← Anthropic Interview Insights
I started with the data model which felt straightforward, just adding a depends_on list of task ids.
Start by outlining the data model changes needed to represent dependencies, then explain how to enforce transition rules at the application and database levels, and finally discuss how to reject invalid state changes with clear errors. Emphasize trade-offs between simplicity and robustness, and consider concurrency and performance implications.
Pro tip: Mention that dependency checks should be atomic with the state transition to avoid race conditions, and consider using database constraints or transactions to enforce this. Also, discuss how to handle cycles in dependencies to prevent deadlocks.
Introduce a separate table or field to represent dependencies, such as a task_dependencies table with task_id and depends_on_task_id. Ensure it supports multiple dependencies and prevents self-references.
When a task is moved to in_progress or done, check that all its dependencies are in done state. This can be done in application code, but consider database triggers or constraints for stronger guarantees.
Use transactions with appropriate isolation levels to ensure that dependency checks and state updates happen atomically, preventing race conditions where a dependency might change concurrently.
Return clear error messages indicating which dependencies are not done. Optionally, log the attempt and consider whether to allow overriding with admin privileges.
Talk about performance implications of dependency checks, indexing strategies, and how to handle cycles. Mention possible extensions like soft dependencies or conditional dependencies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the problem as cycle detection in a directed graph, then propose an incremental algorithm like DFS with recursion stack or topological sort (Kahn's algorithm) that checks for cycles when adding a new edge. Finally, discuss where this logic should live in the system—likely in the service layer that manages task dependencies, with caching or precomputed reachability for performance.
Pro tip: Mention that you can optimize by checking if the new edge creates a cycle only if the source is reachable from the target; precomputing transitive closure or using union-find for undirected graphs can be alternatives, but for directed graphs, DFS with colors is standard. Also, consider concurrency and transactional integrity when adding edges.
Confirm that the dependency graph is directed and acyclic (DAG) and that adding an edge from task A to task B means A depends on B. Ask about scale, frequency of updates, and whether the graph is stored in a database or in-memory.
For a single edge addition, use DFS with a recursion stack (or colors: white, gray, black) to detect back edges. Alternatively, use Kahn's algorithm for topological sorting; if the new edge creates a cycle, the topological sort will fail.
Instead of running a full cycle check on the entire graph, check if there is a path from B to A (the target to the source). If such a path exists, adding A->B creates a cycle. Use bidirectional search or precomputed reachability for efficiency.
Place the cycle detection in the service layer that handles task dependencies, ideally within a transaction to ensure consistency. If the graph is large, consider a dedicated graph service or using a database with graph capabilities (e.g., Neo4j) or a cached reachability index.
Mention trade-offs between full graph traversal (O(V+E)) and incremental checks (O(V+E) worst-case but often faster). Address concurrency: use locks or optimistic concurrency control. Also, consider if the graph is too large for in-memory and how to handle persistence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.