L2 · Working engineerStrings~8 min · 5 tests#25

Find All Occurrences of a Substring

Return every start index where a substring occurs in a string, overlaps included, using str.find in a loop. Covers the O(n·m) cost and KMP follow-up.

The problem

Return a list of every index where sub starts inside s. Overlapping matches count: in "aaaa" the substring "aa" starts at 0, 1, 2.

Assume sub is non-empty.

Examples

Example 1
Input
```
find_all('abababab', 'ab')
```
Expected output
```
[0, 2, 4, 6]
```
Example 2
Input
```
find_all('aaaa', 'aa')
```
Expected output
```
[0, 1, 2]
```

+ 3 hidden tests on Submit — sub longer than s.

Edge cases to ask about

No match
Overlapping matches
sub longer than s

Hints

0/3

How an interviewer scores this

0/9

1. Understand the problemRestate it, ask about edge cases and input size.
2. Write a brute-force solutionCorrect first, clever later.
3. Get the correct outputautoAll visible examples pass.
4. Analyse time complexityautoCommit to a Big-O in the Complexity Lab.
5. Analyse space complexityautoExtra memory, not the input.
6. Identify the bottleneckautoMeasure your code — where does it bend?
7. OptimiseUsually: a set or dict instead of a scan.
8. Explain why it worksSay it out loud to Solvi.
9. Test the edge casesautoSubmit passes every hidden test.

Python 3.13 · find_all

def find_all(s, sub):
    """Every start index of sub in s, overlaps included."""
    # Write your solution here
    pass

⌘/Ctrl + Enter runs the examples

Your code runs in real CPython inside your browser — nothing is sent anywhere. The first run downloads the interpreter (about 6 MB, once). Your code is saved on this device as you type.

Complexity Lab

What does this cost as n grows?

Interviewers score the analysis as much as the code. Commit to an answer first — then check it, and measure your code against the optimal one at growing input sizes.

Time complexity of the optimal solution

Space complexity (extra memory)

Pick both to reveal the answer.

Measure it

Runs the function on inputs of size 250 up to 16,000 and records the time and peak memory. Slow solutions stop early — a short curve is itself the answer.

From brute force to optimal

The progression an interviewer wants to hear, one step at a time.

Approach	Time	Space	Idea
Compare a slice at every index	O(n·m)	O(m)	Each slice is a copy of length m.
str.find from i + 1	O(n·m) worst	O(1)	find is implemented in C and skips fast; restarting at i + 1 keeps overlaps.
bestKMP	O(n + m)	O(m)	Prefix table means no character is re-compared.

Walkthrough of the optimal approach (try it yourself first)

Loop on s.find(sub, i + 1) until it returns -1. Restarting at i + 1 (not i + len(sub)) is what makes overlapping matches count.

The theoretical answer is O(n·m) worst case. If the interviewer pushes, describe KMP: a prefix-function table lets you avoid re-comparing characters, giving O(n + m).

Complexity: O(n·m) time, O(1) space. In the worst case each of the n start positions compares up to m characters. KMP brings this to O(n + m).

Reveal the reference solution

def find_all(s, sub):
    out = []
    i = s.find(sub)
    while i != -1:
        out.append(i)
        i = s.find(sub, i + 1)
    return out

The brute force, for comparison

def find_all(s, sub):
    m = len(sub)
    return [i for i in range(len(s) - m + 1) if s[i:i + m] == sub]

Follow-ups interviewers ask

Non-overlapping matches only.
Implement KMP.

Frequently asked interview questions

Core interview concepts, complexities, and follow-ups scored by hiring teams.

What is the time complexity of Find All Occurrences of a Substring in Python?

The optimal solution runs in O(n·m) time and O(1) auxiliary space. In the worst case each of the n start positions compares up to m characters. KMP brings this to O(n + m).

What is the brute-force approach, and how do you optimise it?

Compare a slice at every index: O(n·m) time, O(m) space. Each slice is a copy of length m. str.find from i + 1: O(n·m) worst time, O(1) space. find is implemented in C and skips fast; restarting at i + 1 keeps overlaps. KMP: O(n + m) time, O(m) space. Prefix table means no character is re-compared.

What follow-up questions do interviewers ask about Find All Occurrences of a Substring?

Non-overlapping matches only. Implement KMP.