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DORA Metrics: Vital Signs for Your CI/CD Pipeline

AI-drafted, machine-checkedSource: Wikipedia: DORA Metricsintermediate
DORA Metrics: Vital Signs for Your CI/CD Pipeline

DORA metrics are four vital signs for your software delivery process, balancing speed and stability. They benchmark DevOps performance from commit to production. The main footgun is optimizing for speed while ignoring stability, leading to frequent outages.

WHY IT EXISTS: Before the DORA metrics, 'engineering performance' was hard to quantify and often relied on vanity metrics like lines of code. Teams struggled to prove that DevOps practices were working or to identify which process changes actually improved delivery without causing instability.

THE MENTAL MODEL: Think of DORA metrics as the vital signs for your software delivery process. Two metrics measure your speed and throughput (Deployment Frequency, Lead Time for Changes), while two measure your stability and resilience (Change Failure Rate, Time to Restore Service). They provide a balanced, outcome-oriented view of system health, not individual output.

HOW IT WORKS: The four metrics are collected from version control and CI/CD systems. First, Deployment Frequency tracks how often you successfully release to production. Second, Lead Time for Changes measures the time from a commit to it running in production. Third, Change Failure Rate is the percentage of deployments that cause a production failure. Fourth, Time to Restore Service is how long it takes to recover from a production failure.

WHEN TO USE IT: Use DORA metrics to establish a baseline for your team's performance and track improvements over time. They are excellent for justifying investments in tooling, automation, or process changes. They help shift conversations from 'are we busy?' to 'are we delivering value effectively and reliably?'.

WHEN NOT TO USE IT: Never use DORA metrics for individual performance reviews. This incentivizes gaming the system (e.g., making tiny, frequent commits to boost deployment numbers) and destroys psychological safety. They are system-level metrics, not a scorecard for individual engineers. Also, avoid comparing your absolute numbers to 'elite' performers without context; focus on your own team's trend of improvement.

ONE CANONICAL EXAMPLE: A platform team notices their Lead Time for Changes is high, taking weeks. Digging in, they find that manual QA and staging environment contention are the bottlenecks. They invest in better automated testing and on-demand preview environments. Over the next quarter, they see Lead Time decrease to days and Deployment Frequency increase, all without a rise in the Change Failure Rate. The metrics prove their investment paid off.

Read the original → en.wikipedia.org

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