Research on Real-Time Data Processing Architecture Based on Kafka and Flink
基于Kafka与Flink的实时数据处理架构研究

摘要

针对传统批处理架构在数据时效性、峰值承载能力和故障恢复方面的不足，提出一种以Kafka为消息缓冲与分发 中心、以Flink为流式计算引擎的实时数据处理架构。该架构按照数据源层、消息接入层、实时计算层、存储与服务层 进行分层设计，并通过分区并行、事件时间、水位线、状态管理、检查点及端到端一致性机制，实现多源异构数据的 持续接入、低延迟计算与可靠输出。研究进一步分析了反压传播、数据倾斜、状态膨胀和跨系统一致性等关键问题， 给出分区规划、并行度配置、状态后端选择、检查点调优及监控告警策略。结果表明，该架构能够在解耦数据生产与 消费的同时形成可扩展、可恢复、可治理的实时处理链路，适用于业务监控、日志分析、风险预警和物联网计算等场景。

Abstract

To address the deficiencies of traditional batch-processing architectures in data timeliness, peak load capacity, and fault recovery, this paper proposes a real-time data processing architecture that takes Kafka as the message buffering and distribution center and Flink as the stream computing engine. The architecture is designed in layers, including the data source layer, message access layer, real-time computing layer, and storage and service layer. Through mechanisms such as partition parallelism, event time, watermarks, state management, checkpoints, and end-to-end consistency, it realizes continuous access, low-latency computing, and reliable output of multi-source heterogeneous data. The study further analyzes key issues such as backpressure propagation, data skew, state expansion, and cross-system consistency, and provides strategies for partition planning, parallelism configuration, state backend selection, checkpoint tuning, and monitoring and alarm management. The results show that the architecture can decouple data production and consumption while forming a scalable, recoverable, and governable real-time processing chain, making it suitable for business monitoring, log analysis, risk early warning, Internet of Things computing, and other scenarios.