This article explains how the @RateLimiter annotation in SpringBoot can replace manual, verbose rate‑limiting code with a concise declarative approach, covering basic usage, custom strategies, real‑world examples, and the benefits of reduced complexity and improved system stability.
This article explains why rate limiting is essential in high‑concurrency and transaction‑processing systems, introduces Google Guava's RateLimiter as a token‑bucket implementation, walks through its source code—including class hierarchy, core algorithms, and usage examples—and discusses practical considerations and extensibility.
This article provides a comprehensive walkthrough of Google Guava's RateLimiter, covering its practical rate‑limiting scenarios, underlying token‑bucket algorithm, detailed source‑code structure, core classes such as RateLimiter, SmoothRateLimiter, SmoothBursty and SmoothWarmingUp, usage examples, and considerations for extending or adapting the component in distributed systems.
This article explains how Guava's RateLimiter implements the token bucket algorithm to provide precise rate limiting in Java, demonstrates usage with code examples, explores the underlying token bucket mechanics, and shows extended implementations for burst capacity and token synchronization.
This article provides an overview of the token bucket algorithm, demonstrates a straightforward Java implementation of a RateLimiter with code examples, compares it to Guava's RateLimiter internals, and includes diagrams and test results to illustrate its behavior.
This article explains why Spark Streaming introduced backpressure, how the dynamic rate‑control mechanism works, and provides a detailed walkthrough of the relevant source code, including the RateController class, its registration, and the execution flow that adjusts ingestion rates to match processing capacity.
