FlashTraceViewer vs. Other Trace Viewers: Which Should You Use?

How to Troubleshoot Traces Quickly Using FlashTraceViewer

1) Load and organize traces fast

• Open trace files (supporting formats) and use bulk-import to load multiple traces at once.
• Use file naming filters (date, service, run ID) and saved views to quickly find relevant traces.

2) Start with a high-level filter

• Filter by time range, service/component, or error status to reduce noise.
• Group by trace duration or error count to surface slow or failing traces first.

3) Use timeline and span heatmaps

• Scan the timeline view to spot long running spans or gaps.
• Heatmaps highlight hotspots (high-latency spans) so you can prioritize investigation.

4) Drill into individual traces efficiently

• Expand the critical spans showing long duration or errors.
• Inspect span tags/attributes (error messages, status codes, resource IDs) and logs attached to spans for root-cause clues.

5) Correlate traces with logs and metrics

• Use built-in links or copy trace IDs to jump to logs/metrics dashboards.
• Compare metric spikes (CPU, DB latency, error rate) with trace times to find systemic causes.

6) Use search and saved queries

• Save common queries (e.g., “500 responses”, “db timeout”) to rerun instantly.
• Use advanced search (tag:value, duration:>500ms) to pinpoint problematic patterns.

7) Compare normal vs. abnormal traces

• Open a baseline (healthy) trace alongside a failing trace to compare span timing and tag differences.
• Look for added retries, unexpected calls, or missing cache hits.

8) Leverage aggregation and root-cause views

• Use aggregated span analytics to see which dependencies cause the most latency or errors across traces.
• Prioritize fixes for high-impact dependencies.

9) Annotate and share findings

• Add notes/annotations to traces and share links with teammates; include suspected root cause and steps to reproduce.
• Export traces or screenshots for incident reports.

10) Actionable next steps checklist

1. Identify top slow/error traces via filters or heatmap.
2. Drill into suspect spans and read tags/logs.
3. Correlate with metrics/logs for system context.
4. Compare to healthy traces to isolate differences.
5. Create a reproducible test or fix and monitor post-deploy traces.

If you want, I can produce a checklist formatted for your team’s incident runbook or tailor these steps to a specific trace format or environment.