Rate Limiting, Throttling and Quota Management Questions

Approach overview
I’d implement a hybrid token-bucket + sliding-window counters system: token-bucket for smooth per-IP and per-account throttling, sliding-window for strict bursts and analytics. Add a global emergency throttle that can be toggled automatically from aggregated metrics.

Algorithms

• Per-IP & per-account: token bucket (tokens refilled per minute). On request, atomically consume token; reject when empty.
• Global emergency: aggregated rate across regions; when threshold exceeded, apply progressive backoff (increase cost per request or short global denylist).
• Analytics: sliding-window counters for forensics and anomaly detection.

Storage & consistency

• Fast path: local in-memory token buckets (per node) for latency.
• Shared state: Redis (clustered, primary-replicas) with Lua scripts for atomic token consume when cross-check needed.
• Multi-region convergence: use CRDT-based counters (e.g., PN-Counters) or Redis GCS with periodic reconciliation for global metrics. CRDTs avoid coordination and eventual-consistent summation for emergency decisions.

Cross-region enforcement

• Default: enforce locally using IP/account mapping to the nearest region; periodically sync counts to central ranking via CRDTs.
• Strong enforcement for critical accounts: use a global Redis write (tradeoff: higher latency) or route auth requests through a global rate-check service (cache results).
• Emergency decisions derived from aggregated CRDT counters and a leader-election (fast path) to avoid split-brain.

Avoiding false positives (NAT/proxies)

• Use progressive checks: if many users share IP, prefer per-account limit over per-IP; raise IP threshold if it maps to known carrier NAT ranges.
• Integrate device fingerprinting and TLS client hello entropy to distinguish users.
• Allow higher burst allowance and CAPTCHA/challenge for suspicious sources instead of hard blocking.
• Maintain dynamic allowlists for known CDNs/proxies and apply weighted tokens per sub-IP (X-Forwarded-For parsing when trusted).

Operational considerations

• Use Redis Lua scripts for atomic operations and metrics emission to Kafka for offline analysis.
• Circuit-breakers and graceful degradation: when Redis unavailable, fall back to local token-bucket with conservative limits.
• Metrics/alerts: SLOs for false positives, request rejection rate, and global throttle activations; automated rollback for misconfigurations.

This balances low-latency enforcement, global visibility, and reduced false positives for users behind shared IPs.

Overview of algorithms

• Token Bucket: Tokens added at rate R; request consumes tokens. Allows bursts up to bucket size. Low cost; common in network/proxy rate limiting.
• Leaky Bucket (queue emulation): Requests enter queue and are drained at fixed rate. Smooths bursts (drops if queue full); less permissive of bursts.
• Fixed Window: Count requests in fixed interval (e.g., minute). Simple but vulnerable to boundary spikes.
• Sliding Window Log: Store timestamps of each request; exact sliding-window semantics. Accurate but storage/CPU heavy.
• Sliding-window Counter (two-window approximation): Keep current and previous window counts weighted by time overlap. Good accuracy/cost tradeoff.

Which for bursty traffic

• Token bucket is best: explicit burst capacity with sustained rate control.
• Leaky bucket limits bursts more strictly (good when you must smooth traffic).
• Fixed window and sliding-window counter can let bursts through at window edges; sliding-window log prevents that but is heavier.

Approximating sliding windows in distributed systems

• Use the two-window counter approximation (current + previous bucket weighted) to avoid per-request logs.
• Implement counters in a fast, centralized store (Redis) with atomic operations or Lua scripts to avoid race conditions.
• Shard keys by user/region with consistent hashing; use local token buckets on each instance refreshed from a central quota to reduce coordination.
• For higher scale, use probabilistic sketches or rate-limiter tokens issued by a small set of leader nodes.

Applying limits: per-user, per-API-key, global

• Enforce hierarchical limits: check most specific → less specific (user/api-key → tenant → global).
• Store counters/namespaces in Redis: rate:user:{id}, rate:api:{key}, rate:global. Use atomic pipeline/Lua to check & decrement multiple keys in one operation to avoid races.
• For performance, combine local fast-path (in-memory token bucket) with periodic reconciliation to central store to allow low-latency bursts while preserving global quotas.
• Apply different policies: stricter global limits for DDoS protection, per-key/user for fair usage, and priority queues for high-value clients.

Recommendations: use token bucket with Redis-backed counters + Lua for distributed correctness; approximate sliding windows with two-bucket weighting; shard and cache to scale while maintaining hierarchical checks.