Rate Limiting • Rate Limit Strategies (Per-User, Per-IP, Global)Medium⏱️ ~3 min
Global Rate Limiting: Service Wide Capacity Protection
Global rate limiting enforces a service wide or cluster wide cap on total request throughput regardless of which users or IPs generate the load. When aggregate traffic across all nodes reaches the limit, additional requests receive 429 responses until capacity frees up. AWS API Gateway enforces regional account level limits around 10,000 requests per second steady state with burst capacity in the thousands, acting as a safety valve to prevent any single customer from exhausting shared infrastructure.
The primary purpose is preventing cascading failures during traffic anomalies. If a viral social media post drives 50x normal traffic or a buggy mobile app loops retries, per user and per IP limits may not trigger fast enough because the load spreads across many principals. A global cap stops the stampede before it overloads databases or downstream services. This buys time for teams to respond with targeted fixes like blocking specific endpoints or deploying emergency capacity.
The danger is self denial of service. If misconfigured too low, the global limit starves legitimate traffic during normal peak hours. Unlike per user limits that only affect individual abusers, a global limit affects everyone simultaneously. Production systems mitigate this with priority classes: critical health checks and payment processing requests bypass the global limiter or draw from a reserved quota, while bulk analytics and backfill jobs hit the limit first. Traffic shaping ensures the most valuable requests always get through.
Implementation faces the distributed consensus challenge. Local per node limits (e.g., each of 100 nodes allows 100 requests per second for a 10,000 total) are fast but permit overage during uneven load distribution. Centralized token buckets in Redis or a coordination service guarantee accuracy but add 0.5 to 2 milliseconds of latency per request plus a single point of failure risk. Most systems choose approximate distributed algorithms: nodes track local usage and periodically sync with a coordinator that adjusts per node quotas based on cluster wide consumption, achieving 95% to 98% accuracy with microsecond local decisions.
💡 Key Takeaways
•AWS API Gateway enforces regional account limits around 10,000 requests per second steady state with burst capacity of several thousand, protecting shared infrastructure from any single tenant
•Global limits prevent cascading failures when per user and per IP limits fail to stop distributed traffic spikes like viral events or buggy client retry loops across thousands of devices
•Self denial of service risk means a misconfigured global limit starves all legitimate users simultaneously; mitigate with priority classes that exempt critical traffic like health checks and payments
•Centralized enforcement guarantees accuracy but adds 0.5 to 2 milliseconds per request plus availability risk; distributed approximation achieves 95% to 98% accuracy with local microsecond decisions
•Implementation often uses hierarchical budgets: allocate tokens per region or per availability zone, then subdivide to nodes, allowing rebalancing without global coordination on every request
•Reserve 10% to 20% of global capacity for retries and background jobs to maintain responsiveness during partial outages when retry rates spike
📌 Examples
A distributed global limiter might allocate each of 100 nodes 100 requests per second locally (10,000 total), then every 5 seconds sync actual usage to a coordinator that redistributes quotas to nodes experiencing higher load
Priority queues can be implemented by reserving 2,000 requests per second of a 10,000 total for Tier 1 traffic (payments, auth) and allowing Tier 2 traffic (analytics, reports) to use only the remaining 8,000