Queue Data Structure

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Queue Data Structure

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* First In First Out (FIFO)
* with time complexity of O(1) for key operations
export class Queue<T>{

/** Enqueues the item in O(1) */
enqueue(item: T): void {


* Dequeues the first inserted item in O(1)
* If there are no more items it returns `undefined`
dequeue(): T | undefined {



* First In First Out (FIFO)
* with time complexity of O(1) for key operations
export class Queue<T>{
private data: { [index: string]: T } = Object.create(null);
private nextEnqueueIndex = 0n;
private nextDequeueIndex = 0n;

/** Enqueues the item in O(1) */
enqueue(item: T): void {
this.data[this.nextEnqueueIndex.toString()] = item;

* Dequeues the first inserted item in O(1)
* If there are no more items it returns `undefined`
dequeue(): T | void {
if (this.nextDequeueIndex !== this.nextEnqueueIndex) {
const dequeued = this.data[this.nextDequeueIndex.toString()];
delete this.data[this.nextDequeueIndex.toString()];
return dequeued;

* Returns the number of elements in the queue
size(): bigint {
return this.nextEnqueueIndex - this.nextDequeueIndex;


00:00 Creating a well performing stack within JavaScript is

00:03 super simple and you can pretty much get away

00:04 by using the built in JavaScript array.

00:06 However, the same is not true

00:08 for creating a well performing queue within JavaScript.

00:11 And in this lesson, we will look at the reason why

00:13 and follow that up by creating your

00:15 own high performance queue.

00:17 So let's go. First,

00:19 let's take a quick look at the built-in methods within

00:22 JavaScript arrays that can be used

00:24 for creating your own stacks and queues.

00:26 Now to add items to an array one by one, the best method

00:29 that we have available is the push method.

00:31 Each call to push adds a new item at the end

00:34 of the JavaScript array.

00:36 Now, if you want to remove the last item that we added,

00:38 this will essentially give us a stack

00:40 that is the last in first out,

00:43 and we can do that with the JavaScript pop method.

00:46 Now, JavaScript arrays have another method called Shift,

00:48 which removes the first item within the array

00:51 and shifts all the other elements one index down.

00:54 However, because it has to update the index

00:57 of all the other elements

00:58 that will still be within the array,

01:00 it is not exactly highly performant.

01:02 Let's look at a quick demo

01:03 to demonstrate this performance issue.

01:06 Here we have a piece of code that creates a JavaScript array

01:09 and then simulates creating a bunch of JavaScript items

01:11 and adding them to the array using the push method,

01:14 and then removes these items in a last in first out order

01:18 using the pop method.

01:20 All of this code is wrapped in consult time,

01:23 so we get a time notification of

01:24 how much time this particular simulation is going to take.

01:28 Now we can repeat the process in a first,

01:29 in first out order, which is required

01:32 by a queue using the JavaScript shift method.

01:35 Now the performance difference over here is highly dependent

01:38 on the number of iterations that we do,

01:40 and over here we're doing a hundred thousand.

01:42 And you can see that the performance difference

01:44 between pop versus shift is quite significant.

01:47 It's five millisecond versus 389.

01:52 Now, even though the performance difference in this

01:54 particular case is quite aggressive

01:56 for most real world applications,

01:58 you probably wouldn't care.

01:59 However, if you are working in a performance intensive

02:02 application or are creating a library for someone

02:04 who cares about performance, then this is a difference

02:07 that you definitely should be aware of PAC

02:09 and create our own Q data structure,

02:11 which performs much better.

02:14 First, we will lay down the structure of

02:17 what we want our Q data structure to be.

02:19 It'll be a generic class,

02:20 just like the built-in JavaScript array.

02:23 It'll have an inq method which can add items in OF one.

02:26 It'll have a DQ method which will remove items in the first,

02:29 in first out order in OF one.

02:32 And that's pretty much it.

02:33 Now, if we didn't know any better,

02:35 we would probably start off

02:36 by creating a JavaScript array for the INQ method.

02:39 We will push it at the end of the array

02:40 and for the DQ method, we will remove it from the start

02:43 of the array using the JavaScript shift method.

02:45 But as we do know better, we know

02:47 that shift is not exactly O fun.

02:49 So let's get rid of this naive implementation.

02:52 Now, one observation that you can make over here is the fact

02:55 that you need some form of O one data structure

02:57 for your DQ operation.

02:59 And whenever you have to think of O one,

03:01 you should probably start thinking about hash maps.

03:03 And the cheapest hash map

03:04 that you can create within JavaScript is just

03:06 using a JavaScript object.

03:08 And that's exactly what we are going to do. Over here.

03:10 We create this JavaScript object that we are going to index

03:13 with a given number and the items within the object are

03:16 going to be of the type T that we want

03:18 to store within the queue.

03:20 Now, the reason why we are using a number index

03:22 for this particular object is that we are going

03:24 to track the items that have been queued

03:26 and D queued from this particular object using two

03:29 simple number variables.

03:31 Now, before we jump into more code,

03:33 let's look at a visualization to explain the strategy

03:36 that we will have for these three variables.

03:38 Think of the items that we are going to add

03:41 as indexes represented

03:42 by a line within this TETA object. We start off

03:45 With both next inq index as well as next

03:49 DQ index initialized to the value zero.

03:52 As we add new item, we will increment the next INQ index

03:56 so that we can add more items to more portions of this line.

03:59 And as we remove items,

04:01 we will increment the next DQ index

04:04 and essentially delete those items

04:06 by deleting the index in the data object,

04:08 which is represented by this line

04:10 and start returning undefined.

04:12 If the next DQ index ever catches up to the next inq index.

04:16 As this means we have run out of items to dq.

04:19 Now with that visualization out of the way, you can see

04:21 that the code for the inq method

04:23 is going to be pretty simple.

04:25 We are going to store the item at the next inq index

04:28 and then increment the next queue index

04:30 for the next inq operation.

04:32 The DQ method is going to be a bit more involved,

04:35 but nothing more complicated beyond basic programming.

04:38 The first thing that we are going to do is to make sure

04:40 that next DQ index has not caught up to the next inq index.

04:45 If it has, then outside of the F block,

04:47 the function is automatically going to return undefined.

04:51 Otherwise, we grab

04:52 The item that we plan to DQ using the next DQ index,

04:56 Delete this item from the data to free up the memory,

04:59 And then increment the next DQ index so it gets closer

05:02 and closer to the next inq index.

05:05 And finally, return the item

05:06 that we have successfully D queued.

05:09 Now that's it for the vital required methods

05:11 for the queue data structure,

05:13 and we can add a lot more methods.

05:14 For example, we can add a method called size

05:17 that returns the number of items

05:19 that are currently within the queue.

05:20 And all that we have to do for this particular case is

05:23 to simply return the difference

05:24 of the next in queue index versus the next DQ index.

05:29 Now let's jump back to a performance test to verify

05:31 that all of this work is not a complete waste.

05:34 We add a new test for the queue data structure.

05:37 We queue items using inq, and then we dq them using dq.

05:41 And if we run this, you can see that the pop

05:44 and the queue are quite similar in performance

05:47 and shift is much, much slower.

05:49 Now, even though for most particular applications,

05:51 this implementation is going to be sufficient,

05:53 there is one minor modification that we can still make.

05:57 JavaScript has this value known as max safe integer,

06:00 which is the maximum integer

06:02 that can be safely represented within JavaScript

06:05 anywhere greater than that might be clipped.

06:07 For example, max Safe integer plus one

06:09 and Max Safe integer plus two are both clipped

06:12 to the same integer value and

06:13 therefore it is not safe

06:15 to use values greater than max safe integer.

06:19 Now this can be problematic as we are using a number index

06:22 and we are implementing it arbitrarily,

06:25 but take an easy fix in modern JavaScript.

06:27 Instead of using the number type, we can use a big INT

06:30 and index by the string representation of that big int.

06:34 So we modify our data to use the string index,

06:37 change our next inq index, as well as the next DQ index

06:41 to big ins by adding the N suffix.

06:44 And then whenever we want to index the data object,

06:47 we make sure to convert the next inq index as well

06:49 as the next DQ index to the string representations.

06:54 Now because theoretically this data structure can hold more

06:57 than number, amount of items, we have

06:59 to modify the return type of the size method to be big int.

07:05 As always, you can find the code

07:06 for this particular lesson on GitHub,

07:08 and this is just one of the many ways

07:10 that you can create your own queue data

07:12 structure within JavaScript.

07:13 And in fact, we looked at another method when we created our

07:16 own W Link list.

07:18 The objective of this particular lesson was to get you

07:20 to create your own queue in the simplest way possible.