面向分布式系统的无侵入式链路追踪方法研究OA

The proliferation of microservices and cloud-native architectures has led to increasingly complex distributed systems,posing significant challenges to observability.Traditional distributed tracing methods often suffer from strong code invasiveness,incomplete link reconstruction,and poor compatibility in heterogeneous environments.To address these issues,this paper proposes a non-invasive distributed tracing method based on multi-source data fusion.This method utilizes the extended Berkeley Packet Filter kernel-level probe to capture network call events and combines them with standardized application logs for context correlation,achieving end-to-end link data collection without modifying business code.Based on the OpenTracing standard,a unified data model is constructed,and a multi-modal storage architecture incorporating relational,time-series,graph,and full-text search databases is adopted to efficiently manage heterogeneous data.Through the collaboration of real-time stream processing and offline batch processing,abnormal patterns such as prohibited calls and cyclic calls are automatically identified.Experimental results show that this method achieves precise reconstruction and visualization of call links while maintaining low resource overhead,significantly improving fault localization efficiency and system operation and maintenance.