Sorting In Data Structures - All Techniques Explained (+Examples)

Sorting is a fundamental concept in data structures and algorithms, where the elements of a collection are arranged in a specific order, such as ascending or descending. This process improves data organization, making it easier to search, analyze, and process. From simple methods like Bubble Sort to more advanced techniques like Quick Sort and Merge Sort, sorting algorithms play a vital role in optimizing performance in various applications. In this article, we’ll explore key sorting techniques, their working mechanisms, and use cases to help you understand their importance and implementation.

What Is Sorting In Data Structures?

Sorting in data structures is the process of arranging data elements in a specific order—ascending or descending—based on a particular criterion. Sorting is a fundamental operation because it makes searching, organizing, and analyzing data more efficient. For instance, sorting a list of names alphabetically or numbers in increasing order simplifies the retrieval process.

Types Of Sorting Techniques

Here’s a table that provides the types of sorting techniques in data structures:

What Is Bubble Sort?

Bubble Sort is a simple comparison-based sorting algorithm that repeatedly steps through the list, compares adjacent elements, and swaps them if they are in the wrong order. This process is repeated until the list is sorted. The name "Bubble Sort" comes from the way smaller elements "bubble" to the top (beginning of the list) with each pass.

How It Works:

1. Compare the first and second elements of the list.
2. If the first element is greater than the second, swap them.
3. Move to the next pair of elements and repeat the process.
4. After each pass, the largest unsorted element is "bubbled" to the end of the list.
5. Repeat the process for the remaining unsorted part of the list.

Bubble Sort Syntax (In C++):

void bubbleSort(int arr[], int n) {
    for (int i = 0; i < n-1; i++) {
        for (int j = 0; j < n-i-1; j++) {
            if (arr[j] > arr[j+1]) {
                // Swap the elements if they are in the wrong order
                int temp = arr[j];
                arr[j] = arr[j+1];
                arr[j+1] = temp;
            }
        }
    }
}

Code Example:

Output: 

Unsorted array: 64 34 25 12 22 11 90
Sorted array: 11 12 22 25 34 64 90

Explanation: 

In the above code example-

1. bubbleSort Function: This function sorts the array using Bubble Sort. It has two nested loops:
• The outer loop runs n-1 times (where n is the number of elements).
• The inner loop compares adjacent elements and swaps them if necessary.
2. printArray Function: This function prints the elements of the array.
3. main() Function: The array is defined, and then both the unsorted and sorted arrays are printed before and after calling the bubbleSort function.

What Is Selection Sort?

Selection Sort is a simple sorting algorithm that divides the list into two parts: the sorted part and the unsorted part. It repeatedly selects the smallest (or largest) element from the unsorted part and moves it to the sorted part.

How It Works:

1. Find the smallest (or largest) element in the unsorted part of the array.
2. Swap it with the first element of the unsorted part.
3. Repeat the process for the remaining unsorted part until the entire array is sorted.

Selection Sort Syntax (In C++)

void selectionSort(int arr[], int n) {
    for (int i = 0; i < n-1; i++) {
        int minIdx = i; // Index of the minimum element
        for (int j = i+1; j < n; j++) {
            if (arr[j] < arr[minIdx]) {
                minIdx = j; // Update index of the smallest element
            }
        }
        // Swap the found minimum element with the first element of the unsorted part
        int temp = arr[minIdx];
        arr[minIdx] = arr[i];
        arr[i] = temp;
    }
}

Code Example:

Output: 

Unsorted array: 64 25 12 22 11
Sorted array: 11 12 22 25 64

Explanation:

In the above code example-

1. In the selectionSort Function:
• The outer loop runs n-1 times (where n is the number of elements).
• For each iteration, the smallest element in the unsorted part is found using the inner loop.
• The smallest element is then swapped with the first element of the unsorted part.
2. printArray Function: This function prints the elements of the array.
3. main() Function: The array is defined, and both the unsorted and sorted arrays are printed before and after calling the selectionSort function.

What Is Insertion Sort?

Insertion Sort is a simple and intuitive sorting algorithm that builds the final sorted array one element at a time. It works similarly to how we sort playing cards in our hands:

1. Start with the second element, compare it with elements before it, and insert it in the correct position.
2. Repeat for all elements, ensuring that the array is sorted after each insertion.

How It Works:

1. Divide the array into a sorted part and an unsorted part.
2. Take the first element of the unsorted part and compare it with the elements of the sorted part.
3. Shift elements of the sorted part that are larger than the current element to make space for it.
4. Insert the current element in its correct position.

Insertion Sort Syntax (In C++)

void insertionSort(int arr[], int n) {
    for (int i = 1; i < n; i++) {
        int key = arr[i];
        int j = i - 1;
        // Shift elements of the sorted part to make space for the key
        while (j >= 0 && arr[j] > key) {
            arr[j + 1] = arr[j];
            j--;
        }
        arr[j + 1] = key; // Insert the key in the correct position
    }
}

Code Example:

Output:

Unsorted array: 12 11 13 5 6
Sorted array: 5 6 11 12 13

Explanation:

In the above code example-

1. In the insertionSort Function:
• The loop starts with the second element (i = 1) because a single element is already sorted.
• For each element, it finds its correct position in the sorted part of the array.
• Elements larger than the current key are shifted to the right to make space.
• The key is then inserted into its correct position.
2. printArray Function: This function prints the elements of the array.
3. main() Function: The array is defined, and the unsorted and sorted arrays are printed before and after calling the insertionSort function.

What Is Merge Sort?

Merge Sort is a divide-and-conquer sorting algorithm that recursively divides the array into two halves until each subarray contains only one element, then merges those subarrays to produce a sorted array. It is one of the most efficient sorting algorithms for large datasets.

How It Works:

1. Divide: Split the array into two halves recursively until each subarray has only one element.
2. Conquer: Merge the subarrays back together in sorted order.
3. Combine: Repeat merging until the entire array is sorted.

Merge Sort involves two main functions:

1. A recursive function to divide the array.
2. A helper function to merge two sorted subarrays.

Code Example: 

Output:

Unsorted array: 12 11 13 5 6 7
Sorted array: 5 6 7 11 12 13

Explanation:

In the above code example-

1. merge Function:
   • Merges two sorted subarrays L and R into a single sorted array.
   • Uses temporary arrays to hold data during merging.
2. mergeSort Function:
   • Recursively splits the array into two halves.
   • Calls the merge function to combine the sorted halves.
3. printArray Function: Prints the elements of the array.
4. main() Function: Initializes the array, calls the mergeSort function, and prints the array before and after sorting.

What Is Quick Sort?

Quick Sort is a highly efficient and widely used divide-and-conquer sorting algorithm. It selects a "pivot" element, partitions the array around the pivot, and recursively sorts the partitions.

How It Works:

1. Choose a Pivot: Select an element as the pivot. Common choices include the first element, the last element, the middle element, or a random element.
2. Partitioning: Reorder the array so that:
• Elements less than the pivot are moved to its left.
• Elements greater than the pivot are moved to its right.
3. Recursion: Recursively apply the above steps to the left and right partitions.

Quick Sort involves two main functions:

1. A partition function that rearranges elements around the pivot.
2. A quickSort function that recursively applies the algorithm to subarrays.

Code Example:

Output: 

Unsorted array: 10 7 8 9 1 5
Sorted array: 1 5 7 8 9 10

Explanation:

In the above code example-

1. partition Function:
• Chooses the pivot as the last element.
• Rearranges the array so elements smaller than the pivot are on the left and greater ones are on the right.
• Returns the index of the pivot.
2. quickSort Function: Recursively applies Quick Sort to the left and right subarrays around the pivot.
3. printArray Function: Prints the elements of the array.
4. main() Function: Initializes the array, calls quickSort, and prints the results.

What Is Heap Sort?

Heap Sort is a comparison-based sorting algorithm that uses a binary heap data structure to sort elements. It is an in-place and non-recursive sorting technique that efficiently organizes and retrieves elements.

How It Works:

1. Build a Max Heap: Construct a binary heap from the input array, ensuring the largest element is at the root.
2. Extract Elements: Repeatedly remove the root (maximum element) of the heap, place it at the end of the array, and reduce the heap size.
3. Heapify: Restore the heap property after each extraction to maintain the max heap structure.

Heap Sort involves two key operations:

1. Heapify: Maintains the max heap property.
2. Heap Sort Function: Builds the heap and sorts the array.

Code Example:

Output:

Unsorted array: 12 11 13 5 6 7
Sorted array: 5 6 7 11 12 13

Explanation:

In the above code example-

1. heapify Function:
• Ensures the max heap property (parent is larger than its children).
• Recursively adjusts the heap if the largest element is not at the root.
2. heapSort Function:
• Builds the initial max heap from the array.
• Extracts the largest element (root) and places it at the end of the array.
• Reduces the heap size and re-applies heapify.
3. printArray Function: Prints the elements of the array.

What Is Radix Sort?

Radix Sort is a non-comparative, stable sorting algorithm that sorts numbers digit by digit, starting from the least significant digit (LSD) to the most significant digit (MSD) or vice versa. It uses counting sort as a subroutine to sort digits.

How It Works:

1. Identify the Maximum Element: Determine the maximum number in the array to find the number of digits.
2. Sort by Each Digit: Starting from the least significant digit (unit place), sort the numbers using counting sort.
3. Repeat for Each Place Value: Move to the next significant digit and repeat until all place values are processed.

Syntax For Radix Sort (In C++)

Radix Sort relies on counting sort to sort numbers at each digit position.

void radixSort(int arr[], int n) {
    // Find the maximum number to determine the number of digits
    int max = *max_element(arr, arr + n);
    // Apply counting sort for each digit
    for (int exp = 1; max / exp > 0; exp *= 10)
        countingSort(arr, n, exp);
}

Code Example:

Output: 

Unsorted array: 170 45 75 90 802 24 2 66
Sorted array: 2 24 45 66 75 90 170 802

Explanation:

In the above code example-

1. countingSort Function:
• Sorts the array based on the current digit (unit, tens, hundreds, etc.).
• Uses a counting array to track occurrences of digits.
2. radixSort Function:
• Determines the number of digits in the largest number.
• Iteratively sorts the array for each digit using countingSort.
3. printArray Function: Prints the elements of the array before and after sorting.

What Is Bucket Sort?

Bucket Sort is a distribution-based sorting algorithm that divides the input into several "buckets," sorts each bucket individually, and then combines them to produce a sorted array. It works well when the input data is uniformly distributed over a range.

How It Works:

1. Divide the Input into Buckets: The input array is divided into several buckets based on a pre-defined range.
2. Sort Each Bucket: Each bucket is sorted individually using another sorting algorithm (commonly Insertion Sort).
3. Concatenate the Sorted Buckets: After sorting each bucket, the contents of all buckets are combined to form the final sorted array.

Code Example:

Output:

Unsorted array: 0.42 0.32 0.23 0.52 0.67 0.31
Sorted array: 0.23 0.31 0.32 0.42 0.52 0.67

Explanation:

In the above code example-

1. insertionSort Function: Sorts each bucket using Insertion Sort, which is efficient for small datasets.
2. bucketSort Function:
• Divides the array into buckets and places elements into the appropriate bucket based on their value.
• Sorts each bucket and combines them back into the original array.
3. printArray Function: Prints the elements of the array before and after sorting.

What Is Counting Sort?

Counting Sort is a non-comparative sorting algorithm that counts the number of occurrences of each element in the array and uses that information to place elements in their correct sorted position. It is best suited for sorting integers or objects that can be mapped to non-negative integer values.

How It Works:

1. Count Occurrences: Count the frequency of each distinct element in the input array.
2. Compute Cumulative Count: Use the frequency counts to compute the cumulative sum, which will indicate the position of each element in the sorted output.
3. Place Elements: Place each element from the input array into the correct position in the output array, using the cumulative count.
4. Rebuild Sorted Array: After placing all elements in their correct position, the output array is the sorted array.

Counting Sort requires:

1. A counting array to store the frequency of each element.
2. A cumulative array to determine the position of elements.

Code Example:

Output:

Unsorted array: 4 2 2 8 3 3 1
Sorted array: 1 2 2 3 3 4 8

Explanation:

In the above code example-

1. countingSort Function:
• First, it determines the maximum element in the array to define the size of the counting array.
• It counts the occurrences of each element in the arr[].
• The count[] array is then updated by adding the previous counts to make it cumulative.
• The elements are placed into their correct position in the output[] array based on the cumulative count.
• Finally, the sorted elements are copied back into the original array.
2. printArray Function: Prints the elements of the array before and after sorting.

What Is Shell Sort?

Shell Sort is an optimization of Insertion Sort that allows the exchange of items that are far apart. The basic idea is to arrange the list of elements so that, starting anywhere, taking every kthk^{th}kth element produces a sorted list. By progressively reducing the gap between elements, Shell Sort efficiently reduces the number of inversions (out-of-order pairs), which leads to improved sorting performance compared to regular Insertion Sort.

How It Works:

1. Gap Sequence: Instead of comparing adjacent elements (like Insertion Sort), Shell Sort compares elements that are far apart. It starts by sorting pairs of elements far apart, then progressively reduces the gap between elements being compared.
2. Insertion Sort: After comparing and potentially swapping far-apart elements, it sorts the sublists using Insertion Sort.
3. Reducing the Gap: The gap between elements is reduced at each step, and this process is repeated until the gap is 1, at which point the algorithm behaves like Insertion Sort.

Code Example:

Output:

Unsorted array: 5 2 9 1 5 6
Sorted array: 1 2 5 5 6 9

Explanation:

In the above code example-

1. shellSort Function:
• Starts with a gap of n/2, where n is the number of elements in the array.
• In each iteration, the gap is halved until it becomes 1.
• For each gap, the array is sorted using a variation of Insertion Sort, where instead of comparing adjacent elements, it compares elements that are gap positions apart.
2. printArray Function: Prints the elements of the array before and after sorting.

Choosing The Right Sorting Algorithm

When selecting a sorting algorithm for a particular problem, several factors should be taken into account to ensure the best performance. The decision depends on the input data characteristics (like size, order, and range) and the specific requirements such as time and space efficiency. Here's a guide to help you choose the right sorting algorithm based on these factors.

Key Factors To Consider:

1. Size of the Input Data:
• Small Data: For small arrays (under 1000 elements), simpler algorithms like Insertion Sort or Selection Sort may perform well despite their higher time complexities, as they have low overhead.
• Large Data: For larger datasets, algorithms with better time complexities, such as Merge Sort, Quick Sort, or Heap Sort, are preferred because of their faster performance for large inputs.
2. Time Complexity:
• Best Time Complexity: Algorithms with O(n log⁡ n) average or worst-case time complexity, like Quick Sort, Merge Sort, and Heap Sort, are efficient for large datasets.
• Worst Time Complexity: For sorting where performance guarantees are critical, Merge Sort is a better choice, as its worst-case time complexity is consistently O(n log n), unlike Quick Sort which can degrade to O(n^2) in the worst case (with bad pivot choices).
3. Space Complexity:
• In-place Sorting: If memory usage is a concern (e.g., for large datasets), in-place sorting algorithms like Quick Sort, Heap Sort, and Insertion Sort are preferred, as they use O(1) extra space.
• Extra Space Required: Algorithms like Merge Sort require O(n) extra space, which can be a disadvantage when dealing with large datasets in memory-constrained environments.
4. Stability:
• Stable Sorting Algorithms maintain the relative order of records with equal keys. Merge Sort and Bubble Sort are stable, whereas Quick Sort and Heap Sort are not.
• If stability is important (e.g., sorting objects based on one attribute while maintaining the relative order of objects with the same attribute), then Bubble Sort, Merge Sort, or Insertion Sort are good choices.
5. Range of Input Elements:
• Counting Sort, Radix Sort, and Bucket Sort can perform much faster than O(n log n) algorithms when the range of input elements is small and can be mapped to integers.
• If the input range is large and non-integer, you may need to use comparison-based algorithms like Quick Sort or Merge Sort.
6. Input Order:
• Partially Sorted Data: Algorithms like Insertion Sort are particularly efficient when the data is already partially sorted. In fact, Insertion Sort can run in O(n) time if the input is already sorted or nearly sorted.
• Randomized or Unsorted Data: Quick Sort, Merge Sort, and Heap Sort generally perform well on random or unsorted data.

Applications Of Sorting

Sorting plays a crucial role in various fields of computer science and real-world applications. It is a fundamental operation that optimizes the performance of other algorithms and systems. Here are some key applications of sorting:

1. Searching Algorithms

• Binary Search: Sorting is essential for efficient searching algorithms like Binary Search. Binary Search can only work on sorted arrays or lists, allowing it to find elements in O(log n) time, much faster than linear search, which has a time complexity of O(n).

2. Data Visualization

• Graphing and Charts: Sorting helps in organizing data for easier visualization, such as arranging data points in ascending or descending order before plotting graphs or creating charts. This makes patterns, trends, and outliers more noticeable.

3. Database Management

• Indexing: Many database management systems use sorting to maintain indexes. Sorted data enables faster retrieval, as the search algorithms can take advantage of the sorted structure to locate records quickly.
• Query Optimization: Sorting improves query performance by optimizing the execution of range queries and join operations.

4. File Systems

• Efficient Storage: In file systems, sorting is used to organize files in directories, ensuring that file listings (e.g., names or sizes) are displayed in a sorted order, allowing for quick access.
• Data Compression: Sorting is used in compression algorithms like Huffman coding, where symbols are arranged in a frequency order to minimize the storage space required.

5. Social Media and Online Platforms

• Ranking Algorithms: Sorting plays a key role in ranking systems on social media platforms, e-commerce websites, and news feeds. It helps in organizing content based on relevance, popularity, or user preferences, ensuring that users see the most important or trending content first.

6. Scheduling Algorithms

• Task Scheduling: In operating systems, sorting helps in scheduling tasks. For example, Shortest Job Next (SJN) scheduling algorithm sorts processes based on their burst time to optimize CPU utilization.
• Deadline-based Scheduling: Sorting helps to order tasks by their deadlines, allowing the system to prioritize tasks efficiently.

7. Data Mining

• Clustering and Classification: Sorting can help with data clustering algorithms by grouping similar data points together. It is also used in decision tree algorithms and other classification techniques to organize and rank data.

8. Merchandise Inventory Management

• Stock Sorting: Sorting algorithms are used in inventory management systems to sort stock items based on factors such as price, quantity, or item number, allowing efficient tracking and reporting.

9. Networking Protocols

• Packet Routing: In networking, sorting helps in routing packets based on priority. For example, Quality of Service (QoS) algorithms prioritize network traffic by sorting packets, ensuring that high-priority packets are transmitted first.

10. E-commerce Systems

• Product Listings: E-commerce platforms like Amazon, eBay, and others use sorting to arrange products based on user preferences, such as sorting by price, rating, or newest arrival, to enhance the shopping experience.

11. Genomic Data Analysis

• Sorting DNA Sequences: Sorting plays a crucial role in bioinformatics, especially in sorting DNA sequences or genomic data. It is used in algorithms for sequence alignment, where sequences need to be sorted based on certain criteria, such as length or nucleotide composition.

Conclusion

In conclusion, sorting is a critical operation in data structures that enables efficient data management and retrieval. Whether you're organizing data for search operations, improving performance in algorithms, or optimizing resource usage in large-scale systems, understanding and choosing the right sorting technique is essential. From simple algorithms like Bubble Sort to advanced methods like Quick Sort and Merge Sort, each sorting technique offers distinct advantages depending on the problem at hand. 

By considering factors such as dataset size, memory constraints, and time complexity, we can make informed decisions about which algorithm to implement. Sorting remains a foundational concept in computer science, with applications spanning across various domains, making it an indispensable skill for anyone working with data.

Frequently Asked Questions

Q. What is the best sorting algorithm for large datasets?

For large datasets, algorithms like Quick Sort, Merge Sort, and Heap Sort are typically the best choices due to their O(n log n) average time complexity. Merge Sort is particularly useful when stability is important, while Quick Sort is often faster in practice for general use.

Q. Is there a sorting algorithm that works in linear time?

Yes, sorting algorithms like Counting Sort, Radix Sort, and Bucket Sort can achieve linear time complexity O(n) under certain conditions, such as when the range of input values is small or the data is uniformly distributed.

Q. What is the difference between stable and unstable sorting algorithms?

A stable sorting algorithm preserves the relative order of equal elements, meaning that if two elements are equal, their order in the sorted array will remain the same as in the original array. Examples of stable algorithms include Bubble Sort, Merge Sort, and Insertion Sort. Unstable algorithms like Quick Sort and Heap Sort do not guarantee this.

Q. When should I use Insertion Sort over other algorithms?

Insertion Sort is ideal for small or nearly sorted datasets. Its simplicity and low overhead make it an efficient choice for small arrays. It also performs exceptionally well when the data is almost sorted, running in linear time O(n) in the best case.

Q. What is the time complexity of Quick Sort, and why is it so popular?

The average time complexity of Quick Sort is O(n log ⁡n), but in the worst case (with poor pivot selection), it can degrade to O(n^2). Despite this, Quick Sort is popular because it often outperforms other algorithms in practice due to its low constant factors and efficient in-place sorting.

Q. Why is Quick Sort often faster than Merge Sort despite having a similar time complexity?

Quick Sort is often faster than Merge Sort in practical use because it has smaller constant factors and performs in-place sorting, which reduces memory usage and overhead. Additionally, Quick Sort is more cache-efficient, making it faster for many real-world datasets. However, Merge Sort guarantees O(n log n) performance in the worst case, while Quick Sort can degrade to O(n^2) if the pivot selection is poor, which is why Quick Sort's performance may vary based on the data.

You may also like to read: 

1. Dynamic Programming - From Basics To Advanced (+Code Examples)
2. Convert String To Date In Java | 3 Different Ways With Examples
3. Super Keyword In Java | Definition, Applications & More (+Examples)
4. How To Find LCM Of Two Numbers In Java? Simplified With Examples
5. How To Find GCD Of Two Numbers In Java? All Methods With Examples
6. Volatile Keyword In Java | Syntax, Working, Uses & More (+Examples)
7. Throws Keyword In Java | Syntax, Working, Uses & More (+Examples)