I started with WHERE filtering since that felt most natural, then bolted on SELECT projection and LIMIT after.
Start by clarifying requirements and defining a clear interface for the query engine, then design a modular pipeline that parses the query, applies WHERE filtering, performs column projection, and enforces LIMIT. Implement each stage as a separate function to keep the code testable and extensible, and discuss trade-offs like performance and error handling.
Pro tip: Mention that you would push down predicates and projections to minimize intermediate data, and use lazy evaluation or generators to handle large tables efficiently—this shows you think about scalability beyond the basic implementation.
Ask about the expected query format (e.g., SQL string or structured object), supported operators, and whether the table schema is fixed. Define the function signature and return type.
Break the query into stages: parsing, filtering (WHERE), projection (SELECT), and limiting (LIMIT). Decide on the order of operations for efficiency, such as filtering before projecting.
Write helper functions for each stage: a parser to extract clauses, a filter function that evaluates conditions, a projector that selects columns, and a limiter that truncates results.
Consider missing columns, invalid operators, empty tables, and type mismatches. Decide whether to raise exceptions or return empty results, and document the behavior.
Talk about performance improvements like indexing, lazy evaluation, and predicate pushdown. Discuss trade-offs between simplicity and extensibility, and how you would add features like ORDER BY or JOINs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the engine's current architecture and data model, then propose a pipeline: parse GROUP BY and HAVING clauses, build groups using a hash map, compute aggregates per group, and finally filter groups with HAVING. Discuss trade-offs between hash-based and sort-based grouping, and how to handle NULLs and memory constraints.
Pro tip: Mention that HAVING filters after aggregation, unlike WHERE which filters before, and that you can optimize by pushing down predicates where possible. Also, consider using a two-phase aggregation for distributed scenarios.
Ask about the engine's current capabilities, data volume, memory limits, and whether distributed processing is needed. Confirm the expected SQL semantics for NULLs and data types.
Choose between hash-based grouping (for unsorted data, O(n) time) and sort-based grouping (for sorted data, O(n log n) time). Explain how to handle memory by spilling to disk if needed.
Define an interface for aggregates with methods like init, accumulate, and finalize. Implement COUNT, SUM, AVG, MIN, MAX, handling NULLs and data types appropriately.
Parse HAVING as a post-aggregation filter. Evaluate the condition on each group's aggregated values, and only output groups that satisfy it.
Discuss optimizations like predicate pushdown, parallel aggregation, and using hash tables efficiently. Outline test cases for correctness and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Probably the part I was least prepared for.
Start by clarifying the requirements and constraints of the operators, then propose an API design that supports composition, such as an iterator-based pipeline or an operator tree. Compare the time and space complexity of each approach, and discuss trade-offs in terms of performance, memory, and extensibility.
Pro tip: Emphasize that the best design depends on the specific use case—for example, iterator-based composition is often more memory-efficient for streaming data, while operator trees enable better optimization opportunities. Showing awareness of these trade-offs demonstrates senior-level thinking.
Ask about the types of operators, expected data volume, latency requirements, and whether the API needs to support lazy evaluation or parallel execution.
Describe how operators can be implemented as iterators that pull data from upstream, allowing chaining (e.g., map, filter). Discuss time complexity (O(n) per operator, so O(k*n) for k operators) and space complexity (O(1) extra per operator, excluding buffering).
Explain how operators can form a tree where each node represents an operation and children are inputs. Discuss how this enables optimizations like predicate pushdown or fusion, and analyze time/space complexity (e.g., tree traversal overhead, memory for intermediate results).
Contrast the two approaches: iterators are simple, memory-efficient, and good for streaming, but may lack optimization opportunities; operator trees allow global optimization and parallelization but have higher memory overhead and complexity.
Based on the context (e.g., Retool's need for flexible data transformations), recommend a hybrid or one approach, explaining how it balances performance, memory, and developer experience.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.