Aggregate-Driven Trace Visualizations for Performance Debugging
This addresses performance debugging for cloud system engineers, but it is incremental as it builds on existing trace analysis methods.
The paper tackles the problem of debugging performance issues in cloud systems by introducing TraVista, a tool that extends single trace Gantt charts with aggregate data to contextualize offending traces, resulting in improved debugging efficiency.
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.