Designing Scalable Rate Limiting Systems: Algorithms, Architecture, and Distributed Solutions

2026-02-12

AI Summary

This work proposes a production-oriented distributed rate-limiting architecture that balances precision, availability, and scalability in distributed systems. Built upon Redis sorted sets and a sliding window algorithm, the design leverages server-side Lua scripts to perform atomic operations, effectively eliminating concurrency contention while ensuring low latency and high accuracy. The system employs a three-tier rule management structure that enables dynamic updates to rate-limiting policies without requiring script modifications. Horizontal scalability and high availability are achieved through integration with Redis Cluster. Guided by the CAP theorem, the architecture explicitly adopts an AP model to prioritize practical engineering trade-offs between consistency and availability. Empirical deployment results demonstrate the system’s effectiveness, robustness, and scalability in real-world scenarios.

Technology Category

• Machine Learning: Scalability of ML Systems
• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Security and Privacy: Blockchains and distributed ledgers

Abstract

Designing a rate limiter that is simultaneously accurate, available, and scalable presents a fundamental challenge in distributed systems, primarily due to the trade-offs between algorithmic precision, availability, consistency, and partition tolerance. This article presents a concrete architecture for a distributed rate limiting system in a production-grade environment. Our design chooses the in-memory cache database, the Redis, along with its Sorted Set data structure, which provides $O(log (N))$ time complexity operation for the key-value pair dataset with efficiency and low latency, and maintains precision. The core contribution is quantifying the accuracy and memory cost trade-off of the chosen Rolling Window as the implemented rate limiting algorithm against the Token Bucket and Fixed Window algorithms. In addition, we explain how server-side Lua scripting is critical to bundling cleanup, counting, and insertion into a single atomic operation, thereby eliminating race conditions in concurrent environments. In the system architecture, we propose a three-layer architecture that manages the storage and updating of the limit rules. Through script load by hashing the rule parameters, rules can be changed without modifying the cached scripts. Furthermore, we analyze the deployment of this architecture on a Redis Cluster, which provides the availability and scalability by data sharding and replication. We explain the acceptance of AP (Availability and Partition Tolerance) from the CAP theorem as the pragmatic engineering trade-off for this use case.

Problem

• rate limiting
• distributed systems
• scalability
• accuracy
• CAP theorem

Innovation

• Rate Limiting
• Redis Sorted Set
• Rolling Window Algorithm
• Lua Scripting
• Distributed System Architecture