I started with a naive list-of-dicts approach per table and it felt too simple, so I second-guessed myself mid-explanation and started overcomplicating the schema representation.
Start by clarifying requirements and constraints, then design a simple but extensible schema and query execution pipeline. Focus on modular components: storage, parser, planner, and executor, and discuss trade-offs for each. Implement a basic version and iterate based on feedback.
Pro tip: Demonstrate awareness of real-world constraints by discussing how to handle concurrent access and memory management, and propose a minimal viable product first before optimizing.
Ask about expected data volume, query complexity, concurrency needs, and performance targets to scope the design appropriately.
Define how tables, rows, and columns are represented in memory, considering indexing and data types for efficient filtering and sorting.
Outline the steps from parsing the query to producing results: parse, validate, plan (filter, project, sort), and execute.
Code insert and query operations, starting with a simple in-memory structure and basic filter/projection, then add sorting.
Talk about performance optimizations (e.g., indexing), concurrency, persistence, and how to extend the system for more complex queries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the conversation got interesting.
Start by clarifying the current database architecture and query patterns, then propose a pluggable index abstraction that supports different index types (e.g., B-tree, hash) for equality and range queries. Explain how indexes integrate with the query planner and executor to accelerate WHERE filtering and ORDER BY, and discuss trade-offs like write overhead and memory usage.
Pro tip: Emphasize the importance of a cost-based optimizer that can choose between index scans and full scans based on statistics, and mention that indexes should be optional and configurable per column to avoid unnecessary overhead.
Ask about the current data model, query workload (read vs write heavy), and performance goals to tailor the index design. Confirm whether indexes should be persistent or in-memory only.
Propose a generic Index interface with methods for insert, delete, update, and lookup. For WHERE filtering, consider hash indexes for equality and B-trees or skip lists for range queries; for ORDER BY, B-trees naturally support ordered traversal.
Modify the query planner to recognize when an index can be used (e.g., matching predicates on indexed columns) and generate index scan plans. For ORDER BY, use the index to avoid sorting if the order matches the index order.
Ensure indexes are updated atomically with data modifications, and handle concurrent access with appropriate locking or MVCC. Discuss write amplification and strategies to mitigate it (e.g., batch updates).
Discuss trade-offs: memory overhead, write latency, and maintenance cost. Mention advanced topics like composite indexes, covering indexes, and adaptive indexing based on workload.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.