I started with a sorted set in Redis and explained why a skiplist makes inserts and rank queries both fast.
Start by clarifying requirements and constraints, then design a minimal single-server solution using an in-memory data structure like a sorted set or heap to track top-K scores. Discuss trade-offs of this approach and outline how it would evolve to handle scale, but keep the initial design simple and focused.
Pro tip: Emphasize that you're starting simple to validate the core functionality and avoid over-engineering; mention that you'd instrument the system to measure performance and guide future scaling decisions.
Ask questions to understand expected traffic, score update frequency, read patterns, and whether scores are per-user or per-item. Confirm that social filtering is out of scope.
Propose an in-memory data structure (e.g., a balanced BST, skip list, or heap) to maintain top-K scores. Describe the API for submitting scores and retrieving the leaderboard.
Discuss time and space complexity of operations, and trade-offs between different data structures. Consider persistence and recovery on restart.
Outline how the design would scale with increased traffic, such as adding caching, sharding, or using a distributed store like Redis sorted sets.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the current scale and requirements, then propose a partitioned and replicated architecture that separates read and write paths. Discuss trade-offs between consistency, latency, and complexity, and justify choices based on Reddit's specific needs.
Pro tip: Mention that leaderboards often have skewed access patterns (e.g., top 100 users get most reads), so consider caching hot entries and using read replicas to offload the primary. Also, discuss how to handle rank updates efficiently, perhaps with a write-optimized store and periodic batch updates to the read-optimized store.
Ask about expected read/write QPS, latency requirements, consistency needs (e.g., eventual vs strong), and data size. This shows you understand the problem before jumping to solutions.
Propose partitioning by user ID or leaderboard ID to distribute load. Discuss hash-based partitioning for even distribution and range-based for efficient range queries, considering the trade-offs.
Suggest using leader-follower replication for read scalability and fault tolerance. Mention multi-leader or leaderless replication if cross-region writes are needed, and discuss consistency implications.
For reads, use caching (e.g., Redis) for hot leaderboards and read replicas. For writes, consider batching, write-behind caching, or using a write-optimized store like Cassandra, then asynchronously updating the read store.
Discuss trade-offs between consistency and availability (CAP theorem), and how to handle rebalancing, hot partitions, and failover. Mention monitoring and auto-scaling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Wasn't expecting this variant and it showed.
Start by clarifying the existing leaderboard design and assumptions, then identify the new requirements: friend relationships and per-user top-K queries. Propose a data model for friendships and an efficient way to compute top-K among friends, discussing trade-offs between precomputation and on-demand queries.
Pro tip: Mention that friend leaderboards are read-heavy and per-user, so caching or precomputing per-user results (e.g., using a fan-out approach) can drastically reduce latency, but be mindful of the write amplification and storage cost.
Confirm the scale (number of users, friends per user, K), read/write patterns, and whether friend relationships are bidirectional. Also check if scores update frequently and if real-time accuracy is required.
Design a storage schema for friendships, such as an adjacency list (user_id, friend_id) with appropriate indexing. Consider using a graph database or a distributed store like Cassandra for scalability.
Propose an approach to retrieve top-K scores from a user's friends. Options include: (a) on-demand: fetch friend IDs, then query their scores and merge; (b) precomputed: maintain a per-user leaderboard updated on score changes or friend changes.
Compare on-demand vs. precomputed based on latency, consistency, and cost. For Reddit-scale, a hybrid approach (e.g., precompute for active users, on-demand for others) or using a cache with TTL might be optimal.
Discuss how to handle score updates and friend additions/removals. For precomputed, consider fan-out on write; for on-demand, use efficient merging (e.g., heap) and caching. Also mention sharding and replication.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.