This one sprawled in ways I didn't expect.
Start by defining a clear data model (e.g., records as dictionaries or structs) and a predicate representation (e.g., expression tree or lambda functions). Then describe a simple execution strategy: iterate over records, apply predicates, and optionally sort/project. Finally, discuss extensibility for AND/OR, sorting, and projection, emphasizing modularity and trade-offs.
Pro tip: Emphasize that the design should be extensible and maintainable: use composable predicates and a clear separation between parsing, planning, and execution. Mention that for in-memory engines, simplicity and performance (e.g., avoiding unnecessary allocations) are key trade-offs.
Represent records as objects or dictionaries with fields like id, name, age. Consider using a schema to enforce types and enable efficient field access.
Use a predicate interface or function that takes a record and returns a boolean. For AND/OR, compose predicates using logical combinators (e.g., AndPredicate, OrPredicate).
Execute by iterating over all records, applying the predicate, and collecting matches. For sorting, collect results and sort by specified fields; for projection, map each record to a subset of fields.
Design a query object that holds a predicate tree, sort criteria, and projection list. This allows easy addition of new operators and optimizations like predicate pushdown.
Mention trade-offs: simplicity vs. performance, e.g., full scan vs. indexing. For in-memory, consider indexing on frequently filtered fields (e.g., hash index on id) to speed up queries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.