Aggregate-Driven Trace Visualizations for Performance Debugging

TL;DR

TraVista is a visualization tool that enhances performance debugging in cloud systems by integrating aggregate data into trace analysis, addressing key challenges in identifying, visualizing, and processing trace data.

Contribution

It introduces a novel visualization extension to Gantt charts that incorporates aggregate metric, temporal, and structural data for better performance debugging.

Findings

Improves debugging efficiency for performance issues.

Enables contextual comparison between individual and aggregate traces.

Addresses challenges in data collection and visualization.

Abstract

Performance issues in cloud systems are hard to debug. Distributed tracing is a widely adopted approach that gives engineers visibility into cloud systems. Existing trace analysis approaches focus on debugging single request correctness issues but not debugging single request performance issues. Diagnosing a performance issue in a given request requires comparing the performance of the offending request with the aggregate performance of typical requests. Effective and efficient debugging of such issues faces three challenges: (i) identifying the correct aggregate data for diagnosis; (ii) visualizing the aggregated data; and (iii) efficiently collecting, storing, and processing trace data. We present TraVista, a tool designed for debugging performance issues in a single trace that addresses these challenges. TraVista extends the popular single trace Gantt chart visualization with three types of aggregate data - metric, temporal, and structure data, to contextualize the performance of the offending trace across all traces.