apim-kql

To write KQL queries for APIM logs in Azure Monitor, you query tables like ApiManagementGatewayLogs to filter telemetry by time, API, or response code. This provides ready-to-use templates for gateway, LLM, and WebSocket logs to enable fast analysis.

The best way to trace APIM errors using KQL is to filter the ApiManagementGatewayLogs table for ResponseCode greater than or equal to 400. This isolates failed requests within your specified timeframe to quickly identify problematic APIs and operations.

Yes, you can query LLM and WebSocket connection logs in Azure Monitor for APIM. The skill provides specific KQL query templates and table schemas that cover these advanced telemetry sources alongside standard gateway logs.

Joining Application Insights data with APIM logs provides end-to-end visibility across your API gateway and backend services. This pattern allows you to correlate gateway request traces with application dependencies to improve performance analysis and error tracing.

No, you do not need extensive prior KQL knowledge to analyze APIM telemetry. The skill provides exact table schemas, query patterns, and common dashboard templates that guide you through performance monitoring, usage analytics, and capacity planning.

Available KQL table schemas for APIM capacity planning include ApiManagementGatewayLogs and related Application Insights tables. These schemas support querying telemetry data to generate usage analytics, monitor performance metrics, and plan capacity requirements.