Tomcat server monitoring and performance management made easy with Site24x7 plugins.
Efficient monitoring of your Apache Tomcat servers is essential as poor performance can have a direct influence on the end-user experience. With Site24x7 Tomcat plugin, get a detailed view of all the critical Tomcat service metrics in a single, intuitive dashboard.
This document details how to configure the Apache Tomcat plugin and the monitoring metrics for providing in-depth visibility into the performance, availability, and usage stats of Tomcat servers.
Apache Tomcat server monitoring metrics:
Monitor your Tomcat server with Site24x7 plugins and get insightful visualizations and timely alerts. Metrics for monitoring Tomcat server falls into three categories: thread pool metrics, overall memory metrics and usage metrics for each memory pool.
Various out-of-the-box metrics we support are:
Total number of threads
The thread_count metric is critical for tracking the resource consumption. This helps to prevent bottlenecks and detect thread pool exhaustion.
Busy threads count and total number of threads allowed
Comparing the thread_busy and thread_count with the thread_allowed, the user can infer on the resource consumption.
Bytes received and bytes sent
Relate the thread count metrics with the bytes_received and bytes_sent to get visibility on the capacity of your web server
Total number of errors
The error_count gives the number of errors per second on all request processors
Number of requests
The request_count metric gives the number of requests per second across all request processors
The free_memory shows the amount of heap space currently available to the Java Virtual Machine (JVM)
The max_memory is the maximum size of the heap space not including the permanent generation area
Time taken for processing the request
The sum of request processing times across all requests handled by the request processors is shown under processing_time
The total_memory is the memory currently allocated to the JVM heap space
Usage stats of each memory pool
Data from the metric usage_"name of the memory pool" gives the user a quick look on the memory usage. This can help manage memory resources proactively and resolve downtimes in production.
How it works?
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