Learn and Understand JavaScript’s Reduce Function with Examples

Learn and Understand JavaScript’s Reduce Function with Examples

Learn and Understand JavaScript’s Reduce Function with Examples. Learn how to use Array.prototype.reduce(). This article assumes that the reader understands other array methods like Map and Filter because it will help in understanding how Reduce works. In order to fully grasp the idea behind Reduce, we will look at a few examples of simple solutions using for loops and then implement those same solutions using the Reduce function, then we will look at some more advanced use cases for the Reduce function.

As the year begins, I have decided to make a series of articles that explain the various APIs (Application Programming Interfaces) in the Javascript language, wherein each article we breakdown a commonly used function in Javascript and try to go through its various applications.

The first function we will be going through is the 'Reduce' higher-order function, mainly because out of all the JS array methods, it took me a bit of time to understand how the Reduce function works.

This article assumes that the reader understands other array methods like Map and Filter because it will help in understanding how Reduce works. In order to fully grasp the idea behind Reduce, we will look at a few examples of simple solutions using for loops and then implement those same solutions using the Reduce function, then we will look at some more advanced use cases for the Reduce function.

Example 1

The first example we will look at is a common one, calculating the sum of items in an array. This requires a simple solution and using a for loop the solution should look like this:

const arrayItems = [1,2,3,4,5,6];
let sum = 0;

for (let i = 0; i < arrayItems.length; i++) {
	sum = sum + arrayItems[i];
}
// sum = 21

The solution above is pretty straightforward, where we add each item in the array and store the result in the sum variable. So the next step is to implement this same solution using Reduce which should look like the code below:

const arrayItems = [1,2,3,4,5,6];

const sum = arrayItems.reduce(function(accumulator, currentItemInArray){
	accumulator = accumulator + currentItemInArray;
    return accumulator;
}, 0);

// sum = 21

Looking at the two examples above it's pretty obvious that the for loop example seems simpler and this has been the cause of some arguments in the ecosystem, but this example is an overkill and we are only using it make it easy to understand how the Reduce function works, so let's work through the example.

We need to, first of all, understand what the Reduce function is. It is a method that exists on every Javascript Array and it enables us to loop through each item in the array and perform a function on each of those items, this is pretty similar to the behavior of the Map function but it has a twist, it allows us to return any value from our function in a particular iteration which will exist as a parameter (argument) in that function in the next iteration (that value is commonly known as the accumulator).

To explain further, the Reduce function takes 2 arguments:

  • Callback function: This is a function that contains 4 parameters typically, but right now we are only concerned with the first accumulator and the second being the current item in the array during that iteration.
  • Initial value: This is the initial value of the accumulator when the iteration starts, in the example above the value is 0, which means the initial value of the accumulator will be 0.

Back to our example:

const arrayItems = [1,2,3,4,5,6];

const sum = arrayItems.reduce(function(accumulator, currentItemInArray){
	accumulator = accumulator + currentItemInArray;
    return accumulator;
}, 0);

// sum = 21

It can be further broken out into the callback function and the initial value:

const arrayItems = [1,2,3,4,5,6];

function callbackFunction(accumulator, currentItemInArray){
    accumulator = accumulator + currentItemInArray;
    return accumulator;
}

const initialValue = 0;

const sum = arrayItems.reduce(callbackFunction, initialValue);

// sum = 21

Now the tricky part for me was how the accumulator works, and to explain it we will go through each iteration in the loop.

Iteration 1

In the first iteration, since our initial value is 0, our accumulator will have a value of 0 so our function will look like this:

const arrayItems = [1,2,3,4,5,6];
// 1 is the current item in the array

function callbackFunction(accumulator = 0, currentItemInArray = 1){
    accumulator = 0 + 1;
    return accumulator // which is 1;
}

callbackFunction will return a value of 1, this will be automatically be used as the next value for the accumulator in the second iteration.

Iteration 2

const arrayItems = [1,2,3,4,5,6];
// 2 is the current item in the array

function callbackFunction(accumulator = 1, currentItemInArray = 2){
    accumulator = 1 + 2;
    return accumulator // which is 3;
}

In this iteration, our accumulator will have a value of 1 which was returned in our iteration 1 and the callbackFunction will return a value of 3 in this iteration which means that our accumulator will have a value of 3 in our third iteration.

Iteration 3

const arrayItems = [1,2,3,4,5,6];
// 3 is the current item in the array

function callbackFunction(accumulator = 3, currentItemInArray = 3){
    accumulator = 3 + 3;
    return accumulator // which is 6;
}

In the third iteration, our accumulator will have a value of 3 which was returned by the callbackFunction in iteration 2 and callbackFunction will return a value of 6 which is to be used as the value of accumulator in iteration 4. These steps will repeat themselves until we get to the last item in the array which is 6.

The example above as I mentioned before can be an overkill, so let's look at a problem that we can implement a solution using Reduce function (this doesn't mean that a for loop cannot be used to implement a working solution).

Example 2

The second example will involve counting the number of occurrences of each element in an array, for example:

//Given an input
const fruits = ['apples', 'apples', 'bananas', 'oranges', 'apples', 'oranges', 'bananas', 'grapes'];

// should give an output of
const count = { 'apples': 3,'oranges': 2,'bananas': 2, 'grapes': 1 };

Let's implement the solution then go through each iteration and see what is happening:

const fruits = ['apples', 'apples', 'bananas', 'oranges', 'apples', 'oranges', 'bananas', 'grapes'];

function countOccurrence(accumulator, currentFruit){
	const currentFruitCount = accumulator[currentFruit];
    // if the fruit exists as a key in the  object, increment its value, else add the fruit as a key to the object with a value of 1

    if(currentFruitCount) {
    	accumulator[currentFruit] = currentFruitCount + 1;
    } else {
    	accumulator[currentFruit] = 1
    }

    return accumulator;
}

const initialValue = {};

const count = fruits.reduce(countOccurrence, initialValue);

The solution is written to be as verbose a possible so we can understand what is going on in the code. As we did before let's go through the first few iterations.

Iteration 1

In the first iteration, since we made our initial value an empty object, the value of accumulator will be an empty object, which means that the countOcurrence function will look this the below code when it is called:

const fruits = ['apples', 'apples', 'bananas', 'oranges', 'apples', 'oranges', 'bananas', 'grapes'];

// current element is 'apples'

function countOccurrence(accumulator = {}, currentFruit = 'apples'){
    // since currentFruit = 'apples' then accumulator[currentFruit] = accumulator['apples']

	const currentFruitCount = accumulator[currentFruit];
    // currentFruitCount will be null since accumulator is an empty object

    if(currentFruitCount) {
    	accumulator[currentFruit] = currentFruitCount + 1;
    } else {
        // this block will run since accumulator is empty
        // currentFruit = 'apples'
    	accumulator['apples'] = 1
        // accumulator should look like this: { 'apples': 1 }
    }

    return accumulator // which is { 'apples': 1 };
}

Since accumulator is an empty object, currentFruitCount will be null which means the else block will run where a new key (apples) with the value of 1 will be added to the accumulator and returned from the function which will be passed as the value of the accumulator in the second iteration.

Iteration 2

In the second iteration, our accumulator will have the value of { 'apples': 1 } which was returned by the countOccurrence function in the first iteration and the countOccurrence function will look like the code below:

const fruits = ['apples', 'apples', 'bananas', 'oranges', 'apples', 'oranges', 'bananas', 'grapes'];

// current element is 'apples'

function countOccurrence(accumulator = { 'apples': 1 }, currentFruit = 'apples'){
    // since currentFruit = 'apples' then accumulator[currentFruit] = accumulator['apples']

	const currentFruitCount = accumulator[currentFruit];
    // currentFruitCount will be 1 

    if(currentFruitCount) {
        // this block will run since currentFruitCount is 1
        // currentFruit = 'apples'
    	accumulator['apples'] = 1 + 1;
        // accumulator should look like this: { 'apples': 2 }
    } else {
    	accumulator[currentFruit] = 1
    }

    return accumulator // which is { 'apples': 2 };
}

Since the accumulator contains a key ('apple') with the value of 1, currentFruit will be 1 which means the if block will be run, in that block the value of the apple key will be incremented by 1 making it 2, this new value will be updated in the accumulator object to make it { 'apples' : 2 } , this value will be returned by the countOccurrence function and passed as the value for the accumulator in the third iteration.

Iteration 3

For our third iteration, accumulator has the value of { apples: 2 } which was returned by countOccurence during the second iteration, the countOccurence function will look like the code below:

const fruits = ['apples', 'apples', 'bananas', 'oranges', 'apples', 'oranges', 'bananas', 'grapes'];

// current element is 'bananas'

function countOccurrence(accumulator = { 'apples': 2 }, currentFruit = 'bananas'){
    // since currentFruit = 'bananas' then accumulator[currentFruit] = accumulator['bananas']

	const currentFruitCount = accumulator[currentFruit];
        // currentFruitCount will be null since accumulator doesn't contain 'bananas'

    if(currentFruitCount) {
        accumulator[currentFruit] = currentFruitCount + 1;
    } else {
        // this block will run since currentFruitCount is null
        // currentFruit = 'bananas'
    	accumulator['bananas'] = 1
    }

    return accumulator // which is { 'apples': 2, 'bananas': 1  };
}

This iteration is similar to the first one, since bananas doesn't exist in accumulator it will be added to the object and given a value of 1 , making accumulator look like this: { 'apples': 2, 'bananas': 1 } and this will become the value of accumulator for the fourth iteration.

The process will repeat itself until the Reduce function has iterated through each element in the array.

Wrapping up

I really hope these examples were clear enough to create a mental model of how the Reduce function works. Thanks!!!

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JavaScript developers should you be using Web Workers?

JavaScript developers should you be using Web Workers?

Do you think JavaScript developers should be making more use of Web Workers to shift execution off of the main thread?

Originally published by David Gilbertson at https://medium.com

So, Web Workers. Those wonderful little critters that allow us to execute JavaScript off the main thread.

Also known as “no, you’re thinking of Service Workers”.

Photo by Caleb Jones on Unsplash

Before I get into the meat of the article, please sit for a lesson in how computers work:

Understood? Good.

For the red/green colourblind, let me explain. While a CPU is doing one thing, it can’t be doing another thing, which means you can’t sort a big array while a user scrolls the screen.

This is bad, if you have a big array and users with fingers.

Enter, Web Workers. These split open the atomic concept of a ‘CPU’ and allow us to think in terms of threads. We can use one thread to handle user-facing work like touch events and rendering the UI, and different threads to carry out all other work.

Check that out, the main thread is green the whole way through, ready to receive and respond to the gentle caress of a user.

You’re excited (I can tell), if we only have UI code on the main thread and all other code can go in a worker, things are going to be amazing (said the way Oprah would say it).

But cool your jets for just a moment, because websites are mostly about the UI — it’s why we have screens. And a lot of a user’s interactions with your site will be tapping on the screen, waiting for a response, reading, tapping, looking, reading, and so on.

So we can’t just say “here’s some JS that takes 20ms to run, chuck it on a thread”, we must think about where that execution time exists in the user’s world of tap, read, look, read, tap…

I like to boil this down to one specific question:

Is the user waiting anyway?

Imagine we have created some sort of git-repository-hosting website that shows all sorts of things about a repository. We have a cool feature called ‘issues’. A user can even click an ‘issues’ tab in our website to see a list of all issues relating to the repository. Groundbreaking!

When our users click this issues tab, the site is going to fetch the issue data, process it in some way — perhaps sort, or format dates, or work out which icon to show — then render the UI.

Inside the user’s computer, that’ll look exactly like this.

Look at that processing stage, locking up the main thread even though it has nothing to do with the UI! That’s terrible, in theory.

But think about what the human is actually doing at this point. They’re waiting for the common trio of network/process/render; just sittin’ around with less to do than the Bolivian Navy.

Because we care about our users, we show a loading indicator to let them know we’ve received their request and are working on it — putting the human in a ‘waiting’ state. Let’s add that to the diagram.

Now that we have a human in the picture, we can mix in a Web Worker and think about the impact it will have on their life:

Hmmm.

First thing to note is that we’re not doing anything in parallel. We need the data from the network before we process it, and we need to process the data before we can render the UI. The elapsed time doesn’t change.

(BTW, the time involved in moving data to a Web Worker and back is negligible: 1ms per 100 KB is a decent rule of thumb.)

So we can move work off the main thread and have a page that is responsive during that time, but to what end? If our user is sitting there looking at a spinner for 600ms, have we enriched their experience by having a responsive screen for the middle third?

No.

I’ve fudged these diagrams a little bit to make them the gorgeous specimens of graphic design that they are, but they’re not really to scale.

When responding to a user request, you’ll find that the network and DOM-manipulating part of any given task take much, much longer than the pure-JS data processing part.

I saw an article recently making the case that updating a Redux store was a good candidate for Web Workers because it’s not UI work (and non-UI work doesn’t belong on the main thread).

Chucking the data processing over to a worker thread sounds sensible, but the idea struck me as a little, umm, academic.

First, let’s split instances of ‘updating a store’ into two categories:

  1. Updating a store in response to a user interaction, then updating the UI in response to the data change
  2. Not that first one

If the first scenario, a user taps a button on the screen — perhaps to change the sort order of a list. The store updates, and this results in a re-rendering of the DOM (since that’s the point of a store).

Let me just delete one thing from the previous diagram:

In my experience, it is rare that the store-updating step goes beyond a few dozen milliseconds, and is generally followed by ten times that in DOM updating, layout, and paint. If I’ve got a site that’s taking longer than this, I’d be asking questions about why I have so much data in the browser and so much DOM, rather than on which thread I should do my processing.

So the question we’re faced with is the same one from above: the user tapped something on the screen, we’re going to work on that request for hopefully less than a second, why would we want to make the screen responsive during that time?

OK what about the second scenario, where a store update isn’t in response to a user interaction? Performing an auto-save, for example — there’s nothing more annoying than an app becoming unresponsive doing something you didn’t ask it to do.

Actually there’s heaps of things more annoying than that. Teens, for example.

Anyhoo, if you’re doing an auto-save and taking 100ms to process data client-side before sending it off to a server, then you should absolutely use a Web Worker.

In fact, any ‘background’ task that the user hasn’t asked for, or isn’t waiting for, is a good candidate for moving to a Web Worker.

The matter of value

Complexity is expensive, and implementing Web Workers ain’t cheap.

If you’re using a bundler — and you are — you’ll have a lot of reading to do, and probably npm packages to install. If you’ve got a create-react-app app, prepare to eject (and put aside two days twice a year to update 30 different packages when the next version of Babel/Redux/React/ESLint comes out).

Also, if you want to share anything fancier than plain data between a worker and the main thread you’ve got some more reading to do (comlink is your friend).

What I’m getting at is this: if the benefit is real, but minimal, then you’ve gotta ask if there’s something else you could spend a day or two on with a greater benefit to your users.

This thinking is true of everything, of course, but I’ve found that Web Workers have a particularly poor benefit-to-effort ratio.

Hey David, why you hate Web Workers so bad?

Good question.

This is a doweling jig:

I own a doweling jig. I love my doweling jig. If I need to drill a hole into the end of a piece of wood and ensure that it’s perfectly perpendicular to the surface, I use my doweling jig.

But I don’t use it to eat breakfast. For that I use a spoon.

Four years ago I was working on some fancy animations. They looked slick on a fast device, but janky on a slow one. So I wrote fireball-js, which executes a rudimentary performance benchmark on the user’s device and returns a score, allowing me to run my animations only on devices that would render them smoothly.

Where’s the best spot to run some CPU intensive code that the user didn’t request? On a different thread, of course. A Web Worker was the correct tool for the job.

Fast forward to 2019 and you’ll find me writing a routing algorithm for a mapping application. This requires parsing a big fat GeoJSON map into a collection of nodes and edges, to be used when a user asks for directions. The processing isn’t in response to a user request and the user isn’t waiting on it. And so, a Web Worker is the correct tool for the job.

It was only when doing this that it dawned on me: in the intervening quartet of years, I have seen exactly zero other instances where Web Workers would have improved the user experience.

Contrast this with a recent resurgence in Web Worker wonderment, and combine that contrast with the fact that I couldn’t think of anything else to write about, then concatenate that combined contrast with my contrarian character and you’ve got yourself a blog post telling you that maybe Web Workers are a teeny-tiny bit overhyped.

Thanks for reading

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Further reading

An Introduction to Web Workers

JavaScript Web Workers: A Beginner’s Guide

Using Web Workers to Real-time Processing

How to use Web Workers in Angular app

Using Web Workers with Angular CLI