val list = List(1, 2, 3, 4, 5)
val result = list.map(_ * 2).webp){kind=logo}
Ask the right questions to secure the right FP talent among an increasingly shrinking pool of talent.
The FP (Functional Programming) computer software programming language was developed by John Backus, a renowned computer scientist, in the late 1970s. It was introduced in his Turing Award lecture as an alternative to the prevailing imperative programming paradigm. FP is a high-level, declarative language that emphasizes mathematical functions and avoids changing-state and mutable data. Its design philosophy has significantly influenced modern functional languages like Haskell and Erlang. The details of its development can be found in Backus's original paper "Can Programming Be Liberated from the von Neumann Style?" published in 1978.
The next 20 minutes of the interview should attempt to focus more specifically on the development questions used, and the level of depth and skill the engineer possesses.
Higher-order functions are functions that take other functions as arguments, return a function as a result, or both. They are a key part of functional programming and they provide a powerful abstraction mechanism.
In functional programming, side effects are handled by isolating them from the rest of the code. This can be done by using monads, which are a kind of container that can handle side effects in a controlled manner.
Imperative programming is about writing code that describes in detail the steps that the computer must take to accomplish the goal. This is done by changing the program state through assignment statements. On the other hand, functional programming is about writing code that describes what it should accomplish without prescribing how to achieve it. It avoids changing-state and mutable data.
Pure functions are a fundamental part of functional programming. A function is considered pure if it always produces the same output for the same set of inputs and it does not have any observable side effects such as network requests, data mutation, or console logs.
Functional programming is a programming paradigm where programs are constructed by applying and composing functions. It is a declarative type of programming style that focuses on what to solve rather than how to solve (procedural programming). Its main features include pure functions, recursion, and avoidance of shared state, mutable data, and side-effects.
Even though developers often work independently, they also need to collaborate with others. The candidate should demonstrate the ability to work well in a team environment.
A strong foundation in data structures and algorithms is important for writing efficient and effective code.
While understanding functional programming principles is important, having experience with the specific language(s) your company uses will allow them to hit the ground running.
Problem-solving is a critical skill for developers. They should be able to demonstrate how they would approach and solve a complex problem.
Communication is key in any development role. The candidate should be able to explain their thought process, solutions, and the logic behind their code.
Understanding the principles of functional programming is crucial for a FP developer. This includes concepts such as immutability, first-class functions, and higher-order functions.
The next 20 minutes of the interview should attempt to focus more specifically on the development questions used, and the level of depth and skill the engineer possesses.
Functional programming has a steep learning curve, especially for developers coming from an imperative background. It can also lead to performance issues due to things like recursion and immutability. Debugging can also be more difficult in functional programming because control flow is not as explicit as in imperative programming.
In functional programming, errors are often handled by returning a special type that represents either a successful result or an error. This could be an 'Either' type, with 'Left' representing an error and 'Right' representing a success, or a 'Maybe' type, with 'Just' representing a value and 'Nothing' representing an absence of a value.
Map, filter, and reduce are higher-order functions that operate on arrays. Map applies a given function to each item of an array and returns a new array with the results. Filter creates a new array with all elements that pass a test implemented by the provided function. Reduce applies a function against an accumulator and each element in the array (from left to right) to reduce it to a single output value.
Immutability in functional programming brings several benefits. It makes the code safer and easier to reason about since data cannot change once it's created. This eliminates a whole class of bugs related to data changes. It also makes it easier to implement undo/redo functionality, to parallelize code, and to compare data by reference instead of by value.
Recursion is a technique where a function calls itself as a subroutine. This can be used as a substitute for iterative control structures. In functional programming, recursion is often used to iterate over data structures, compute factorials, generate Fibonacci series, etc.
At this point, a skilled FP engineer should demonstrate proficiency in functional programming questions, problem-solving abilities, and strong understanding of data structures and algorithms. Red flags include inability to explain concepts clearly, lack of practical experience, or struggles with complex problem-solving.
val list = List(1, 2, 3, 4, 5)
val result = list.map(_ * 2)val list = List(1, 2, 3, 4, 5)
val result = list.foldLeft(0)(_ + _)val list = List(1, 2, 3, 4, 5)
val result = list.filter(_ % 2 == 0)import scala.concurrent.Future
import scala.concurrent.ExecutionContext.Implicits.global
val future = Future { Thread.sleep(1000); 42 }
future.onComplete { case Success(value) => println(value) case Failure(exception) => println(exception) }case class Person(name: String, age: Int)
val john = Person('John', 30)
val olderJohn = john.copy(age = john.age + 1)val list = List(1, 2, 3, 4, 5)
val result = list.flatMap(x => List(x, x*2))The final few interview questions for a FP candidate should typically focus on a combination of technical skills, personal goals, growth potential, team dynamics, and company culture.
A monad is a design pattern used to handle side effects in functional programming. It's a type of functor that provides a way to chain operations together in such a way that the output of one operation is the input of the next. Monads have two basic operations: 'bind' and 'return'. 'Bind' is used to sequence functions that return monads, and 'return' is used to inject values into the monadic chain.
Memoization is a technique used to optimize expensive function calls by storing the results of those calls and reusing the cached result when the same inputs occur again. In functional programming, memoization can be implemented by creating a higher-order function that returns a version of the original function with a cache.
Eager evaluation is where expressions are evaluated as soon as they are bound to a variable. Lazy evaluation is where expressions are not evaluated until their results are needed. This can improve performance by avoiding unnecessary computations and creating potentially infinite data structures.
Currying is a technique in functional programming where a function with multiple arguments is transformed into a sequence of functions, each with a single argument. Curried functions are great to improve code reusability and functional composition.
Recursive functions can be optimized in functional programming using techniques like tail recursion, where the recursive call is the final operation in the function. This allows the compiler or interpreter to optimize the recursion away and turn it into a loop, which can significantly improve performance.
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