Stable vs Unstable Sorting Algorithms
Stability is a property that confuses many learners but is genuinely important in practice. A stable sorting algorithm preserves the relative order of elements that compare as equal. This article makes the concept concrete and explains when you must care about it.
A concrete example
Imagine a list of employees already ordered by name, and you sort them by department. A stable sort keeps everyone in the same department in their original alphabetical-by-name order. An unstable sort might scramble the names within each department. The final order is still 'sorted by department', but the secondary ordering is lost.
Why stability matters
Stability is what makes multi-key sorting work. To sort a spreadsheet by date and then by category, you sort by the least significant key first, then by the most significant key using a stable sort — and the earlier ordering survives. Databases depend on stable sorts for predictable ORDER BY results across multiple columns.
Which algorithms are stable
Stable: Bubble, Insertion, Merge, Counting, Radix, and Tim Sort. Unstable: Selection, Quick, Heap, and Shell Sort. Stability is a consequence of how an algorithm moves elements — Selection Sort, for example, swaps distant elements and can leapfrog equal keys. Unstable sorts can usually be made stable by attaching the original index as a tiebreaker, at the cost of extra memory.
Frequently asked questions
Which sorting algorithms are stable? +
Can Quick Sort be made stable? +
Does stability affect performance? +
See it in motion
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