The scoping part is what tripped me up at first.
Start by clarifying the scope and requirements, then propose a modular architecture with separate components for parsing, planning, and execution. Implement a minimal viable subset (e.g., SELECT with WHERE) and design for extensibility by using interfaces and a clear separation of concerns.
Pro tip: Emphasize extensibility from the start: design the parser and executor to be easily extended for new SQL features, and mention how you would handle schema evolution and query optimization as the engine grows.
Ask questions to understand the expected SQL subset, data types, and performance constraints. Define the initial feature set (e.g., SELECT, FROM, WHERE) and plan for incremental additions.
Outline a modular design with components: parser (SQL to AST), planner/optimizer (AST to logical plan), and executor (logical plan to results). Use interfaces for extensibility.
Start with a simple parser for the basic subset, then build an executor that can handle scans, filters, and projections. Use in-memory data structures like hash maps for tables.
Write unit tests for each component and integration tests for end-to-end queries. Ensure correctness and handle edge cases like empty tables and null values.
Discuss how to add features like JOINs, GROUP BY, and indexes. Mention trade-offs between simplicity and performance, and how to refactor as needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining the query engine's end-to-end pipeline from SQL text to results, then break it into modular components with clear interfaces (consumes/produces). Emphasize extensibility by showing where JOIN and GROUP BY fit as new operators or planner rules without disrupting existing stages.
Pro tip: Frame your architecture around a clean separation between logical planning and physical execution, and explicitly call out how you'd add a new operator (e.g., JOIN) by implementing a common interface—this shows you design for change, not just for today's features.
Describe the end-to-end flow: SQL text → parser → logical plan → optimizer → physical plan → execution → results. This sets the stage for component details.
For each component, state what it consumes and produces. For example: parser consumes SQL string, produces AST; planner consumes AST, produces logical plan; optimizer consumes logical plan, produces optimized logical plan; executor consumes physical plan, produces result set.
Explain the key abstractions: e.g., logical operators (Scan, Filter, Project), physical operators (with open/next/close), and how rows/batches flow between them. Highlight that operators are composable.
Show where these plug in: JOIN as a logical operator that the planner can introduce (e.g., from a join condition) and a physical operator (e.g., hash join, nested loop). GROUP BY as an aggregation operator that consumes rows and produces grouped results. Mention optimizer rules to push down predicates or choose join algorithms.
Discuss design choices that enable future features: e.g., using an iterator model vs. vectorized execution, rule-based vs. cost-based optimizer, and how these impact adding JOIN/GROUP BY. Conclude with how you'd test and validate new operators.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Recursive descent parser was the right call and I knew it, but I second-guessed myself and started sketching a regex-based thing first.
Start by clarifying the scope: which SQL subset, data types, and whether indexes or performance are required. Then outline a modular pipeline: tokenizer, parser to AST, and evaluator that recursively applies WHERE predicates. Implement a clean recursive descent parser for expressions with precedence (OR < AND < comparison) and an evaluator that short-circuits.
Pro tip: Mention that you would separate parsing from evaluation and use the visitor pattern or recursive evaluation on the AST; this shows you understand maintainability and testability, which interviewers value. Also, proactively discuss handling NULLs and type coercion, as these are common pitfalls.
Ask about the SQL dialect, supported operators, data types, and whether performance or indexing matters. Confirm the expected input format (e.g., in-memory rows or a file).
Propose a modular design: tokenizer -> parser -> AST -> evaluator. Explain how the WHERE clause will be represented as an expression tree with AND/OR nodes and comparison leaves.
Use recursive descent with precedence climbing: parse OR expressions, then AND, then comparisons. Handle parentheses and literals. Return an AST.
Traverse the AST for each row, evaluating comparisons and combining with short-circuit AND/OR. Apply the predicate to filter rows and return matching ones.
Walk through examples, including operator precedence, parentheses, NULL handling, and type mismatches. Mention unit tests for parser and evaluator.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by outlining the overall architecture of the query engine, emphasizing modularity and extensibility. Then, for each feature (ORDER BY/LIMIT, GROUP BY with aggregates, INNER JOIN), describe the necessary changes to the parser, planner, and execution engine, highlighting trade-offs and design decisions. Conclude with how you would test and validate the extensions.
Pro tip: Demonstrate awareness of performance implications: for example, discuss how to avoid sorting the entire dataset for LIMIT by using a top-N heap, and how to choose join algorithms based on data size and indexes.
Ask clarifying questions about the expected SQL syntax, data volumes, performance requirements, and whether the engine is in-memory or disk-based. This ensures you focus on the most relevant aspects.
Explain how to extend the parser to recognize ORDER BY, LIMIT, GROUP BY, aggregate functions, and JOIN clauses, and how to represent them in the AST.
Describe the logical plan changes (e.g., adding Sort, Limit, Aggregate, Join nodes) and physical operator choices (e.g., sort algorithms, hash vs. nested-loop join, streaming aggregation).
Detail how to modify the execution engine to process these operators, including handling of intermediate results, memory management, and pipelining where possible.
Outline a testing strategy (unit tests, integration tests, performance benchmarks) and discuss potential optimizations like predicate pushdown, index usage, and parallel execution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through left-deep vs bushy join trees and mentioned that join order matters a lot for intermediate result sizes.
Start by explaining the mechanics of multi-table joins, including join types and syntax, then discuss how the query optimizer determines join order and the performance implications. Use a concrete example to illustrate how different join orders can lead to vastly different execution times and resource usage.
Pro tip: Mention that while the optimizer usually picks a good join order, you can influence it with hints or by restructuring the query, but always measure before and after to avoid premature optimization.
Describe how joins combine rows from multiple tables using conditions, and mention common join types (INNER, LEFT, etc.) and syntax.
Explain that the database optimizer chooses the join order based on statistics and cost estimates, and that the order can significantly impact performance.
Provide a concrete scenario where a different join order reduces intermediate result sizes, I/O, and CPU usage, leading to faster execution.
Mention techniques like ensuring proper indexes, using EXPLAIN to analyze plans, and occasionally using hints or query restructuring to guide the optimizer.
Conclude with best practices: rely on the optimizer, but monitor and tune when necessary, and always test with realistic data volumes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
HAVING was easy to place (after GROUP BY, before project).
Start by explaining how you would extend the AST to represent HAVING clauses and subqueries, then describe the executor changes needed to evaluate them. Emphasize the separation of concerns: parsing, semantic analysis, and execution. Use a concrete example to illustrate the flow.
Pro tip: Mention that HAVING is essentially a filter applied after aggregation, and subqueries can be handled by treating them as separate query plans that are executed and their results integrated. This shows you understand the underlying relational algebra.
Add new node types for HAVING and subqueries. For HAVING, add a clause to the SELECT statement node. For subqueries, introduce a SubqueryExpression node that can appear in WHERE, FROM, or SELECT clauses.
Modify the grammar to recognize HAVING after GROUP BY and subqueries within parentheses. Ensure the parser builds the corresponding AST nodes.
Validate that HAVING references aggregate functions or grouped columns, and that subqueries are correlated or uncorrelated appropriately. Resolve column references and types.
For HAVING, apply the filter after the aggregation step. For subqueries, execute them as separate plans, possibly caching results for uncorrelated subqueries, and integrate their results into the outer query.
Discuss potential optimizations like pushing down predicates, decorrelating subqueries, and reusing subquery results. Mention how these affect the executor design.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Predicate pushdown was the obvious answer and I gave it.
Start by clarifying the system context and where query optimization fits in the data flow, then describe the optimizer's role and a specific rewrite with expected impact. Emphasize trade-offs and how you would measure improvement.
Pro tip: Mention that you would validate the rewrite with EXPLAIN plans and A/B testing, and consider the cost of maintaining the optimization. This shows you think about production readiness and long-term maintainability.
Briefly describe the system architecture, including data sources, query patterns, and where the query optimizer would sit (e.g., in the database layer, middleware, or application).
Explain what the optimizer does: parse queries, apply rules, estimate costs, and generate execution plans. Highlight its importance for performance and scalability.
Select a common rewrite (e.g., predicate pushdown, join reordering, subquery unnesting) and explain why it helps performance in your context.
Acknowledge potential downsides (e.g., increased complexity, stale statistics) and describe how you would measure the impact (e.g., latency, throughput, resource usage).
Tie the optimization back to user experience, cost savings, or scalability, showing alignment with company objectives.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I said property-based testing against a reference implementation (like SQLite) and a suite of golden input/output pairs per feature.
Start by clarifying what 'this engine' is and what correctness means for it, then propose a layered testing strategy that scales with feature growth. Emphasize automated regression tests, property-based testing, and observability to catch issues early, and discuss how you'd prioritize tests based on risk and usage.
Pro tip: Frame your answer around building a test pyramid with fast unit tests, targeted integration tests, and a few end-to-end tests, and mention that you'd invest in test infrastructure and tooling to keep feedback loops short as the codebase grows.
Ask questions to understand what the engine does, its inputs/outputs, and what 'correct' means (e.g., functional correctness, performance, edge cases). This ensures your testing strategy is aligned with the actual requirements.
Start with unit tests for core components, integration tests for interactions, and a few end-to-end tests for critical paths. Use these as a safety net for future changes.
Introduce property-based testing to cover edge cases, contract tests for interfaces, and fuzzing for robustness. Automate regression tests and run them in CI to catch regressions quickly.
Add observability (logging, metrics, tracing) to detect issues in production. Use canary releases and feature flags to limit blast radius. Continuously refine tests based on bugs found and new features.
Regularly review test coverage and flakiness. Prioritize tests for high-risk areas and refactor tests as the code evolves to keep them fast and reliable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.