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I went with sliding window counter because it smooths out burst traffic better than fixed window without the memory cost of a full log.
Start by clarifying requirements: per-key limits, configurable window, and expected scale (QPS, number of keys). Then compare common algorithms (fixed window, sliding window, token bucket, leaky bucket) with trade-offs, and choose one (e.g., sliding window counter or token bucket) that balances accuracy, memory, and performance. Finally, discuss distributed implementation using Redis or a similar store, including atomic operations and handling of edge cases like clock skew and hot keys.
Pro tip: Mention that rate limiting is often implemented at multiple layers (e.g., edge, service, and per-user) and that you should consider returning standard headers like X-RateLimit-Remaining and Retry-After to help clients. Also, highlight the importance of monitoring and dynamically adjusting limits based on traffic patterns.
Ask about scale (requests per second, number of unique keys), latency requirements, and whether limits are global or per-region. Confirm if the system needs to be highly available and if eventual consistency is acceptable.
Briefly explain fixed window, sliding window log, sliding window counter, token bucket, and leaky bucket. Discuss their pros and cons in terms of memory usage, accuracy, and burst handling.
Choose one algorithm (e.g., sliding window counter for a good balance) and explain why it fits the requirements. Mention how it handles bursts and its memory footprint.
Describe how to store counters in a distributed cache like Redis, using atomic operations (e.g., INCR, EXPIRE) or Lua scripts for atomicity. Discuss sharding by key to distribute load and handling of hot keys.
Cover clock skew, race conditions, failure modes (e.g., Redis down), and how to degrade gracefully. Mention monitoring, logging, and dynamic configuration updates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the rate limiter's requirements (e.g., algorithm, distributed vs. single-node) and then explain the concurrency challenges for a shared key. Describe how you would use synchronization primitives (locks, atomic operations) or lock-free data structures to ensure thread safety, and discuss trade-offs like contention and performance.
Pro tip: Mention that in distributed systems, thread safety also requires coordination across nodes (e.g., using Redis with Lua scripts or a centralized store), and highlight the importance of choosing the right granularity of locking to avoid bottlenecks.
Ask whether the rate limiter is single-node or distributed, and which algorithm (token bucket, sliding window, etc.) is used. This determines the concurrency strategy.
Explain that the counter or timestamp for a key is shared mutable state, and concurrent updates can lead to lost updates or incorrect limits.
Propose using per-key locks (e.g., striped locks), atomic operations (e.g., compare-and-swap), or lock-free data structures to ensure atomicity. Discuss trade-offs between simplicity and performance.
If distributed, describe using a centralized store like Redis with atomic operations (INCR, Lua scripts) or a consensus protocol to coordinate across nodes.
Discuss contention, lock granularity, and potential bottlenecks. Mention optimizations like sharding, local caching with periodic sync, or using approximate algorithms.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the problem: at high QPS, a single-node limiter becomes a bottleneck, so you need to shard state across nodes while ensuring correctness. Discuss sharding strategies (e.g., by user ID, API key, or region), distributed coordination (e.g., using Redis with Lua scripts, or a gossip protocol), and trade-offs between consistency and performance (e.g., eventual vs strong consistency).
Pro tip: Emphasize that perfect global consistency is often unnecessary; instead, use techniques like local rate limiting with periodic sync or probabilistic data structures to achieve approximate limits with high performance. This shows you understand real-world trade-offs.
Ask about the expected QPS, latency requirements, consistency needs (hard vs soft limits), and failure tolerance. This sets the stage for design decisions.
Decide how to partition limiter state: by user ID, API key, IP, or a combination. Consider consistent hashing to distribute load evenly and minimize rebalancing.
Select a coordination mechanism: centralized store (Redis, etcd) with atomic operations, or decentralized (gossip, CRDTs). Discuss how to handle node failures and network partitions.
Analyze trade-offs: strong consistency (e.g., via consensus) ensures accurate limits but adds latency; eventual consistency or local limiting improves performance but may allow temporary over-limit. Choose based on business needs.
Explain how to scale horizontally (add shards), handle hot shards, and monitor for correctness (e.g., drift in counters). Mention fallback strategies like local rate limiting during outages.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.