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= Spring Data Envers

This project is an extension of the https://github.com/SpringSource/spring-data-jpa[Spring Data JPA] project to allow access to entity revisions managed by Hibernate Envers. The sources mostly originate from a contribution of Philipp Hügelmeyer https://github.com/hygl[@hygl].

The core feature of the module consists of an implementation of the `RevisionRepository` of Spring Data Commons.

[source,java]
----
public interface RevisionRepository<T, ID extends Serializable, N extends Number & Comparable<N>> {

	Revision<N, T> findLastChangeRevision(ID id);

	Revisions<N, T> findRevisions(ID id);

	Page<Revision<N, T>> findRevisions(ID id, Pageable pageable);
}
----

You can pull in this functionality to your repositories by simply additionally extending the interface just mentioned:

[source,java]
----
interface PersonRepository extends RevisionRepository<Person, Long, Integer>, CrudRepository<Person, Long> {

  // Your query methods go here
}
----

To successfully activate the Spring Data Envers repository factory use the Spring Data JPA repositories namespace element's `factory-class` attribute:

[source,xml]
----
<jpa:repositories base-package="com.acme.repositories"
                  factory-class="….EnversRevisionRepositoryFactoryBean" />
----

== Contributing to Spring Data

Here are some ways for you to get involved in the community:

* Create https://github.com/spring-projects/spring-data-envers/issues[Github issues] for bugs and new features and comment and vote on the ones that you are interested in.
* Github is for social coding: if you want to write code, we encourage contributions through pull requests from https://help.github.com/forking/[forks of this repository]. If you want to contribute code this way, please reference a JIRA ticket as well covering the specific issue you are addressing.
* Watch for upcoming articles on Spring by https://spring.io/blog[subscribing] to spring.io.

Before we accept a non-trivial patch or pull request we will need you to https://cla.pivotal.io/sign/spring[sign the Contributor License Agreement]. Signing the contributor’s agreement does not grant anyone commit rights to the main repository, but it does mean that we can accept your contributions, and you will get an author credit if we do. If you forget to do so, you'll be reminded when you submit a pull request. Active contributors might be asked to join the core team, and given the ability to merge pull requests.

If you're an Eclipse user make sure you activate automatic application of the formatter (located at `etc/eclipse-formatter.xml`) and activate automatic formatting and organizing imports on save.

= Running CI tasks locally

Since this pipeline is purely Docker-based, it's easy to:

* Debug what went wrong on your local machine.
* Test out a a tweak to your `test.sh` script before sending it out.
* Experiment against a new image before submitting your pull request.

All of these use cases are great reasons to essentially run what the CI server does on your local machine.

IMPORTANT: To do this you must have Docker installed on your machine.

1. `docker run -it --mount type=bind,source="$(pwd)",target=/spring-data-envers-github adoptopenjdk/openjdk8:latest /bin/bash`
+
This will launch the Docker image and mount your source code at `spring-data-envers-github`.
+
2. `cd spring-data-envers-github`
+
Next, run your tests from inside the container:
+
3. `./mvnw clean dependency:list test -Dsort` (or whatever profile you need to test out)

Since the container is binding to your source, you can make edits from your IDE and continue to run build jobs.

If you test building the artifact, do this:

1. `docker run -it --mount type=bind,source="$(pwd)",target=/spring-data-envers-github adoptopenjdk/openjdk8:latest /bin/bash`
+
This will launch the Docker image and mount your source code at `spring-data-envers-github`.
+
2. `cd spring-data-envers-github`
+
Next, try to package everything up from inside the container:
+
3. `./mvnw -Pci,snapshot -Dmaven.test.skip=true clean package`

NOTE: Docker containers can eat up disk space fast! From time to time, run `docker system prune` to clean out old images.