QA Made Easy with KQL in Azure Application Insights
In today’s world of modern DevOps and continuous delivery, having the ability to analyze application behavior quickly and efficiently is key to Quality Assurance (QA).
Azure Application Insights offers powerful telemetry collection, but what makes it truly shine is the Kusto Query Language (KQL)—a rich, expressive query language that enables deep-dive analytics into your application’s performance, usage, and errors. Whether you’re testing a web app, monitoring API failures, or validating load test results, KQL can become your best QA companion.
What is KQL?
KQL stands for Kusto Query Language, and it’s used to query telemetry data collected by Azure Monitor, Application Insights, and Log Analytics. It’s designed to be read like English, with SQL-style expressions, yet much more powerful for telemetry analysis.
Challenges Faced with Application Insights in QA
1. Telemetry data doesn’t always show up immediately after execution, causing delays in debugging and test validation.
2.When testing involves thousands of records, isolating failed requests or exceptions becomes tedious and time-consuming.
3.The default portal experience lacks intuitive filters for QA-specific needs like test case IDs, custom payloads, or user roles.
4.Repeated logs from expected failures (e.g., negative test cases) can clutter insights, making it hard to focus on actual issues.
5.Out-of-the-box telemetry doesn’t group actions by test scenario or user session unless explicitly configured, making traceability difficult during test case validation.
To overcome these limitations, QA teams need more than just default dashboards—they need flexibility, precision, and speed in analyzing telemetry. This is where Kusto Query Language (KQL) becomes invaluable. With KQL, testers can write custom queries to filter, group, and visualize telemetry exactly the way they need, allowing them to focus on real issues, validate test scenarios, and make data-driven decisions faster and more efficiently.
Let’s take some examples for better understanding:
Some Common scenarios where a KQL proves to be very effective.
Check if the latest deployment introduced new exceptions
Example:
exceptions
| where timestamp > ago(1d)
| summarize count() by type, outerMessage, operation_Name
| order by count_ desc
Find all failed requests
Example:
requests
| where success == “False”
| project timestamp, name, url, resultCode, duration
| order by timestamp desc
Analyse performance of a specific page or operation
Example:
requests
| where name == “GET Home/Index”
| summarize avg(duration) by bin(timestamp, 1h)
Correlate request with exceptions
Example:
requests
| join kind=inner (
exceptions
| project operation_Id, exceptionType = type, exceptionMessage = outerMessage
) on operation_Id
| project timestamp, name, exceptionType, exceptionMessage
Validate custom event tracking (like button clicks)
Example:
customEvents
| where name == “SubmitOrderButton_Click”
| project timestamp, name, customDimensions
Track specific user sessions for end-to-end QA testing
Example:
requests
| where user_Id == “”
| order by timestamp desc
Test API performance under load
Example:
requests
| where name contains “api/order”
| summarize avg(duration), count() by bin(timestamp, 5m)
All of this can be Visualized too – You can pin your KQL queries to Azure Dashboards or even Power BI for real-time tracking during QA sprints.
To conclude, KQL is not just for developers or DevOps. QA engineers can significantly reduce manual log-hunting and accelerate issue detection by writing powerful queries in Application Insights. By incorporating KQL into your testing lifecycle, you add an analytical edge to your QA process—making quality not just a gate but a continuous insight loop.
Start with a few basic queries, and soon you’ll be building powerful dashboards that QA, Dev, and Product can all share!
Hope this helps !
I hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com.
