I started with the basic follow graph using a dict of sets, which was fine.
Start by clarifying requirements and scale, then propose a hybrid storage model: an append-only event log for timestamped follow events and a graph index for efficient point-in-time and 2-hop queries. For point-in-time queries, use a temporal index (e.g., interval trees or versioned adjacency lists) to check if A followed B at time t. For 2-hop recommendations, precompute or compute on-the-fly mutual connections using the graph index, ranking candidates by the count of mutual intermediaries.
Pro tip: Emphasize the trade-offs between storage, query latency, and consistency, and mention how you would handle scale (e.g., sharding by user ID) and updates (e.g., using a lambda architecture with batch and speed layers).
Ask about expected read/write throughput, latency requirements, and whether the system needs to support historical queries at scale. Clarify if the 2-hop recommendation should be real-time or can be precomputed.
Propose an append-only log of follow events (A, B, timestamp) and a temporal index to support point-in-time queries. Discuss options like interval trees, versioned adjacency lists, or bitemporal modeling.
Explain how to check if A followed B at time t using the temporal index. For example, store for each (A,B) pair a list of intervals when the follow was active, and binary search for t.
Describe how to find candidates who are followed by people that A follows (2-hop). Use the graph index to get A's followees, then their followees, and count mutual intermediaries. Rank by count.
Discuss sharding, caching, and precomputation strategies. Mention trade-offs between consistency and latency, and how to handle updates to the graph.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: snapshot isolation, efficient get_followers, and top-k recommendations. Then propose a versioned graph design using copy-on-write or persistent data structures, and outline algorithms for each operation with complexity analysis. Finally, discuss trade-offs between memory, latency, and consistency.
Pro tip: Emphasize that snapshots should be immutable and cheap to create, ideally O(1) by sharing structure. Mention that top-k recommendations on a snapshot can leverage precomputed follower counts or approximate algorithms to balance accuracy and speed.
Ask about expected scale (users, follows), snapshot frequency, read/write patterns, and latency/consistency requirements. Confirm that snapshots are read-only and that follow() operations after snapshot creation should not affect it.
Propose a versioned or persistent data structure (e.g., copy-on-write, immutable adjacency lists, or versioned edges with timestamps). Explain how to create a snapshot in O(1) or O(log n) by capturing a version pointer or root node.
For a given user and snapshot, retrieve followers efficiently. Suggest storing reverse edges (follower lists) per user per version, or using a persistent hash map. Discuss indexing and caching strategies to achieve O(1) or O(log n) lookup.
Define a scoring function (e.g., mutual follows, PageRank, or follower count) and compute top-k users. Propose algorithms: precompute scores per snapshot, use heap-based selection, or approximate methods like count-min sketch for large-scale. Analyze time/space complexity.
Compare approaches: memory overhead vs. snapshot creation speed, exact vs. approximate top-k, and consistency guarantees. Suggest optimizations like incremental updates, lazy evaluation, or compression for old snapshots.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about storing deltas between snapshots instead of full copies, and copy-on-write using immutable frozen sets.
Start by clarifying the system's requirements and constraints, then outline a scalable architecture that addresses bottlenecks. For the tradeoff question, compare deep-copying snapshots with alternatives like copy-on-write, incremental snapshots, and reference counting, discussing their impact on performance, consistency, and cost.
Pro tip: Demonstrate awareness of OpenAI's scale and real-time constraints by emphasizing latency and consistency tradeoffs, and mention how you'd measure and monitor the chosen approach in production.
Ask questions to understand the expected read/write patterns, consistency needs, latency requirements, and budget. This ensures your scaling strategy is grounded in reality.
Analyze where the system will break under load (e.g., database, compute, network) and determine whether to scale horizontally or vertically, and which components need partitioning or caching.
Outline a high-level design that handles millions of users, such as sharding, replication, load balancing, and asynchronous processing. Mention specific technologies if relevant.
Explain deep-copying snapshots and alternatives like copy-on-write, incremental snapshots, and log-based replication. Discuss tradeoffs in terms of memory, I/O, consistency, and complexity.
Choose an approach based on the requirements, and justify it by weighing the tradeoffs. Mention how you would validate and monitor the solution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.