# Advantages and Disadvantages of Quick Sort

The purpose of this article is to explain the “**Advantages and Disadvantages of Quick Sort**“.

Algorithms are discussed every day. But what’s the exact purpose of an algorithm? The algorithm is a set of instructions to be followed step-by-step to arrive at the desired result. Essentially, algorithms are methods for solving problems. Since the introduction of the computer, the term “algorithm” may seem very new. The first algorithm was developed in Ancient Greece, and the first algorithm was captured at that time. Technological advancements have led to the development of more complex algorithms every day. A quick sort algorithm is one such algorithm whose advantages and disadvantages will be examined in this analysis.

This algorithm involves sorting, as its name suggests. Dividing and conquering techniques are used to solve the problem. This algorithm can be used to identify the worst, average, and best scenarios. So let’s examine quick sort’s pros and cons to get a better understanding of what it has to offer.

## Advantages and Disadvantages of quicksort

**Advantages of quicksort**

You can solve your problems quickly with the quick sort algorithm. It is an efficient algorithm that can solve a wide variety of problems. In detail, it has a number of advantages.

**1. Fast and efficient**

To perform sorting functions quickly and efficiently, quicksort is the most preferred method. Other sorting algorithms are slower than this, so a faster result is achieved. For example, suppose you are planning to classify an array of items. Any value from the list can be selected by this algorithm and act as a pivot. The categories will be separated by this algorithm.

We will also use pivots to further divide all categories. Small sections remaining will not be divided further. Those sections can now be sorted by type of category. The algorithm pivots an element in order to divide the array, which may appear simple at first glance. As the array is sorted, each partition has recursively applied the algorithm. Organizing an extensive list of items using it has proven particularly useful.

**2. Cache friendly**

Quicksort’s in-place sorting is its most talked-about feature. The algorithm does not require additional storage space at the time of sorting. Sorting occurs in a given area. Thus, the sorted and unsorted lists share the same storage and sorting occurs there. The auxiliary array is necessary for other sorting algorithms. Quicksort beats other algorithms in that category in that situation. Quicksort algorithm also exhibits good cache locality, thus making it faster in an environment like virtual memory.

**3. Time complexity**

Algorithm time complexity is measured by how long it takes for an algorithm to execute completely. The quicksort algorithm has a better time complexity than any other sorting algorithm. Let’s break this down into best-case, average-case, and worst-case scenarios to understand its time complexity.

**A. Best-case time complexity**

If you select the mean element as the pivot, you are considered to have the best case for quicksort. Arrays are divided by a pivot, and when a mean element is chosen, the arrays are divided into equal subarrays. In this case, there are log2 n levels of partitioning, so the tree’s height is minimum. Because of this, the good case time complexity of the quicksort is O(n log n).

**B. Average-case time complexity**

It is believed that quicksort is slower than average when we do not receive evenly balanced partitions, with an O(n log) time complexity. The next scenario may be obvious.

**C. Worst-case time complexity**

In terms of the worst case of quicksort, it occurs when either the smallest or largest element in the list is selected as the pivot element. Such instances typically occur when the list is close to being sorted. The array is divided into n-1 if the pivot is the largest element. A significant difference in tree height will result from the partitioning not being equal. In this case, we have n partitioning levels, n-1, n-2, … etc. Therefore, in the worst case, the quicksort has a time complexity of O(n2).

**4. Space complexity**

Fast sort has a space complexity of O(log N), which makes it a suitable algorithm in situations where space is limited. Space complexity can be defined as the amount of memory required by an algorithm to solve a problem. The number of containers that must be created for quicksort is N, which means its space complexity will also be N. This is instead accomplished by using the array given.

**Disadvantages of quicksort**

There are advantages and disadvantages to each algorithm, and quicksort is no exception. The following are two disadvantages of quicksort.

**1. Unstable**

You might want to consider re-evaluating your options for stability if you use Quick Sort. Quick Sort is undoubtedly an efficient and fast sorting algorithm. Since this sorting algorithm does not maintain the original order of the key and value pairs, it is considered unstable. As opposed to stable sorting algorithms for which the original order of the key-value pairs is remembered even though they have been sorted. In this case, quick sort won’t preserve the element order, so it is possible to see the same elements flipped when displaying a sorted list.

**2. Worst-case time complexity**

The worst-case time complexity was discussed in detail in the above section. Fast sorting has this disadvantage. It usually occurs when the largest or smallest element is the pivot, or when all the elements are the same size. Both of these worst-case scenarios greatly affect the speed of the quick sort. It is possible to get a StackOverflowException when selecting a nearly sorted array as the array is large and the recursion is deep. The shuffle method is one way of dealing with this situation. The quicksort algorithm performs better when elements are shuffled, which introduces randomness into the elements.

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