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About project

Databucket is an Open Source Test Data Management Tool. It delivers features to effectively create and maintain test data and metadata for extensive tests on multiple projects, environments, and various integrated tools at the same time. Databucket stores data in elastic structures, which gives a ready approach to maintain constantly changing test data in the software development process.

Getting started with Databucket

• First you can read about key features on databucket.pl
• Next, you can dive deeper in the Wiki page.

Upgrading

New tables and columns are added automatically on startup. Changes that can't be done that way come as SQL scripts in upgrade/, named after the version that needs them. When upgrading an existing database, run the scripts of the versions you skip, in order, e.g.:

psql -h <host> -U <user> -d <database> -f upgrade/3.7.0.sql

• 3.7.0 — user emails must be unique (single sign-on matches users by email). The script normalizes the emails and adds the unique constraint; when several users share an email it lists them and changes nothing.

Contribution rules

1. During development, we are following GitHub flow. In order to provide change or fix in project, one has to:

   1. Create a branch from the repository.
   2. Create, edit, rename, move, or delete files.
   3. Send a pull request from your branch with your proposed changes to kick off a discussion.
   4. Make changes on your branch as needed. Your pull request will update automatically.
   5. Merge the pull request once the branch is ready to be merged.
   6. Tidy up your branches using the delete button in the pull request or on the branches page.

   For more information see Understanding the GitHub flow.

2. Each commit must contain clear and descriptive commit messages.

3. Source code must be properly formatted

4. Source code must be checked according to checkstyle rules

5. Changes must be well-tested. See also Testing strategy chapter.

Setup

Prerequisites

Installed below dependencies:

• Java 17
• NodeJS 22 — only for the Vite dev server (npm run dev). The Gradle build downloads its own Node.js.

To try Databucket without installing anything but Docker, docker/docker-compose.yaml starts it together with PostgreSQL and Keycloak (single sign-on): cd docker && docker compose up --build, then open http://databucket.localhost.

Useful commands

command	what it does
./gradlew clean assemble	compiles java classes
./gradlew test	runs all tests
./gradlew buildFrontend	installs frontend dependencies and builds frontend
./gradlew copyFrontend	buildFrontend and copies frontend file to java resources
export SPRING_PROFILES_ACTIVE=dev && ./gradlew bootRun	starts app with dev profile
./gradlew clean build	compiles java classes, builds frontend, runs tests and prepares all-in-one runnable jar