Databricks·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026Remote

Summary

Databricks SWE interview with a design question that looked straightforward on the surface but had a few gotchas worth thinking through carefully. The snapshot iteration part is where most of the interesting tradeoffs live.

Questions Asked (1)

Design and implement a mutable collection that supports snapshot-based iteration. The collection stores unique values and must support add, remove, contains, and an iterator() method that returns a snapshot of the collection at the time it was called. Mutations after calling iterator() should not affect what the iterator returns. Discuss time and space complexity for each operation.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

My first instinct was a HashSet with a synchronized copy on iterator() call, which works fine but I fumbled explaining why the lack of ordering requirements actually matters here.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a design that balances simplicity and efficiency, such as a persistent data structure or versioned snapshot. Implement the core operations with careful attention to immutability and thread-safety, and analyze time/space complexity for each operation. Discuss trade-offs between different approaches (e.g., copy-on-write vs. persistent trees) and justify your choice.

Pro tip: Mention that snapshots can be implemented efficiently using structural sharing (e.g., persistent balanced trees) to avoid full copies, and highlight that this is similar to how Databricks' Delta Lake provides snapshot isolation for data versions.

1. Clarify Requirements and Constraints

Ask about expected usage patterns, concurrency needs, and performance requirements. Confirm that the iterator must reflect the state at call time and that mutations afterward should not affect it.

2. Choose a Data Structure

Select a data structure that supports efficient snapshots. Consider persistent balanced trees (e.g., AVL, red-black) or copy-on-write with versioning. Discuss trade-offs between simplicity and performance.

3. Implement Core Operations

Design add, remove, contains, and iterator. For iterator, return a snapshot by capturing the root of the persistent structure or by copying the current state. Ensure mutations create new versions without altering existing snapshots.

4. Analyze Complexity

For each operation, state time and space complexity. For persistent trees, add/remove/contains are O(log n) time and O(log n) space per operation due to path copying. Iterator creation is O(1) if it just captures the root, and iteration is O(n).

5. Discuss Trade-offs and Alternatives

Compare with copy-on-write (O(n) snapshot creation but O(1) mutations) and versioned arrays. Explain why persistent trees offer a good balance for frequent snapshots and mutations.

Key Points to Mention

Persistent data structures (e.g., balanced trees) enable efficient snapshots via structural sharing.
Time complexity: add/remove/contains O(log n), iterator creation O(1), iteration O(n).
Space complexity: O(log n) per mutation due to path copying, O(n) total for all versions.
Thread-safety: immutable snapshots allow lock-free reads and safe concurrent iteration.
Alternative: copy-on-write with O(n) snapshot creation but simpler implementation.
Use cases: versioned data, snapshot isolation in databases (e.g., Delta Lake).

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