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Design a Distributed Rate Limiter

Last updated: Jun 13, 2026

Quick Overview

This question evaluates a candidate's understanding of distributed systems, rate limiting strategies, API design, consistency and scalability trade-offs, and operational concerns such as observability and failure handling.

  • medium
  • Bytedance
  • System Design
  • Software Engineer

Design a Distributed Rate Limiter

Company: Bytedance

Role: Software Engineer

Category: System Design

Difficulty: medium

Interview Round: Onsite

Design a distributed rate limiter for a large-scale API platform. The rate limiter should protect backend services from abuse and traffic spikes while allowing legitimate users to proceed with minimal latency. Requirements: - Support limits such as requests per user, API key, IP address, endpoint, and global service limits. - Example quotas: 100 requests per minute per user, 1,000 requests per minute per API key, and configurable endpoint-specific limits. - Work across many stateless application servers and multiple data centers or regions. - Return a clear rejection response when a client exceeds its quota. - Keep request-path latency very low, preferably single-digit milliseconds. - Support dynamic configuration updates without redeploying services. - Provide observability for allowed requests, blocked requests, hot keys, and configuration errors. Discuss the API, architecture, storage choices, algorithms, consistency trade-offs, failure handling, and monitoring.

Quick Answer: This question evaluates a candidate's understanding of distributed systems, rate limiting strategies, API design, consistency and scalability trade-offs, and operational concerns such as observability and failure handling.

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Bytedance
Jun 2, 2026, 12:00 AM
Software Engineer
Onsite
System Design
0
0

Design a distributed rate limiter for a large-scale API platform.

The rate limiter should protect backend services from abuse and traffic spikes while allowing legitimate users to proceed with minimal latency.

Requirements:

  • Support limits such as requests per user, API key, IP address, endpoint, and global service limits.
  • Example quotas: 100 requests per minute per user, 1,000 requests per minute per API key, and configurable endpoint-specific limits.
  • Work across many stateless application servers and multiple data centers or regions.
  • Return a clear rejection response when a client exceeds its quota.
  • Keep request-path latency very low, preferably single-digit milliseconds.
  • Support dynamic configuration updates without redeploying services.
  • Provide observability for allowed requests, blocked requests, hot keys, and configuration errors.

Discuss the API, architecture, storage choices, algorithms, consistency trade-offs, failure handling, and monitoring.

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