The content discusses the importance of monitoring micrometrics to effectively forecast application performance and availability, particularly focusing on memory, thread, network, and storage metrics. By analyzing metrics like garbage collection throughput, thread states, and IOPS, organizations can identify potential issues early, enhancing application reliability and performance.
Engineers utilize Garbage Collection (GC) log analysis not only for troubleshooting and tuning purposes but also to lower AWS costs, catch performance defects during testing, prevent production failures, and identify issues during code commits. By leveraging micro metrics, organizations can optimize memory settings and enhance application performance effectively.
GC logs detail the time taken for GC events, reporting 'user', 'sys', and 'real' times. 'Real' time is the elapsed clock time, while 'user' and 'sys' times reflect CPU time in user-mode and kernel, respectively. Real time can exceed user + sys time due to heavy I/O activity or lack of CPU, indicating potential performance issues.
On a major B2B application different GC algorithms behaviors were studied. This application is basically a web service provider servicing SOAP and REST requests from its clients.