Trace Sampling 2.0: Code Knowledge Enhanced Span-level Sampling for Distributed Tracing
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909792725893120 |
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| author | Wu, Yulun Yu, Guangba Jiang, Zhihan Li, Yichen Lyu, Michael R. |
| author_facet | Wu, Yulun Yu, Guangba Jiang, Zhihan Li, Yichen Lyu, Michael R. |
| contents | Distributed tracing is an essential diagnostic tool in microservice systems, but the sheer volume of traces places a significant burden on backend storage. A common approach to mitigating this issue is trace sampling, which selectively retains traces based on specific criteria, often preserving only anomalous ones. However, this method frequently discards valuable information, including normal traces that are essential for comparative analysis. To address this limitation, we introduce Trace Sampling 2.0, which operates at the span level while maintaining trace structure consistency. This approach allows for the retention of all traces while significantly reducing storage overhead. Based on this concept, we design and implement Autoscope, a span-level sampling method that leverages static analysis to extract execution logic, ensuring that critical spans are preserved without compromising structural integrity. We evaluated Autoscope on two open-source microservices. Our results show that it reduces trace size by 81.2% while maintaining 98.1% faulty span coverage, outperforming existing trace-level sampling methods. Furthermore, we demonstrate its effectiveness in root cause analysis, achieving an average improvement of 8.3%. These findings indicate that Autoscope can significantly enhance observability and storage efficiency in microservices, offering a robust solution for performance monitoring. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13852 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Trace Sampling 2.0: Code Knowledge Enhanced Span-level Sampling for Distributed Tracing Wu, Yulun Yu, Guangba Jiang, Zhihan Li, Yichen Lyu, Michael R. Software Engineering Distributed tracing is an essential diagnostic tool in microservice systems, but the sheer volume of traces places a significant burden on backend storage. A common approach to mitigating this issue is trace sampling, which selectively retains traces based on specific criteria, often preserving only anomalous ones. However, this method frequently discards valuable information, including normal traces that are essential for comparative analysis. To address this limitation, we introduce Trace Sampling 2.0, which operates at the span level while maintaining trace structure consistency. This approach allows for the retention of all traces while significantly reducing storage overhead. Based on this concept, we design and implement Autoscope, a span-level sampling method that leverages static analysis to extract execution logic, ensuring that critical spans are preserved without compromising structural integrity. We evaluated Autoscope on two open-source microservices. Our results show that it reduces trace size by 81.2% while maintaining 98.1% faulty span coverage, outperforming existing trace-level sampling methods. Furthermore, we demonstrate its effectiveness in root cause analysis, achieving an average improvement of 8.3%. These findings indicate that Autoscope can significantly enhance observability and storage efficiency in microservices, offering a robust solution for performance monitoring. |
| title | Trace Sampling 2.0: Code Knowledge Enhanced Span-level Sampling for Distributed Tracing |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2509.13852 |