Any agile DevOps team knows testing and analysis are vital to successful production cycles. However the best ones understand how to test and analyze their own workflow in order to optimize their own processes. That’s where MTTR and MTTI come in.
MTTR and MTTI: Critical DevOps Metrics
What is MTTR? Mean Time to Resolve (MTTR) is the average time between the start and resolution of an incident. But first you have to identify the problem.
That’s why Mean Time to Identify (MTTI) is also an important key performance indicator (KPI).
As DevOps teams release more often and automate more, performance and availability problems have increased. The result is Ops is spending more time troubleshooting and development is drawn into production troubleshooting. Reducing MTTR and MTTI is more crucial than ever.
How to Improve Your MTTR
By monitoring deployments in real time, you can drastically improve MTTI from days to minutes. Sumo Logic delivers a comprehensive strategy for monitoring application and system events, stats, network traffic and logs all in real time. So you can be proactive in identifying unexpected conditions and undesired behaviors.
Once you’ve identified an issue, Sumo Logic provides tools to help you to quickly troubleshoot issues, perform root-cause analysis and dramatically decrease MTTR.
For example, tools like Live Tail let you tail log files and apply pattern searches to bring up near real-time metrics within seconds. This can significantly lower the time developers spend troubleshooting issues in production and ultimately reduce MTTR.
Using Machine Learning for Better MTTR
Existing approaches for application monitoring and application performance management are no longer sufficient to provide the complete view into the volume, variety, and velocity of data being generated across the full stack, from bare metal to microservices.
Using the Sumo Logic LogReduce engine and LogCompare tool, you can harness the power of machine learning to reduce the noise within your logs and identify key patterns. Using these patterns, developers can identify and remediate bugs and issues within code or the application, among other things.
LogCompare takes this one step further, providing the ability to compare the key log patterns and signatures from one period of time to another.
With built-in pattern detection, anomaly detection, transaction analytics, outlier detection, and predictive analytics, Sumo Logic provides real-time visibility across thousands of data streams and seamlessly detects and predicts conditions that indicate potential performance, reliability or security issues.
Demo of LogReduce from Sumo Logic
See how Sumo Logic’s LogReduce can become an integral part of your DevOps toolbox.