I started with fixed window because it's easy to explain, but they pushed back pretty fast asking what happens at the boundary.
Start by clarifying requirements: per-user or per-API-key limits, distributed environment, accuracy vs. performance trade-offs. Then compare algorithms like token bucket, leaky bucket, fixed window, sliding window log, and sliding window counter, discussing their pros and cons. Finally, propose a scalable design using a distributed store like Redis, and address edge cases like synchronization and failure modes.
Pro tip: Emphasize that the choice of algorithm depends on the specific requirements (e.g., burst tolerance, memory constraints) and that a hybrid approach (e.g., token bucket with sliding window) can often balance trade-offs. Also, mention the importance of monitoring and dynamic adjustment of limits.
Ask about scale (requests per second, number of users), distribution (single vs. multiple servers), accuracy needs, and whether bursts are allowed. This sets the context for algorithm selection.
Explain token bucket, leaky bucket, fixed window, sliding window log, and sliding window counter. For each, describe how it works, its pros and cons (e.g., memory usage, burst handling, accuracy).
Highlight trade-offs: memory vs. accuracy, burst tolerance vs. smoothness, simplicity vs. precision. Relate these to the requirements from step 1.
Propose using a centralized data store like Redis with atomic operations (e.g., Lua scripts) to enforce limits across multiple servers. Discuss sharding, replication, and consistency.
Cover handling of race conditions, clock skew, failure of the rate limiter (fail-open vs. fail-closed), and how to scale the rate limiter itself (e.g., local caching with periodic sync).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
API gateway vs sidecar vs in-process library.
Start by outlining the possible placements in the request path (client, edge, gateway, service, backend) and then compare them based on trade-offs like latency, accuracy, scalability, and complexity. Emphasize that the optimal choice depends on the specific requirements and architecture, and often a combination is used.
Pro tip: Mention that rate limiting at the edge is great for DDoS protection but can be bypassed, while service-level limiting offers granular control but adds overhead; a layered approach is often best.
List the common locations: client-side, CDN/edge, API gateway, individual services, and backend datastore. Briefly describe each.
For each option, discuss advantages (e.g., early rejection, reduced load) and disadvantages (e.g., limited context, added latency, complexity).
Tie the choice to factors like scale, security needs, accuracy, and existing infrastructure. For example, if you need per-user limits, service-level might be necessary.
Propose a layered approach or a specific placement based on the scenario, and justify why it balances the trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Redis with atomic Lua scripts was my go-to answer.
Start by clarifying requirements (scale, latency, accuracy) and then propose a centralized store like Redis with atomic operations (e.g., Lua scripts) as the primary solution. Discuss trade-offs of alternatives (e.g., gossip, local buckets) and explicitly address consistency models and clock skew mitigation techniques.
Pro tip: Mention that using Redis with Lua scripts ensures atomicity and avoids clock skew, but also discuss how to handle Redis failures with fallback strategies like local rate limiting or circuit breakers.
Ask about scale (requests per second, number of instances), latency tolerance, and accuracy needs (hard vs. soft limits). This shapes the choice of algorithm and storage.
Propose a centralized data store like Redis or Memcached that supports atomic operations. Explain why it's suitable for low-latency, high-throughput rate limiting.
Use Lua scripts or transactions to atomically check and update counters, ensuring consistency across instances. Discuss algorithms like token bucket or sliding window.
Avoid relying on local clocks by using a centralized time source (e.g., Redis TIME command) or logical timestamps. Alternatively, use algorithms that don't require precise time (e.g., token bucket with refill rate).
Discuss fallback strategies (e.g., local rate limiting, fail-open vs. fail-closed) and trade-offs between consistency, availability, and latency (CAP theorem).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Token bucket naturally handles bursts so I leaned on that.
Start by clarifying requirements: burst handling (allow short bursts above steady rate) and fairness (prevent one tenant from starving others). Then propose a two-layer design: a global rate limiter for overall capacity and per-tenant limiters with a fair scheduling algorithm. Discuss trade-offs between strict fairness and burst tolerance, and how to implement with token buckets and weighted fair queuing.
Pro tip: Mention that fairness doesn't mean equal rates; it means proportional to tenant weights or SLAs, and that burst handling often requires borrowing from a shared pool with repayment to avoid abuse.
Ask about burst size, rate limits, number of tenants, and fairness definition (equal vs. weighted). Confirm if bursts are allowed and for how long.
Propose a global limiter to protect overall system capacity and per-tenant limiters to enforce individual quotas. Use token buckets for burst tolerance.
Explain how to schedule requests from different tenants fairly, ensuring no tenant monopolizes resources. Use weights to reflect tenant priority or SLA.
Allow tenants to temporarily exceed their rate by borrowing from a shared burst pool, but track debt and repay over time to maintain fairness.
Compare strict vs. relaxed fairness, centralized vs. distributed rate limiting, and how to handle synchronization in a distributed system (e.g., using Redis or a gossip protocol).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
HTTP 429 with a Retry-After header, pretty standard.
Start by framing the rate limiter as a critical component that needs visibility into both its own health and its impact on clients. Then outline the key metrics (counters, latency, saturation) and how they should be exposed (e.g., Prometheus). Finally, describe the client-facing response (HTTP 429, Retry-After) and internal alerting to detect misconfigurations or abuse.
Pro tip: Emphasize that observability must include both allow and reject decisions, and that the response should be consistent and informative to help clients back off gracefully. Also mention that metrics should be labeled by policy, client, and endpoint to enable debugging.
Clarify what you want to monitor: rate limiter effectiveness, system health, and client impact. This guides metric selection.
Choose metrics like request counts (allowed/rejected), latency of rate limit checks, current usage vs. limits, and error rates. Include both counters and gauges.
Specify the HTTP status code (429 Too Many Requests), include Retry-After header, and a clear error message. Ensure consistency across services.
Set up alerts for high rejection rates, latency spikes, or misconfigured limits. Create dashboards to visualize trends and aid debugging.
Provide client-side guidance (e.g., backoff strategies) and log rejected requests for auditing. Ensure the system can adapt limits dynamically if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.