Start by clarifying requirements and scale, then model the core entities (users, cameras, groups) and their relationships, focusing on how nested groups affect access. Propose a data model and access-check algorithm, discuss trade-offs (e.g., precomputed vs. on-the-fly), and address the reverse query (users with access to all cameras) with an efficient approach.
Pro tip: Emphasize that access control is a graph problem: nested groups form a DAG, and you need to handle cycles and inheritance carefully. Also, mention that the reverse query is essentially a set intersection problem, which can be optimized with precomputation or indexing.
Ask about expected number of users, cameras, groups, query volume, and latency requirements. Clarify whether access is granted via direct assignment, group membership, or both, and whether groups can be nested arbitrarily.
Define entities: User, Camera, Group, and relationships: user-group membership, group-group nesting (DAG), and access grants (user/group to camera). Consider using adjacency lists or closure tables for efficient traversal.
For 'does user U have access to camera C?', traverse the group hierarchy from U's groups (including nested) and check if any group has access to C. Discuss caching or precomputation for performance.
For 'which users have access to all cameras?', compute the intersection of user sets that have access to each camera. Discuss efficient set intersection using precomputed indexes or bitsets.
Compare on-the-fly traversal vs. precomputed access lists (materialized views). Address consistency, update propagation, and handling large-scale graphs (e.g., sharding, caching).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I said check for cycles on write using a DFS before committing the edge.
Start by clarifying the requirements: whether the hierarchy is a tree or DAG, and whether cycles are prevented at write time or detected later. Then propose a concrete algorithm (e.g., DFS with visited states or union-find) and discuss trade-offs like performance, concurrency, and data integrity.
Pro tip: Mention that cycle prevention should be enforced at the database level (e.g., using a recursive CTE or a closure table) to avoid race conditions, and that application-level checks alone are insufficient under concurrency.
Ask whether the hierarchy is a tree (single parent) or a DAG (multiple parents), and whether cycles must be prevented on every write or can be detected asynchronously. Also clarify expected scale and concurrency.
For a tree, a simple parent-pointer walk with a visited set works. For a DAG, use DFS with three-color marking (white/gray/black) or union-find for incremental edge additions. Explain the time and space complexity.
Propose enforcing the check within a transaction or using database constraints (e.g., a recursive CTE in a CHECK or a trigger) to prevent race conditions. Discuss optimistic vs. pessimistic locking.
Compare performance of different approaches (e.g., DFS per write vs. maintaining a closure table) and mention when to use each. Consider read-heavy vs. write-heavy workloads.
Conclude with a recommended solution and note edge cases like self-references, deep hierarchies, and batch updates. Mention monitoring and repair strategies for existing cycles.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Precomputing felt obviously better for read-heavy access checks but then they asked about incremental updates when a group changes.
Start by clarifying the access patterns and scale (read/write ratio, graph size, query latency requirements) before comparing precomputation vs on-demand traversal. Then walk through the tradeoffs in terms of latency, storage, consistency, and operational complexity, and propose a hybrid or adaptive strategy based on the specific use case.
Pro tip: Mention that the right answer often depends on the read/write ratio and the acceptable staleness of results—showing you can quantify the tradeoff (e.g., precompute for hot paths, traverse for cold or deep queries) demonstrates senior-level judgment.
Ask about graph size, update frequency, query patterns (depth, frequency), latency SLAs, and consistency needs. This grounds the tradeoff discussion in real numbers.
Explain how you would precompute transitive closures (e.g., materialized views, denormalized tables) and the costs: storage blowup (O(n^2) worst case), write amplification, and staleness.
Explain graph traversal (BFS/DFS) at query time, its costs: higher latency for deep graphs, CPU/memory per query, but always fresh and no storage overhead.
Contrast latency vs storage, read vs write performance, consistency vs freshness, and operational complexity. Use concrete examples or estimates.
Suggest caching hot results, precomputing for shallow depths or frequent queries, and falling back to traversal for deep or rare queries. Mention monitoring and invalidation strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about event-driven propagation with a message queue and eventual consistency.
Start by clarifying the scope: which systems consume authorization decisions (APIs, caches, services, UI) and what consistency guarantees are needed. Then propose a publish-subscribe or event-driven mechanism where group membership changes emit events that trigger cache invalidation and policy re-evaluation across all consumers, with idempotency and versioning to handle ordering and duplicates.
Pro tip: Emphasize the trade-off between strong and eventual consistency: for most authorization systems, eventual consistency with a short propagation delay is acceptable, but you must bound that delay and provide a way to detect and reconcile drift. Mentioning a reconciliation job or audit log shows operational maturity.
Ask about the number of services, expected propagation latency, consistency requirements (strong vs eventual), and failure modes. Identify all authorization consumers: API gateways, microservices, caches, and client-side UI.
Propose an event-driven approach: when group membership changes, publish an event to a message bus (e.g., Kafka, SNS/SQS). Subscribers (authorization services, caches) consume the event and invalidate or update their local state.
Include a version or timestamp in events to handle out-of-order delivery, and make consumers idempotent. Use a centralized authorization service (e.g., policy decision point) as the source of truth to avoid divergent logic.
Implement retries with dead-letter queues for failed event processing. Periodically run a reconciliation job that compares cached permissions with the source of truth and corrects drift.
Add metrics and alerts for event processing latency and cache staleness. Define an SLO for propagation delay and ensure the system meets it, with fallbacks (e.g., bypass cache on critical operations) if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a read-through cache keyed on user-camera pairs with TTL-based invalidation, plus a secondary index on group membership tables.
Start by clarifying the access-check query pattern and scale (e.g., read-heavy, low-latency, high cardinality of users/resources). Then propose a layered strategy: indexing for efficient lookups, caching for hot data, and invalidation for consistency, while discussing trade-offs like staleness vs. latency.
Pro tip: Emphasize that caching access decisions requires careful invalidation on permission changes; mention using a versioned cache key or short TTLs to bound staleness, and consider negative caching for denied checks to prevent repeated expensive lookups.
Ask about query volume, latency SLOs, data size, and how often permissions change. This determines whether to optimize for reads, writes, or both.
Propose composite indexes on (user_id, resource_id) or (resource_id, user_id) depending on query direction, and consider covering indexes to avoid table lookups. Mention partitioning for large datasets.
Cache at multiple levels: in-process (e.g., LRU) for hottest checks, distributed cache (e.g., Redis) for shared decisions, and possibly a read-through cache for user-role mappings. Discuss cache key design and negative caching.
Explain strategies like TTL-based expiry, event-driven invalidation on permission changes, and versioned cache keys. Discuss trade-offs between staleness and performance, and how to handle cache misses gracefully.
Compare with precomputed materialized views or denormalized permission tables. Discuss monitoring cache hit rates and index usage, and when to consider a dedicated authorization service (e.g., Zanzibar-style).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Sharded by user ID for the membership store, but then they pointed out that camera-centric queries cross shard boundaries.
Start by clarifying the service's current architecture and data model, then identify bottlenecks as users and cameras grow. Propose a sharding strategy based on natural partitioning (e.g., by customer or camera), and discuss scaling reads/writes, storage, and handling hot shards. Emphasize trade-offs and operational complexity.
Pro tip: Highlight the importance of choosing a shard key that avoids hotspots and supports future growth, and mention how you'd handle rebalancing and failure recovery. Showing awareness of operational burden (e.g., monitoring, backups) sets you apart.
Ask about the existing system design, data flow, and what 'scaling' means (users, cameras, video storage, queries). Identify the main bottlenecks (e.g., database writes, storage, bandwidth).
Propose sharding by customer/tenant or camera ID to distribute load evenly. Discuss trade-offs: tenant-based simplifies isolation but may cause hotspots; camera-based distributes better but complicates cross-camera queries.
Address scaling of video storage (e.g., object storage with tiering), metadata databases (sharded SQL/NoSQL), and streaming/processing (horizontal scaling with load balancers). Mention caching and CDNs for video playback.
Explain how to detect and mitigate hot shards (e.g., splitting shards, consistent hashing, dynamic rebalancing). Discuss automation and monitoring to maintain balance as usage patterns change.
Summarize trade-offs: complexity vs. scalability, consistency vs. availability, cost. Mention operational aspects: deployment, monitoring, backups, and disaster recovery for a sharded system.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Append-only audit log, time-stamped events for every membership change, separate read path from the main access-check service.
Start by clarifying the requirements: what events need auditing (view, export, share), retention period, and compliance needs. Then propose a high-level architecture with an append-only audit log, capturing immutable events with actor, camera, action, timestamp, and context. Discuss data modeling, storage choices, and how to ensure reliability and query performance.
Pro tip: Emphasize that audit logs must be tamper-evident and immutable, and mention partitioning by time and camera for efficient queries—this shows you understand real-world operational needs.
Ask about the scope of auditing (e.g., live view, playback, export), retention period, compliance standards (e.g., GDPR, HIPAA), and expected query patterns.
Specify the fields to capture: actor (user/service), action, resource (camera ID), timestamp, source IP, and outcome. Consider including a request ID for traceability.
Propose an append-only, immutable log (e.g., using a write-once store or blockchain-like hash chaining). Discuss partitioning by time and camera for scalability.
Explain how to efficiently query logs (e.g., indexing by actor, camera, time) and how to secure access to the audit logs themselves (e.g., role-based access, encryption).
Discuss ensuring no events are lost (e.g., async logging with retries, dead-letter queues) and implementing retention policies with archival and deletion.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.