We pride ourselves by going the extra mile in our support channels, and we often get asked for help for both Scala and Sbt issues where Hydra is not always involved. It’s time to share some of the lessons we learned.
In this first installment we lay the foundation to understand how sbt works. We cover what a setting is, and we build an intuition of why it takes long for sbt to load a build in memory (on large projects loading the build can easily take more than 30 seconds!). On the way, we learn about a few utilities that are readily available at our fingertips and that can brilliantly help us debug problems (say goodbye to println statements!).
Although ephemeral builds can be nice to test changes in isolation, there are trade-offs for doing so. One of those trade-offs is dependency download times.
In Develocity 2019.3, the dependency download metrics in the performance and trends dashboards can be used to look at dependency download times and debug issues. For example, let’s look at the dependency download times for the Gradle project itself for all builds run in the last four weeks.
Changing one dependency declaration can have unexpected side effects through its transitive dependencies. For an example of tightly-coupled dependencies, let’s look at a simple Spring Boot project with the following build.gradle file.
Have you ever had an infrastructure failure break your build, or perhaps been forced to stop working in order to fix broken build configuration? Changes to development infrastructure often happen unbeknownst to the engineers it serves. Many problems go unreported, but chip away at our productivity much more than we realize. It is important to identify and stop non-verification failures (those not caused by routine development, such as compile and test failures) before they disrupt your organization. To combat this, we are pleased to introduce new failure analysis tools.
Whether you use a mono-repository or multi-repository approach for your sources, managing dependencies is central to productivity by reusing artifacts and ensuring a quality build. A single incorrect dependency can break existing code, introduce a vulnerability or simply prevent your project from building.
Android builds can be complex and challenging to optimize, yet Android teams usually have the fewest resources to work on the build. In this webinar, Jason Schroeder from Salesforce and Rooz Mohazzabi from Gradle discuss top Android build issues an how to solve them.
We found out that compilation speed is not affected much by instance size, while the price doubles with every step. The most cost-effective instance is thus m5.large (2 cores). But what happens when we throw in our Hydra parallel Scala compiler?
Do you ever watch your IDE sync or build run and think to yourself: “This is slow. I think this used to be much faster”? I do, more often than I’d like to admit.
Whether you use a mono-repository or multi-repository approach for your sources, managing dependencies is central to productivity by reusing artifacts and ensuring a quality build.
Accelerate
Troubleshoot
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