Mean Time Between Incidents (MTBI) helps measure the reliability of a system by calculating the average time between consecutive incidents or failures. And we believe this is a supremely important metric in the world of Site Reliability Engineering (SRE).
A high MTBI indicates that the system is stable and functioning well, which translates to a better user experience and minimal disruptions in service. For example, if my payment service keeps going down every Tuesday between 8 PM to 10 PM due to high error rates, I know I need to remediate this to improve my MTBI.
MTBI is often used in conjunction with other metrics, such as Mean Time to Recover (MTTR) and Service Level Objectives (SLOs), to evaluate a system's overall reliability and performance. By tracking these metrics, SRE teams can gain insights into the overall health and resilience of the system, and continuously improve and optimize system reliability.
Additional reading — Do check out Sidu’s post on MTTRs. He argues that the management of engineering health is stuck in the stone age and why MTTRs should be a vital business metric to measure the efficacy of engineering.
Particularly in the world of SRE, measuring MTBI can help teams identify areas where improvements need to be made, such as increasing redundancy, improving monitoring, or reducing the system's complexity.
It's worth noting that MTBI is not the only metric that should be used to evaluate system reliability, as it only measures the time between incidents and not the severity or impact of those incidents. Therefore, using MTBI in conjunction with other metrics is essential to get a complete picture of system reliability.
However, this is a valuable metric to track because it provides an objective measure of the reliability of a system over time. By continuously measuring and tracking MTBI, SRE teams can identify trends and patterns that can help them proactively address potential issues before they become major incidents.
How do you track MTBI
To track MTBI, you must first define what constitutes an "incident" or "failure" for your system. This could include service outages, errors, crashes, or other issues impacting user experience. Once you have defined what constitutes an incident, you can start tracking the time between incidents.
To calculate MTBI, you divide the total uptime of the system by the number of incidents that occurred during that uptime period. For example, if your system had 100 days of uptime and experienced ten incidents during that time, the MTBI would be ten days. Although this is not an exact measurement, this can be a starting point to measure MTBI.
To track MTBI over time, you should record the uptime and incidents for your system regularly, such as daily or weekly. You can then calculate the MTBI for each period and track how it changes over time.
At Last9, we believe SLOs and early warnings can help improve MTBI over time as it accurately depicts system health because incidents are inevitable. An Improving SLO will result in lesser incidents and higher MTBI.
An example of improving MTBI: A company could reduce the frequency and impact of database-related incidents by adding additional database replicas. This simple task helped reduce recurring database outages and improved overall reliability.
Over time, the team reduced the MTBI from several days to several weeks, translating it into a much more stable and reliable system. Their improvements improved not only the experience for their customers but also increased the overall business value of the system.
This anecdote highlights the importance of tracking MTBI and other metrics to identify areas where improvements can be made. By continuously monitoring and analyzing system behavior, SRE teams can proactively address potential issues and improve system reliability and performance.
MTBI vs. MTTD vs. MTTR
MTBI measures the average time between incidents or failures, while MTTD measures the average time to detect an incident or failure. MTBI and MTTD (Mean Time to Detect) are important metrics for evaluating system reliability and performance, but they measure different aspects of system behavior.
Some other metrics that could be useful to track alongside MTBI include the following:
- Mean Time to Detect (MTTD): This metric measures the average time it takes to detect an incident or failure. A low MTTD can indicate that monitoring and alerting systems are working effectively, which can help prevent incidents from occurring in the first place.
- Mean Time to Repair (MTTR): This metric measures the average time it takes to repair a system after an incident or failure. A low MTTR can indicate that incident response processes are practical and efficient, which can help minimize downtime and reduce the impact of incidents on users.
- Error rates: This metric measures the frequency and severity of errors in the system. By tracking error rates, SRE teams can identify areas where improvements need to be made to reduce the likelihood of errors occurring.
- Resource utilization: This metric measures how effectively system resources are being used. By tracking resource utilization, SRE teams can identify areas where resource usage can be optimized, which can help improve system performance and reliability.
By tracking and analyzing multiple metrics, SRE teams can gain a more comprehensive understanding of their system.
Want to know more about Last9? Check out last9.io; we're building SRE tools to make running systems at scale fun and embarrassingly easy. 🟢