4 Key DevOps Metrics – How To Measure and Improve It
To fulfil the promises of DevOps – delivering more quality products quicker – teams have to gather, analyze and measure a wide range of metrics. These DevOps metrics provide the crucial information DevOps teams need to have visibility and control over their development pipeline.
What are DevOps Metrics?
DevOps metrics are the data points that show the performance of a DevOps software development pipeline and help identify and eliminate the issue in the process. These metrics are used to monitor the technical capabilities and team processes.
In essence, DevOps blurs the border between operations and development teams, allowing for greater collaboration between system administrators and developers. Metrics enable DevOps teams to monitor and evaluate collaborative workflows and keep track of progress in achieving high-level objectives, such as improved quality, speedier releases, and enhanced application performance.
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4 Essential DevOps Metrics
Although many measures are used to evaluate DevOps performance, each DevOps team should track the four most important metrics.
1. Lead time for changes
One of the crucial DevOps metrics to monitor is the lead time for the changes. Don’t confuse this with cycle time (discussed below). Lead time for changes measures the time it takes for committed code to enter production.
This metric determines how quickly your team responds to application-specific issues. Shorter lead times are preferable, although a longer lead time does not always indicate a problem. It might just show a complicated project that takes longer than expected. Lead time for changes helps teams determine how effective their process are.
To calculate lead time for changes, you must record when the commit occurred and when the deployment occurred. Implementing quality assurance testing across numerous development environments and automating testing and DevOps processes are two major strategies to increase this metric.
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2. Change failure rate
The change failure rate is the percentage of deployments that fail in production and necessitate a bug repair or rollback.
The change failure rate considers how many deployments were tried and how many of those deployments failed when released to production. This measure assesses the stability and efficiency of your DevOps procedures. To compute the change failure rate, you’ll need the total number of deployments and the ability to link them to bug-related incident reports, labels on GitHub problems, issue management systems, etc.
A change failure rate of more than 40% may suggest weak testing processes, implying that teams may need to make more changes than necessary, degrading productivity.
Evaluating the change failure rate is to automate additional DevOps operations. Increased automation results in more consistent and dependable software that is more likely to succeed in production.
Read more: The DevOps RoadMap for Developers
3. Deployment Frequency
The deployment frequency is the frequency a team successfully releases to production.
More firms implement continuous integration/continuous delivery (CI/CD), and teams may release more regularly, typically several times daily. A high deployment frequency allows businesses to offer bug fixes, upgrades, and new features faster. It also means that developers may get crucial real-world input faster, allowing them to prioritize repairs and new features that will have the greatest impact.
Deployment frequency measures both short-term and long-term efficiency. For example, you may calculate how effectively your team responds to process changes by analyzing deployment frequency on a daily or weekly basis. Tracking deployment frequency over time might reveal whether your deployment velocity is increasing. It can also show any bottlenecks or service delays that must be resolved.
4. Mean time to restore service
The mean time to restore (MTTR) service determines how long it takes an organization to recover from a production failure.
In a world where 99.999% availability is the norm, evaluating MTTR is essential for ensuring resilience and stability. In unforeseen outages or service degradations, MTTR assists teams in determining which response procedures require improvement. To calculate MTTR, you must know when an issue happened and when it was successfully handled. It’s also helpful to know what deployment fixed the issue and to assess user experience data to determine whether the service has been properly restored.
Most systems have an MTTR of less than one hour, while some have an MTTR of less than one day. Anything that takes more than a day might suggest a lack of warning or monitoring, resulting in more affected systems.
To achieve quick MTTR metrics, implement software in tiny increments to decrease risk and deploy automated monitoring tools to prevent failure.
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How can we measure, apply and improve the DevOps metrics
Lead time for changes
High-performing teams often track lead times in hours, whereas medium and low-performing teams track lead times in days, weeks, or even months.
Test automation, trunk-based development, and working in small batches are major factors in reducing lead time. These methods allow developers to obtain immediate feedback on the quality of the code they contribute, allowing them to detect and correct any flaws. Long lead times are usually the result of developers working on major changes in different branches and relying on manual testing for quality control.
Change failure rate
Change failure rates in high-performing teams range from 0 to 15%.
The same practices that enable shorter lead times — test automation, trunk-based development, and working in small batches — correlate with reducing change failure rates. All of these methods make it much easier to discover and correct flaws.
Track and report change failure rates not just for discovering and correcting issues but also to guarantee that new code releases match security standards.
Deployment Frequency
High-performing teams can deliver changes on-demand and frequently do so throughout the day. Lower-performing teams are frequently confined to weekly or monthly deployments.
The ability to deploy on-demand necessitates using an automated deployment pipeline that integrates the automated testing and feedback methods mentioned in earlier sections and minimizes the need for human intervention.
Mean time to restore service
High-performing teams recover rapidly from system failures — generally in less than an hour — While lower-performing teams may take up to a week.
The capacity to recover rapidly from a failure is dependent on the ability to detect a failure promptly and deliver a repair or roll back any modifications that caused the failure. This is often accomplished by continually monitoring system health and notifying operations personnel in the event of a failure. The operations team must have the proper protocols, tools, and permissions to address incidents.
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Other related metrics
Cycle time is another important measure to consider. This is the time a team spends working on an item until it is ready for dispatch. Cycle time in development refers to the time it takes from when developers commit to when it is deployed to production. This important DevOps metric assists project managers and engineering managers in understanding what works well within the development pipeline. Consequently, they may better align their work with stakeholders’ and consumers’ expectations, allowing their team to ship quicker.
Cycle time reports enable project managers to create a baseline for the development pipeline, which can then be used to evaluate future procedures. Developers often have less work in progress and fewer unproductive procedures when teams optimize for cycle time.
For Lean product management, the focus is on Value stream mapping, a visual representation of the flow of information from the product or feature idea to delivery. DevOps metrics are the crucial data points needed for efficient Value stream management and mapping but must be complemented by other metrics for product and business to ensure a comprehensive end-to-end analysis. For instance, charting sprint burndowns gives an insight into the effectiveness of planning and estimation processes. Net Promoter Scores indicate the extent to which the final product aligns with customers’ needs.
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In conclusion
If you want to take DevOps to the next level, I’m confident our list of DevOps metrics will help you think about what to measure and improve. The purpose of DevOps is to increase developer involvement in the deployment process and application monitoring. If you need assistance monitoring your applications, let’s consider our DevOps Services.