Logstash is a popular open source data processing pipeline used for collecting, enriching, and transforming logs and other events. With a wide range of plugins, Logstash can easily be integrated with other tools and services like Elasticsearch, Kibana, and AWS services. It is a great tool for centralized log management and analytics.
The prerequisites for monitoring Logstash with Netdata are to have Logstash 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 Logstash monitoring please read the collector documentation.
You should now see the Logstash 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 Threads are the threads that are running inside the Java Virtual Machine (JVM). Monitoring JVM Threads is important, as it provides insight into the amount of work that the JVM is doing. By monitoring the number of threads, we can detect if there is an increase in the number of threads, which can indicate resource contention or other performance issues.
The JVM Memory Heap is a collection of memory blocks that are allocated to the JVM for running Java applications. Monitoring the Heap provides insight into the amount of memory that is being used for the application. By monitoring the Heap, we can detect if the application is running out of memory, or if there is an increase in memory usage, which can indicate a performance issue.
The JVM Memory Pools are collections of memory blocks that are allocated to the JVM for running Java applications. Monitoring the Memory Pools provides insight into the amount of memory that is being used for the application. By monitoring the Memory Pools, we can detect if the application is running out of memory, or if there is an increase in memory usage, which can indicate a performance issue.
JVM GC Collector Count is the number of garbage collection cycles that are executed by the JVM. Monitoring the GC Collector Count provides insight into the performance of the JVM. By monitoring the GC Collector Count, we can detect if there is an increase in the number of garbage collection cycles, which can indicate a performance issue.
JVM GC Collector Time is the amount of time that the JVM spends performing garbage collection cycles. Monitoring the GC Collector Time provides insight into the performance of the JVM. By monitoring the GC Collector Time, we can detect if there is an increase in the amount of time spent performing garbage collection cycles, which can indicate a performance issue.
Open File Descriptors are the number of open files that the JVM has open. Monitoring Open File Descriptors provides insight into the amount of resources that the JVM is using. By monitoring Open File Descriptors, we can detect if there is an increase in the number of open files, which can indicate a resource contention issue.
Events are the number of events that are processed by Logstash. Monitoring Events provides insight into the amount of work that Logstash is doing. By monitoring Events, we can detect if there is an increase in the number of events, which can indicate a performance issue.
Event Duration is the amount of time that events take to be processed by Logstash. Monitoring Event Duration provides insight into the performance of Logstash. By monitoring Event Duration, we can detect if there is an increase in the amount of time that events are taking to be processed, which can indicate a performance issue.
Uptime is the amount of time that Logstash has been running. Monitoring Uptime provides insight into the availability of Logstash. By monitoring Uptime, we can detect if Logstash is not available, which can indicate an availability issue.
Pipeline Events are the number of events that are processed by Logstash pipelines. Monitoring Pipeline Events provides insight into the amount of work that Logstash pipelines are doing. By monitoring Pipeline Events, we can detect if there is an increase in the number of events, which can indicate a performance issue.
Pipeline Event Duration is the amount of time that events take to be processed by Logstash pipelines. Monitoring Pipeline Event Duration provides insight into the performance of Logstash pipelines. By monitoring Pipeline Event Duration, we can detect if there is an increase in the amount of time that events are taking to be processed, which can indicate a performance issue.