What are DORA Metrics in DevOps & How to Improve Them?<\/p>\n
The aim of DevOps is to improve the software development process through better communication and collaboration between the development and operations teams. DevOps culture has four basic principles as per the CAMS model:<\/p>\n
- \n
- Culture<\/li>\n
- Automation<\/li>\n
- Measurement<\/li>\n
- Sharing<\/li>\n<\/ol>\n
The third principle, that is measurement, is what entails the metrics and Key Performance Indicators (KPIs) used to evaluate the DevOps performance of companies. These metrics and KPIs also help companies find loopholes in their development and deployment process. There are several metrics and KPIs, but four of them have been set as standard by DORA.<\/p>\n
Let\u2019s see what is DORA and it\u2019s 4 standard metrics\u2026<\/p>\n
What are DORA Metrics?<\/h2>\n
DevOps Research and Assessment (DORA)<\/strong> is a research program launched by Gene Kim<\/em>, Jez Humble<\/em>, and Dr. Nicole Forsgren<\/em>. It is a team at Google Cloud that \u201cseeks to understand the capabilities that drive software delivery and operations performance.\u201d (DORA<\/a>)With years of research, DORA has identified 4 key metrics that help to measure the DevOps performance of businesses. These key metrics can be categorized based on what they measure.<\/p>\n
The first category measures the throughput<\/em> (or velocity) which refers to how fast changes are being made. It includes:<\/p>\n
- \n
- Deployment frequency<\/li>\n
- Lead time for changes<\/li>\n<\/ol>\n
The second one measures stability<\/em> which refers to the quality of the changes and the ability of the team to fix any failures. It includes:<\/p>\n
- \n
- Change failure rate<\/li>\n
- Mean time to recovery<\/li>\n<\/ol>\n
Based on these metrics, DORA classifies the DevOps performance of companies into four: Elite, High, Medium and Low. <\/em>After understanding all the metrics, you can also attempt the DORA Quick Check<\/a> to evaluate these metrics for your company.<\/p>\n
So let\u2019s discuss each one of the metrics and how to improve them one by one\u2026<\/p>\n
Deployment frequency<\/h2>\n
Deployment frequency tells how often new codes are deployed to production. It can be simply calculated by counting the number of codes deployed over a period of time. It can range from multiple times a day (for high-performing teams) to once every 6 months or more (for low-performing teams).<\/p>\n
How to improve deployment frequency?<\/h2>\n
- \n
- Smaller deployments:<\/strong> When you receive a proposal for change, try to break it down into smaller changes. This makes it look less intimidating and more doable. Moreover, it becomes easy to test with smaller changes and rollback in case of failures.<\/li>\n
- Automation:<\/strong> Automating manual or repetitive tasks like testing or deploying can help reduce time spent on individual deployments. This allows the workforce to focus on more creative and complex tasks instead, thus improving the frequency of updates.<\/li>\n<\/ul>\n
Change lead time<\/h2>\n
Change lead time or the lead time for changes indicates the efficiency of the CI\/CD pipeline based on how much time it takes for a change to get successfully deployed to production. In other words, it is \u201cthe difference in hours between the date and time of the author\u2019s commit and the date and time of the deployment containing that commit.\u201d (Pluralsight<\/a>)<\/p>\n
It can range from less than an hour (for high-performing teams) to more than 6 months (for low-performing teams).<\/p>\n
How to reduce change lead time?<\/h2>\n
- \n
- Smaller deployments and automation:<\/strong> As said before, smaller batches of codes and automated testing and deployment help speed up the deployment process and reduce change lead time.<\/li>\n
- Optimize communication:<\/strong> Smooth communication and collaboration help in faster decisions and less delays.<\/li>\n<\/ul>\n
Change failure rate<\/h2>\n
Change failure percentage or change failure rate (CFR) is the percentage of deployments that lead to failures in production. A \u2018change failure\u2019 means any negative impact like crashes, low performance, or security vulnerabilities caused by a change or update. It can be calculated using the following formula:<\/p>\n
CFR = (number of failed changes\/total number of changes)*100<\/p>\n
A CFR lower than 15% is considered good while anywhere between 16 to 30% is considered high. A lower CFR indicates a reliable CI\/CD pipeline and effective testing.<\/p>\n
How to reduce the change failure rate?<\/h2>\n
- \n
- Automated testing:<\/strong> Test automation and code reviewing at every step help to ensure the correctness of codes and reduce human errors.<\/li>\n<\/ul>\n
Mean time to restore service (MTTR)<\/h2>\n
Also known as failed deployment recovery time, MTTR is the time taken to recover from a partial service interruption or a total failure. It includes the time spent diagnosing and repairing the issue and redeploying the new code. It can be calculated using the following formula:<\/p>\n
MTTR = (Total downtime\/Number of incidents)*100<\/p>\n
It ranges from less than an hour (for high-performing teams) to 6 months (for low-performing teams).<\/p>\n
How to reduce MTTR?<\/h2>\n
- \n
- Real-time monitoring and alerting systems: <\/strong>Continuous monitoring helps to instantly detect anomalies and resolve them quickly.<\/li>\n
- Automation:<\/strong> Use automation tools for detecting and resolving faults speedily.<\/li>\n
- Improve communication:<\/strong> Improve communication between teams to reduce delays and keep everyone updated.<\/li>\n
- Infrastructure as Code (IaC):<\/strong> IaC improves collaboration and allows easy rollback mechanisms in case the new code does not work.<\/li>\n<\/ul>\n
The above metrics can be summarized using the table given below:<\/p>\n
![\"\"](\"https:\/\/devopscurry.com\/wp-content\/uploads\/2024\/08\/Calculating_the_metrics_frOhcbp.max-2000x2000-1-400x259.jpg\")
<\/p>\n - Automation:<\/strong> Use automation tools for detecting and resolving faults speedily.<\/li>\n
- Real-time monitoring and alerting systems: <\/strong>Continuous monitoring helps to instantly detect anomalies and resolve them quickly.<\/li>\n
- Optimize communication:<\/strong> Smooth communication and collaboration help in faster decisions and less delays.<\/li>\n<\/ul>\n
- Automation:<\/strong> Automating manual or repetitive tasks like testing or deploying can help reduce time spent on individual deployments. This allows the workforce to focus on more creative and complex tasks instead, thus improving the frequency of updates.<\/li>\n<\/ul>\n
- Smaller deployments:<\/strong> When you receive a proposal for change, try to break it down into smaller changes. This makes it look less intimidating and more doable. Moreover, it becomes easy to test with smaller changes and rollback in case of failures.<\/li>\n