Databricks·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026

Summary

Databricks software engineering interview with a pretty meaty design question around lazy evaluation. The kind of problem that sounds clean on paper but has a lot of subtle correctness issues once you start thinking about memoization and chaining.

Questions Asked (1)

Design and implement a Lazy Array data structure where operations like map, filter, and transform are deferred until an element is actually accessed by index. Support chaining, memoize each computed element, and avoid doing any work for elements that are never accessed.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

I started with a naive pipeline approach where each transformation just wraps the previous one as a callable, which is fine for basic laziness.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design a lazy array as a chain of operation nodes (source, map, filter, etc.) that store transformations without executing them. Implement an index access method that recursively evaluates only the needed elements, caching results in each node to avoid recomputation. Discuss trade-offs like memory overhead, thread safety, and handling of infinite sequences.

Pro tip: Emphasize that memoization should be per-node and immutable to ensure thread safety, and mention that filter operations may require scanning ahead, so caching negative results (e.g., 'no element found up to index N') can prevent redundant work.

1. Clarify Requirements and Constraints

Ask about expected operations, data types, size limits, thread safety, and whether infinite sequences need support. Confirm that only index-based access triggers computation.

2. Design the Lazy Array Structure

Model the lazy array as a chain of operation nodes (e.g., Source, Map, Filter) where each node holds a reference to its parent and a transformation function. Each node maintains a cache (e.g., array or map) for computed elements.

3. Implement Index Access with Memoization

For a given index, check the cache; if absent, recursively compute the value by applying the transformation to the parent's value at the appropriate index (for map) or by scanning parent indices until a match (for filter). Store the result in the cache before returning.

4. Handle Chaining and Edge Cases

Ensure that chaining operations (e.g., map().filter().map()) builds a new lazy array without executing. Handle out-of-bounds, negative indices, and infinite sequences gracefully (e.g., throw or return undefined).

5. Analyze Trade-offs and Optimizations

Discuss time/space complexity, memory overhead of caching, potential for stack overflow with deep chains, and optimizations like caching filter scan positions or using iterative evaluation to avoid recursion depth issues.

Key Points to Mention

Lazy evaluation: defer computation until index access
Memoization per node to avoid recomputation
Chaining operations by composing nodes without execution
Handling filter's variable-length output and scanning
Thread safety and immutability considerations
Trade-offs: memory vs. speed, recursion depth, infinite sequences

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