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It encourages you to write pure functions. A pure function must satisfy both of the following properties:. Let's illustrate with a few examples. First, the multiply function is an example of a pure function. It always returns the same output for the same input, and it causes no side effects. The following are examples of impure functions. The canRide function depends on the captured heightRequirement variable.
Can you state which of the two properties each one does not satisfy? Living in a perfect world in which all our functions are pure would be nice, but as you can tell from the list above, any meaningful program will contain impure functions. Most of the time we will need to make an Ajax call, check the current date, or get a random number. If a function you're writing or using is void i.
If the function has no return value, then either it's a no-op or it's causing some side effect. Along the same lines, if you call a function but do not use its return value, again, you're probably relying on it to do some side effect, and it is an impure function.
Let's return to the concept of captured variables. Above, we looked at the canRide function. We decided that it is an impure function, because the heightRequirement could be reassigned. Here is a contrived example of how it can be reassigned with unpredictable results:. Let me reemphasize that captured variables do not necessarily make a function impure. We can rewrite the canRide function so that it is pure by simply changing how we declare the heightRequirement variable.
Declaring the variable with const means that there is no chance that it will be reassigned. If an attempt is made to reassign it, the runtime engine will throw an error; however, what if instead of a simple number we had an object that stored all our "constants"?
That means that we cannot call mutator methods such as array. Instead of pushing an item onto the existing array, we can create a new array with all the same items as the original array, plus the one additional item. In fact, every mutator method can be replaced by a function that returns a new array with the desired changes.
The same thing goes when using a Map or a Set. We can avoid mutator methods by returning a new Map or Set with the desired changes. If you call Object. Because of how Maps and Sets are represented internally, calling Object.
But it's easy enough to tell the compiler that you would like them to be read-only. Okay, so we can create new objects instead of mutating existing ones, but won't that adversely affect performance?
Yes, it can. Be sure to do performance testing in your own app.
If you need a performance boost, then consider using Immutable. This is the same technique used internally by functional programming languages such as Clojure and Scala. Remember thinking, "When am I ever going to use this? There are libraries such as Ramda and lodash that provide a more elegant way of composing functions. Instead of simply passing the return value from one function to the next, we can treat function composition in the more mathematical sense.
We can create a single composite function made up from the others i.
Let's recall the definition of factorial from mathematics:. That is, n! We can write a loop that computes that for us easily enough.
Notice that both product and i are repeatedly being reassigned inside the loop. This is a standard procedural approach to solving the problem. How would we solve it using a functional approach? We would need to eliminate the loop and make sure we have no variables being reassigned. Recursion is one of the most powerful tools in the functional programmer's toolbelt.
Recursion asks us to break down the overall problem into sub-problems that resemble the overall problem. Computing the factorial is a perfect example. To compute n! That's the same thing as saying:. We found a sub-problem to solve n-1!
There's one more thing to take care of: The base case tells us when to stop the recursion. If we didn't have a base case, then recursion would go on forever. In practice, you'll get a stack overflow error if there are too many recursive calls. What is the base case for the factorial function? With this information in mind, let's write a recursive factorial function. Okay, so let's compute recursiveFactorial , because When we do, we get this:.
So what's going on here? We got a stack overflow error! It's not because of infinite recursion. We know that we handled the base case i. It's because the browser has a finite stack and we have exceeded it.
Each call to recursiveFactorial causes a new frame to be put on the stack. We can visualize the stack as a set of boxes stacked on top of each other. Each time recursiveFactorial gets called, a new box is added to the top. The following diagram shows a stylized version of what the stack might look like when computing recursiveFactorial 3. Note that in a real stack, the frame on top would store the memory address of where it should return after executing, but I have chosen to depict the return value using the variable r.
Is there anything we can do about that? It turns out that, yes, there is something we can do about it. As part of the ES aka ES6 specification, an optimization was added to address this issue. It's called the proper tail calls optimization PTC. It allows the browser to elide, or omit, stack frames if the last thing that the recursive function does is call itself and return the result. Actually, the optimization works for mutually recursive functions as well, but for simplicity we're just going to focus on a single recursive function.
You'll notice in the stack above that after the recursive function call, there is still an additional computation to be made i. That means that the browser cannot optimize it using PTC; however, we can rewrite the function in such a way so that the last step is the recursive call. One trick to doing that is to pass the intermediate result in this case the product into the function as an argument. Let's visualize the optimized stack now when computing factorial 3. As the following diagram shows, in this case the stack never grows beyond two frames.
The reason is that we are passing all necessary information i. So, after the product has been updated, the browser can throw out that stack frame. You'll notice in this diagram that each time the top frame falls down and becomes the bottom frame, the previous bottom frame gets thrown out.
It's no longer needed. Functional programming is a software paradigm that will radically change the way in which you approach any programming endeavor.
This is evident in this simple example:. While this program is very trivial, without any effort, it can quickly become problematic. Or change the target element? Inevitably, you would have to rewrite this statement again. Consider wrapping it all inside a function and making these data points function parameters.
Combining simple functions to create more meaningful programs is the central theme of functional programming.
The good news is that functional programming and object-oriented programming are not mutually exclusive and can be used simultaneously. In fact, platforms like Scala and F have blended both paradigms successfully into a single language, making them very pragmatic and geared towards productivity.
In order to understand functional programming, you must first understand functions in the mathematical sense as being a mapping of types.
Simply, the function isEmpty—used to validate whether a string has length zero—is a mapping from String to Boolean. This is written as:. Functional programming is based on the premise that you will build immutable programs solely based on pure functions. A pure function has the following qualities:. Both points refer to the presence of side effects in your code; behavior that is to be avoided at all costs:.
The concept of pure functions leads into another concept in functional programming: Referential transparency RT is a more formal way of defining a pure function. Hence, if a function consistently yields the same result on the same input, it is said to be referentially transparent.
The multiplier and adder functions shown earlier were RT. The problems with this program are due to its exposure to side effects by relying on external variables: If any of these variables change in-between calls to average, it yields different results. RT exercise: A noticeable quality of functional programs is the absence of the standard looping mechanisms: Instead, we will take advantage of high-order functions—like: You can download Lodash. This function iterates over the elements of a collection, invoking the provided iteratee function on each one.
The callback function is supplied 3 arguments: Mapping functions onto containers is an important part of functional programming. For arrays, I can use a mapping function to transform its contents into an array of similar length as the original, while keeping the original intact. Given a function:. Filter transforms an array by mapping a predicate function function with a boolean return value onto each element. The resulting array is made up of, potentially, a subset of elements for which the predicate returns true.
Each successive invocation is supplied the return value of the previous. The accumulator function is supplied the current running total or accumulated value , the current value, index, and the array. Map, reduce, filter exercise: Functions like map, filter, and reduce are just a few of an entire library of functions in Lodash. Chaining functions this way leads to very declarative code, which describes what the program should do rather than how it does it.
Function chains allow you to describe the parts of your application without actually running it. Recursion has many uses in software, especially when solving self-similar types of problems such as traversing trees or mathematical progressions like Fibonacci. Traversing arrays recursively originates from realizing arrays are self-defined as collections, each having a head and a tail, both available as Lodash operations.
Hence, the responsibility of moving through the elements in an array is ceded entirely to the language runtime. Functional programming provides very powerful abstractions that allow you to create functions from the definition of other functions or to augment the evaluation of existing functions.
Currying is a technique that converts a multivariable function into a step-wise sequence of unary functions.
In other words, a function with parameters: Consider the case of a function name that returns first and last name:. As you can see from the example above, name is a function of two arguments. From this curried version, a family of functions is born by partially applying only the first argument:. Currying exercise: This idea of partially applying arguments has another modality, known as partial application. This is especially useful when implementing presets or default function arguments.
Consider the following log function:. I can use this function to log a message with different levels and target either the console or an alert box. Consider these two examples:. Both currying and partial application are extremely useful when combined with composition. Composition is the process used to group together the execution of simpler functions. In essence, composition is a function derived from other functions.
You can visualize the composition of two functions as a mapping of sets linking their inputs and outputs:. In simple terms, this means the output of g will be passed into the input of f. Currying and composition exercise: Currying and composition provide abstractions over functions—the drivers for behavior in your application.
Functional data types like functors and monads provide abstractions over the data. Functional data types part from the notion of mapping functions onto containers. In a similar manner, we can map functions over any type. The idea behind containerizing is very simple but has far-reaching applications.
The goal is to place values in a container. Once we wrap a value, the rule is that you can only map functions onto it to transform and retrieve it. This idea is rather simple: We can make this more powerful if we could continue mapping functions over the container multiple times, just like map.
Consider this variation of Wrapper. As you can see now, aside from mapping the function over the value, the result is placed back into the container, which allows for a chainable sequence of operations to take place:. In essence, a functor is nothing more than a type that implements a mapping function with the purpose of lifting values into a container. Hence, a functor F defines a mapping function as follows:. The function F. Diagraming the example code above:.
For instance, what happens if I wrap a null object and attempt to map functions over it? Functor exercise: Monads are data types similar to functors, but they also define the rules by which values are contained within them. The monad type, as such, is an abstract data type with the following interface:.
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