I went with a deque per user key, dropping timestamps outside the window before each check.
Start by clarifying requirements: window semantics (fixed vs sliding), inclusivity of boundaries, and whether timestamps are monotonically increasing. Then design a per-user data structure (e.g., deque or sorted list) to track request timestamps, and for each request, evict expired timestamps and check the count against N. Discuss trade-offs between memory, time complexity, and distributed considerations.
Pro tip: Mention that in a real system like Roblox, you'd likely use a distributed cache (e.g., Redis) with atomic operations or a token bucket algorithm for scalability, but for this problem, focus on the core sliding window logic and edge cases.
Ask about window definition (e.g., [t-W, t) or (t-W, t]), whether timestamps are sorted, and if the stream is per-user or global. Confirm that each request is processed in order.
For each user, maintain a queue (deque) of timestamps of allowed requests within the current window. Alternatively, use a circular buffer or a balanced BST if out-of-order timestamps are possible.
For each request (user, t): remove timestamps from the front of the user's queue that are <= t - W (or < t - W depending on inclusivity). If the queue size < N, allow the request and append t; else deny.
Time: O(1) amortized per request (each timestamp added/removed once). Space: O(N) per user. Handle edge cases: exactly at boundary, multiple requests same timestamp, user with no history.
For distributed systems, consider using Redis sorted sets with ZREMRANGEBYSCORE and ZCARD, or a token bucket for smoother limiting. Mention trade-offs: sliding window is precise but memory-heavy; fixed window is simpler but allows bursts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where it got interesting and also where I tripped up.
Design a two-phase rate limiter that first checks both user and game limits atomically, then consumes capacity only if both checks pass. Use a sliding window or token bucket algorithm for each limiter, and ensure thread-safe operations to avoid race conditions. Discuss trade-offs between strict atomicity and performance, and consider distributed scenarios.
Pro tip: Mention that you would use a two-phase commit or a lock-free approach with atomic operations to ensure that capacity is not consumed if either check fails, and highlight the importance of idempotency and handling partial failures in distributed systems.
Ask about the expected scale (number of users, games, requests per second), latency requirements, and whether the system is distributed. Clarify if limits are per time window (e.g., per second) and if they are hard or soft limits.
Select appropriate rate limiting algorithms (e.g., sliding window log, sliding window counter, token bucket) for both user and game levels. Consider using a hash map for user limits and another for game limits, with efficient time-based eviction.
Outline a process: first, check if both user and game limits allow the request without consuming; if both pass, then consume from both. Ensure atomicity to prevent race conditions, possibly using locks or atomic operations.
Discuss how to handle concurrent requests in a single node (e.g., mutexes, atomic counters) and in a distributed system (e.g., Redis with Lua scripts for atomicity, or a centralized rate limiter service).
Compare approaches: strict atomicity vs. eventual consistency, performance overhead, and failure modes. Discuss edge cases like clock skew, partial failures, and how to handle retries without double-counting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about moving state to Redis, using sorted sets to replicate the deque logic, and acknowledged the race condition problem with check-then-act.
Start by restating the single-node rate limiter's data structures and guarantees, then identify what breaks when multiple nodes share state. Propose a distributed architecture (e.g., centralized Redis, sharded counters, or gossip-based) and explicitly discuss the trade-offs between consistency, latency, and accuracy. Conclude with how you'd handle failures and edge cases like clock skew and hot keys.
Pro tip: Mention that perfect global rate limiting is often unnecessary; instead, use a hybrid approach like local token buckets with periodic global synchronization to reduce coordination overhead while accepting slight over-admission. This shows you understand real-world trade-offs and can optimize for Roblox's massive scale.
Ask about scale (requests per second, number of nodes), latency tolerance, and whether strict global limits are required. This determines whether you need strong consistency or can accept eventual consistency.
Explain that in-memory counters and locks don't work across nodes; you need shared state or coordination. Mention issues like race conditions, network partitions, and clock skew.
Suggest using a centralized store like Redis with atomic operations (INCR, EXPIRE) or a distributed cache like Memcached. For higher scale, consider sharding counters by user or region, or using a gossip protocol for approximate counts.
Compare strong consistency (e.g., Redis with Lua scripts) vs eventual consistency (e.g., local buckets synced periodically). Highlight the latency vs accuracy trade-off and how to handle failures (e.g., fallback to local limits).
Cover hot keys (e.g., a single user hitting many nodes), clock synchronization (use NTP or logical clocks), and monitoring. Suggest techniques like consistent hashing to distribute load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.