Performance Insights
Overview
Metrics, KPI's, and trends for all local and CI build and test performance data, including cache overhead, dependency download time, parallelism, test method, user/project-specific data, and much more.
Performance Insights is enabled by the Develocity Performance and Trends Dashboards. They provide increased observability with intuitive visualizations of build and test performance data and key metrics for both local and CI builds.
Key Features
Observability to key performance metrics: For the first time you can get quick access to some fundamental data points, such as # of builds executed over a specified time period and aggregated build execution time; serial execution time (for assessing the strength of your parallelism implementation); savings from reduced feedback cycle time and cache overhead; dependency download time.
Performance metric filtering: All the above metrics can be filtered for arbitrary subsets of builds. For example, you can filter by a specific user, specific projects, local vs. CI builds, builds from a specific branch, builds of a particular type, local builds with changes not yet committed to version control, and many other criteria.
Drillable performance trends: You can also see the performance trends for every test class and every test method or test method subset. By using custom values, you can filter by the same powerful criteria to find, for example, performance differences between locations or hardware configurations.
Derived performance metrics: With ready access to standard metrics, you can easily derive additional metrics and KPIs of interest like the total developer wait time for local or pull request builds to complete and associated aggregate costs. You can also use custom values and tags to compare metrics between different locations and different hardware configurations.
ROI/value quantification: In practical terms, it's easy to quantify the value of wasted R&D investment due to unnecessarily slow builds or the value of shaving one minute off the average build time in terms of engineering years or additional R&D budget. For even a moderate-sized project these numbers are often eye-popping.
Scalable data store: S3 disk storage provides a cost-effective solution for storing massive amounts of data that can be leveraged in mining for insight and trend patterns.