部分抽样分布迹的估计
Estimation from Partially Sampled Distributed Traces
摘要：
抽样通常是减少分布式跟踪的处理和存储成本的必要手段。在这项工作中，我们描述了一个可扩展的和自适应的采样方法，可以更好地保存感兴趣的事件比广泛使用的头为基础的采样方法。采样率可以单独和独立地为每个跨度选择，允许考虑跨度属性和本地资源约束。所得到的迹线通常仅部分而不是完全采样，这使得统计分析复杂化。为了充分利用给定的信息，提出了一种无偏估计算法。即使它不需要知道迹线是否完整，但与仅考虑完整迹线相比，它在许多情况下减少了估计误差。
英文摘要：
Sampling is often a necessary evil to reduce the processing and storage costs of distributed tracing. In this work, we describe a scalable and adaptive sampling approach that can preserve events of interest better than the widely used head-based sampling approach. Sampling rates can be chosen individually and independently for every span, allowing to take span attributes and local resource constraints into account. The resulting traces are often only partially and not completely sampled which complicates statistical analysis. To exploit the given information, an unbiased estimation algorithm is presented. Even though it does not need to know whether the traces are complete, it reduces the estimation error in many cases compared to considering only complete traces.
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