I went straight to a tree structure with a parent_id foreign key and spent too long on the basic hierarchy before they nudged me toward permissions.
Start by clarifying requirements and scale, then propose a normalized schema with a nodes table for files and folders, using a parent_id foreign key to represent hierarchy. Explain how permission inheritance works by traversing ancestors or using materialized paths, and discuss trade-offs between simplicity and performance for operations like listing, moving, and permission checks.
Pro tip: Mention that permission inheritance can be optimized with a closure table or path enumeration to avoid recursive queries, and highlight the need for efficient permission checks at scale, possibly with caching or denormalization.
Ask about expected scale (number of users, files, depth of hierarchy), read/write patterns, and consistency requirements. This informs schema and indexing choices.
Propose a nodes table with id, name, type (file/folder), parent_id, owner_id, and timestamps. Use a self-referential foreign key for parent-child relationships.
Introduce a permissions table linking users/groups to nodes with roles (e.g., read, write, admin). Explain that permissions inherit down the tree unless overridden.
Discuss techniques like materialized paths, closure tables, or nested sets to efficiently query descendants, ancestors, and inherited permissions.
Compare normalization vs. denormalization, recursive CTEs vs. precomputed paths, and caching strategies for permission checks. Mention sharding or partitioning if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining the core entities (users, groups, links, domains) and the permission hierarchy (owner > editor > commenter > viewer). Then explain how permissions are resolved when multiple sources apply, emphasizing the principle of least privilege and explicit overrides. Finally, discuss the data model and API design that supports efficient permission checks and sharing operations.
Pro tip: Mention that permission checks should be centralized in a single authorization service to avoid inconsistencies, and that you'd use bitwise flags or a similar compact representation for permission levels to enable fast checks and easy extensibility.
Clearly outline the four permission levels (owner, editor, commenter, viewer) and what actions each allows. Explain that owner has full control including sharing and deletion, editor can modify content, commenter can add comments but not edit, and viewer can only read.
Describe the different entities that can be granted permissions: individual users, groups, public links, and entire domains. Explain how each target type is represented in the data model and how permissions are assigned to them.
Explain how to resolve permissions when a user is affected by multiple grants (e.g., direct user grant, group membership, domain-wide grant). Describe the precedence rules, such as explicit user grant overriding group grant, and how to handle conflicts (e.g., most permissive wins or most restrictive wins).
Outline the database schema for storing permissions (e.g., a permissions table with resource_id, grantee_type, grantee_id, permission_level) and the API endpoints for sharing (e.g., POST /resources/{id}/share with grantee and permission). Mention the need for efficient queries to check permissions.
Discuss edge cases like link expiration, domain verification, permission inheritance for nested resources, and revocation. Emphasize security considerations such as avoiding privilege escalation and ensuring audit logs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Probably the hardest part of the whole interview.
Start by clarifying the ACL model (e.g., inheritance, explicit deny/allow, precedence rules) and the desired semantics for propagation. Then describe a recursive or iterative algorithm that updates inherited permissions while preserving explicit overrides, and discuss trade-offs like performance, consistency, and conflict resolution.
Pro tip: Mention that explicit overrides should take precedence, but also consider how to handle conflicts when a parent's ACL change would otherwise remove access—sometimes you need to re-evaluate effective permissions or notify stakeholders.
Ask about the ACL system: inheritance rules, explicit vs inherited permissions, deny/allow precedence, and whether changes should be atomic or eventual. Confirm the expected behavior for child overrides.
Propose a traversal (DFS/BFS) that updates inherited permissions on children, skipping or merging with explicit overrides. Consider whether to recompute effective permissions or store deltas.
Explain how to resolve conflicts: explicit overrides win, but if a parent change would revoke access, decide whether to keep the override, flag it, or require manual review. Discuss deny vs allow precedence.
Discuss optimizations: lazy propagation, caching effective permissions, batching updates, or using a permission graph. Mention trade-offs between consistency and latency.
Cover edge cases: deep hierarchies, concurrent modifications, circular references, and rollback. Explain how to ensure atomicity or eventual consistency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I leaned hardest on what I knew.
Start by clarifying the access control model (e.g., RBAC, ABAC, ReBAC) and the latency/scale requirements. Then compare denormalized permission caches and relationship-tuple-based systems (like Zanzibar) in terms of data structures, consistency, and performance. Finally, discuss trade-offs and propose a hybrid approach if appropriate.
Pro tip: Emphasize that access control is a read-heavy, latency-sensitive problem, so caching and precomputation are key. Mention that relationship-tuple systems like Zanzibar use graph traversal with memoization to achieve low latency at scale.
Ask about the access control model, expected QPS, latency SLA, consistency requirements, and scale of users/resources.
Explain how permissions can be precomputed and stored in a fast key-value store (e.g., Redis) with a key like user:resource, and how invalidation works on policy changes.
Explain the Zanzibar model: tuples like (object, relation, user), stored in a graph database, with recursive expansion and memoization to answer queries.
Discuss latency, consistency, scalability, and complexity: caches are fast but can be stale; tuple systems are flexible but may have higher latency due to graph traversal.
Suggest combining both: use tuple system as source of truth and cache computed permissions with short TTL or event-driven invalidation for low latency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: what events need to be logged, who needs access, retention policies, and compliance needs. Then propose a high-level architecture that captures events, stores them immutably, and provides efficient querying and alerting. Finally, dive into data modeling, scalability, and security considerations.
Pro tip: Emphasize that audit logs must be tamper-evident and immutable; suggest using append-only storage with cryptographic hashing or a ledger database. Also, discuss how to handle high write throughput without impacting primary application performance, e.g., via asynchronous logging.
Ask about the scope: which events (file access, permission changes), volume, retention period, compliance standards (e.g., SOC2, GDPR), and who will consume the logs (admins, auditors, automated systems).
Propose a pipeline: event producers (file service, auth service) -> message queue (e.g., Kafka) -> log processor -> storage (e.g., append-only DB, data lake) -> query/alerting interface. Ensure decoupling for scalability and fault tolerance.
Define the schema for audit events: timestamp, user ID, action type, resource ID, old/new permissions, IP, user agent, status, etc. Consider indexing for efficient queries (e.g., by user, resource, time).
Choose storage that supports immutability (e.g., WORM storage, blockchain-like hash chain) and scalability. Implement retention policies (e.g., hot storage for recent, cold for archival) and ensure compliance with legal holds.
Ensure logs are encrypted at rest and in transit, and access is restricted via RBAC. Implement tamper detection (e.g., checksums, digital signatures) and audit the auditors.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They explicitly said 'briefly' and I still rambled for four minutes.
Start by clarifying requirements (file types, size, sync frequency, offline support) and then propose a high-level architecture that separates storage (e.g., object storage with metadata DB) from sync (e.g., delta sync with versioning) and conflict resolution (e.g., last-write-wins with vector clocks or CRDTs). Emphasize trade-offs between consistency, latency, and complexity, and tie choices back to HarveyAI's domain (legal AI) where data integrity and auditability are critical.
Pro tip: Mention that conflict resolution should be domain-specific: for legal documents, you might prefer manual merge or version branching over automatic LWW to avoid data loss. Also, highlight the importance of idempotent operations and client-side retries for reliability.
Ask about file types, sizes, expected sync frequency, offline support, and consistency needs. This shows you don't jump to solutions without understanding the problem.
Propose using object storage (e.g., S3) for blobs and a metadata database (e.g., PostgreSQL) for file info, versioning, and permissions. Discuss encryption, durability, and access control.
Outline a sync protocol: clients track local changes, send deltas to server, and receive updates via push (WebSocket) or pull (polling). Use version vectors or timestamps to detect changes.
Explain strategies: last-write-wins (simple but lossy), operational transforms (for text), or CRDTs (for automatic merge). For legal docs, consider manual resolution or version branching.
Compare consistency vs. availability, latency vs. bandwidth, and complexity vs. correctness. Mention how the design scales with number of users and file sizes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.