Watch 5 Sorting Algorithms Race | Bubble vs Merge vs Quick Sort

Veröffentlicht am: 25 Juni 2026
auf dem Kanal: Code Clarity DSA

Sorting algorithms — finally explained the visual way. We take one messy array,
[7, 3, 5, 2, 8, 1], and watch 5 different algorithms race to sort it. No dry theory,
no scary math — just clean animations and simple intuition.

By the end you'll understand exactly how Bubble, Selection, Insertion, Merge, and
Quick Sort actually work — plus how to compare them by time, space, and stability,
and which one to reach for in real life.

Perfect for students, placement & interview prep, and self-taught programmers.

⏱ CHAPTERS
00:00 Same array, five strategies (intro)
00:28 What is sorting?
00:59 How to judge a sorting algorithm
01:36 Bubble Sort
02:32 Selection Sort
03:20 Insertion Sort
04:11 Why simple sorts get slow
04:38 Merge Sort
05:38 Quick Sort
06:43 Full comparison table (Time / Space / Stable)
07:17 Which algorithm should you use?
08:04 The code (Python)
08:31 Recap

🎯 WHAT YOU'LL LEARN
• What sorting is and why it matters
• Time complexity & space complexity — explained visually
• Big-O notation without the headache
• Stability, comparisons, and swaps
• The exact use case for each algorithm
• When Quick Sort drops to O(n²) — and why pivots matter

📌 RUNNING EXAMPLE: [7, 3, 5, 2, 8, 1] → [1, 2, 3, 5, 7, 8]

📊 QUICK COMPARISON
• Bubble — O(n²) | O(1) | stable | learning the basics
• Selection — O(n²) | O(1) | not stable | when swaps are costly
• Insertion — O(n²)/O(n) | O(1) | stable | small or nearly sorted data
• Merge — O(n log n) | O(n) | stable | predictable / linked lists
• Quick — O(n log n)* | O(log n) | not stable | fast in-memory sorting
*worst case O(n²) with bad pivots

💻 Full Python + Java code for all 5 algorithms 👉 [GitHub link]

If this made sorting finally click, subscribe to Code Clarity DSA for clear, visual
DSA explanations built for beginners and interviews. 🔔

▶ Next up: Recursion, Explained Visually
▶ Related: Heaps & Priority Queues

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