I started with a dict of table names mapping to lists of row dicts, which felt right.
Start by clarifying requirements and defining the API, then design the data model and core operations (insert, query with projection, filter, sort). Implement a clean, modular solution with attention to edge cases and performance, and discuss trade-offs and possible optimizations.
Pro tip: Mention that you would use stable sorting to preserve insertion order for equal keys, and consider indexing to speed up filtering and sorting for large datasets.
Ask clarifying questions about expected operations, data types, concurrency, and performance. Define the public methods for insert, query, filter, and sort.
Choose an in-memory representation: a map from table names to tables, where each table stores rows as lists of dictionaries or objects. Consider schema flexibility and memory efficiency.
Implement insert by appending to the table's row list. Implement query by iterating rows, applying filters, projecting columns, and sorting the result.
Address missing tables/columns, type mismatches, empty results, and multiple sort keys. Discuss indexing, caching, or lazy evaluation for performance.
Walk through example scenarios, test correctness, and discuss trade-offs between simplicity and performance, and possible extensions like transactions or persistence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the query patterns and data characteristics, then propose an index structure (e.g., hash, B-tree, or trie) that balances lookup speed, memory overhead, and update cost. Explain how you would build and maintain the index in memory, and discuss trade-offs like concurrency, persistence, and eviction.
Pro tip: Emphasize that in-memory indexes must be designed for the workload: point lookups favor hash indexes, range queries favor balanced trees, and prefix searches favor tries. Also mention that index maintenance (inserts/updates) can be as important as query speed.
Ask about query types (point, range, prefix), data size, read/write ratio, and latency/throughput goals. This determines the index type and whether multiple indexes are needed.
Select a structure that matches the workload: hash map for O(1) point lookups, balanced tree (e.g., red-black, B-tree) for range queries, trie for prefix searches, or a combination. Consider memory overhead and cache efficiency.
Explain how to build the index initially (e.g., scan data and insert) and keep it updated on writes. Discuss concurrency control (locks, copy-on-write, lock-free) and memory management (eviction, compaction).
Discuss trade-offs: memory vs. speed, update cost vs. query speed, and complexity. Mention optimizations like partial indexes, composite keys, or adaptive indexing based on access patterns.
Propose how to measure performance (benchmarks, profiling) and iterate on the design. Mention fallback strategies if the index becomes a bottleneck.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.