Bytedance·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate

Jul 2026

Summary

Bytedance SWE coding round, two problems back to back. Both were algorithmic, one binary search and one string multiplication. Felt okay on the first, got a bit tangled explaining complexity on the second.

Questions Asked (2)

Given an array of piles and a maximum number of hours, find the minimum processing speed k such that all piles can be finished within the time limit. Each hour you can process at most k items from one pile.

Algorithms & Data Structures

Author's notes

I knew this was binary search pretty quickly, the monotonic property is obvious once you think about it.

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AI HintsAI Generated

Suggested Approach

Recognize that the answer lies between 1 and the maximum pile size, and that feasibility is monotonic: if speed k works, any larger speed also works. Use binary search on k, and for each candidate speed, compute the total hours required by summing ceil(pile/k) for all piles, checking if it's within the limit.

Pro tip: Mention that the lower bound can be tightened to ceil(total_items / max_hours) to reduce the search space, and always discuss integer overflow and edge cases like empty piles or hours less than number of piles.

1. Clarify the problem and constraints

Confirm that each hour you can process at most k items from one pile, and that you cannot split work across piles in the same hour. Ask about input size, value ranges, and whether k must be an integer.

2. Define the feasibility function

For a given speed k, compute the total hours needed as sum(ceil(pile / k)). The speed is feasible if this sum is <= max_hours.

3. Identify monotonicity and choose binary search

Explain that if speed k is feasible, any speed > k is also feasible. Therefore, binary search on the answer between 1 and max(piles).

4. Implement binary search with careful bounds

Set low = 1 (or ceil(total/max_hours)) and high = max(piles). While low < high, compute mid, check feasibility, and adjust bounds accordingly. Return low.

5. Analyze complexity and test edge cases

Time complexity is O(n log(max(pile))), space O(1). Test cases: single pile, hours equal to number of piles, very large piles, and hours less than number of piles (impossible).

Key Points to Mention

Monotonicity of the feasibility condition enables binary search.
Feasibility check uses ceiling division: (pile + k - 1) // k to avoid floating point.
Binary search bounds: low = 1, high = max(piles); optionally tighten low to ceil(total_items / max_hours).
Time complexity O(n log M) where M is the maximum pile size; space O(1).
Edge cases: empty array, max_hours < number of piles (return -1 or handle as impossible), and large values requiring 64-bit integers.
Alternative approach: priority queue simulation is inefficient; binary search is optimal.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Multiply two large non-negative integers represented as strings without converting the full strings to built-in numeric types. Return the result as a string.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Classic problem but I kept second-guessing my index math.

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AI HintsAI Generated

Suggested Approach

Use a digit-by-digit multiplication approach, simulating the manual multiplication process with an array to store intermediate results. Iterate through each digit of the second number and multiply it with each digit of the first number, accumulating results with proper carry handling. Finally, convert the array to a string, removing leading zeros.

Pro tip: Clarify constraints upfront (e.g., maximum length, leading zeros) and discuss trade-offs between time and space complexity. Mention that you can optimize by using a single array and processing from right to left to avoid extra space.

1. Clarify requirements and edge cases

Ask about input constraints (max length, leading zeros, empty strings) and expected output format. Confirm that built-in big integer conversion is not allowed.

2. Design the algorithm

Explain the schoolbook multiplication method: use an array of size m+n to store intermediate sums, iterate over digits from right to left, and handle carries.

3. Implement the solution

Write code that converts characters to digits, performs nested loops, updates the result array, and manages carries. Ensure no built-in numeric conversion of the full strings.

4. Handle edge cases and optimize

Test with zeros, single-digit numbers, and large inputs. Discuss potential optimizations like using a single array or early termination for zeros.

5. Analyze complexity and trade-offs

State time complexity O(m*n) and space O(m+n). Mention alternative algorithms (e.g., Karatsuba) and when they might be beneficial.

Key Points to Mention

Time and space complexity analysis (O(m*n) time, O(m+n) space)
Handling of carries and digit accumulation
Edge cases: zero, leading zeros, empty strings
Avoiding built-in big integer conversion
Potential optimizations (e.g., using a single array, early termination)
Alternative algorithms like Karatsuba for very large numbers

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.