses Erlang.
Many other companies like AT&T, Bank of America, Galois, Intel, and NVIDIA use Haskell. Foursquare uses Scala. Honing your FP skills, especially in a language relevant to your target company, is a worthwhile investment and will help you nail your big-tech interview.
Here are some FP based interview questions to help you evaluate your understanding of the topic:
- What are pure and impure functions?
- What is immutability?
- What are algebraic types, monads, continuations, and tail calls?
- What is lazy evaluation?
- What is the difference between expression evaluation and instructions execution?
- What are the advantages of FP?
- Talk about a time when you used FP; what were the benefits, tradeoffs you had to make, and alternatives you had to consider?
- What is the difference between functional, reactive, declarative, and any other programming paradigms you know about?
- Can you do functional programming in C++? If yes, how?
Functional Programming FAQs
1. What is “modular design,” and why is it important?
Modular design involves using small modules independent from each other, which can be coded quickly, re-used often, and tested separately. This increases productivity and makes development more efficient.
2. What are first-class and higher-order functions?
First-class functions have no regular restrictions on use, can be treated just like variables, and can be found anywhere in the code.
Higher-order functions either take other functions as arguments or return other functions as results, or both.
3. What are pure and impure functions?
In pure functions, Inputs are declared as inputs and aren’t ever hidden. Outputs are also declared as outputs, and each time the output is the same if given the same parameters. They act on their parameters and are inefficient if they’re not returning anything. In impure functions, at least some inputs and outputs are hidden, and since they have dependencies, they cannot be used or tested separately.
num1 = num1*5;
4. What is the concept of side effects in functional programming?
State changes that occur anywhere other than inside of a called function are called side effects, which FP aims to minimize as much as possible by separating them from the remaining software.
5. How does a capable compiler run code written in functional programming style?
A capable compiler would run by parallelizing the instructions written in FP style code, would wait for the results to be evaluated, and can memorize the result as needed.
How to Prepare Functional Programming for FAANG Interviews
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