← Pinterest Interview Insights
Started with BFS on hasAccess and felt pretty good about it until they pushed on what happens when the graph gets large and hasAccess is called constantly.
First, clarify the requirements: is the graph static or dynamic? What are the expected query patterns? Then, model the access control as reachability in a directed graph. For grantAccess, add a directed edge from 'from' to 'to'. For hasAccess, perform a reachability query from user to resource. Discuss trade-offs between precomputing transitive closure (fast queries, slow updates) and on-the-fly traversal (slow queries, fast updates), and propose a hybrid approach if needed.
Pro tip: Mention that in real systems, access control often involves more than simple reachability—consider inheritance, negative permissions, and revocation. Also, discuss how to handle cycles and ensure scalability with caching or incremental updates.
Ask about graph size, update frequency, query frequency, and whether access can be revoked. Determine if the graph is static or dynamic, and if there are constraints like cycles.
Represent the graph using adjacency lists (e.g., HashMap<Node, Set<Node>>) for efficient edge addition and traversal. For precomputed reachability, consider a transitive closure matrix or bitsets.
Add a directed edge from 'from' to 'to'. If using precomputation, update the transitive closure incrementally (e.g., for all ancestors of 'from', add all descendants of 'to').
If using on-the-fly traversal, perform BFS/DFS from user to resource. If using precomputed closure, do a constant-time lookup. Discuss caching frequent queries.
Compare time/space complexity of approaches. For dynamic graphs, consider incremental algorithms or hybrid approaches. Address scalability, concurrency, and persistence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's requirements: scale, consistency needs, and access patterns. Then compare on-the-fly reachability computation (e.g., graph traversal) with precomputed closure (e.g., materialized transitive closure), discussing trade-offs in latency, storage, and update cost. Finally, propose a hybrid or adaptive solution based on the specific context.
Pro tip: Emphasize that the choice depends on the read/write ratio and consistency requirements; for Pinterest's scale, a hybrid approach with caching and incremental updates often balances performance and freshness.
Ask about scale, read/write patterns, consistency needs, and latency SLAs to ground your answer in the specific system.
Describe how revocation works in each model: on-the-fly checks traverse the graph per request, while precomputed closure updates the stored closure upon revocation.
Discuss latency, storage, update cost, and consistency: on-the-fly is simple but slow for deep graphs; precomputed is fast for reads but expensive to update and store.
Recommend a hybrid approach, such as caching reachability results with TTL and invalidating on revocation, or using incremental updates for the closure.
Mention handling of cycles, large fan-outs, and eventual consistency, and how to monitor and adapt the solution over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.