Debug Production Errors in Application Insights
When production breaks, every second counts. Debugging requires you to connect exception messages to user sessions, trace the request lifecycle, and identify the exact dependency that failed—all while the system is under load.
Application Insights captures the signals: exceptions, traces, requests, and dependencies. But writing the KQL to correlate them across tables and time windows is the hard part. Below are practical patterns for turning production symptoms into actionable insights.
Find High Error Rate Endpoints
Identify API endpoints with elevated error rates in the last hour to prioritize investigation.
requests
| where TimeGenerated > ago(1h)
| summarize TotalRequests = count(), ErrorCount = countif(resultCode >= 500) by name
| extend ErrorRate = (ErrorCount * 100.0) / TotalRequests
| where ErrorRate > 5
| order by ErrorRate descTrace Exception to Session
Link exception messages to specific user sessions for faster root cause analysis.
exceptions
| where TimeGenerated > ago(24h)
| join (traces | where severityLevel >= 2) on operation_Id
| project TimeGenerated, outerMessage, severityLevel, user_Id, operation_Id
| where outerMessage has "NullReferenceException"
| summarize ExceptionCount = count() by user_Id, outerMessage
| order by ExceptionCount descDebug faster. Ship faster.
KQL Remix lets you describe your debugging intent in plain language. Get production-ready KQL in seconds. No more syntax hunting when you're on-call.
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