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Reddit·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Reddit coding screen for a software engineer role, one meaty question about merging overlapping chat context windows efficiently. The problem looked like a straightforward dedup task until the time complexity constraint showed up.

Questions Asked (1)

Q1

You have a chat history where messages have unique strictly-increasing integer IDs. An external API returns a context window around a given message ID (up to N messages before and after it). Given a list of IDs, implement a class that fetches the context window for each ID and returns a single deduplicated, sorted list of messages, running in better than O(M log M) time where M is the total number of fetched messages.

Algorithms & Data StructuresAPI & IntegrationsTechnical Trade-offs
Author's notes

The dedup part I figured out pretty fast, just track seen IDs in a set.

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AI HintsAI Generated

Suggested Approach

Clarify the API's behavior and constraints, then design a solution that leverages the sorted nature of message IDs to deduplicate and merge efficiently. Use a data structure like a balanced BST or a hash set combined with sorting to achieve better than O(M log M) time, possibly by exploiting the fact that each context window is contiguous and sorted.

Pro tip: Mention that if the API returns messages in sorted order, you can merge the sorted lists in O(M) time using a k-way merge, and deduplicate on the fly. Also, consider caching or batching API calls to reduce latency, and discuss trade-offs between time and space.

1. Understand the problem and constraints

Ask clarifying questions about the API: Does it return messages sorted by ID? Are the context windows contiguous? What is N? Can we batch requests? This helps determine the optimal approach.

2. Choose the right data structure

Since IDs are strictly increasing, use a balanced BST (e.g., TreeSet) or a hash set for deduplication, but to achieve better than O(M log M), consider using a boolean array or bitset if ID range is known, or merge sorted lists.

3. Design the algorithm

If each context window is sorted, perform a k-way merge using a min-heap of size K (number of IDs) to merge in O(M log K) time. If K is small, this is better than O(M log M). Alternatively, if windows overlap, use interval merging to deduplicate in O(M) time.

4. Implement and optimize

Implement the class with methods to fetch and merge. Optimize by batching API calls, caching results, and using efficient data structures. Analyze time and space complexity, ensuring it's better than O(M log M).

5. Test and discuss trade-offs

Test with edge cases: empty list, overlapping windows, large N. Discuss trade-offs between time and space, and how the solution scales with M and K.

Key Points to Mention

  • Leverage the strictly increasing IDs to maintain sorted order and deduplicate efficiently.
  • Use a k-way merge with a min-heap to combine sorted context windows in O(M log K) time, where K is the number of IDs.
  • If context windows are contiguous and overlapping, merge intervals to deduplicate in O(M) time.
  • Consider using a bitset or boolean array if the ID range is bounded, achieving O(M) time.
  • Batch API requests to reduce network overhead and improve performance.
  • Analyze time and space complexity, and discuss trade-offs between different approaches.

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