AI 中文总结
研究分布式跟踪诊断中的比较难题，提出Contrast系统，通过跟踪投影对象分离跟踪表示与比较语义，利用SpectroViz和Parallax接口展示能力，经实验验证其有效性和效率。
AI 中文摘要
使用分布式跟踪进行诊断本质上是一项比较任务，而跟踪比较具有挑战性，因为执行之间的有用差异可能体现在多个维度，且没有单一诊断接口能捕捉所有差异。本文提出Contrast系统，引入跟踪投影对象（TPO），将跟踪表示与比较语义分离，允许不同接口选择性地推理特定维度。通过SpectroViz和Parallax两个互补接口展示了该能力，并通过实验证明了Contrast的有效性和效率。
英文摘要
Diagnosis using distributed traces is fundamentally a comparative task: operators seek to understand how an anomalous execution differs from expected behavior, how a deployment changes system execution, or how two individual executions differ. Trace comparison is challenging because useful differences between executions can manifest across multiple dimensions, and no single diagnostic interface is effective at capturing all of them. Moreover, the relevant dimensions and comparison populations are often not known a priori; operators construct and refine comparison sets dynamically as they develop hypotheses about system behavior. This paper presents Contrast, a system for multi-dimensional comparative trace analysis. Contrast introduces the Trace Projection Object (TPO), a mergeable representation that captures structural, temporal, critical-path, and semantic properties of trace populations while enabling efficient construction of arbitrary comparison sets at query time. Unlike approaches that define a fixed notion of trace difference, Contrast separates trace representation from comparison semantics, allowing diverse interfaces to selectively reason about specific dimensions. This separation enables the composition of complementary interfaces, allowing operators to combine insights from multiple dimensions for more effective diagnosis. We demonstrate this capability through two complementary interfaces: (i) SpectroViz, a critical-path-based visual interface for localizing execution differences; and (ii) Parallax, a natural language interface for generating explanations of trace differences using LLMs. We demonstrate the effectiveness and efficiency of Contrast through controlled experiments on traces from DeathStarBench and evaluation on production traces from Uber.
