The schema flexibility part is what tripped me up at first.
Start by clarifying requirements and constraints, then propose a data model that balances flexibility and performance, such as a table of rows where each row is a map of column names to values, plus inverted indexes for fast lookups. Implement the core operations (insert, find-by-column, find-by-column-value) with clean interfaces, and write comprehensive tests covering edge cases like missing columns, duplicate values, and empty results.
Pro tip: Discuss trade-offs between different indexing strategies (e.g., full scan vs. inverted index) and mention how you would handle schema evolution or type consistency, showing you think beyond the basic implementation.
Ask about expected data volume, read/write ratio, concurrency needs, and whether columns have types. This ensures your design meets the actual use case.
Propose representing each row as a dictionary (or map) of column names to values, stored in a collection (e.g., list or hash map keyed by row ID). Consider adding inverted indexes for columns to speed up lookups.
Write methods for insert (add a new row), find-by-column (return all rows that have a given column), and find-by-column-value (return all rows where a specific column equals a given value). Use indexes if available, otherwise fall back to scanning.
Create tests for basic functionality, edge cases (empty database, non-existent column, duplicate values), and performance (if applicable). Include tests for mixed schemas and null/empty values.
Mention how you would handle updates/deletes, concurrency, persistence, or more complex queries (e.g., range queries). Highlight the trade-offs of your design choices.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and constraints, then propose a design that balances flexibility and performance, such as a bitmap index or inverted index. Discuss tradeoffs between insert and query costs, and explain how to handle range queries and missing columns with appropriate data structures and query planning.
Pro tip: Demonstrate awareness of real-world constraints by mentioning that no single solution fits all; propose a hybrid approach and discuss how you'd measure and adapt based on workload characteristics.
Ask about data volume, query patterns, latency requirements, and update frequency to understand the problem space. This ensures your design aligns with actual needs.
Suggest using bitmap indexes, inverted indexes, or columnar storage to support arbitrary predicate combinations. Explain how each structure enables efficient filtering.
Discuss how index maintenance affects write throughput and how query performance benefits from precomputed structures. Mention techniques like LSM trees or write-optimized indexes for heavy write workloads.
For range queries, propose ordered indexes (e.g., B-trees) or zone maps. For missing columns, discuss sparse indexes, default values, or null handling strategies.
Conclude with a recommended approach based on the clarified requirements, highlighting how it addresses the tradeoffs and edge cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.