I started with the high-level pipeline and they kept pushing me down each layer.
Start by clarifying the scope and constraints, then walk through the pipeline from SQL string to results: tokenization, parsing into an AST, building a logical plan, optimizing, and executing. For each stage, explain the key data structures and algorithms, and discuss trade-offs like simplicity vs. performance.
Pro tip: Emphasize separation of concerns: keep parsing, planning, and execution decoupled so you can swap components (e.g., different execution engines) without rewriting the whole system. Also, mention how you'd handle errors gracefully at each stage.
Ask about expected SQL subset, data volume, concurrency, and whether it's in-memory or disk-based. This shows you think about constraints before diving into design.
Describe how to break the SQL string into tokens (keywords, identifiers, literals, operators) and then parse them into an AST using recursive descent or a parser generator. Mention handling of clauses like WHERE, GROUP BY, ORDER BY.
Convert the AST into a logical plan (e.g., relational algebra tree) with nodes for scans, filters, aggregates, sorts. Discuss simple optimizations like predicate pushdown and projection pruning.
Explain how to execute the logical plan: use an iterator model (volcano-style) where each operator has next() to pull tuples. Describe how to implement aggregates (hash or sort-based) and sorting (in-memory or external).
Talk about trade-offs: simplicity vs. performance, memory usage, support for indexes, transactions, etc. Mention how you'd extend to more complex queries or larger data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a row-store approach, slice of maps basically, and they asked why not columnar.
Start by clarifying the engine's requirements—workload type, data size, and performance goals—then propose a concrete in-memory layout (e.g., columnar or row-based) with justification. Describe a simple indexing scheme (like hash or sorted arrays) that balances lookup speed and memory overhead, and discuss trade-offs.
Pro tip: Emphasize that the best design depends on the workload; show you can adapt by briefly contrasting alternatives and explaining why your choice fits. Mention that you'd prototype and benchmark to validate assumptions.
Ask about the engine's purpose, data characteristics (size, schema, update frequency), and performance targets (latency, throughput). This ensures your design is tailored.
Choose a layout (e.g., row-oriented for transactional, columnar for analytical) and detail how data is stored in memory (arrays, structs, pointers). Explain alignment and padding considerations.
Select an indexing method (e.g., hash table for point lookups, sorted array for range queries) and describe its structure and operations. Keep it simple but effective.
Compare your choices against alternatives in terms of memory usage, speed, and complexity. Acknowledge limitations and potential optimizations.
Conclude with how you'd test the design (e.g., benchmarks, profiling) and iterate. Highlight that the design is a starting point.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Tacked on at the end with like five minutes left.
Start by clarifying the current engine's architecture and query execution model, then propose a JOIN implementation that fits naturally into that design. Discuss the core algorithm choices (e.g., hash join, sort-merge join, nested loop) and how to integrate them with existing components like the planner, executor, and storage layer. Emphasize trade-offs in performance, memory, and complexity, and outline a phased rollout with testing and metrics.
Pro tip: Anchor your answer in the existing engine's abstractions—show you'd extend rather than rewrite—and proactively mention how you'd measure success (e.g., latency, throughput, memory) to demonstrate production maturity.
Ask about the engine's query model, data layout, and existing operators to ground your design. Confirm the types of JOINs needed (inner, outer, etc.) and scale expectations.
Select appropriate algorithms (hash join for equi-joins, sort-merge for sorted data, nested loop for small tables) and decide between in-memory vs. disk-based execution. Consider partitioning and spilling for large datasets.
Extend the query planner to recognize JOIN syntax and generate join operators. Modify the executor to handle join nodes, including predicate pushdown and join order optimization.
Implement memory management, parallelism, and spill-to-disk strategies. Add statistics collection to inform cost-based optimization and join order selection.
Build correctness tests (including edge cases like nulls and duplicates) and performance benchmarks. Roll out incrementally with feature flags and monitor key metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.