My first instinct was to just walk up the tree and union the user sets as I go.
First, clarify the data model and constraints (e.g., hierarchy depth, read/write ratio, consistency needs). Then outline an algorithm for query-time ancestor traversal and compare it with a precomputed reverse index, discussing trade-offs in performance, storage, and maintenance. Finally, recommend an approach based on the expected workload.
Pro tip: Mention that the optimal solution depends on the read/write ratio and hierarchy depth; for read-heavy systems with shallow hierarchies, query-time traversal may suffice, but for deep hierarchies or frequent queries, a precomputed index is better. Also, consider caching or hybrid approaches.
Ask about hierarchy depth, read/write frequency, consistency requirements, and scale (number of users, accounts, assignments). This determines the suitable approach.
Explain how to walk up the ancestor chain from the given accountId, collecting all ancestor accountIds, then filter assignments for those accounts. Discuss time complexity O(depth + assignments) and potential optimizations like indexing on accountId.
Describe building a mapping from each account to all users with inherited roles, either by propagating assignments down the hierarchy or by precomputing ancestor sets. Discuss update complexity and storage overhead.
Analyze query-time traversal (simple, no extra storage, but slower for deep hierarchies and frequent queries) versus precomputed index (fast reads, but higher storage and update cost, and potential staleness).
Based on the constraints, recommend an approach or a hybrid (e.g., caching, materialized views) and explain why it fits the scenario.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.