Enabling Multi-Dimensional Distributed Trace Comparison with Contrast

2026-07-21

AI Summary

This work addresses the challenge in distributed tracing diagnostics where anomalies often differ from normal behavior across multiple unknown dimensions—such as structure, timing, critical paths, and semantics—making them difficult to capture effectively through a single interface. To tackle this, the authors propose Contrast, a novel system that decouples trace representation from comparison semantics and introduces a composable Trace Projection Object (TPO) to uniformly encode multidimensional features. This design enables dynamic construction of arbitrary comparison sets at query time. Contrast further integrates two complementary interfaces: interactive visualization and large language model–generated explanations, facilitating flexible and in-depth root cause analysis. Experimental evaluation demonstrates that Contrast significantly improves both multidimensional comparison efficiency and diagnostic accuracy on the DeathStarBench benchmark and in Uber’s production environment.

Abstract

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.

Problem

• distributed tracing
• trace comparison
• multi-dimensional analysis
• diagnostic interfaces
• anomaly diagnosis

Innovation

• distributed tracing
• multi-dimensional comparison
• Trace Projection Object
• diagnostic interfaces
• LLM-based explanation