Merge branch 'MicrosoftDocs:main' into main

Author: Daya-Patil

articles/application-gateway/for-containers/migrate-from-agic-to-agc.md

@@ -5,7 +5,7 @@ services: application gateway
 author: mbender-ms
 ms.service: azure-appgw-for-containers
 ms.topic: concept-article
-ms.date: 10/28/2024
+ms.date: 7/22/2025
 ms.author: mbender
 # Customer intent: As a Kubernetes administrator, I want to migrate services from Application Gateway Ingress Controller to Application Gateway for Containers, so that I can leverage improved performance, seamless scaling, and modern API compatibility without experiencing downtime during the transition.
 ---
@@ -43,7 +43,6 @@ Prior to migration, it is important to identify any dependencies on Application
 
 Such dependencies include:
 
-- Web Application Firewall (WAF)
 - Private IP
 - Ports other than 80 and 443
 - Configurable request timeout values
@@ -65,7 +64,7 @@ Here's a summarized list of AGIC annotations and whether Application Gateway for
 | [Request timeout](migrate-from-agic-to-agc.md#request-timeout) | appgw.ingress.kubernetes.io/request-timeout | Non-configurable | Non-configurable |
 | [Frontend port other than 80 and 443](migrate-from-agic-to-agc.md#frontend-port-override) | appgw.ingress.kubernetes.io/override-frontend-port | Not supported | Not supported |
 | [Private frontend](migrate-from-agic-to-agc.md#private-frontend) | appgw.ingress.kubernetes.io/use-private-ip | Not supported | Not supported |
-| [WAF](migrate-from-agic-to-agc.md#waf) | appgw.ingress.kubernetes.io/waf-policy-for-path | Not supported | Not supported |
+| [Web Application Firewall (WAF)](migrate-from-agic-to-agc.md#waf) | appgw.ingress.kubernetes.io/waf-policy-for-path | [Web Application Firewall (WAF) (Preview)](web-application-firewall.md) | Not supported |
 | [Custom health probe](migrate-from-agic-to-agc.md#custom-health-probes) | appgw.ingress.kubernetes.io/health-probe-hostname | [HealthCheckPolicy](migrate-from-agic-to-agc.md#healthcheckpolicy) | [HealthCheckPolicy](migrate-from-agic-to-agc.md#healthcheckpolicy) |
 | [Custom health probe](migrate-from-agic-to-agc.md#custom-health-probes) | appgw.ingress.kubernetes.io/health-probe-port |  [HealthCheckPolicy](migrate-from-agic-to-agc.md#healthcheckpolicy) | [HealthCheckPolicy](migrate-from-agic-to-agc.md#healthcheckpolicy) |
 | [Custom health probe](migrate-from-agic-to-agc.md#custom-health-probes) | appgw.ingress.kubernetes.io/health-probe-path | [HealthCheckPolicy](migrate-from-agic-to-agc.md#healthcheckpolicy) | [HealthCheckPolicy](migrate-from-agic-to-agc.md#healthcheckpolicy) |
@@ -298,7 +297,9 @@ AGIC annotation
 
 Application Gateway for Containers implementation
 
-WAF isn't supported by Application Gateway for Containers.
+#### Web Application Firewall Policy
+
+The equivalent is a new WebApplicationFirewallPolicy resource with a reference to a defined resource or resource section. More details can be found in the [Web Application Firewall](web-application-firewall.md) document.
 
 ### Custom Health probes
 

articles/automation/manage-runtime-environment.md

@@ -313,7 +313,7 @@ PATCH
 https://management.azure.com/subscriptions/<subscriptionId>/resourceGroups/<resourceGroup>/providers/Microsoft.Automation/automationAccounts/<accountName>/runbooks/<runbookName>?api-version=2024-10-23
 { 
   "properties": { 
-    "type": "PowerShell" 
+    "type": "PowerShell",
     "runtimeEnvironment": "<runtimeEnvironmentName>" 
   } 
 } 

articles/automation/troubleshoot/extension-based-hybrid-runbook-worker.md

@@ -120,9 +120,6 @@ You can remove this limit and make it unrestricted with the following steps:
   1. systemctl daemon-reload
   1. systemctl restart hwd.service
 
-For more information, see [Linux hybrid jobs get stuck in Running state even if each CPU core usage is less than 25% - Overview](https://supportability.visualstudio.com/AAAP_Code/_wiki/wikis/AAAP/1980067/KI-Linux-hybrid-jobs-get-stuck-in-Running-status-even-if-each-CPU-core-usage-is-less-than-25-).
-
-
 ### Scenario: Runbooks go into a suspended state on a Hybrid Runbook Worker when using a custom account on a server with User Account Control (UAC) enabled
 
 #### Issue

articles/azure-app-configuration/howto-telemetry-javascript.md

@@ -91,82 +91,12 @@ In this tutorial, you use telemetry in your Node.js application to track feature
     export APPLICATIONINSIGHTS_CONNECTION_STRING='applicationinsights-connection-string'
     ```
 
-1. Run the application, [see step 4 of Use variant feature flags](./howto-variant-feature-flags-javascript.md#run-the-application).
-
-1. Create 10 different users and log into the application. As you log in with each user, you get a different message variant for some of them. ~50% of the time you get no message. 25% of the time you get the message "Hello!" and 25% of the time you get "I hope this makes your day!"
-
-1. With some of the users click the **Like** button to trigger the telemetry event.
-
-    > [!div class="mx-imgBorder"]
-    > ![Screenshot of the application with like button clicked.](./media/howto-telemetry-javascript/like-button.png)
-
-1. Open your Application Insights resource in the Azure portal and select **Logs** under **Monitoring**. In the query window, run the following query to see the telemetry events:
-
-    ```kusto
-    // Step 1: Get distinct users and their Variant from FeatureEvaluation
-    let evaluated_users =
-        customEvents
-        | where name == "FeatureEvaluation"
-        | extend TargetingId = tostring(customDimensions.TargetingId),
-                Variant = tostring(customDimensions.Variant)
-        | summarize Variant = any(Variant) by TargetingId;
-
-    // Step 2: Get distinct users who emitted a "Like"
-    let liked_users =
-        customEvents
-        | where name == "Liked"
-        | extend TargetingId = tostring(customDimensions.TargetingId)
-        | summarize by TargetingId;
-
-    // Step 3: Join them to get only the evaluated users who also liked
-    let hearted_users =
-        evaluated_users
-        | join kind=inner (liked_users) on TargetingId
-        | summarize HeartedUsers = dcount(TargetingId) by Variant;
-
-    // Step 4: Total evaluated users per variant
-    let total_users =
-        evaluated_users
-        | summarize TotalUsers = dcount(TargetingId) by Variant;
-
-    // Step 5: Combine results
-    let combined_data =
-        total_users
-        | join kind=leftouter (hearted_users) on Variant
-        | extend HeartedUsers = coalesce(HeartedUsers, 0)
-        | extend PercentageHearted = strcat(round(HeartedUsers * 100.0 / TotalUsers, 1), "%")
-        | project Variant, TotalUsers, HeartedUsers, PercentageHearted;
-
-    // Step 6: Add total row
-    let total_sum =
-        combined_data
-        | summarize
-            TotalUsers = sum(TotalUsers),
-            HeartedUsers = sum(HeartedUsers)
-        | extend
-            Variant = "All",
-            PercentageHearted = strcat(round(HeartedUsers * 100.0 / TotalUsers, 1), "%")
-        | project Variant, TotalUsers, HeartedUsers, PercentageHearted;
-
-    // Step 7: Output
-    combined_data
-    | union (total_sum)
-    ```
-
-    > [!div class="mx-imgBorder"]
-    > ![Screenshot of Application Insights showing the results table with four rows; All, Simple, Long, and None with their respective user counts and percentages.](./media/howto-telemetry-javascript/telemetry-results.png)
-
-    You see one "FeatureEvaluation" event for each time the quote page was loaded and one "Liked" event for each time the like button was clicked. The "FeatureEvaluation" event has a custom property called `FeatureName` with the name of the feature flag that was evaluated. Both events have a custom property called `TargetingId` with the name of the user that liked the quote.
+## Collect telemetry
 
-    For more information about the "FeatureEvaluation" event, go to the [Feature flag telemetry reference](./feature-flag-telemetry-reference.md)
+Deploy your application to begin collecting telemetry from your users. To test its functionality, you can simulate user activity by creating many test users. Each user will experience a different variant of greeting messages, and they can interact with the application by clicking the heart button to like a quote. As your user base grows, you can monitor the increasing volume of telemetry data collected in Azure App Configuration. Additionally, you can drill down into the data to analyze how each variant of the feature flag influences user behavior.
+- [Review telemetry results in App Configuration](./howto-telemetry.md#review-telemetry-results-in-azure-app-configuration).
 
 ## Additional resources
 
 - [Quote of the Day sample](https://github.com/Azure-Samples/quote-of-the-day-javascript)
-
-## Next steps
-
-For the full feature rundown of the JavaScript feature management library, refer to the following document.
-
-> [!div class="nextstepaction"]
-> [JavaScript Feature Management](./feature-management-javascript-reference.md)
+- For the full feature rundown of the JavaScript feature management library you can refer to the [JavaScript Feature Management reference documentation](./feature-management-javascript-reference.md)

articles/azure-app-configuration/howto-telemetry-python.md

@@ -105,70 +105,10 @@ In this tutorial, you use telemetry in your Python application to track feature
     export APPLICATIONINSIGHTS_CONNECTION_STRING='applicationinsights-connection-string'
     ```
 
-1. Run the application, [see step 2 of Use variant feature flags](./howto-variant-feature-flags-python.md#build-and-run-the-app).
-
-1. Create 10 different users and log into the application. As you log in with each user, you get a different message variant for some of them. ~50% of the time you get no message. 25% of the time you get the message "Hello!" and 25% of the time you get "I hope this makes your day!".
-
-1. With some of the users select the **Like** button to trigger the telemetry event.
-
-1. Open your Application Insights resource in the Azure portal and select **Logs** under **Monitoring**. In the query window, run the following query to see the telemetry events:
-
-    ```kusto
-    // Step 1: Get distinct users and their Variant from FeatureEvaluation
-    let evaluated_users =
-        customEvents
-        | where name == "FeatureEvaluation"
-        | extend TargetingId = tostring(customDimensions.TargetingId),
-                Variant = tostring(customDimensions.Variant)
-        | summarize Variant = any(Variant) by TargetingId;
-
-    // Step 2: Get distinct users who emitted a "Like"
-    let liked_users =
-        customEvents
-        | where name == "Liked"
-        | extend TargetingId = tostring(customDimensions.TargetingId)
-        | summarize by TargetingId;
-
-    // Step 3: Join them to get only the evaluated users who also liked
-    let hearted_users =
-        evaluated_users
-        | join kind=inner (liked_users) on TargetingId
-        | summarize HeartedUsers = dcount(TargetingId) by Variant;
-
-    // Step 4: Total evaluated users per variant
-    let total_users =
-        evaluated_users
-        | summarize TotalUsers = dcount(TargetingId) by Variant;
-
-    // Step 5: Combine results
-    let combined_data =
-        total_users
-        | join kind=leftouter (hearted_users) on Variant
-        | extend HeartedUsers = coalesce(HeartedUsers, 0)
-        | extend PercentageHearted = strcat(round(HeartedUsers * 100.0 / TotalUsers, 1), "%")
-        | project Variant, TotalUsers, HeartedUsers, PercentageHearted;
-
-    // Step 6: Add total row
-    let total_sum =
-        combined_data
-        | summarize
-            TotalUsers = sum(TotalUsers),
-            HeartedUsers = sum(HeartedUsers)
-        | extend
-            Variant = "All",
-            PercentageHearted = strcat(round(HeartedUsers * 100.0 / TotalUsers, 1), "%")
-        | project Variant, TotalUsers, HeartedUsers, PercentageHearted;
-
-    // Step 7: Output
-    combined_data
-    | union (total_sum)
+## Collect telemetry
 
-    ```
-
-    > [!div class="mx-imgBorder"]
-    > ![Screenshot of Application Insights showing the results table with four rows; All, Simple, Long, and None with their respective user counts and percentages.](./media/howto-telemetry-python/telemetry-results.png)
-
-    You see one "FeatureEvaluation" for each time the quote page was loaded and one "Liked" event for each time the like button was clicked. The "FeatureEvaluation" event have a custom property called `FeatureName` with the name of the feature flag that was evaluated. Both events have a custom property called `TargetingId` with the name of the user that liked the quote.
+Deploy your application to begin collecting telemetry from your users. To test its functionality, you can simulate user activity by creating many test users. Each user will experience a different variant of greeting messages, and they can interact with the application by clicking the heart button to like a quote. As your user base grows, you can monitor the increasing volume of telemetry data collected in Azure App Configuration. Additionally, you can drill down into the data to analyze how each variant of the feature flag influences user behavior.
+- [Review telemetry results in App Configuration](./howto-telemetry.md#review-telemetry-results-in-azure-app-configuration).
 
 ## Additional resources
 - [Flask Quote of the Day sample](https://github.com/Azure-Samples/quote-of-the-day-python)