Openai·Software Engineer·Onsite - Coding / Algorithms·Senior

SeniorPrefer not to say

Jun 2026

Summary

OpenAI SWE coding round with one meaty interval-management problem that looks like a merge-intervals warm-up but has enough edge cases to chew through your whole session if you're not careful.

Questions Asked (2)

Design a shard management system where each shard has an id, start key, and end key. Implement add_shard, remove_shard, and a rebalance() method that ensures no more than `limit` shards overlap at any key point. When overlap would exceed the limit, trim the newer shard's range. Fill any gaps that open up after trimming to keep coverage contiguous.

Algorithms & Data StructuresSystem Design

Author's notes

I went straight to merge-intervals instinct and that got me most of the way there, but the gap-fill requirement blindsided me.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints, then propose a data structure that efficiently supports range-based operations and overlap counting. Outline the algorithms for add_shard, remove_shard, and rebalance, focusing on how to enforce the overlap limit and maintain contiguous coverage. Discuss trade-offs and potential optimizations.

Pro tip: Consider using an interval tree or a balanced BST keyed by start keys to quickly find overlapping shards and gaps, and handle edge cases like shards with zero-length ranges or limit=0.

1. Clarify requirements and assumptions

Ask about expected shard count, update frequency, whether keys are integers or strings, and if coverage must be contiguous from -infinity to +infinity or within a bounded domain.

2. Choose data structures

Select a data structure that supports efficient insertion, deletion, and range queries, such as an interval tree, segment tree, or a balanced BST with augmented overlap counts.

3. Design add_shard and remove_shard

For add_shard, insert the new shard and then call rebalance to enforce the overlap limit. For remove_shard, delete the shard and then fill any gaps that appear to maintain contiguous coverage.

4. Design rebalance algorithm

Traverse shards in order of insertion (or by start key), and for each key point where overlap exceeds limit, trim the newer shard's range. Then identify gaps and extend adjacent shards to fill them.

5. Analyze complexity and edge cases

Discuss time and space complexity of each operation, and address edge cases like overlapping shards with identical ranges, limit=0, and empty shard set.

Key Points to Mention

Use of interval tree or segment tree for efficient overlap detection and range queries
Handling of shard trimming: when overlap exceeds limit, trim the newer shard (by insertion time or id) to reduce overlap
Gap filling: after trimming, extend adjacent shards to cover any gaps, ensuring contiguous coverage
Complexity analysis: aim for O(log n + k) per operation where k is number of affected shards
Edge cases: limit=0, empty shard set, shards with zero-length ranges, and overlapping shards with same start/end
Trade-offs between different data structures and potential optimizations like lazy rebalancing

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

How does your rebalance logic handle the case where multiple shards all start at the same key and the limit is small? Walk through what happens step by step.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This is where I got tripped up.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

First, clarify the rebalance algorithm's goal and constraints, then walk through a concrete example with multiple shards sharing the same start key and a small limit. Explain how the algorithm selects shards, handles ties, and ensures fairness and progress, highlighting any edge cases and trade-offs.

Pro tip: Demonstrate awareness of real-world constraints like avoiding starvation and minimizing data movement; mention how you'd test this scenario with unit tests and metrics to validate correctness and performance.

1. Clarify the scenario and assumptions

Restate the problem: multiple shards have the same starting key, and the limit is small (e.g., 1 or 2). Confirm whether the limit is per-shard or global, and whether shards are ordered.

2. Describe the selection logic

Explain how the algorithm picks which shard(s) to process first. If using a priority queue or round-robin, detail how ties are broken (e.g., by shard ID, load, or random).

3. Walk through step-by-step execution

Simulate the process: with a small limit, only a few shards are processed. Show how the algorithm advances the start key for processed shards and what happens to unprocessed shards.

4. Address fairness and progress

Discuss how the algorithm prevents starvation (e.g., rotating shards) and ensures all shards eventually make progress, even with a small limit.

5. Highlight edge cases and trade-offs

Mention potential issues like hot spots, increased latency, or redundant work, and how you'd mitigate them (e.g., batching, dynamic limits).

Key Points to Mention

Tie-breaking strategy for shards with identical start keys (e.g., round-robin, shard ID order, or load-based).
Definition of 'limit'—whether it's a global cap or per-shard, and its impact on selection.
Fairness mechanisms to avoid starvation, such as rotating the starting shard or using a queue.
Progress guarantees: ensuring each shard eventually advances its key range.
Performance considerations: overhead of coordination, potential for increased latency with small limits.
Testing approach: unit tests for tie-breaking, integration tests with multiple shards, and monitoring metrics like shard processing skew.

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