Puppet is an open source configuration management tool used to deploy, manage and automate applications across multiple environments. It allows users to easily automate the process of setting up, configuring and managing infrastructure, applications and services.
The prerequisites for monitoring Puppet with Netdata are to have Puppet and Netdata installed on your system.
Netdata auto discovers hundreds of services, and for those it doesn’t turning on manual discovery is a one line configuration. For more information on configuring Netdata for Puppet monitoring please read the collector documentation.
You should now see the Puppet section on the Overview tab in Netdata Cloud already populated with charts about all the metrics you care about.
Netdata has a public demo space (no login required) where you can explore different monitoring use-cases and get a feel for Netdata.
JVM Heap is the memory allocated by the JVM to store objects created in the Java application. It is important to monitor heap memory usage to ensure that the application is not running out of memory, which can lead to application crashes and degraded performance. The two main metrics to monitor are the committed memory (allocated from the OS) and the used memory (actual use). If the committed memory is consistently close to the maximum amount of memory allocated, it may indicate that the application is running out of memory and an increase in memory may be required.
JVM Non-Heap is the memory allocated by the JVM for storing class definitions and other objects. It is important to monitor non-heap memory usage to ensure that the application is not running out of memory, which can lead to application crashes and degraded performance. The two main metrics to monitor are the committed memory (allocated from the OS) and the used memory (actual use). If the committed memory is consistently close to the maximum amount of memory allocated, it may indicate that the application is running out of memory and an increase in memory may be required.
CPU usage is an important metric to monitor as it indicates the amount of processing and computing power that is being used by the application. Monitoring CPU usage can help identify potential performance and bottleneck issues, as well as indicate when additional resources are needed to meet the demands of the application. The two main metrics to monitor are the execution and GC (taken by garbage collection). If the execution is consistently high and the GC is low, this could be an indication that the application is under-utilizing the available resources.
File descriptors are an important metric to monitor as they indicate the number of open files or resources that are being used by the application. Monitoring file descriptors can help identify potential performance issues or bottlenecks, as well as indicate when additional resources are needed to meet the demands of the application. The two main metrics to monitor are the max and used. If the used file descriptors is consistently close to the maximum number of allowed file descriptors, it may indicate that the application is running out of memory and an increase in resources may be required.