Merge branch 'MicrosoftDocs:release-aio-2507' into release-aio-2507

Author: yujinkim-msft

.openpublishing.redirection.json

@@ -6894,6 +6894,11 @@
       "redirect_url": "/azure/governance/policy/samples/hipaa-hitrust",
       "redirect_document_id": false
     },
+    {
+      "source_path": "articles/reliability/migrate-workload-aks-mysql.md",
+      "redirect_url": "/azure/reliability/availability-zones-migration-overview",
+      "redirect_document_id": true
+    },
     {
        "source_path": "articles/private-5g-core/disconnected-mode.md",
        "redirect_url": "/azure/private-5g-core/azure-stack-edge-disconnects",
@@ -6904,6 +6909,7 @@
       "redirect_url": "/azure/signups/overview",
       "redirect_document_id": false
     }
+    
   ]
 }
 

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/enable-dynamic-configuration-gin-web-app.md

@@ -40,7 +40,7 @@ Finish the quickstart: [Create a Gin web app with Azure App Configuration](./qui
     > [!TIP]
     > You can set the `Interval` property of the `RefreshOptions` to specify the minimum time between configuration refreshes. In this example, you use the default value of 30 seconds. Adjust to a higher value if you need to reduce the number of requests made to your App Configuration store.
 
-2. Update your `main.go` file to register a callback function for configuration updates:
+1. Update your `main.go` file to register a callback function for configuration updates:
 
     ```golang
     // Existing code
@@ -59,11 +59,21 @@ Finish the quickstart: [Create a Gin web app with Azure App Configuration](./qui
             return
         }
     })
+
+    // The rest of existing code
+    //... ...
     ```
 
-3. Add a configuration refresh middleware. Update *`main.go`* with the following code.
+1. Add a configuration refresh middleware. Update *`main.go`* with the following code.
 
     ```golang
+    // Existing code
+    // ... ...
+    type App struct {
+        Name      string
+        Port      int
+    }
+
     func configRefreshMiddleware(provider *azureappconfiguration.AzureAppConfiguration) gin.HandlerFunc {
         return func(c *gin.Context) {
             // Start refresh in a goroutine to avoid blocking the request
@@ -79,9 +89,12 @@ Finish the quickstart: [Create a Gin web app with Azure App Configuration](./qui
             c.Next()
         }
     }
+
+    // The rest of existing code
+    //... ...
     ```
 
-4. Use the configuration refresh middleware:
+1. Use the configuration refresh middleware:
 
     ```golang
     // Existing code
@@ -110,20 +123,21 @@ Now that you've set up dynamic configuration refresh, let's test it to see it in
 1. Run the application.
 
    ```bash
-   go run main.go
+    go mod tidy
+    go run .
    ```
 
-2. Open a web browser and navigate to `http://localhost:8080` to access your application. The web page looks like this:
+1. Open a web browser and navigate to `http://localhost:8080` to access your application. The web page looks like this:
 
     :::image type="content" source="./media/quickstarts/gin-app-refresh-before.png" alt-text="Screenshot of the gin web app refresh before.":::
 
-3. Navigate to your App Configuration store and update the value of the `Config.Message` key.
+1. Navigate to your App Configuration store and update the value of the `Config.Message` key.
 
     | Key                    | Value                                  | Content type       |
     |------------------------|----------------------------------------|--------------------|
     | *Config.Message*       | *Hello from Azure App Configuration - now with live updates!*               | Leave empty        |
 
-4. After refreshing the browser a few times, you'll see the updated content once the ConfigMap is updated in 30 seconds.
+1. After refreshing the browser a few times, you'll see the updated content once the ConfigMap is updated in 30 seconds.
 
     :::image type="content" source="./media/quickstarts/gin-app-refresh-after.png" alt-text="Screenshot of the gin web app refresh after.":::
 

articles/azure-app-configuration/enable-dynamic-configuration-go-console-app.md

@@ -41,26 +41,27 @@ In this quickstart, you'll enhance a basic Go console application to dynamically
     > [!TIP]
     > You can set the `Interval` property of the `RefreshOptions` to specify the minimum time between configuration refreshes. In this example, you use the default value of 30 seconds. Adjust to a higher value if you need to reduce the number of requests made to your App Configuration store.
 
-2. Open the file *`unmarshal_sample.go`* and add the following code to your main function:
+2. Open the file *`main.go`* and add the following code to your main function:
 
     ```golang
-    // Existing code in unmarshal_sample.go
+    // Existing code in main.go
     // ... ...
-    fmt.Printf("Timeout: %d seconds\n", config.App.Settings.Timeout)
-    fmt.Printf("Retry Count: %d\n", config.App.Settings.RetryCount)
-
-    // Register refresh callback to update and display the configuration
-    provider.OnRefreshSuccess(func() {
-        // Re-unmarshal the configuration
-        err := appCfgProvider.Unmarshal(&updatedConfig, nil)
-        if err != nil {
-            log.Printf("Error unmarshalling updated configuration: %s", err)
-            return
-        }
-        
-        // Display the updated configuration
-        displayConfig(config)
-    })
+	fmt.Println("\nRaw JSON Configuration:")
+	fmt.Println("------------------------")
+	fmt.Println(string(jsonBytes))
+	
+    // Register refresh callback to update the configuration
+	provider.OnRefreshSuccess(func() {
+		var updatedConfig Config
+		// Re-unmarshal the configuration
+		err := provider.Unmarshal(&updatedConfig, nil)
+		if err != nil {
+			log.Printf("Error unmarshalling updated configuration: %s", err)
+			return
+		}
+
+		fmt.Printf("Message: %s\n", updatedConfig.Message)
+	})
 
     // Setup a channel to listen for termination signals
     done := make(chan os.Signal, 1)
@@ -99,7 +100,8 @@ In this quickstart, you'll enhance a basic Go console application to dynamically
 1. Run your application:
 
    ```bash
-   go run unmarshal_sample.go
+    go mod tidy
+    go run .
    ```
 
 2. Keep the application running.

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)