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Databricks·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
May 2026Remote

Summary

Databricks system design round focused entirely on one meaty problem: build a snapshot-able set with MVCC semantics. The whole session was basically a deep dive into one data structure, which I wasn't expecting from a coding-style round.

Questions Asked (1)

Q1

Design a snapshot-able set backed by MVCC. The API needs add, remove, takeSnapshot, get (returns elements at a given snapshot), contains (value + snapshotId), and an iterator over elements present at a given snapshot. Walk through your data structure choices, how you keep snapshot creation cheap, iterator stability guarantees, and the complexity of each operation.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

I started with the naive approach of copying the whole set on each snapshot and they shut that down immediately, which I kind of expected but still felt embarrassing to say out loud.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a versioned data structure using immutable snapshots and a persistent map. Explain how snapshot creation is O(1) by capturing a version number, and detail iterator stability through versioned nodes. Finally, analyze time and space complexity for each operation.

Pro tip: Emphasize that MVCC snapshots are immutable and cheap because they only capture a version, not copy data. Discuss trade-offs like memory overhead and garbage collection to show depth.

1. Clarify Requirements and Constraints

Ask about expected workload (read/write ratio, snapshot frequency), memory constraints, and concurrency needs. Confirm that snapshots are immutable and that get/contains/iterator must reflect the state at snapshot creation.

2. Choose Core Data Structures

Propose a persistent (immutable) balanced tree or hash map with versioned nodes, where each update creates a new version. Alternatively, use a versioned linked list or skip list. Explain why persistence enables cheap snapshots.

3. Design Snapshot Mechanism

Snapshots are represented by a version number or a pointer to the root of the persistent structure at that time. Creation is O(1) because it only records the current version. Discuss how to handle concurrent writes (e.g., using atomic version counters).

4. Ensure Iterator Stability

Iterators are bound to a snapshot version and traverse the persistent structure as of that version. Since the structure is immutable, the iterator sees a consistent view even if new writes occur. Mention that iterators may need to skip deleted elements based on version.

5. Analyze Complexity and Trade-offs

Provide time complexity for add, remove, get, contains, and iterator (typically O(log n) for tree-based, O(1) average for hash-based). Snapshot creation is O(1). Space complexity increases with versions; discuss garbage collection of old versions when no snapshots reference them.

Key Points to Mention

  • MVCC uses versioning to provide consistent snapshots without locking.
  • Persistent data structures (e.g., immutable balanced trees) allow O(1) snapshot creation by capturing a version.
  • Iterator stability is guaranteed because the underlying data structure is immutable for that version.
  • Time complexity: add/remove/get/contains are O(log n) for tree-based, O(1) average for hash-based; snapshot is O(1); iterator is O(n) to traverse but O(1) per element.
  • Space overhead: each version adds nodes; use path copying and garbage collection to reclaim memory when snapshots are no longer needed.
  • Concurrency: use atomic operations to update the current version pointer; readers can safely access old versions.

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