Our team aims to make deployments fast and frequent, minimizing the risk of bugs each time we push new changes live. We built the Deploy feature to help teams measure performance and improve stability of their mainline systems. In addition to key metrics like deployment frequency, you can also see which repositories take the longest to deploy or fail to deploy most often.
✨ What’s new?
- Time from open to deploy: average time from opening to deploying a pull request
- Deployment frequency: count of production deployments per developer per day
- Time between deployments: average time between successful deployments
- Deployment run time: average time elapsed per deployment
- Slowest deployments: repositories that take the longest to deploy
- Deployment success rate: count of successful production deployments vs. the total over time
- Least successful deployments: repositories that fail to deploy most frequently
- Deployment batch size: average number of code changes per deployment
💭 How does it work?
- Go to our Deploys feature to see your data. It works out-of-the-box with our existing GitHub integration if you’re using GitHub Actions, deployments, or releases.
- We automatically detect how you deploy each repository, but you can also manually assign specific workflow runs, check runs, deployments, and releases as production deployments for each repository.
👋 Latest from our team
- Brett wrote a guest post for InsideBigData: Why You Should Add DevOps Metrics to Your Data Fabric.