Impact Analysis

Impact analysis compares metrics before and after an event to help you understand whether a deployment, rollback, or other change made things better or worse.

How It Works

1. Event occurs — A deployment, rollback, or other change is recorded on the timeline
2. Metrics are collected — Connected analytics providers continuously report metrics (error rates, response times, page views, etc.)
3. Before/after comparison — OpsTrails compares metric values in a configurable window before and after the event
4. Impact assessment — Significant changes are flagged, helping you correlate deployments with metric movements

Comparison Windows

When querying impact, you can specify the comparison window size:

• 1h — Tight comparison for quick-impact changes
• 2h — Default window, good for most deployments
• 4h — Wider view for gradual impacts
• 6h, 12h, 24h — Broader windows for slow-rolling changes

Using with AI

When connected via MCP, AI assistants automatically use the get_metrics_around_event tool to assess impact. You can ask:

• “Did the error rate spike after the last deployment?”
• “How did page load times change after the v2.1.0 release?”
• “Compare metrics before and after the rollback”

Example Metrics

Common metrics used for impact analysis:

• error_rate — Percentage of requests resulting in errors
• p50_latency — Median response time
• p99_latency — 99th percentile response time
• page_views — Total page views from analytics
• crash_free_rate — Percentage of sessions without crashes
• throughput — Requests per second