jib VS Bazel

You need to build Docker images for each app. This is specific to the JHipster application used in this tutorial which uses Jib to build the images. Make sure you are logged into Docker using docker login. Navigate to each app folder (store, invoice, product) and run the following command:

JIB

It is pretty easy to do with https://github.com/GoogleContainerTools/jib.

Save yourself the dockerfile and use jib: https://github.com/GoogleContainerTools/jib/tree/master/jib-maven-plugin

I first learned about Distroless because it was the default option in Google's Jib. Jib is a Maven plugin to create Docker containers without dependency on Docker. Note that the default has changed now.

Optimize your Dockerfile by using a small base Java Image, use either Spring Boot's layers tools or Google Jib to build your docker file, and increase CPU/Memory requests and limits if you can.

In this section, we will setup the automated generation and deployment of a Docker container image. You will need a Docker Hub account and the Jib Gradle Plugin.

If you don't want to hack on your own scripts to package your app into a container, I can recommend the JIB maven plugin. A gradle version is also available.

At some point in the future it might be enabled by default

To achieve reproducibility, your build process must control for environmental differences like timestamps, file ordering, or machine-specific configurations. Tools like Bazel or Nixprovide deterministic build systems that lock down these variables. For instance, Bazel uses a content-addressable cache, meaning the same source code and dependencies always result in the same build outputs, even when run on different machines.

Despite following all the steps (on both Windows and Unix), I couldn't get the cmake build to succeed. After several hours of debugging, I decided to try another build method provided by the project, using bazel, which was much simpler.

Many big companies have built their own tools to reign in this complexity and make it easier and faster for developers to work on large, multi-language code bases. Meta has buck, Amazon has brazil, and Google has bazel. But from my experience, especially, with brazil, these tools also have some rough edges, so understanding how they work can go a long way.

Discover Bazel

Wow, if you curl it, there's a lot of boilerplate code there.

Maybe built using Bazel?

https://bazel.build

Bazel by Google

Luckily a feature to limit the disk cache size is in development: https://github.com/bazelbuild/bazel/issues/5139

This is a problem that Bazel (https://bazel.build) solves in a very convenient way. You can just keep using the paths relative to the repository root, and as long as you properly declare your test needs that file it will access it without problems. Or you can use the runfile libraries to access them too.