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GLU.Monitoring Deployment Guidelines

The GLU.Engine, which runs on a Java Virtual Machine (JVM), leverages Java Management Extensions (JMX) to present internal metrics and performance data. JMX, a standard for managing and monitoring Java applications, enables the GLU.Engine and its surrounding JVM to expose management information and control capabilities to remote clients.

List of the compatible monitoring tools

As such this allows GLU.Engines to provide monitoring support for many of the top monitoring tools through JMX hooks , including:

  1. Nagios
  2. Zabbix
  3. Datadog
  4. New Relic
  5. AppDynamics
  6. SolarWinds
  7. Splunk
  8. Prometheus
  9. InfluxDB

These tools provide plugins or integrations that allow you to monitor specific metrics exposed by the GLU.Engine via JMX. With JMX monitoring, you can track important performance metrics such as heap memory usage, garbage collection times, and thread count, as well as monitor custom MBeans and inbound and outbound connections. JMX monitoring can be performed locally on the same host as the Java application, or remotely over JMX protocol. The best tool for your organisation will depend on your specific requirements and the size and complexity of your Java application.

In addition to the above list which GLU will support through JMX hooks GLU has also tested the integration of monitoring through Hawtio and Dynatrace.

Example of GLU integrated with Hawtio to view metrics

Hawtio is an open-source, web-based management console for Java applications. It provides a unified and flexible way to manage and monitor Java applications, microservices, and containers. More details available at http://hawt.io

This example below shows how a hawtio monitoring tool can be configured to point at a GLU.Engine through the server port.Below are the key values which need to be put in hawtio to setup the connection.

Use this link to download and run a Hawtio executable app on your local machine.

HostIP Address the GLU.Engine is running on

Once the details are the following screen will appear.

After the Welcome screen has been displayed the screen showing the metrics will be displayed.

The Hawtio metrics screen displays a wide range of metrics, including those for all API inbound connectors, as well as connectors handled through orchestration. In addition to these, the screen also displays key performance metrics such as heap memory usage, garbage collection times, and thread counts.

Hawtio is able to retrieve data from the JMX hooks, allowing users to get a real-time view of how their GLU.Engine is performing.

Hawtio goes beyond just monitoring JMX data, providing additional features that allow users to dive deep into the inner workings of their GLU.Engine. With Hawtio, users can view detailed information about their engine’s threads, memory usage, and garbage collection times. Additionally, Hawtio provides powerful tools for analyzing the performance of their engine, such as a profiler and heap analyzer.

One of the key benefits of using Hawtio and JMX to monitor the GLU.Engine is that it allows users to quickly identify performance bottlenecks and issues. For example, if the engine’s heap memory usage is increasing rapidly, this could indicate that there is a memory leak. By using Hawtio to monitor the engine, users can quickly identify the cause of the issue and take corrective action.

Another benefit of using Hawtio and JMX to monitor the GLU.Engine is that it allows users to track key performance metrics over time. This allows them to identify trends and patterns in their engine’s performance, which can help them to optimize their engine and ensure that it continues to perform well even under heavy workloads.

In conclusion, the Java Management Extensions (JMX) and Hawtio provide a powerful set of tools for managing and monitoring the GLU.Engine. With these tools, users can get a real-time view of their engine’s performance, identify performance bottlenecks, and track key performance metrics over time. By using JMX and Hawtio, users can ensure that their GLU.Engine continues to perform well, even under heavy workloads.

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