Apache Kafka has become a foundational platform for high throughput event streaming, enabling real time analytics, financial transaction processing, industrial telemetry, and large scale data driven systems. Despite its maturity and widespread adoption, consolidated research on reusable architectural design patterns and reproducible benchmarking methodologies remains fragmented across academic and industrial publications. This paper presents a structured synthesis of forty two peer reviewed studies published between 2015 and 2025, identifying nine recurring Kafka design patterns including log compaction, CQRS bus, exactly once pipelines, change data capture, stream table joins, saga orchestration, tiered storage, multi tenant topics, and event sourcing replay. The analysis examines co usage trends, domain specific deployments, and empirical benchmarking practices using standard suites such as TPCx Kafka and the Yahoo Streaming Benchmark, as well as custom workloads. The study highlights significant inconsistencies in configuration disclosure, evaluation rigor, and reproducibility that limit cross study comparison and practical replication. By providing a unified taxonomy, pattern benchmark matrix, and actionable decision heuristics, this work offers practical guidance for architects and researchers designing reproducible, high performance, and fault tolerant Kafka based event streaming systems.

翻译：Apache Kafka已成为高吞吐量事件流处理的基础平台，支持实时分析、金融交易处理、工业遥测和大规模数据驱动系统。尽管其技术成熟且应用广泛，关于可复用架构设计模式和可复现基准测试方法的系统性研究在学术界与工业界文献中仍呈现碎片化。本文对2015年至2025年间发表的42项同行评审研究进行了结构化综述，识别出九种重复出现的Kafka设计模式，包括日志压缩、CQRS总线、精确一次处理管道、变更数据捕获、流表连接、Saga编排、分层存储、多租户主题和事件溯源重放。研究通过TPCx Kafka、Yahoo流处理基准测试等标准套件及自定义工作负载，分析了模式协同使用趋势、领域特定部署方案及实证基准测试实践。该研究揭示了配置披露不完整、评估严谨性不足和可复现性缺陷等显著问题，这些问题限制了跨研究比较与实际系统复现。通过提出统一分类体系、模式-基准关联矩阵及可操作的决策启发规则，本研究为设计可复现、高性能且容错的Kafka事件流系统的架构师与研究者提供了实践指导。