Databricks·Software Engineer·Take-home Assignment·Senior

Senior

Apr 2026

Summary

Databricks SWE interview that threw a query optimizer problem at me. You get a tree of SQL plan nodes and have to implement real optimizations on it, not just describe them. Harder than I expected to get right under pressure.

Questions Asked (1)

Given a tree structure representing a SQL query plan, implement transformations to produce a more efficient equivalent plan. You must include predicate pushdown and at least one additional optimization such as projection pruning or constant folding. Your solution needs to run against a provided test harness.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The predicate pushdown part felt manageable once I mapped out the cases where pushing a Filter below a Join is actually safe versus when it blows up semantically.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the tree representation and the test harness expectations, then design a recursive transformation framework that applies predicate pushdown and projection pruning in a single bottom-up pass. Implement each optimization as a modular rule, ensuring correctness by preserving semantics and validating against the provided tests.

Pro tip: Emphasize that predicate pushdown must respect operator semantics (e.g., outer joins, aggregations) and that projection pruning requires careful handling of column references to avoid breaking the plan. Mention that combining optimizations in one pass reduces overhead and avoids conflicting rewrites.

1. Understand the plan representation and test harness

Examine the tree node types (e.g., Scan, Filter, Project, Join) and the test harness API to know how to traverse and modify the plan. Identify how predicates and projections are represented.

2. Design a recursive transformation framework

Create a function that recursively processes each node, applying optimization rules bottom-up. Ensure the framework can compose multiple rules and handle plan rewrites safely.

3. Implement predicate pushdown

For each Filter node, push its predicate down through Project, Join (respecting join type), and other operators as far as possible, combining with existing filters. Stop at nodes that block pushdown (e.g., aggregations, limits).

4. Implement projection pruning

Traverse the plan to determine which columns are actually needed by ancestors, then remove unused columns from Project and Scan nodes. Update parent references accordingly to maintain a valid plan.

5. Validate and test

Run the transformed plan against the test harness to ensure it produces the same results as the original. Check for edge cases like outer joins, subqueries, and complex predicates.

Key Points to Mention

Predicate pushdown rules: push filters through projections, joins (inner vs outer), and unions; combine predicates; avoid pushing through aggregations or limits.
Projection pruning: compute required columns top-down, remove unused columns from projections and scans, and handle column aliases and expressions.
Correctness: ensure transformations preserve query semantics, especially with outer joins, nulls, and side effects.
Performance: discuss how these optimizations reduce data shuffling, I/O, and computation, and mention the importance of a single-pass approach.
Test harness integration: describe how to plug the optimizer into the provided harness, including any required interfaces or callbacks.
Trade-offs: mention when pushdown might not be beneficial (e.g., highly selective filters after a join) and how to decide which optimizations to apply.

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