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Databricks·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
May 2026Remote

Summary

Databricks technical screen focused on a data structure design problem that sounds deceptively simple until you're actually in it. The question was meaty enough that I was still thinking about edge cases after the call ended.

Questions Asked (1)

Q1

Design a 'lazy array' data structure that supports set(i, v), get(i), and a range update like fill(l, r, v) or addRange(l, r, v). Point queries must return the correct value accounting for all pending updates. Discuss the technique you'd use (lazy propagation on a segment tree, or layered diff arrays) and the time complexity of each operation.

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

I started with the diff array approach because it felt simpler to explain, but then the interviewer pushed on what happens when you mix set() calls with range updates and suddenly my clean O(1) range update story falls apart.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints (e.g., array size, update/query frequency, value types). Then present a segment tree with lazy propagation as the primary solution, explaining how it supports point queries and range updates in O(log n) time. Optionally, discuss alternative approaches like sqrt decomposition or difference arrays for specific cases, and compare their trade-offs.

Pro tip: Demonstrate awareness of real-world constraints: if updates are much more frequent than queries, a difference array with periodic rebuilding might be more efficient; if queries dominate, a segment tree is better. Mentioning this trade-off shows maturity.

1. Clarify requirements and constraints

Ask about array size, number of operations, update/query patterns, and whether values are integers or floats. This determines the optimal data structure.

2. Propose segment tree with lazy propagation

Explain that a segment tree can handle range updates and point queries in O(log n) by storing pending updates at nodes and pushing them down when needed.

3. Detail operations and complexity

Describe set(i, v): update leaf and propagate; get(i): traverse from root to leaf, accumulating lazy values; fill/addRange: update range with lazy tags. All operations O(log n).

4. Discuss alternative approaches

Mention sqrt decomposition (O(√n) per operation) and difference arrays (O(1) update, O(n) query) with periodic rebuilding, comparing trade-offs.

5. Conclude with recommendation

Summarize that segment tree with lazy propagation is generally optimal for balanced workloads, but choose based on specific constraints.

Key Points to Mention

  • Segment tree with lazy propagation for O(log n) range updates and point queries
  • Lazy tags: store pending updates at nodes and push down during queries
  • Time complexity: set, get, fill, addRange all O(log n) with segment tree
  • Alternative: sqrt decomposition with O(√n) per operation, simpler to implement
  • Alternative: difference array for O(1) range updates but O(n) point queries, with periodic rebuilding
  • Trade-offs based on update/query frequency and array size

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