Building a Log Analytics Dashboard for Application Monitoring in Azure

Monitoring application performance and logs in real-time is essential for maintaining high availability and performance in modern web applications. In this blog, we’ll explore how to create a Log Analytics Dashboard using Azure Log Analytics, track metrics and logs of a web application, and implement critical alerts for issue detection.

Project 5: Building a Log Analytics Dashboard for Application Monitoring in AzureBuilding a Log Analytics Dashboard for Application Monitoring in Azure

Overview of Log Analytics Dashboard

The project involves:

1. Setting up centralized log collection using Azure Log Analytics.

2. Writing queries using Kusto Query Language (KQL) to extract insights from logs.

3. Creating a custom Azure Monitor dashboard for performance tracking.

4. Configuring Azure Alerts for critical log patterns.

   
   

By the end of this project, you will have a real-time monitoring solution to ensure your application runs smoothly.

Step-by-Step Guide

Step 1: Set Up Azure Log Analytics Workspace

1. Log in to Azure Portal.

2. Navigate to Monitor > Log Analytics Workspaces.

3. Click Create:

   • Choose your subscription and resource group.

   • Provide a name for your workspace (e.g., MyAppLogWorkspace).

   • Select the appropriate region.

4. Click Review + Create and then Create.

   
   

Step 2: Connect Your Web Application to Log Analytics

1. Navigate to your Web App in Azure.

2. Go to Diagnostics settings under the Monitoring section.

3. Click + Add diagnostic setting:

   • Give your setting a name (e.g., AppDiagnostics).

   • Check Send to Log Analytics workspace and select your workspace.

   • Select the logs and metrics you want to collect (e.g., AppServiceHTTPLogs, ApplicationLogs, and PerformanceCounters).

4. Click Save.

   
   

Step 3: Writing KQL Queries to Analyze Logs

Now that logs are being sent to your Log Analytics Workspace, you can use KQL to query the data.

1. Navigate to your Log Analytics Workspace and click on Logs.

2. Try some sample queries:

3. View application logs:

kql

AppServiceHTTPLogs | where TimeGenerated > ago(1h) | project TimeGenerated, StatusCode, RequestUri, DurationMs

Find slow requests:

kql

AppServiceHTTPLogs | where DurationMs > 1000 | order by DurationMs desc

Count errors by status code:

kql
AppServiceHTTPLogs | summarize Count = count() by StatusCode

Save your queries for later use in the dashboard.

Step 4: Build a Custom Azure Monitor Dashboard

1. Navigate to Monitor > Dashboards.

2. Click New Dashboard and choose + Add Tile.

3. Select Log Analytics and link your workspace.

4. Use your saved KQL queries to add visualizations:

   • Charts for request duration.

   • Tables for error counts and status codes.

5. Organize tiles for a clean layout and save your dashboard.

   
   

Step 5: Set Up Alerts for Critical Patterns

1. Navigate to Monitor > Alerts > + New Alert Rule.

2. Define the scope:

   • Select the Log Analytics Workspace associated with your application.

3. Set a condition:

4. Use a Log Query and input a critical condition:

kql

AppServiceHTTPLogs | where StatusCode >= 500 | summarize Count = count() | where Count > 5

1. Configure the action group:

   • Choose Email, SMS, or Webhook for notifications.

2. Provide an alert name (e.g., HighErrorRateAlert) and Enable the alert.

   
   

Best Practices

• Query Optimization: Optimize your KQL queries by filtering unnecessary data to improve performance.

• Alert Fine-Tuning: Test and adjust alert thresholds to minimize noise from false positives.

• Dashboards: Regularly review and enhance dashboard visualizations for clarity and usability.

Conclusion

By leveraging Azure Log Analytics, KQL, and Azure Monitor, you can set up a robust monitoring solution for your web application. This ensures real-time insights into performance and error patterns, empowering you to respond to issues proactively.

With this setup, you’re equipped to deliver a reliable, high-performance application to your users.