Toma·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Toma SWE interview with a coding problem centered on building a simple audio mixer class. The problem looked manageable at first but the overlap/overwrite semantics on same-track writes took a bit to think through clearly.

Questions Asked (1)

Design and implement an AudioStream class that supports multiple named tracks on a shared timeline. It needs a write(track, data, t) method that places float values starting at time t, where later writes to the same track overwrite earlier ones at overlapping indices. It also needs a read(t1, t2) method that returns the summed output across all tracks for each time in [t1, t2). Walk through your data structure choices and the complexity of each operation.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

My first instinct was a 2D array but that falls apart immediately if the time range is unbounded.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints (e.g., time resolution, track count, memory limits). Propose a data structure that balances write and read efficiency, such as a hash map of tracks to sparse arrays or interval trees. Then analyze the time and space complexity of each operation, discussing trade-offs and potential optimizations.

Pro tip: Mention that you would use a sparse representation (e.g., a balanced BST or skip list) for each track to efficiently handle overwrites and range queries, and discuss how to merge tracks during read without materializing the entire timeline.

1. Clarify requirements and assumptions

Ask about expected data volume, time resolution, number of tracks, and whether reads and writes are interleaved. Confirm that time is discrete and that writes overwrite existing values at overlapping indices.

2. Choose data structures

For each track, use a data structure that supports efficient point updates and range queries, such as a balanced binary search tree (e.g., red-black tree) or a skip list, mapping time indices to float values. Alternatively, consider a segment tree if the time range is bounded and known.

3. Implement write operation

For write(track, data, t), locate the track's data structure and insert or update the values starting at time t. If using a balanced BST, each insertion/update is O(log n) per element; if data is a contiguous block, consider bulk insertion.

4. Implement read operation

For read(t1, t2), iterate over all tracks, query each track's data structure for the range [t1, t2), and sum the values at each time index. Use an efficient range query (e.g., in-order traversal of the BST within the range) and merge results.

5. Analyze complexity and trade-offs

Discuss time complexity: write is O(k log n) for k values, read is O(m log n + total_points) where m is number of tracks. Space is O(total stored points). Compare with alternatives like dense arrays (O(1) write/read but high memory) and explain why sparse is better for large timelines.

Key Points to Mention

Use of sparse data structures (e.g., balanced BST, skip list) to avoid allocating memory for silent periods.
Overwrite semantics: later writes replace earlier values at overlapping indices, which can be handled by updating the data structure.
Read operation sums across tracks: need to efficiently merge multiple tracks' data for the queried range.
Time complexity: write O(k log n), read O(m log n + output size), where n is number of stored points per track, m is number of tracks.
Space complexity: O(total number of stored points) which is efficient for sparse audio.
Trade-offs: dense arrays offer O(1) operations but waste memory; interval trees or segment trees can optimize range queries but increase write complexity.

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