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2ccd7c8280
See #2192.
44 lines
2.0 KiB
Plaintext
44 lines
2.0 KiB
Plaintext
= Continuous Integration
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image:https://jenkins.spring.io/buildStatus/icon?job=spring-data-jpa%2Fmain&subject=Moore%20(main)[link=https://jenkins.spring.io/view/SpringData/job/spring-data-jpa/]
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image:https://jenkins.spring.io/buildStatus/icon?job=spring-data-jpa%2F2.1.x&subject=Lovelace%20(2.1.x)[link=https://jenkins.spring.io/view/SpringData/job/spring-data-jpa/]
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image:https://jenkins.spring.io/buildStatus/icon?job=spring-data-jpa%2F1.11.x&subject=Ingalls%20(1.11.x)[link=https://jenkins.spring.io/view/SpringData/job/spring-data-jpa/]
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== Running CI tasks locally
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Since this pipeline is purely Docker-based, it's easy to:
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* Debug what went wrong on your local machine.
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* Test out a a tweak to your test routine before sending it out.
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* Experiment against a new image before submitting your pull request.
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All of these use cases are great reasons to essentially run what the CI server does on your local machine.
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IMPORTANT: To do this you must have Docker installed on your machine.
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1. `docker run -it --mount type=bind,source="$(pwd)",target=/spring-data-jpa-github adoptopenjdk/openjdk8:latest /bin/sh`
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This will launch the Docker image and mount your source code at `spring-data-jpa-github`.
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2. `cd spring-data-jpa-github`
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Next, run the tests from inside the container:
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3. `./mvnw clean dependency:list test -Dsort -Dbundlor.enabled=false -B` (or with whatever profile you need to test out)
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Since the container is binding to your source, you can make edits from your IDE and continue to run build jobs.
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If you need to package things up, do this:
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1. `docker run -it --mount type=bind,source="$(pwd)",target=/spring-data-jpa-github adoptopenjdk/openjdk8:latest /bin/sh`
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This will launch the Docker image and mount your source code at `spring-data-jpa-github`.
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2. `cd spring-data-jpa-github`
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Next, package things from inside the container doing this:
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3. `./mvnw clean dependency:list package -Dsort -Dbundlor.enabled=false -B`
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NOTE: Docker containers can eat up disk space fast! From time to time, run `docker system prune` to clean out old images.
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