{"id":16521,"date":"2025-10-02T14:29:22","date_gmt":"2025-10-02T14:29:22","guid":{"rendered":"https:\/\/www.kaashivinfotech.com\/blog\/?p=16521"},"modified":"2025-10-02T14:29:22","modified_gmt":"2025-10-02T14:29:22","slug":"quicksort-algorithm-explained","status":"publish","type":"post","link":"https:\/\/www.kaashivinfotech.com\/blog\/quicksort-algorithm-explained\/","title":{"rendered":"\ud83d\ude80 QuickSort Algorithm Explained: Why Every Developer Should Master It in 2025"},"content":{"rendered":"<p>If you\u2019re a developer, sooner or later you\u2019ll bump into the <strong>QuickSort algorithm<\/strong>. Whether you\u2019re sitting in a coding interview, working on large datasets, or simply curious about how your favorite programming language sorts numbers under the hood \u2014 QuickSort keeps showing up.<\/p>\n<p>Here\u2019s a quick fact: <strong>C++\u2019s <code class=\"\" data-line=\"\">std::sort()<\/code> and Java\u2019s <code class=\"\" data-line=\"\">Arrays.sort()<\/code> (for primitives) use QuickSort or its variants<\/strong>. That means every time you write <code class=\"\" data-line=\"\">sort()<\/code>, you\u2019re relying on an algorithm that\u2019s been around since 1960, when Tony Hoare first introduced it. More than six decades later, it\u2019s still one of the fastest and most practical sorting techniques.<\/p>\n<p>But why does QuickSort matter so much today?<\/p>\n<ul>\n<li>\ud83d\udca1 Because it\u2019s still the <strong>fastest general-purpose sorting algorithm<\/strong> when stability isn\u2019t required.<\/li>\n<li>\ud83d\udca1 Because in interviews, hiring managers often test your ability to implement it from scratch.<\/li>\n<li>\ud83d\udca1 Because it teaches you the art of <em>divide-and-conquer<\/em> \u2014 a strategy that shows up in countless algorithms beyond sorting.<\/li>\n<\/ul>\n<p>Think about it: if Big Tech relies on QuickSort inside standard libraries, isn\u2019t it worth knowing how and why it works?<\/p>\n<hr \/>\n<h3>\u2728 Key Highlights of This Guide<\/h3>\n<ul>\n<li>\ud83d\udccc <strong>What is QuickSort?<\/strong> A divide-and-conquer sorting algorithm used in almost every modern programming language.<\/li>\n<li>\ud83d\udccc <strong>Pivot selection strategies<\/strong> that make or break your sorting speed.<\/li>\n<li>\ud83d\udccc <strong>Partitioning methods<\/strong> (Naive, Lomuto, Hoare) explained with examples.<\/li>\n<li>\ud83d\udccc <strong>Step-by-step walkthrough<\/strong> with real arrays so you can visualize the process.<\/li>\n<li>\ud83d\udccc <strong>QuickSort implementations<\/strong> in C, Java, C++, and Python \u2014 ready to run.<\/li>\n<li>\ud83d\udccc <strong>Time and space complexity<\/strong> (and why QuickSort sometimes fails).<\/li>\n<li>\ud83d\udccc <strong>Advantages, disadvantages, and best practices<\/strong> for real-world use.<\/li>\n<li>\ud83d\udccc <strong>Applications in tech and interviews<\/strong> \u2014 where you\u2019ll actually use QuickSort in your career.<\/li>\n<\/ul>\n<p>By the end, you won\u2019t just know how to code QuickSort. You\u2019ll understand why companies like Google, Microsoft, and Amazon care if you know it too.<\/p>\n<hr \/>\n<h3>\ud83e\udde9 What is QuickSort?<\/h3>\n<p>At its core, <strong>QuickSort is a sorting algorithm<\/strong> that follows the <em>divide and conquer<\/em> principle. Instead of comparing every element with every other element (like Bubble Sort), it picks one element \u2014 called the <strong>pivot<\/strong> \u2014 and rearranges the array so that:<\/p>\n<ul>\n<li>All elements <strong>smaller than the pivot<\/strong> go to its left.<\/li>\n<li>All elements <strong>greater than the pivot<\/strong> go to its right.<\/li>\n<li>The pivot itself lands in its correct sorted position.<\/li>\n<\/ul>\n<p>This process is called <strong>partitioning<\/strong>. Once partitioning is done, the same logic is applied recursively to the left and right halves until the entire array is sorted.<\/p>\n<p>Why is this so powerful?<br \/>\nBecause on average, QuickSort runs in <strong>O(n log n)<\/strong> time \u2014 making it faster than simple algorithms like Insertion or Selection Sort. In fact, with a good pivot strategy, it rivals or even beats MergeSort in practice because it works <em>in-place<\/em> (meaning it doesn\u2019t need extra memory).<\/p>\n<p>\ud83d\udc49 Example:<br \/>\nUnsorted array: <code class=\"\" data-line=\"\">[9, 4, 8, 3, 7, 1, 6, 2, 5]<\/code><br \/>\nPivot = <code class=\"\" data-line=\"\">5<\/code><br \/>\nAfter partitioning \u2192 <code class=\"\" data-line=\"\">[4, 3, 1, 2, 5, 8, 6, 9, 7]<\/code><\/p>\n<p>Notice how <code class=\"\" data-line=\"\">5<\/code> is in its correct spot, and everything smaller is on the left while everything larger is on the right. That\u2019s the magic of QuickSort.<\/p>\n<p>And here\u2019s the kicker: this same trick powers sorting functions in <strong>C, C++, Java, and Python (under the hood in Timsort hybrids)<\/strong>. That\u2019s why if you\u2019re learning algorithms for interviews, QuickSort isn\u2019t optional \u2014 it\u2019s essential.<\/p>\n<figure id=\"attachment_16531\" aria-describedby=\"caption-attachment-16531\" style=\"width: 300px\" class=\"wp-caption aligncenter\"><img fetchpriority=\"high\" decoding=\"async\" class=\"size-medium wp-image-16531\" src=\"https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/What-is-QuickSort-300x169.webp\" alt=\"What is QuickSort\" width=\"300\" height=\"169\" srcset=\"https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/What-is-QuickSort-300x169.webp 300w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/What-is-QuickSort-1024x576.webp 1024w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/What-is-QuickSort-768x432.webp 768w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/What-is-QuickSort-380x214.webp 380w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/What-is-QuickSort-800x450.webp 800w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/What-is-QuickSort-1160x653.webp 1160w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/What-is-QuickSort.webp 1280w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><figcaption id=\"caption-attachment-16531\" class=\"wp-caption-text\">What is QuickSort<\/figcaption><\/figure>\n<hr \/>\n<h2>\u2699\ufe0f How QuickSort Algorithm Works<\/h2>\n<p>To really \u201cget\u201d the <strong>QuickSort algorithm<\/strong>, you need to see how it actually plays out step by step. Forget the abstract theory for a moment\u2014imagine you\u2019re sorting a messy pile of books by height. Instead of comparing every book with every other book (painful, right?), you:<\/p>\n<ol>\n<li><strong>Pick one book as the pivot<\/strong> \ud83d\udcd8<\/li>\n<li>Move all shorter books to the left, taller books to the right.<\/li>\n<li>Repeat the process for each side until no pile needs sorting.<\/li>\n<\/ol>\n<p>That\u2019s QuickSort in a nutshell: <strong>pick, partition, recurse.<\/strong><\/p>\n<p>Let\u2019s break it down more formally:<\/p>\n<ul>\n<li><strong>Step 1: Choose a pivot<\/strong> \u2192 This is the heart of QuickSort, and the pivot choice affects efficiency.<\/li>\n<li><strong>Step 2: Partition the array<\/strong> \u2192 Rearrange so smaller values go left, bigger go right.<\/li>\n<li><strong>Step 3: Recursively sort<\/strong> \u2192 Apply the same steps on the left and right halves.<\/li>\n<li><strong>Step 4: Base condition<\/strong> \u2192 Stop when the subarray has 0 or 1 elements.<\/li>\n<\/ul>\n<p>\ud83d\udc49 Example walk-through:<br \/>\nArray = <code class=\"\" data-line=\"\">[9, 4, 8, 3, 7, 1, 6, 2, 5]<\/code><\/p>\n<ul>\n<li>Pivot = <code class=\"\" data-line=\"\">5<\/code><\/li>\n<li>Partitioned \u2192 <code class=\"\" data-line=\"\">[4, 3, 1, 2, 5, 8, 6, 9, 7]<\/code><\/li>\n<li>Recurse on <code class=\"\" data-line=\"\">[4, 3, 1, 2]<\/code> and <code class=\"\" data-line=\"\">[8, 6, 9, 7]<\/code><\/li>\n<li>Keep going until everything is sorted.<\/li>\n<\/ul>\n<p>Why is this so effective? Because instead of tackling the full problem at once, QuickSort <strong>divides the array into smaller, easier problems<\/strong>\u2014and that\u2019s exactly why it\u2019s faster than simple O(n\u00b2) sorting methods like Bubble or Insertion Sort.<\/p>\n<hr \/>\n<h2>\ud83c\udfaf Pivot Selection Strategies in QuickSort<\/h2>\n<p>The pivot is the \u201ccaptain\u201d of QuickSort. Choose wisely, and you get blazing speed. Choose poorly, and you risk a painful O(n\u00b2) performance.<\/p>\n<p>Here are the most common <strong>pivot selection strategies<\/strong> developers use:<\/p>\n<ol>\n<li><strong>First element as pivot<\/strong>\n<ul>\n<li>Easiest to implement.<\/li>\n<li>\u26a0\ufe0f Worst for already sorted arrays \u2192 leads to worst-case O(n\u00b2).<\/li>\n<\/ul>\n<\/li>\n<li><strong>Last element as pivot<\/strong>\n<ul>\n<li>Also simple.<\/li>\n<li>\u26a0\ufe0f Same problem: fails on sorted or reverse-sorted inputs.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Random pivot<\/strong>\n<ul>\n<li>Picks a pivot at random.<\/li>\n<li>\u2705 Great for average performance because it reduces the chance of consistently bad splits.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Median-of-three pivot<\/strong>\n<ul>\n<li>Compare the first, middle, and last elements, and pick the median.<\/li>\n<li>\u2705 Often the best practical choice: balances partitions and avoids worst-case on sorted data.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>\ud83d\udca1 <strong>Developer Insight<\/strong>: Many real-world QuickSort implementations (like in C++ STL) use hybrid strategies\u2014random pivot or median-of-three\u2014because they make the algorithm much more stable for diverse inputs.<\/p>\n<p>So, when someone asks in an interview, \u201cHow would you choose the pivot?\u201d you now know the smart answer: <strong>randomized or median-of-three<\/strong>.<\/p>\n<figure id=\"attachment_16533\" aria-describedby=\"caption-attachment-16533\" style=\"width: 300px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" class=\"size-medium wp-image-16533\" src=\"https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Pivot-Selection-Strategies-in-QuickSort-300x200.webp\" alt=\"Pivot Selection Strategies in QuickSort\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Pivot-Selection-Strategies-in-QuickSort-300x200.webp 300w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Pivot-Selection-Strategies-in-QuickSort-1024x683.webp 1024w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Pivot-Selection-Strategies-in-QuickSort-768x512.webp 768w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Pivot-Selection-Strategies-in-QuickSort-380x253.webp 380w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Pivot-Selection-Strategies-in-QuickSort-800x533.webp 800w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Pivot-Selection-Strategies-in-QuickSort-1160x773.webp 1160w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Pivot-Selection-Strategies-in-QuickSort.webp 1536w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><figcaption id=\"caption-attachment-16533\" class=\"wp-caption-text\">Pivot Selection Strategies in QuickSort<\/figcaption><\/figure>\n<hr \/>\n<h2>\ud83d\udd00 Partitioning Methods in <strong>QuickSort<\/strong>\u00a0Algorithm<\/h2>\n<p>Partitioning is where QuickSort earns its name. You split the array around the pivot, and this choice of method directly affects efficiency.<\/p>\n<p>There are <strong>three main partitioning techniques<\/strong> worth knowing in <strong>QuickSort<\/strong> Algorithm:<\/p>\n<ol>\n<li><strong>Naive Partition<\/strong>\n<ul>\n<li>Create temporary arrays for left and right of pivot, then combine.<\/li>\n<li>\u274c Uses extra memory, not in-place. Rarely used in practice.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Lomuto Partition<\/strong> (used in many textbooks)\n<ul>\n<li>Easy to code.<\/li>\n<li>Uses last element as pivot, and a single pointer to divide smaller and greater elements.<\/li>\n<li>Example:\n<pre><code class=\"language-python\" data-line=\"\">def lomuto_partition(arr, low, high):\n    pivot = arr[high]\n    i = low - 1\n    for j in range(low, high):\n        if arr[j] &lt;= pivot:\n            i += 1\n            arr[i], arr[j] = arr[j], arr[i]\n    arr[i + 1], arr[high] = arr[high], arr[i + 1]\n    return i + 1\n<\/code><\/pre>\n<\/li>\n<\/ul>\n<\/li>\n<li><strong>Hoare Partition<\/strong> (preferred in practice)\n<ul>\n<li>Uses two pointers moving inward from both ends.<\/li>\n<li>\u2705 Faster and performs fewer swaps compared to Lomuto.<\/li>\n<li>Example (C++-style pseudocode):\n<pre><code class=\"language-cpp\" data-line=\"\">int hoarePartition(int arr[], int low, int high) {\n    int pivot = arr[low];\n    int i = low - 1, j = high + 1;\n    while (true) {\n        do { i++; } while (arr[i] &lt; pivot);\n        do { j--; } while (arr[j] &gt; pivot);\n        if (i &gt;= j) return j;\n        swap(arr[i], arr[j]);\n    }\n}\n<\/code><\/pre>\n<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>\ud83d\udca1 <strong>Best practice<\/strong>: Use Hoare for efficiency, Lomuto for simplicity when teaching or interviewing.<\/p>\n<hr \/>\n<h2>\ud83c\udfeb Step-by-Step QuickSort Example in Action<\/h2>\n<p>Imagine this: a teacher has a list of students standing in random order, and she wants to <strong>sort them by height<\/strong> from shortest to tallest. Instead of comparing every student with every other student (which would take forever), she decides to use the <strong>QuickSort algorithm<\/strong>.<\/p>\n<p>Here\u2019s how she does it:<\/p>\n<p>The teacher&#8217;s list of students&#8217; heights:<\/p>\n<p><code class=\"\" data-line=\"\">[150, 160, 145, 170, 155, 165, 140, 180]<\/code><\/p>\n<h3>Step-by-Step QuickSort Process:<\/h3>\n<h4><strong>Step 1: Pick a pivot student<\/strong><\/h4>\n<p>The teacher picks a student\u2019s height to act as the &#8220;pivot.&#8221; Let&#8217;s say she picks <strong>155 cm<\/strong> as the pivot.<\/p>\n<h4><strong>Step 2: Partition the students<\/strong><\/h4>\n<p>Now, the teacher will compare each student\u2019s height with the pivot (155 cm) and divide them into two groups:<\/p>\n<ul>\n<li>Students shorter than 155 cm<\/li>\n<li>Students taller than 155 cm<\/li>\n<\/ul>\n<p><strong>Partitioning process:<\/strong><\/p>\n<ul>\n<li><strong>Shorter than 155 cm:<\/strong> <code class=\"\" data-line=\"\">[150, 145, 140]<\/code><\/li>\n<li><strong>Pivot:<\/strong> <code class=\"\" data-line=\"\">[155]<\/code><\/li>\n<li><strong>Taller than 155 cm:<\/strong> <code class=\"\" data-line=\"\">[160, 170, 165, 180]<\/code><\/li>\n<\/ul>\n<p>So, after partitioning, the list looks like:<\/p>\n<ul>\n<li>Left group: <code class=\"\" data-line=\"\">[150, 145, 140]<\/code><\/li>\n<li>Pivot: <code class=\"\" data-line=\"\">[155]<\/code><\/li>\n<li>Right group: <code class=\"\" data-line=\"\">[160, 170, 165, 180]<\/code><\/li>\n<\/ul>\n<h4><strong>Step 3: Sort the left group<\/strong><\/h4>\n<p>Now, the teacher will sort the left group <code class=\"\" data-line=\"\">[150, 145, 140]<\/code> using QuickSort:<\/p>\n<ul>\n<li>Pick a pivot (say 145).<\/li>\n<li><strong>Shorter than 145 cm:<\/strong> <code class=\"\" data-line=\"\">[140]<\/code><\/li>\n<li><strong>Pivot:<\/strong> <code class=\"\" data-line=\"\">[145]<\/code><\/li>\n<li><strong>Taller than 145 cm:<\/strong> <code class=\"\" data-line=\"\">[150]<\/code><\/li>\n<\/ul>\n<p>So now we have:<\/p>\n<ul>\n<li>Left group: <code class=\"\" data-line=\"\">[140]<\/code><\/li>\n<li>Pivot: <code class=\"\" data-line=\"\">[145]<\/code><\/li>\n<li>Right group: <code class=\"\" data-line=\"\">[150]<\/code><\/li>\n<\/ul>\n<p>This group is now sorted as <code class=\"\" data-line=\"\">[140, 145, 150]<\/code>.<\/p>\n<h4><strong>Step 4: Sort the right group<\/strong><\/h4>\n<p>Now, let\u2019s sort the right group <code class=\"\" data-line=\"\">[160, 170, 165, 180]<\/code>:<\/p>\n<ul>\n<li>Pick a pivot (say 170).<\/li>\n<li><strong>Shorter than 170 cm:<\/strong> <code class=\"\" data-line=\"\">[160, 165]<\/code><\/li>\n<li><strong>Pivot:<\/strong> <code class=\"\" data-line=\"\">[170]<\/code><\/li>\n<li><strong>Taller than 170 cm:<\/strong> <code class=\"\" data-line=\"\">[180]<\/code><\/li>\n<\/ul>\n<p>So now we have:<\/p>\n<ul>\n<li>Left group: <code class=\"\" data-line=\"\">[160, 165]<\/code><\/li>\n<li>Pivot: <code class=\"\" data-line=\"\">[170]<\/code><\/li>\n<li>Right group: <code class=\"\" data-line=\"\">[180]<\/code><\/li>\n<\/ul>\n<h4><strong>Step 5: Continue sorting the smaller groups<\/strong><\/h4>\n<ul>\n<li>For the left group <code class=\"\" data-line=\"\">[160, 165]<\/code>, pick 165 as the pivot.\n<ul>\n<li><strong>Shorter than 165 cm:<\/strong> <code class=\"\" data-line=\"\">[160]<\/code><\/li>\n<li><strong>Pivot:<\/strong> <code class=\"\" data-line=\"\">[165]<\/code><\/li>\n<li><strong>Taller than 165 cm:<\/strong> <code class=\"\" data-line=\"\">[]<\/code> (empty)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>So now we have <code class=\"\" data-line=\"\">[160, 165]<\/code>.<\/p>\n<h4><strong>Step 6: Combine all the groups<\/strong><\/h4>\n<p>Now we can combine all the sorted groups to form the final list:<\/p>\n<ul>\n<li>Left group: <code class=\"\" data-line=\"\">[140, 145, 150]<\/code><\/li>\n<li>Pivot: <code class=\"\" data-line=\"\">[155]<\/code><\/li>\n<li>Right group: <code class=\"\" data-line=\"\">[160, 165, 170, 180]<\/code><\/li>\n<\/ul>\n<p>Final sorted list of students by height:<\/p>\n<p><code class=\"\" data-line=\"\">[140, 145, 150, 155, 160, 165, 170, 180]<\/code><\/p>\n<hr \/>\n<h3>Why QuickSort is Efficient:<\/h3>\n<p>QuickSort works efficiently because it continuously divides the problem (sorting the students) into smaller sub-problems (sorting smaller groups). This makes it faster than other algorithms, like <strong>Bubble Sort<\/strong>, because it reduces the number of comparisons needed to organize the entire list.<\/p>\n<h3>Recap:<\/h3>\n<ul>\n<li><strong>Teacher selects a pivot student\u2019s height.<\/strong><\/li>\n<li><strong>Divides<\/strong> the students into two groups: one shorter and one taller.<\/li>\n<li><strong>Recursively sorts<\/strong> those groups until all students are ordered by height.<\/li>\n<\/ul>\n<p>This makes QuickSort a fast and effective way for the teacher to organize her students based on height!<\/p>\n<hr \/>\n<h2>\ud83d\udcbb <strong>QuickSort<\/strong> Algorithm Code Implementations (C, Java, C++, Python)<\/h2>\n<p>Let\u2019s explore how QuickSort looks in different languages \u2014 and why each version has its own quirks.<\/p>\n<hr \/>\n<h3>\ud83d\udd39 QuickSort in C<\/h3>\n<pre><code class=\"language-c\" data-line=\"\">#include &lt;stdio.h&gt;\n\nvoid swap(int* a, int* b) {\n    int t = *a;\n    *a = *b;\n    *b = t;\n}\n\nint partition(int arr[], int low, int high) {\n    int pivot = arr[high];\n    int i = (low - 1);\n    for (int j = low; j &lt; high; j++) {\n        if (arr[j] &lt;= pivot) {\n            i++;\n            swap(&amp;arr[i], &amp;arr[j]);\n        }\n    }\n    swap(&amp;arr[i + 1], &amp;arr[high]);\n    return (i + 1);\n}\n\nvoid quickSort(int arr[], int low, int high) {\n    if (low &lt; high) {\n        int pi = partition(arr, low, high);\n        quickSort(arr, low, pi - 1);\n        quickSort(arr, pi + 1, high);\n    }\n}\n\nint main() {\n    int arr[] = {160, 175, 155, 170, 165, 180, 150};\n    int n = sizeof(arr) \/ sizeof(arr[0]);\n    quickSort(arr, 0, n - 1);\n    printf(&quot;Sorted array: &quot;);\n    for (int i = 0; i &lt; n; i++) printf(&quot;%d &quot;, arr[i]);\n    return 0;\n}\n<\/code><\/pre>\n<p>\ud83d\udca1 <strong>Developer Insight (C)<\/strong>:<\/p>\n<ul>\n<li>C gives you <strong>full control<\/strong> over memory and recursion depth.<\/li>\n<li>But be careful: recursion in C can cause <strong>stack overflow<\/strong> for very large arrays.<\/li>\n<li>Best practice \u2192 switch to <strong>iterative QuickSort<\/strong> or use tail recursion elimination for safety.<\/li>\n<\/ul>\n<hr \/>\n<h3>\ud83d\udd39 QuickSort Java Example<\/h3>\n<pre><code class=\"language-java\" data-line=\"\">public class QuickSort {\n    public static int partition(int arr[], int low, int high) {\n        int pivot = arr[high];\n        int i = (low - 1);\n        for (int j = low; j &lt; high; j++) {\n            if (arr[j] &lt;= pivot) {\n                i++;\n                int temp = arr[i];\n                arr[i] = arr[j];\n                arr[j] = temp;\n            }\n        }\n        int temp = arr[i + 1];\n        arr[i + 1] = arr[high];\n        arr[high] = temp;\n        return i + 1;\n    }\n\n    public static void quickSort(int arr[], int low, int high) {\n        if (low &lt; high) {\n            int pi = partition(arr, low, high);\n            quickSort(arr, low, pi - 1);\n            quickSort(arr, pi + 1, high);\n        }\n    }\n\n    public static void main(String args[]) {\n        int arr[] = {160, 175, 155, 170, 165, 180, 150};\n        quickSort(arr, 0, arr.length - 1);\n        System.out.print(&quot;Sorted array: &quot;);\n        for (int num : arr) System.out.print(num + &quot; &quot;);\n    }\n}\n<\/code><\/pre>\n<p>\ud83d\udca1 <strong>Developer Insight (Java)<\/strong>:<\/p>\n<ul>\n<li>Java handles recursion more safely than C, but it\u2019s <strong>slower<\/strong> because of object overhead.<\/li>\n<li>In practice, Java\u2019s <code class=\"\" data-line=\"\">Arrays.sort()<\/code> uses a <strong>dual-pivot QuickSort<\/strong> for primitives, which is faster in real-world cases.<\/li>\n<li>If you\u2019re coding interviews, <strong>stick with single-pivot<\/strong> (like above) for clarity.<\/li>\n<\/ul>\n<hr \/>\n<h3>\ud83d\udd39 QuickSort program in C++<\/h3>\n<pre><code class=\"language-cpp\" data-line=\"\">#include &lt;iostream&gt;\nusing namespace std;\n\nvoid swap(int* a, int* b) {\n    int t = *a;\n    *a = *b;\n    *b = t;\n}\n\nint partition(int arr[], int low, int high) {\n    int pivot = arr[high];\n    int i = (low - 1);\n    for (int j = low; j &lt; high; j++) {\n        if (arr[j] &lt;= pivot) {\n            i++;\n            swap(&amp;arr[i], &amp;arr[j]);\n        }\n    }\n    swap(&amp;arr[i + 1], &amp;arr[high]);\n    return (i + 1);\n}\n\nvoid quickSort(int arr[], int low, int high) {\n    if (low &lt; high) {\n        int pi = partition(arr, low, high);\n        quickSort(arr, low, pi - 1);\n        quickSort(arr, pi + 1, high);\n    }\n}\n\nint main() {\n    int arr[] = {160, 175, 155, 170, 165, 180, 150};\n    int n = sizeof(arr) \/ sizeof(arr[0]);\n    quickSort(arr, 0, n - 1);\n    cout &lt;&lt; &quot;Sorted array: &quot;;\n    for (int i = 0; i &lt; n; i++) cout &lt;&lt; arr[i] &lt;&lt; &quot; &quot;;\n}\n<\/code><\/pre>\n<p>\ud83d\udca1 <strong>Developer Insight (C++)<\/strong>:<\/p>\n<ul>\n<li>C++ allows you to write QuickSort with <strong>templates<\/strong> to handle multiple data types.<\/li>\n<li>But in real-world C++, you don\u2019t need to implement this: just use <strong><code class=\"\" data-line=\"\">std::sort()<\/code><\/strong> \u2192 which is a hybrid of QuickSort, HeapSort, and Insertion Sort.<\/li>\n<li>Pro tip: always prefer <strong>STL algorithms<\/strong> in production. Only hand-write QuickSort for learning or interviews.<\/li>\n<\/ul>\n<hr \/>\n<h3>\ud83d\udd39 QuickSort in Python<\/h3>\n<pre><code class=\"language-python\" data-line=\"\">def partition(arr, low, high):\n    pivot = arr[high]\n    i = low - 1\n    for j in range(low, high):\n        if arr[j] &lt;= pivot:\n            i += 1\n            arr[i], arr[j] = arr[j], arr[i]\n    arr[i + 1], arr[high] = arr[high], arr[i + 1]\n    return i + 1\n\ndef quick_sort(arr, low, high):\n    if low &lt; high:\n        pi = partition(arr, low, high)\n        quick_sort(arr, low, pi - 1)\n        quick_sort(arr, pi + 1, high)\n\narr = [160, 175, 155, 170, 165, 180, 150]\nquick_sort(arr, 0, len(arr) - 1)\nprint(&quot;Sorted array:&quot;, arr)\n<\/code><\/pre>\n<p>\ud83d\udca1 <strong>Developer Insight (Python)<\/strong>:<\/p>\n<ul>\n<li>Python\u2019s built-in <code class=\"\" data-line=\"\">sort()<\/code> and <code class=\"\" data-line=\"\">sorted()<\/code> don\u2019t use QuickSort at all \u2014 they use <strong>Timsort<\/strong> (a hybrid of MergeSort and Insertion Sort).<\/li>\n<li>Why? Because Timsort is <strong>stable<\/strong> and optimized for partially sorted data (which is common in the real world).<\/li>\n<li>Best practice \u2192 use <code class=\"\" data-line=\"\">sorted()<\/code> in production; only implement QuickSort for <strong>learning, interviews, or algorithm competitions<\/strong>.<\/li>\n<\/ul>\n<hr \/>\n<h2>\u23f1\ufe0f QuickSort Time Complexity<\/h2>\n<p>When you talk about the <strong>time complexity of QuickSort<\/strong>, the magic (and the risk) lies in how the pivot splits the array.<\/p>\n<p>Let\u2019s break it down:<\/p>\n<ul>\n<li><strong>Best Case (\u03a9(n log n))<\/strong>\n<ul>\n<li>Happens when the pivot splits the array into <strong>two equal halves<\/strong> every time.<\/li>\n<li>Example: pivot always ends up near the median.<\/li>\n<li>Recurrence relation: <code class=\"\" data-line=\"\">T(n) = 2T(n\/2) + O(n)<\/code> \u2192 Solves to <code class=\"\" data-line=\"\">O(n log n)<\/code>.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Average Case (\u03b8(n log n))<\/strong>\n<ul>\n<li>On random input, the pivot usually gives \u201cgood enough\u201d splits.<\/li>\n<li>That\u2019s why QuickSort is considered <strong>efficient in practice<\/strong>.<\/li>\n<li>Most interviewers expect you to explain this case.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Worst Case (O(n\u00b2))<\/strong>\n<ul>\n<li>Happens if you consistently pick the <strong>smallest or largest element as pivot<\/strong>.<\/li>\n<li>Example: already sorted or reverse-sorted input with first\/last pivot.<\/li>\n<li>Recurrence relation: <code class=\"\" data-line=\"\">T(n) = T(n-1) + O(n)<\/code> \u2192 Solves to <code class=\"\" data-line=\"\">O(n\u00b2)<\/code>.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>\ud83d\udc49 <strong>Interview tip<\/strong>: If an interviewer asks <em>\u201cWhen does QuickSort become O(n\u00b2)?\u201d<\/em>, the safe answer is: <strong>when pivot selection is poor<\/strong> (like always picking first\/last element in sorted data).<\/p>\n<p>\ud83d\udcca Quick comparison with MergeSort:<\/p>\n<table>\n<thead>\n<tr>\n<th>Algorithm<\/th>\n<th>Best Case<\/th>\n<th>Average Case<\/th>\n<th>Worst Case<\/th>\n<th>Space<\/th>\n<th>Stability<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>QuickSort<\/td>\n<td>O(n log n)<\/td>\n<td>O(n log n)<\/td>\n<td>O(n\u00b2)<\/td>\n<td>O(log n) avg, O(n) worst<\/td>\n<td>Not Stable<\/td>\n<\/tr>\n<tr>\n<td>MergeSort<\/td>\n<td>O(n log n)<\/td>\n<td>O(n log n)<\/td>\n<td>O(n log n)<\/td>\n<td>O(n)<\/td>\n<td>Stable<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\ud83d\udca1 <strong>Real-world note<\/strong>: Despite its scary worst case, QuickSort is still preferred in many systems because with a <strong>good pivot strategy<\/strong> (random\/median-of-three), the worst case almost never happens.<\/p>\n<figure id=\"attachment_16536\" aria-describedby=\"caption-attachment-16536\" style=\"width: 300px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" class=\"size-medium wp-image-16536\" src=\"https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/QuickSort-Time-Complexity-infographic-300x200.webp\" alt=\"QuickSort Time Complexity infographic\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/QuickSort-Time-Complexity-infographic-300x200.webp 300w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/QuickSort-Time-Complexity-infographic-1024x683.webp 1024w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/QuickSort-Time-Complexity-infographic-768x512.webp 768w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/QuickSort-Time-Complexity-infographic-380x253.webp 380w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/QuickSort-Time-Complexity-infographic-800x533.webp 800w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/QuickSort-Time-Complexity-infographic-1160x773.webp 1160w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/QuickSort-Time-Complexity-infographic.webp 1536w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><figcaption id=\"caption-attachment-16536\" class=\"wp-caption-text\">QuickSort Time Complexity infographic<\/figcaption><\/figure>\n<hr \/>\n<h2>\ud83d\udce6 Space Complexity of <strong>QuickSort<\/strong>\u00a0Algorithm<\/h2>\n<p><strong>QuickSort<\/strong> Algorithm is fast, but what about memory?<\/p>\n<ul>\n<li><strong>Average Case<\/strong>: <code class=\"\" data-line=\"\">O(log n)<\/code>\n<ul>\n<li>This comes from the recursive calls. On balanced splits, the recursion depth is about <code class=\"\" data-line=\"\">log n<\/code>.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Worst Case<\/strong>: <code class=\"\" data-line=\"\">O(n)<\/code>\n<ul>\n<li>If partitions are highly unbalanced (like sorted input with bad pivot), the recursion stack can grow to <code class=\"\" data-line=\"\">n<\/code>.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>\ud83d\udc49 In contrast, MergeSort always requires <strong>O(n)<\/strong> extra space, which makes QuickSort <strong>more space-efficient<\/strong> in most practical cases.<\/p>\n<p>\ud83d\udca1 <strong>Developer Best Practice<\/strong>:<\/p>\n<ul>\n<li>In C\/C++, tail recursion optimization or iterative QuickSort reduces space usage.<\/li>\n<li>In Python, recursion depth is limited (default ~1000). For very large arrays, use <strong>iterative versions<\/strong> or switch to Python\u2019s built-in <code class=\"\" data-line=\"\">sorted()<\/code>.<\/li>\n<\/ul>\n<figure id=\"attachment_16538\" aria-describedby=\"caption-attachment-16538\" style=\"width: 300px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"size-medium wp-image-16538\" src=\"https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Space-Complexity-of-QuickSort-Algorithm-300x200.webp\" alt=\"Space Complexity of QuickSort\u00a0Algorithm\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Space-Complexity-of-QuickSort-Algorithm-300x200.webp 300w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Space-Complexity-of-QuickSort-Algorithm-1024x683.webp 1024w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Space-Complexity-of-QuickSort-Algorithm-768x512.webp 768w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Space-Complexity-of-QuickSort-Algorithm-380x253.webp 380w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Space-Complexity-of-QuickSort-Algorithm-800x533.webp 800w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Space-Complexity-of-QuickSort-Algorithm-1160x773.webp 1160w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Space-Complexity-of-QuickSort-Algorithm.webp 1536w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><figcaption id=\"caption-attachment-16538\" class=\"wp-caption-text\">Space Complexity of QuickSort\u00a0Algorithm<\/figcaption><\/figure>\n<hr \/>\n<h2>\u2696\ufe0f Advantages &amp; Disadvantages of <strong>QuickSort<\/strong>\u00a0Algorithm<\/h2>\n<p>Like any algorithm, <strong>QuickSort<\/strong> Algorithm isn\u2019t perfect. Here\u2019s the truth every developer should know:<\/p>\n<h3>\u2705 Advantages<\/h3>\n<ul>\n<li><strong>Blazing fast in practice<\/strong>: With average O(n log n) time, it\u2019s faster than MergeSort in many cases because it\u2019s in-place and cache-friendly.<\/li>\n<li><strong>Widely used<\/strong>: C++ STL <code class=\"\" data-line=\"\">std::sort<\/code> and Java <code class=\"\" data-line=\"\">Arrays.sort<\/code> (for primitives) are based on QuickSort variants.<\/li>\n<li><strong>Good space efficiency<\/strong>: Needs only O(log n) extra space on average.<\/li>\n<li><strong>Teaches divide and conquer<\/strong>: Learning QuickSort helps you understand recursion, partitioning, and pivot strategies \u2014 all core CS concepts.<\/li>\n<\/ul>\n<h3>\u274c Disadvantages<\/h3>\n<ul>\n<li><strong>Not stable<\/strong>: Equal elements may not maintain their original order. If stability matters (like sorting records by multiple fields), MergeSort or Timsort is better.<\/li>\n<li><strong>Worst-case O(n\u00b2)<\/strong>: Bad pivot choices make it painfully slow.<\/li>\n<li><strong>Recursion depth issues<\/strong>: Can cause stack overflow for very large arrays without optimization.<\/li>\n<\/ul>\n<p>\ud83d\udca1 <strong>Career Insight<\/strong>:<\/p>\n<ul>\n<li>In <strong>interviews<\/strong>, <strong>QuickSort<\/strong> Algorithm shows up not just as a coding task but also as a discussion point \u2014 \u201cWhen does it fail?\u201d, \u201cHow do you fix it?\u201d, \u201cWhy use it over MergeSort?\u201d<\/li>\n<li>In <strong>real-world projects<\/strong>, developers rarely write QuickSort from scratch \u2014 instead, they rely on built-in library implementations. But understanding it is like knowing how the engine of a car works \u2014 it makes you a stronger problem-solver.<\/li>\n<\/ul>\n<hr \/>\n<h2>\ud83c\udf0d Applications of <strong>QuickSort<\/strong>\u00a0Algorithm<\/h2>\n<p><strong>QuickSort<\/strong> Algorithm isn\u2019t just a classroom algorithm \u2014 it\u2019s powering some of the tools you use every day. Here\u2019s where it shows up in the real world:<\/p>\n<ol>\n<li><strong>Database Sorting<\/strong>\n<ul>\n<li>Imagine a database query asking: <em>\u201cGive me the top 10 students with highest marks.\u201d<\/em><\/li>\n<li>QuickSort (or its cousin QuickSelect) helps efficiently arrange records.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Library &amp; Standard Functions<\/strong>\n<ul>\n<li><strong>C++ <code class=\"\" data-line=\"\">std::sort<\/code><\/strong>, <strong>Java <code class=\"\" data-line=\"\">Arrays.sort<\/code><\/strong> (for primitives), and many other built-in sorting functions rely on tuned QuickSort variants.<\/li>\n<li>Why? Because it\u2019s cache-friendly and fast on large datasets.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Data Analytics<\/strong>\n<ul>\n<li>Sorting is the backbone of many algorithms in statistics and machine learning. QuickSort handles millions of data points quickly without consuming extra memory.<\/li>\n<\/ul>\n<\/li>\n<li><strong>File Systems &amp; OS<\/strong>\n<ul>\n<li>Operating systems use QuickSort for tasks like scheduling and memory management when large unsorted lists need ordering.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Graphics &amp; Computational Geometry<\/strong>\n<ul>\n<li>In algorithms like <strong>convex hull<\/strong> or line sweeping, QuickSort helps preprocess points efficiently.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Competitive Programming<\/strong>\n<ul>\n<li>Fast, in-place, easy to code \u2192 QuickSort is a favorite for contests where performance matters.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>\ud83d\udc49 Bottom line: <strong>QuickSort<\/strong> Algorithm\u00a0<strong>is everywhere<\/strong> \u2014 from your database queries to your coding interviews.<\/p>\n<figure id=\"attachment_16539\" aria-describedby=\"caption-attachment-16539\" style=\"width: 300px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"size-medium wp-image-16539\" src=\"https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Applications-of-QuickSort-Algorithm-300x200.webp\" alt=\"Applications of QuickSort\u00a0Algorithm\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Applications-of-QuickSort-Algorithm-300x200.webp 300w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Applications-of-QuickSort-Algorithm-1024x683.webp 1024w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Applications-of-QuickSort-Algorithm-768x512.webp 768w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Applications-of-QuickSort-Algorithm-380x253.webp 380w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Applications-of-QuickSort-Algorithm-800x533.webp 800w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Applications-of-QuickSort-Algorithm-1160x773.webp 1160w, https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2025\/10\/Applications-of-QuickSort-Algorithm.webp 1536w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><figcaption id=\"caption-attachment-16539\" class=\"wp-caption-text\">Applications of QuickSort\u00a0Algorithm<\/figcaption><\/figure>\n<hr \/>\n<h2>\u2753 FAQ: QuickSort Explained for Beginners<\/h2>\n<p>Here are some common beginner questions that pop up when learning QuickSort:<\/p>\n<p><strong>Q1. What does \u201cin-place\u201d mean in QuickSort algorithm?<\/strong><br \/>\n\ud83d\udc49 In-place means QuickSort doesn\u2019t need extra arrays (like MergeSort does). It rearranges elements within the same array, saving memory.<\/p>\n<p><strong>Q2. Why is QuickSort algorithm not stable?<\/strong><br \/>\n\ud83d\udc49 Stability means equal elements keep their original order after sorting. Since QuickSort swaps elements freely around the pivot, it doesn\u2019t guarantee this.<\/p>\n<p><strong>Q3. What is a pivot?<\/strong><br \/>\n\ud83d\udc49 A pivot is just the \u201cchosen element\u201d QuickSort algorithm uses to split the array. All smaller items go to the left, all larger items go to the right. Think of it like a \u201cdivider.\u201d<\/p>\n<p><strong>Q4. What\u2019s the difference between Lomuto and Hoare partition schemes?<\/strong><br \/>\n\ud83d\udc49 Lomuto is easier to understand and implement (one loop, pivot at the end). Hoare is more efficient in practice (fewer swaps, pivot can be the first element).<\/p>\n<p><strong>Q5. Why does QuickSort sometimes perform badly?<\/strong><br \/>\n\ud83d\udc49 If the pivot is chosen poorly (like smallest or largest element in an already sorted array), the partitions are unbalanced \u2192 recursion depth grows \u2192 O(n\u00b2).<\/p>\n<p><strong>Q6. Should I ever write my own QuickSort in real projects?<\/strong><br \/>\n\ud83d\udc49 Usually, no. Most programming languages already provide highly optimized sort functions. But knowing how QuickSort works will help you in interviews and when debugging performance issues.<\/p>\n<hr \/>\n<h2>\ud83d\ude80 Conclusion: Why QuickSort Still Matters<\/h2>\n<p>At this point, you\u2019ve seen that <strong>QuickSort<\/strong> Algorithm isn\u2019t just another algorithm\u2014it\u2019s a <strong>workhorse of computer science<\/strong>. From database engines to coding interview whiteboards, it continues to prove why it\u2019s one of the most important algorithms to master.<\/p>\n<p>Here\u2019s the real takeaway:<\/p>\n<ul>\n<li><strong>QuickSort<\/strong> Algorithm teaches you <strong>divide and conquer<\/strong>.<\/li>\n<li>It forces you to think about <strong>efficiency vs worst-case trade-offs<\/strong>.<\/li>\n<li>And it sharpens your understanding of recursion, memory, and algorithm design.<\/li>\n<\/ul>\n<p>Whether you\u2019re a beginner writing your first sorting function or a developer optimizing large-scale systems, QuickSort is a <strong>tool you\u2019ll never regret learning deeply<\/strong>.<\/p>\n<p>\ud83d\udd25 <strong>Call to Action:<\/strong><br \/>\nDon\u2019t just read about it \u2014 try <strong>QuickSort<\/strong> Algorithm in <strong>C, Java, Python, and C++<\/strong> yourself. Then, push further: experiment with pivot strategies, compare performance with MergeSort, and even test it on real-world datasets. That\u2019s how you go from <em>knowing<\/em> QuickSort to <strong>mastering<\/strong> it.<\/p>\n<p>\ud83d\udc49 Ready to take the next step? Try building your own QuickSelect (a QuickSort variant) to find the <em>kth smallest element<\/em>. That\u2019s when you\u2019ll realize just how powerful this algorithm really is.<\/p>\n<hr \/>\n<h2>\ud83d\udcda Related Reads<\/h2>\n<p>If you enjoyed learning about <strong>QuickSort<\/strong>\u00a0Algorithm, you might also want to check out these guides on other sorting algorithms and their complexities:<\/p>\n<ul>\n<li>\ud83d\udd17 <a href=\"https:\/\/www.wikitechy.com\/what-is-sorting-techniques-best-algorithm-2025\/\" target=\"_blank\" rel=\"noopener\">What Is Sorting? A Complete Guide to Sorting Techniques &amp; the Best Sorting Algorithm<\/a><\/li>\n<li>\ud83d\udd17 <a href=\"https:\/\/www.wikitechy.com\/selection-sort-algorithm-explained\/\" target=\"_blank\" rel=\"noopener\">What is Selection Sort Algorithm (2025 Guide): Examples, and Best Practices<\/a><\/li>\n<li>\ud83d\udd17 <a href=\"https:\/\/kaashivinfotech.com\/blog\/insertion-sort-algorithm-examples\/\">\ud83d\ude80 Insertion Sort Algorithm in 2025 \u2013 Must-Know Facts, Examples in C, Java, Python &amp; More<\/a><\/li>\n<li>\ud83d\udd17 <a href=\"https:\/\/www.wikitechy.com\/master-merge-sort-algorithm-examples-definition\/\" target=\"_blank\" rel=\"noopener\">Merge Sort Algorithm [2025] \u2013 Step by Step Explanation, Example, Code in C, C++, Java, Python, and Complexity \ud83d\ude80<\/a><\/li>\n<li>\ud83d\udd17 <a href=\"https:\/\/www.wikitechy.com\/bubble-sort-algorithm-guide-2025\/\" target=\"_blank\" rel=\"noopener\">\ud83d\ude80 Bubble Sort Algorithm: A Complete Guide with Examples in Java and C<\/a><\/li>\n<li>\ud83d\udd17 <a href=\"https:\/\/www.kaashivinfotech.com\/blog\/insertion-sort-time-complexity-guide\/\">Insertion Sort Time Complexity: Complete Guide for Beginners &amp; Pros<\/a><\/li>\n<\/ul>\n<hr \/>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>If you\u2019re a developer, sooner or later you\u2019ll bump into the QuickSort algorithm. Whether you\u2019re sitting in a coding interview, working on large datasets, or simply curious about how your favorite programming language sorts numbers under the hood \u2014 QuickSort keeps showing up. Here\u2019s a quick fact: C++\u2019s std::sort() and Java\u2019s Arrays.sort() (for primitives) use [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":16540,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3356,3203,3236],"tags":[1729,9555,9553,9562,9554,9564,9567,9563,9558,9568,9566,9556,9557,9560,9561,9569,9559,9565,8366],"class_list":["post-16521","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-java","category-programming","category-python","tag-divide-and-conquer","tag-quick-sort-in-c","tag-quicksort","tag-quicksort-advantages","tag-quicksort-algorithm","tag-quicksort-applications","tag-quicksort-code-examples","tag-quicksort-disadvantages","tag-quicksort-example","tag-quicksort-for-beginners","tag-quicksort-guide","tag-quicksort-in-java","tag-quicksort-in-python","tag-quicksort-partitioning","tag-quicksort-pivot-strategies","tag-quicksort-step-by-step","tag-quicksort-time-complexity","tag-quicksort-tutorial","tag-sorting-algorithms"],"_links":{"self":[{"href":"https:\/\/www.kaashivinfotech.com\/blog\/wp-json\/wp\/v2\/posts\/16521","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.kaashivinfotech.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.kaashivinfotech.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.kaashivinfotech.com\/blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kaashivinfotech.com\/blog\/wp-json\/wp\/v2\/comments?post=16521"}],"version-history":[{"count":0,"href":"https:\/\/www.kaashivinfotech.com\/blog\/wp-json\/wp\/v2\/posts\/16521\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.kaashivinfotech.com\/blog\/wp-json\/wp\/v2\/media\/16540"}],"wp:attachment":[{"href":"https:\/\/www.kaashivinfotech.com\/blog\/wp-json\/wp\/v2\/media?parent=16521"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.kaashivinfotech.com\/blog\/wp-json\/wp\/v2\/categories?post=16521"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.kaashivinfotech.com\/blog\/wp-json\/wp\/v2\/tags?post=16521"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}