My first instinct was to just pass a callback predicate and call it a day, but I could tell they wanted something more structured.
Start by defining a discriminated union for the condition object, where each variant specifies a field and a comparison operator, ensuring type safety. Then, implement a generic filter function that uses a type guard to narrow the Sim type based on the condition, and apply the appropriate comparison logic. Finally, discuss trade-offs between simplicity and extensibility, and mention potential performance considerations.
Pro tip: Demonstrate awareness of TypeScript's type system by using a mapped type to derive the condition type from the Sim type, ensuring that any changes to Sim automatically update the condition type. Also, mention that for production code, you'd consider using a library like zod for runtime validation if conditions come from external sources.
Clearly define the Sim interface with fields like age, occultType, and salary. Then, define a discriminated union for the condition, where each variant includes a field, an operator (e.g., 'eq', 'gt', 'lt'), and a value of the appropriate type.
Write a generic function that takes an array of Sim and a condition, and returns a filtered array. Use a switch on the condition's field or operator to apply the correct comparison, ensuring type safety by leveraging TypeScript's narrowing.
If needed, use a type guard to narrow the Sim type based on the condition, especially if the condition can be on a field that might have different types. Consider making the function generic over the field to maintain type safety.
Talk about the trade-offs between a simple switch-based approach and a more extensible one using a map of operators. Mention how to extend the condition type to support multiple conditions or logical operators.
Mention that the function should handle empty arrays and invalid conditions gracefully. Discuss performance implications of filtering large arrays and potential optimizations like early termination or indexing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.