Research on Streaming Data Analysis Methods for Real-Time Business Monitoring
面向实时业务监控的流式数据分析方法研究

摘要

针对传统批处理监控存在数据更新滞后、指标口径固化、乱序事件导致统计偏差以及突发流量下告警延迟升高等 问题，提出一种面向实时业务监控的流式数据分析方法。该方法以消息队列和分布式流处理引擎为基础，构建数据接 入、事件时间治理、增量指标计算、动态异常识别、结果服务和运行保障一体化链路；通过水位线与迟到数据旁路机 制提高窗口统计完整性，利用可组合状态实现多粒度指标增量更新，并结合动态规则、鲁棒统计阈值和告警抑制策略 降低误报与告警风暴。仿真原型结果表明，与定时批处理和基础流式方案相比，本文方法在峰值负载下能够保持较低 的端到端延迟，并改善乱序窗口完整率和故障恢复效率。 

Abstract

To address problems in traditional batch-processing monitoring, such as delayed data updates, fixed indicator definitions, statistical deviations caused by out-of-order events, and increased alarm latency under burst traffic, this paper proposes a streaming data analysis method for real-time business monitoring. Based on message queues and distributed stream processing engines, the method constructs an integrated chain covering data access, event-time governance, incremental indicator calculation, dynamic anomaly identification, result services, and operational support. Window statistics completeness is improved through watermarking and a bypass mechanism for late-arriving data. Multi-granularity incremental updating of indicators is realized by using composable states, while false alarms and alarm storms are reduced through dynamic rules, robust statistical thresholds, and alarm suppression strategies. The simulation prototype results show that, compared with scheduled batch processing and basic streaming schemes, the proposed method can maintain lower end-to-end latency under peak loads and improve out-of-order window completeness and fault recovery efficiency.