Measuring your DevOps’ success rate in your organization is not like a regular term paper you turn in. There is no guaranteed way to give it an A+ because it looks perfect or a B- just because it has some loopholes, according to you. There are several factors associated with it that you need to pay attention to. It stems from how you define DevOps at your workplace.
The word DevOps can mean different things to different people. Some perceive it as a culture, and every vendor in the industry claims that their tools help with DevOps. DevOps can be defined as everything that relates to deploying and monitoring the applications. In many ways, this bends over to site reliability engineering. Sometimes developers deploy directly to the cloud and don’t even have an operations team to collaborate with. How is DevOps going in your organization?
If you need any assistance measuring just how well it is going, here is a list we prepared for some key DevOps metrics to track. These metrics can help you understand how your team is doing over some time.
Here is a list of DevOps metrics to track :
- Measuring lead time
- Measuring mean time to recover
- Measuring change failure rate
- Changing failure rate
Now Let us understand these in detail.
Measuring Lead Time
Lead time gives the time required to implement, test, and deliver a single bit of functionality. Measuring lead time needs starting a clock when development starts and stopping it when said code enters the stage of production. You need to integrate a feature/issue tracker and/or source control data to gather this data. The final implementation depends on how your team performs. Let us analyze a case. The implementation could use pull requests. A developer may open a pull request when they start working and close it when verified after production.
The lead time can also be calculated from the time of the first commit, or when the PR was opened to when it was done merging which signifies that code has been accepted in production. This solution depends on how the team works with branches. The technical process for measuring lead time in your team can be viewed as an implementation detail. It’s more crucial to pick the start and stop points and automate the measurement from there. If it’s not automated, then it won’t happen.
Measuring Deployment frequency
Tracking how often you enforce the deployments is a critical DevOps metric. Ultimately, the goal is to do much smaller deployments as often as possible. Reducing the size of deployments makes it easier to test and release. Counting both production and non-production deployments separately is highly recommended. The frequency of deployments to Quality Assurance or in pre-production environments is also essential. You need to deploy quickly and often in Quality Assurance to ensure time for testing. Finding bugs in QA is essential to keep the defect escape rate under control.
Measuring Mean Time to Recover
This metric helps track how long exactly it takes to recover from failures. A key metric for the business is minimizing failures and recovering from them as fast as possible. It is usually measured in hours and can be referred to as business hours, not clock hours. Having well-performing application monitoring tools to quickly identify the issues and deploy the fix as soon as possible is vital to reducing your MTTR.
Measuring Change Failure Rate
The change failure rate can be measured as the total failed deploys divided by the total deploys. Deployment Frequency provides that value. The first value can be found by an implementation. A small implementation can just count the number of outages or production issues coming from monitoring software. That may be fine to start but neglects a subtly induced distinction.
This metric ideally reveals the flaws in your deployment pipeline and not present in the outside world. Code and changes the team introduces and the outside world may both cause failures and only need to know the former. Consider the case where your infrastructure provider goes down. That shouldn’t act against you. If you can, it may be worth it to flag the production issues and tag them with the related system. This data can be used to calculate the global failure as a whole and the failure rate per system.
Tracking the error rates within your application is very important. They act as an indicator of quality problems, but they are also a reflection of the ongoing performance and uptime-related issues. Proper exception handling practices are critical for implementing any suitable software.
- Bugs – Identify new exceptions being thrown in your code after a deployment
- Production issues – Capture issues with database connections, query timeouts, and other related issues.
Errors are bound to occur for most applications. A few errors here and there form just the noise in a busy system. However, pulse on your error rates must be monitored and look for spikes.
Change failure rate
The change failure rate estimates how often deployment failures occur in production that requires an immediate remedy, for example, particularity, rollbacks.
If you want to scale newer heights with DevOps at your organization, our list of DevOps metrics will hopefully assist and give you some ideas of what to track and improve. DevOps’ goal is collaboration, getting developers more involved in the deployment process, application monitoring, and building a fantastic product/service.
How Cloudanix can help?
We at Cloudanix help audit. monitor and secure your cloud workloads. As a DevOps practicing team, you can focus on the core business issues and leave the grunt work for the platform which we have created to help you.