Databricks·Software Engineer·Onsite - System Design / Architecture·Senior

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Jun 2026

Summary

Databricks system design round focused entirely on a song list class, which sounds deceptively simple until you're 20 minutes deep into debating skip lists versus ordered maps and second-guessing every trade-off you mentioned.

Questions Asked (1)

Design a song list class that supports adding a song, removing a song, and reordering the list. Walk through multiple data structure options and justify your choice.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I went straight to arrays and linked lists like most people probably do, but the interviewer kept pushing on reorder cost.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (e.g., frequency of operations, need for indexing, concurrency) and then systematically compare data structures like arrays, linked lists, and balanced trees, focusing on time complexity trade-offs. Choose the structure that best aligns with the expected usage pattern and justify with Big-O analysis and practical considerations.

Pro tip: Demonstrate awareness of real-world constraints: mention that in-memory structures may need persistence or concurrency handling, and that Databricks values scalable, distributed solutions—so hint at how your choice could extend to a distributed setting.

1. Clarify Requirements

Ask about expected operation frequencies, list size, need for random access, and whether the list must be thread-safe or persistent. This ensures your design targets the right priorities.

2. Enumerate Data Structure Options

List plausible structures: dynamic array, doubly linked list, balanced BST (e.g., order-statistic tree), and skip list. Briefly describe how each would support add, remove, and reorder.

3. Analyze Trade-offs

Compare time and space complexity for each operation across structures. Highlight that reordering is O(1) for linked list (pointer swaps) but O(n) for array (shifting), while random access is O(1) for array but O(n) for linked list.

4. Select and Justify

Choose the structure that best fits the clarified requirements. For example, if reordering is frequent and random access is rare, a doubly linked list is optimal; if random access dominates, a dynamic array may be better.

5. Discuss Extensions and Edge Cases

Mention how to handle concurrency (e.g., locks or concurrent data structures), persistence (e.g., serialization), and scalability (e.g., sharding or distributed lists). Also address edge cases like empty list, duplicate songs, and invalid indices.

Key Points to Mention

Time complexity of add, remove, and reorder for each data structure (e.g., array: O(n) for add/remove at arbitrary position, O(1) for reorder if swapping; linked list: O(1) for add/remove given node reference, O(1) for reorder via pointer manipulation).
Space overhead: linked lists require extra pointers, arrays may have unused capacity, trees have node overhead.
Cache performance: arrays have better locality, linked lists suffer from pointer chasing.
Concurrency considerations: thread-safe implementations may require locks or lock-free algorithms, affecting choice.
Real-world usage: music playlists often require frequent reordering and sequential access, making linked lists attractive, but if the app needs to jump to a song by index, an array or a hybrid (e.g., indexed skip list) might be better.
Scalability: for large-scale systems, consider distributed data structures or databases, but for an in-memory class, stick to core data structures.

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