Sorting Stability and When to Use Which Sort in DSA Go - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does it mean for a sorting algorithm to be stable?

A stable sorting algorithm keeps the original order of equal elements the same after sorting. For example, if two items have the same value, they stay in the order they appeared before sorting.

beginner

Name a common stable sorting algorithm.

Merge Sort is a common stable sorting algorithm. It keeps equal elements in their original order while sorting.

intermediate

Why might you choose an unstable sort like Quick Sort over a stable sort?

Quick Sort is often faster and uses less memory than stable sorts. If you don't need to keep the order of equal elements, Quick Sort is a good choice for speed.

intermediate

When is it important to use a stable sorting algorithm?

Use a stable sort when you want to sort by multiple criteria step-by-step. For example, first sort by age, then by name. Stability keeps the previous order intact.

intermediate

What is a key difference between Merge Sort and Heap Sort regarding stability?

Merge Sort is stable, meaning it keeps equal elements in order. Heap Sort is unstable, so equal elements might change order after sorting.

Which sorting algorithm is stable by default?

A Merge Sort

B Quick Sort

C Heap Sort

D Selection Sort

Merge Sort keeps the order of equal elements, so it is stable. Quick Sort, Heap Sort, and Selection Sort are generally unstable.

Why choose a stable sort when sorting by multiple keys?

A To keep the order of equal elements from previous sorts

B Because stable sorts are always faster

C To use less memory

D Because unstable sorts can't sort multiple keys

Stable sorts keep the order of equal elements, which helps when sorting by multiple keys step-by-step.

Which sorting algorithm is usually fastest but unstable?

A Bubble Sort

B Quick Sort

C Insertion Sort

D Merge Sort

Quick Sort is often the fastest in practice but is unstable.

If you want to sort a large list with limited memory and don't care about stability, which sort is best?

A Counting Sort

B Merge Sort

C Bubble Sort

D Quick Sort

Quick Sort uses less memory than Merge Sort and is fast, making it good for large lists when stability is not needed.

Which sorting algorithm is stable and good for linked lists?

A Heap Sort

B Quick Sort

C Merge Sort

D Selection Sort

Merge Sort is stable and works well with linked lists because it doesn't require random access.

Explain what sorting stability means and why it matters when sorting by multiple criteria.

Think about how sorting twice by different keys keeps the order.

Describe when you would choose Quick Sort over Merge Sort and the trade-offs involved.

Consider performance and stability needs.

Practice

1. What does it mean when a sorting algorithm is called stable?

easy

A. It keeps the original order of equal elements after sorting

B. It sorts elements faster than any other algorithm

C. It uses less memory than unstable sorts

D. It only works on numbers, not strings

Solution

1. Step 1: Understand stability in sorting

   A stable sort keeps the relative order of items that compare equal the same as before sorting.

2. Step 2: Compare options

   Only It keeps the original order of equal elements after sorting describes this property correctly. Other options describe speed, memory, or data type restrictions, which are unrelated to stability.

3. Final Answer:

   It keeps the original order of equal elements after sorting -> Option A

4. Quick Check:

   Stable sort = keeps equal items order [OK]

Hint: Stable means equal items keep their original order [OK]

Common Mistakes:

• Confusing stability with speed
• Thinking stability means less memory use
• Believing stability depends on data type

2. Which of the following Go code snippets correctly uses the sort.SliceStable function to sort a slice of structs by an integer field Age?

easy

A. sort.SliceStable(people, func(i, j int) bool { return people[i].Age > people[j].Age })

B. sort.Slice(people, func(i, j int) bool { return people[i].Age > people[j].Age })

C. sort.Slice(people, func(i, j int) bool { return people[i].Age < people[j].Age })

D. sort.SliceStable(people, func(i, j int) bool { return people[i].Age < people[j].Age })

Solution

1. Step 1: Identify stable sort usage

   The question asks for stable sorting by Age ascending. sort.SliceStable is the stable function.

2. Step 2: Check comparison function

   The comparison must be people[i].Age < people[j].Age for ascending order.

3. Final Answer:

   sort.SliceStable(people, func(i, j int) bool { return people[i].Age < people[j].Age }) -> Option D

4. Quick Check:

   Stable + ascending = sort.SliceStable(people, func(i, j int) bool { return people[i].Age < people[j].Age }) [OK]

Hint: Use SliceStable with less-than for ascending stable sort [OK]

Common Mistakes:

• Using sort.Slice instead of sort.SliceStable
• Reversing comparison for ascending order
• Mixing stable and unstable sort functions

3. Given the following Go code, what will be the output after sorting?

type Item struct {
  Name string
  Rank int
}

items := []Item{{"A", 2}, {"B", 1}, {"C", 2}}
sort.Slice(items, func(i, j int) bool { return items[i].Rank < items[j].Rank })
for _, item := range items {
  fmt.Print(item.Name, " ")
}

medium

A. A C B

B. B A C

C. B C A

D. C A B

Solution

1. Step 1: Understand sorting criteria

   The slice is sorted by Rank ascending using sort.Slice, which is unstable.

2. Step 2: Analyze order of equal Rank elements

   Items with Rank 2 are "A" and "C". Since sort.Slice is unstable, their relative order may change. The output shows "B" first (Rank 1), then "C", then "A".

3. Final Answer:

   B C A -> Option C

4. Quick Check:

   Unstable sort may change equal items order [OK]

Hint: Unstable sort may reorder equal keys; check output carefully [OK]

Common Mistakes:

• Assuming stable order with sort.Slice
• Mixing up item names and ranks
• Ignoring unstable sort behavior

4. The following Go code attempts to sort a slice of strings stably by length but does not work as expected. What is the error?

words := []string{"cat", "bat", "apple", "dog"}
sort.Slice(words, func(i, j int) bool { return len(words[i]) < len(words[j]) })

medium

A. Using sort.Slice instead of sort.SliceStable causes unstable sort

B. The comparison function should use > instead of <

C. The slice must be converted to []struct before sorting

D. The code is correct and stable by default

Solution

1. Step 1: Identify sorting function used

   The code uses sort.Slice, which is unstable.

2. Step 2: Understand stability requirement

   To sort stably by length, sort.SliceStable must be used instead.

3. Final Answer:

   Using sort.Slice instead of sort.SliceStable causes unstable sort -> Option A

4. Quick Check:

   Stable sort requires SliceStable, not Slice [OK]

Hint: Use SliceStable for stable sorting, not Slice [OK]

Common Mistakes:

• Thinking comparison direction affects stability
• Believing slice type conversion is needed
• Assuming sort.Slice is stable

5. You have a list of employees with fields Name and Department. You want to sort them first by Department alphabetically and then by Name alphabetically within each department, preserving the order of employees with the same name. Which sorting approach is best in Go?

hard

A. Use one unstable sort by Department only

B. Use two stable sorts: first by Name, then by Department

C. Use one stable sort by concatenated Department+Name string

D. Use two unstable sorts: first by Department, then by Name

Solution

1. Step 1: Understand multi-level sorting with stability

   To sort by Department then Name, stable sorts allow sorting by the secondary key first, then primary key.

2. Step 2: Apply stable sorts in correct order

   Sort first by Name (secondary key) stably, then by Department (primary key) stably. This preserves Name order within Departments.

3. Final Answer:

   Use two stable sorts: first by Name, then by Department -> Option B

4. Quick Check:

   Two stable sorts in reverse order = correct multi-level sort [OK]

Hint: Sort by secondary key first, then primary key with stable sorts [OK]

Common Mistakes:

• Using unstable sorts causing order loss
• Sorting primary key first losing secondary order
• Trying one sort on combined keys without stability