Why Sorting Algorithm Choice Matters
Every programmer encounters sorting, yet the nuances between algorithms matter enormously at scale.
| Algorithm | Best | Average | Worst | Stable | In-place |
|---|---|---|---|---|---|
| Bubble Sort | O(n) | O(n²) | O(n²) | ✅ | ✅ |
| Merge Sort | O(n log n) | O(n log n) | O(n log n) | ✅ | ❌ |
| Quick Sort | O(n log n) | O(n log n) | O(n²) | ❌ | ✅ |
| Heap Sort | O(n log n) | O(n log n) | O(n log n) | ❌ | ✅ |
| Timsort | O(n) | O(n log n) | O(n log n) | ✅ | ❌ |
When to Use Each
- Timsort — default choice (Python, Java, Swift use it)
- Quick Sort — cache-friendly, great average performance
- Merge Sort — stable, ideal for linked lists and external sorting
- Counting/Radix Sort — integers or fixed-alphabet strings, O(n+k)
- Insertion Sort — nearly-sorted data, O(n) best case