I started with the obvious stuff, hashmaps keyed by user id pointing to sets of role ids, roles pointing to sets of permission strings.
Start by clarifying requirements and scale, then design a normalized data model with users, roles, permissions, and junction tables. Discuss efficient permission checks using caching or precomputed sets, and outline the core APIs for managing entities and assignments.
Pro tip: At Stripe, emphasize security and auditability: mention how you would log permission changes and ensure least privilege. Also, consider using an inverted index for fast permission checks.
Ask about expected number of users, roles, permissions, and query patterns. Determine if permissions are hierarchical or flat, and if real-time updates are needed.
Propose tables: users, roles, permissions, user_roles (many-to-many), role_permissions (many-to-many). Include indexes on foreign keys for efficient lookups.
Outline CRUD operations for users, roles, permissions, and assignment endpoints. Specify how to check permissions (e.g., GET /users/{id}/permissions/{permission}).
Discuss caching strategies (e.g., Redis) or precomputing user-permission mappings. Consider using bitmasks or sets for fast membership tests.
Mention audit logging, role hierarchy, and handling high read throughput. Discuss sharding or replication if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints, such as the depth of hierarchy, frequency of permission checks, and whether roles can have multiple parents. Then propose a data model (e.g., directed acyclic graph) and an algorithm to resolve permissions efficiently, discussing trade-offs between precomputation and on-the-fly resolution. Finally, address edge cases like cycles, caching, and consistency.
Pro tip: Mention that you would enforce acyclic constraints at write time and consider caching resolved permissions with invalidation on hierarchy changes, showing awareness of production concerns.
Ask about hierarchy depth, multiple inheritance, performance needs, and consistency requirements to scope the solution.
Represent roles as nodes in a directed acyclic graph (DAG) with parent-child relationships, ensuring no cycles.
Decide between precomputing effective permissions (e.g., via topological sort) or resolving on-demand with caching, weighing trade-offs.
Address cycle detection, cache invalidation, and efficient lookups for deep hierarchies or high query volume.
Compare approaches like materialized paths, closure tables, or graph databases, and justify your choice based on requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the trickiest part of the whole question.
Start by clarifying the requirements: what types of resources, how permissions are assigned, and the expected scale. Then propose a data model that captures resource-scoped permissions, such as a permission table with a resource_id column or a separate resource_permissions table. Discuss trade-offs between flexibility, performance, and complexity, and outline how to enforce these permissions in the authorization layer.
Pro tip: Mention that resource-scoped permissions can be modeled as a tuple (subject, action, resource) and that this aligns with common authorization frameworks like RBAC with resource qualifiers or ABAC. Also, consider how to handle wildcard or hierarchical resources (e.g., 'edit' on all resources of a type) to show foresight.
Ask questions to understand the scope: what resources need scoping, how permissions are granted (per user, per role), and the expected query patterns (e.g., checking if a user can edit a specific resource).
Suggest extending the existing permission model with a resource identifier. For example, add a resource_id column to the permissions table, or create a new table linking permissions to resources. Discuss normalization vs. denormalization.
Explain how the authorization check would work: given a user, action, and resource, query the permission store to see if a matching permission exists. Consider caching strategies for performance.
Compare approaches: a single table with nullable resource_id vs. separate tables for global and scoped permissions. Consider impact on query complexity, indexing, and migration.
Mention how to support wildcards (e.g., 'edit' on all resources of a type), hierarchical resources (e.g., parent-child), and bulk operations. Also discuss auditing and revocation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They saved this as a wrap-up discussion and it felt like a mini oral exam.
Start by framing the problem: you need to support permission checks across multiple extensions with O(1) amortized time. Then walk through your data structure choices (e.g., hash maps, bitsets, or tries) and explain how they enable constant-time lookups, including how you handle dynamic updates and amortization.
Pro tip: Emphasize that amortized O(1) often comes from careful design of update operations (like rehashing or path compression) and that you measure worst-case latency, not just average, to ensure consistent performance.
Restate the problem: permission checks must be O(1) amortized across all extensions, implying frequent reads and occasional writes. Ask about scale, update frequency, and consistency requirements.
Select structures like hash maps for direct key lookups, bitsets for compact permission flags, or tries for hierarchical permissions. Explain why each fits the access pattern.
Describe how lookups achieve constant time: e.g., precomputed permission sets, caching, or union-find with path compression. Discuss how updates (e.g., adding an extension) are handled to maintain amortized bounds.
Explain how the design extends to new extensions without degrading performance, such as using consistent hashing or sharding. Mention any trade-offs (memory vs. speed).
Provide amortized analysis (e.g., using potential method) and discuss edge cases like concurrent updates or permission revocation. Suggest monitoring and fallback strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.