← Perplexity Interview Insights
My first mistake was only tracking which tasks a given task depended on, not which tasks depended on it.
Model tasks as nodes in a directed acyclic graph (DAG) with edges representing dependencies. Use topological sorting and in-degree tracking to determine when tasks become READY, and propagate status changes (BLOCKED, READY, SUCCEEDED, FAILED) through the graph as tasks complete or fail. Discuss handling cycles, concurrency, and scalability.
Pro tip: Explicitly address how you would detect and handle cycles in the dependency graph, as real-world systems must prevent deadlocks. Also, mention idempotency and atomic status updates to ensure consistency in distributed or concurrent environments.
Ask about expected scale, concurrency needs, persistence, and whether tasks can be retried or have timeouts. Confirm the exact status transitions and whether dependencies are only success-based or also failure-based.
Represent tasks as nodes with attributes (id, status, dependencies) and build a directed graph. Use adjacency lists for dependencies and reverse adjacency for dependents to efficiently propagate status changes.
Specify rules: a task is BLOCKED if any dependency is not SUCCEEDED; it becomes READY when all dependencies are SUCCEEDED; if any dependency FAILS, it becomes FAILED (or BLOCKED depending on policy). On task completion, update dependents' in-degree and status accordingly.
Use a queue or priority queue to manage READY tasks. When a task finishes, decrement in-degree of dependents; if in-degree becomes zero and no failures, mark READY; if a failure occurs, cascade FAILED to all transitive dependents.
Handle cycles via DFS or Kahn's algorithm, discuss concurrency control (locks, optimistic updates), and consider distributed scheduling with message queues or databases for persistence and fault tolerance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.