sierra·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Sierra SWE interview had a pretty meaty coding problem around stream merging. One round, focused on algorithm design and complexity analysis. Not a lot of fluff, they wanted to see if you actually knew your data structures.

Questions Asked (1)

Design a MergeStream class that takes multiple sorted integer streams (each with next() and hasNext()) and returns the globally smallest next value across all streams until they're all exhausted. Walk through the time complexity of each next() call and explain how you'd handle streams that are empty or finite.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I knew the min-heap approach going in but fumbled a bit explaining why it's O(log k) per call.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then propose a min-heap solution that stores the current head of each stream. Walk through the algorithm step-by-step, analyze the time complexity of each next() call, and discuss edge cases like empty or finite streams.

Pro tip: Mention that you can optimize by only adding non-empty streams to the heap initially and after each pop, and highlight that the heap size is bounded by the number of streams, making it efficient for many streams.

1. Clarify Requirements and Constraints

Ask about the number of streams, whether they can be empty, if streams are finite, and if there are memory constraints. Confirm that next() should return the smallest value and advance that stream.

2. Design the Data Structure

Use a min-heap (priority queue) to store the current head of each non-empty stream. Each heap element contains the value and a reference to its stream. Initialize the heap with the first element of each stream that hasNext().

3. Implement next() and hasNext()

For next(): pop the minimum from the heap, advance that stream, and if the stream still hasNext(), push its new head into the heap. Return the popped value. For hasNext(): return true if the heap is not empty.

4. Analyze Time Complexity

Each next() call involves a heap pop and possibly a heap push, both O(log k) where k is the number of streams. Overall, for n total elements, total time is O(n log k). Space is O(k) for the heap.

5. Handle Edge Cases

Discuss handling empty streams (skip them), finite streams (remove from heap when exhausted), and all streams empty (return null or throw exception). Also consider if streams can be infinite, but the algorithm still works.

Key Points to Mention

Use a min-heap to efficiently get the smallest current element across all streams.
Time complexity per next() is O(log k) where k is the number of streams.
Space complexity is O(k) for the heap.
Handle empty streams by not adding them to the heap initially or after exhaustion.
For finite streams, when a stream is exhausted, simply don't push its next element.
Consider tie-breaking if multiple streams have the same value (order doesn't matter for correctness).

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