The cost of service downtime is growing up. There are reliable reports stating that the cost of downtime ranges from $100,000-$72,000 per minute. Identifying the root-cause (mean-time-to-identification (MTTI) generally takes hours. For a complex situation, the process may run into days. The MTTI is lengthy due to various reasons. There are not many tools to speed up the MTTI process. We need competent tools that enrich the value by correlating the data from different IT tools, such as APM, ITSM, SIEM, and ITOM with open API connectors. As microservices and their instances run on containers, IT teams need to manage millions of data points. This transition mandates for highly advanced and automated tools. The pioneering AI algorithms will be commonly used to automate for precisely finding the root-causes.
Root-cause analysis is being touted as an important post-deployment activity for exactly pinpointing bugs and their roots in any software applications. As articulated previously, any standardized APM solution shows you a stack trace for every error and exception that your application throws. At the top of every exception, the APM software tells the class and method where the error gets originated. But the essence of root-cause analysis is to go further down to the root to understand the causes. There are a few software solutions for simplifying the root-cause analyzes.
The continuous adoption of continuous delivery by significantly shortening testing and release cycles from weeks to days is exponentially increasing the likelihood of introducing errors into production.
OverOps (https://www.overops.com/) analyzes code in staging and production to automatically detect and deliver the root-causes for all errors with no dependency on logging. OverOps shows you a stack trace for every error and exception. However, it also shows you the complete source code, objects, variables, and values that caused that error or exception to be thrown. This assists in identifying the root-cause of when your code breaks. OverOps injects a hyperlink into the exception's link, and you'll be able to jump directly into the source code and actual variable state that cause it. OverOps can co-exist in production alongside all the major APM agents and profilers. Using OverOps with your APM allows monitoring server slowdowns and errors, along with the ability to drill down into the real root-cause of each issue.