Scalable Distributed Rate Limiter

Scalable Distributed Rate Limiter

Design a distributed rate limiting system capable of handling 1 million requests per second. The system must support various limiting strategies (e.g., sliding window, token bucket) and allow for per-user or per-API-key quotas. Key constraints include sub-5ms latency overhead, high availability with fail-open semantics, and the ability to scale horizontally as traffic grows. Explain your choice of storage, consistency models, and how you would handle 'hot keys' for extremely popular users.

RedisLuagRPCAPI GatewayKubernetesNoSQL

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Distributed API Rate Limiter for Large-Scale AI Services

Design a distributed rate-limiting system capable of handling 1M+ requests per minute for an AI provider like OpenAI. The system must support complex metrics including Requests Per Minute (RPM) and Tokens Per Minute (TPM) across hierarchical levels (Organizations and API Keys). Focus on achieving sub-5ms latency, high availability with fail-open capabilities, and handling the unique challenge of 'token-based' limiting where the exact cost is only known after the request completes.

RedisGogRPCPostgreSQLLuaKubernetesAPI GatewayLRU Cache

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Scalable Distributed Rate Limiter

Design a high-performance distributed rate limiting system capable of handling 10 million requests per second with sub-2ms latency. The system must support various granularities (User, IP, API Key) and dynamic rule updates. Focus on high availability, the trade-offs between consistency and performance, and ensuring the system does not become a single point of failure for the entire architecture.

RedisLuagRPCPostgreSQLToken BucketAPI GatewaySidecar Pattern

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Scalable Distributed Rate Limiter

Design a high-performance, distributed rate limiting system capable of handling over 1 million requests per second. The system must support multiple identification strategies (User ID, IP address, API Key) and different quota tiers. Key constraints include a sub-2ms latency overhead and high availability, with a focus on how to handle race conditions in a distributed environment and how the system should behave during partial failures.

RedisLuagRPCPostgreSQLConsistent HashingAPI GatewayKubernetes

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Scalable Distributed Rate Limiter

Design a high-performance, distributed rate-limiting system capable of handling millions of requests per second across a global API infrastructure. The system must support multiple limiting strategies (e.g., sliding window), ensure sub-millisecond overhead, and maintain high availability even during partial network partitions or cache failures. Address how you would handle race conditions in a distributed environment and discuss the trade-offs between accuracy and system latency.

RedisLuaAPI GatewayDistributed CacheCircuit BreakerSliding Window

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Scalable Distributed Rate Limiter

Design a distributed rate-limiting system for a global high-traffic API. The system must support millions of users, maintain sub-5ms latency overhead, and handle different limiting tiers (e.g., free vs. premium). Discuss the choice of algorithm, handling of race conditions in a distributed environment, and the trade-offs between system availability and rate-limiting accuracy during partial network failures.

RedisLuaAPI GatewayNoSQLRedis Cluster

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Scalable Distributed Rate Limiter Design

Design a high-performance, distributed rate limiting service capable of handling millions of requests per second across a global API infrastructure. The system must support flexible windowing algorithms (e.g., sliding window), ensure sub-millisecond latency overhead, and remain resilient to component failures. Detail the trade-offs between consistency and availability, the choice of storage for real-time counters, and how to handle global synchronization challenges.

RedisPostgreSQLgRPCKafkaLuaKubernetesNoSQL

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Distributed Rate Limiter Design

Design a distributed rate limiting system capable of handling 50,000 peak QPS. The system must support various algorithms (e.g., Sliding Window), provide sub-5ms latency, and ensure high availability across multiple application nodes. Discuss how you would handle race conditions, rule management, and system failures without significantly impacting the user experience.

RedisLua ScriptingAPI GatewayPostgreSQL

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Scalable Distributed Rate Limiter

Design a high-performance distributed rate-limiting system capable of handling millions of requests per second across a global microservices architecture. The system must support various limiting algorithms (e.g., Sliding Window, Token Bucket) and offer different granularities such as per-user, per-IP, and per-API key. Key constraints include sub-millisecond latency overhead, high availability even during storage failures, and the ability to handle 'hot' keys effectively. Explain your choice of state management, how you handle race conditions in a distributed environment, and your strategy for fail-soft operations.

RedisLuaAPI GatewayConsistent HashingCircuit BreakerL1/L2 Caching

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