I jumped straight to a hashmap and felt good about it for about two minutes.
Start by clarifying the data model and access patterns, then propose a normalized structure with unique identifiers for each entity and explicit parent-child links. For traversal, use a depth-first search with a visited set or a recursive aggregation that passes down a context to avoid double-counting. Emphasize that the solution should handle cycles and shared references gracefully.
Pro tip: Mention that in real systems like Stripe, you often need to aggregate across levels for reporting or risk checks, so the traversal should be efficient and avoid N+1 queries by using batched lookups or in-memory joins.
Ask about the expected scale, read/write patterns, and whether the hierarchy can have cycles or shared nodes. Confirm what 'double-counting' means in this context (e.g., same payment instrument linked to multiple accounts).
Propose a normalized structure: User { id, ... }, Account { id, userId, ... }, SubAccount/PaymentInstrument { id, accountId, ... }. Use unique IDs and explicit foreign keys. Consider adding a type field to distinguish sub-accounts from payment instruments.
For a given user, start from the user node, traverse to accounts, then to sub-accounts/payment instruments. Use DFS or BFS with a visited set to avoid revisiting nodes if cycles are possible. Alternatively, use recursive aggregation with memoization.
Write pseudocode for the traversal that collects or aggregates data. For example, a recursive function that takes a node and a visited set, processes the node, then recurses on children. Ensure each entity is processed exactly once.
Discuss time and space complexity (O(N) for N nodes). Address edge cases: empty hierarchy, cycles, shared nodes, and large fan-out. Suggest optimizations like caching or denormalization for read-heavy workloads.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.