# Computer Algorithms: Stack and Queue

## Introduction

Every developer knows that computer algorithms are tightly related to data structures. Indeed many of the algorithms depend on a data structures and can be very effective for some data structures and ineffective for others. A typical example of this is the heapsort algorithm, which depends on a data structure called “heap”. In this case although the stack and the queue are data structures instead of pure algorithms it’s imporant to understand their structure and the way they operate over data.

However, before we continue with the concrete realization of the stack and the queue, let’s first take a look on the definition of this term. A data structure is a logical abstraction that “models” the real world and presents (stores) our data in a specific format. The access to this data structure is often predefined thus we can access directly every item containing data. This help us to perform a different kind of tasks and operations over different kind of data structures – insert, delete, search, etc.. A typical data structures are the stack, the queue, the linked list and the tree.

All these structures help us perform specific operations effectively. For instance searching in a balanced tree is faster than searching in a linked list.

It is also very important to note that data structures can be represented in many different ways. We can model them using arrays or pointers, as shown in this post. In fact the most important thing is to represent the logical structure of the data structure you’re modeling. Thus the stack is a structure that follows the LIFO (Last In First Out) principle and it doesn’t matter how it is represented in our program (whether it will be coded with an array or with pointers). The important thing into a stack representation is to follow the LIFO principle correctly. In this case if the stack is an array only its top should be accessible and the only operation must be inserting new top of the stack.
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# Computer Algorithms: Bubble Sort

## Overview

It’s weird that bubble sort is the most famous sorting algorithm in practice since it is one of the worst approaches for data sorting. Why is bubble sort so famous? Perhaps because of its exotic name or because it is so easy to implement. First let’s take a look on its nature.

Bubble sort consists of comparing each pair of adjacent items. Then one of those two items is considered smaller (lighter) and if the lighter element is on the right side of its neighbour, they swap places. Thus the lightest element bubbles to the surface and at the end of each iteration it appears on the top. I’ll try to explain this simple principle with some pictures.

### 1. Each two adjacent elements are compared

Here “2” appears to be less than “4”, so it is considered lighter and it continues to bubble to the surface (the front of the array).
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# JavaScript Performance: for vs. while

## JavaScript Loops

If you have read some preformance tests on JavaScript loops, you may have heard that “while” is faster than “for”. However the question is how faster is “while”? Here are some results, but first let’s take a look on the JavaScript code.

#### The for experiment

```console.time('for'); for (var i = 0; i < 10000000; i++) { i / 2; } console.timeEnd('for');```

#### The while experiment

```console.time('while'); var i = 0; while (i++ < 10000000) { i / 2; } console.timeEnd('while');```

Note – these tests are performed and measured with Firebug on Firefox.

## Results

It’s a fact, that you’ll get different results as many times as you run this snippet. It depends also on the enviroment and software/hardware specs. That is why I performed them 10 times and then I took the average value. Here are the values of my performance tests. Note that both for and while perform 10,000,000 iterations.

## And the Winner Is

While is the winner with an average result of 83.5 milliseconds, while “for” result is 88 average milliseconds.

As the diagram bellow shows, the while loop is slightly faster. However we should be aware that these performance gains are significant for large number of iterations!