root-cause-localization

Root-cause localization from logs and traces analyzes time alignment and call chains within a fault window to identify true failure initiators. It distinguishes victims from triggers by reasoning about dependency graphs and failure propagation paths.

Trace failure propagation by analyzing dependency graphs and call chains to distinguish root-cause components from affected victims. This approach uses time-aligned diagnostic traces to converge on the true initiators of failures within your defined fault window.

You need to provide a fault window, candidate components, and diagnostic traces as inputs. The analysis outputs structured root-cause items that can feed upstream orchestrators and RCA coordination workflows.

Yes, the root-cause localization supports identifying multiple root causes when necessary. It analyzes time-aligned logs and traces to converge on all true failure initiators within the given fault window, delivering structured root-cause entries for downstream orchestration.

Yes, fault diagnosis with this tool requires Python dependencies including pandas, numpy, drain3, scikit-learn, networkx, and pytz. These libraries enable time-aligned analysis of logs and traces and support dependency graph reasoning for root-cause localization.

Time alignment matters because it allows the root-cause localization to accurately correlate events across logs and traces within a fault window. This temporal correlation is essential for tracing call chains and determining the true initiators of failures.