Lynn Reporting - HEALTH

This report presents a layout similar to the mapping offered by Application Map, a feature of Azure monitoring. It is an extensible Application Performance Management (APM) service for web developers across multiple platforms.

The application map allows you to see the complete topology of the application across multiple levels of related components. The application map locates components by following the HTTP dependency calls made between servers with the Application Insights SDK installed.

Application Insights is a service offered by Microsoft Azure that provides tools for monitoring, analyzing, and detecting performance errors in applications hosted in the cloud. Additionally, it offers the ability to insert custom traces and log errors in these applications.

It has three buttons in the upper left corner; they are listed from left to right:

1. To go back in case you have entered a specific node.
2. To adjust the view if you have zoomed in on the diagram.
3. To change the view of the diagram, either to an organic view or a hierarchical view.

A key objective of the application map experience is to help visualize the application's topology.

Next to each connector, the number of calls between the different components and services of the application is displayed, including both directions if applicable.

The connections between the various elements of the application are represented by lines (or call lines), which show the paths that requests follow. These lines indicate the number of calls and the average response time between components.

When clicking on the nodes, a table will appear on the left side with details for consulting and further investigating the data of your application. You can view the communication logs sorted by date, and it is possible to expand each one within the table to see additional details. By 'node,' we refer to the connector linked to the communication channels configured in the tenant and the log source servers.

When an error occurs, the connectors will be highlighted in red, indicating the occurrence of error calls expressed as a percentage. This translates into assistance for service health investigations and allows for reducing resolution time by highlighting only the failures that need to be investigated.

Additional service health data

If you want to see more reports than those shown in the table, a Show More button will appear, opening a view with all the records contained in the selected date range for analysis.

This section of the report provides an overview of the performance and stability of the application concerning the tenant in question.

All the charts that appear in the report include an interactive legend that allows for a more dynamic exploration of the data. Each element of the legend represents a data group. By clicking on one of these elements, the values associated with that variable disappear from the chart, allowing you to focus on the behavior of the other categories and improving interpretation.

Logs Table

This table displays the records generated by the application over time. These records, also known as logs, are essential for monitoring and diagnosing the application's performance.

It contains three columns:

1. Date: Shows the date when the event occurred.
2. Status: Includes the status codes that web servers return in response to HTTP requests.
   • 1xx (Informational): Indicates that the request was received and the process is continuing. These are not commonly used in server logs.
   • 2xx (Success): Indicates that the request was received, understood, and successfully processed.
   • 3xx (Redirection): Indicates that further action is needed to complete the request.
   • 4xx (Client Error): Refers to issues with the request from the client.
   • 5xx (Server Error): Indicates that the server encountered an error while processing the request.
3. This column contains a button that allows you to view more details in the Log Details table.

Performance Chart

It shows a line chart that represents the number of error logs and successful logs over time, allowing for the visualization of the application's performance trends and the detection of behavior patterns.

The X-axis represents time, showing how the logs change over different intervals.

The Y-axis shows the number of logs, allowing you to see how many events (successful and with errors) occurred at a specific time.

The chart has two lines to display the information:

• Error Line: Represents the number of error logs. This line can help identify peaks or patterns in the errors.
• Successful Logs Line: Shows the number of successful logs. This line allows you to observe the expected behavior and its relation to errors.

By analyzing the trend of both lines, it is possible to identify correlations between increases in errors and decreases in successful logs, which may indicate problems in system performance or stability. Furthermore, analyzing this chart over time helps visualize the service's reliability and assess whether errors are decreasing, remaining constant, or increasing.

Time Monitoring

It shows a line chart that represents the average response time of an application and the average latency time, which is the time it takes to receive a response from the service provider of the configured channel.

On the X-axis, time is represented. This axis may be divided into intervals such as days or hours, depending on the period being analyzed.

The Y-axis shows the time in milliseconds, allowing for the observation of the performance metrics of the application and the bot.

Lines of the Chart:

• Average Response Time Line of the Application: This line represents the time it takes for the application to respond to user requests. A lower response time indicates optimal performance, while an increase in this time may signal performance issues or system overload.
• Average Latency Time Line of the Bot: This line indicates the time the bot takes to process information and generate a response. The bot's latency can be affected by various factors, such as server load, the complexity of queries, or wait times in connecting with other APIs.

By observing both lines, you can identify behavior patterns. For example, if the average response time of the application increases while the bot's latency remains constant, it may indicate that the application is experiencing issues, regardless of the bot's performance.

Log Details

It displays a table with the details of the selected record in the Logs Table, providing detailed and specific information about each record in a clear and easy-to-interpret format. The table has two columns:

• Key: Represents the name or identifier of the data field.
• Value: Shows the specific content or data associated with each key for that particular record.

This format allows for quick analysis of relevant information about a specific event or record. Additionally, it makes searching for details easier, as each key represents a unique and well-defined field.