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44 lines
2.1 KiB
Plaintext
44 lines
2.1 KiB
Plaintext
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= Continuous Integration
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image:https://jenkins.spring.io/buildStatus/icon?job=spring-data-elasticsearch%2Fmaster&subject=Moore%20(master)[link=https://jenkins.spring.io/view/SpringData/job/spring-data-elasticsearch/]
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image:https://jenkins.spring.io/buildStatus/icon?job=spring-data-elasticsearch%2F3.1.x&subject=Lovelace%20(3.1.x)[link=https://jenkins.spring.io/view/SpringData/job/spring-data-elasticsearch/]
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image:https://jenkins.spring.io/buildStatus/icon?job=spring-data-elasticsearch%2F2.1.x&subject=Ingalls%20(2.1.x)[link=https://jenkins.spring.io/view/SpringData/job/spring-data-elasticsearch/]
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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.sh` script 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="$(pwd)",target=/spring-data-elasticsearch-github adoptopenjdk/openjdk8:latest /bin/bash`
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This will launch the Docker image and mount your source code at `spring-data-elasticsearch-github`.
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2. `cd spring-data-elasticsearch-github`
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Next, run your tests from inside the container:
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3. `./mvnw clean dependency:list test -Dsort` (or whatever profile you need to test out)
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Since the container is binding to your source, you can make edits from your IDE and continue to run build jobs.
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If you need to package things up, do this:
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1. `docker run -it --mount type=bind,source="$(pwd)",target=/spring-data-elasticsearch-github adoptopenjdk/openjdk8:latest /bin/bash`
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This will launch the Docker image and mount your source code at `spring-data-elasticsearch-github`.
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2. `cd spring-data-elasticsearch-github`
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Next, try to package everything up from inside the container:
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3. `./mvnw -Pci,snapshot -Dmaven.test.skip=true clean package`
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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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