Design a high-throughput distributed rate limiter

Q: Design a high-throughput distributed rate limiter

This question evaluates distributed systems architecture, rate-limiting algorithms, data modeling, sharding and hot-key mitigation, consistency and fault-tolerance strategies, API design, and operational capacity planning within the System Design domain.

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Question

System Design: High-Throughput Distributed Rate Limiting Service

Context

You are designing a multi-tenant rate limiting platform for an edge/gateway layer that protects downstream services. The system must enforce both per-user and global limits, tolerate bursts, and approximate a sliding window across multiple regions at peak 10M requests/second.

Requirements

Design a distributed rate limiting service that:

Supports per-user and global limits with burst tolerance and approximately sliding-window semantics.
Handles 10M RPS peak across multiple regions with low latency.
Specifies:
1. Public API (check, configure, introspect) and enforcement integration on the request path.
2. Algorithms (token bucket, leaky bucket, sliding window) and why.
3. Data model for counters/buckets/policies.
4. Sharding and hot-key mitigation (e.g., consistent hashing, key splitting).
5. Storage choices (in-memory vs. Redis vs. custom), replication, and time coordination.
6. Failure handling and partial outages; fairness and consistency (eventual vs strong) where needed.
7. Scale-out plan: capacity planning formulas, machines needed for a 2× traffic spike, autoscaling signals, and backpressure/throttling strategies.

Design a high-throughput distributed rate limiter

Quick Overview

System Design: High-Throughput Distributed Rate Limiting Service

Context

Requirements

Solution

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