74 lines
2.7 KiB
YAML
74 lines
2.7 KiB
YAML
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# Template docker-push
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# This template allows you to build and push your docker image to a Docker Hub account.
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# The workflow allows running tests, code linting and security scans on feature branches (as well as master).
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# The docker image will be validated and pushed to the docker registry after the code is merged to master.
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# Prerequisites: $DOCKERHUB_USERNAME, $DOCKERHUB_PASSWORD setup as deployment variables
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image: atlassian/default-image:2
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definitions:
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services:
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docker:
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memory: 3072
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pipelines:
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default:
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- parallel:
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- step:
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name: Build and Test
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script:
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- IMAGE_NAME=$BITBUCKET_REPO_SLUG
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- docker build . --file Dockerfile --tag ${IMAGE_NAME}
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services:
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- docker
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caches:
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- docker
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- step:
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name: Lint the Dockerfile
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image: hadolint/hadolint:latest-debian
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script:
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- hadolint Dockerfile
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branches:
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master:
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- step:
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name: Build and Test
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script:
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- IMAGE_NAME=linode-autoscaler
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- docker build . --file linode-autoscaler/Dockerfile --tag ${IMAGE_NAME}
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- docker save ${IMAGE_NAME} --output "${IMAGE_NAME}.tar"
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services:
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- docker
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caches:
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- docker
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artifacts:
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- "*.tar"
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- step:
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name: Push COTURN docker image to GCR
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image: google/cloud-sdk:alpine
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script:
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# Authenticating with the service account key file
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- echo $GCLOUD_API_KEYFILE | base64 -d > ./gcloud-api-key.json
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- gcloud auth activate-service-account --key-file gcloud-api-key.json
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- gcloud config set project $GCLOUD_PROJECT
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- IMAGE_NAME="linode-autoscaler"
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- docker load --input "${IMAGE_NAME}.tar"
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- VERSION="prod-0.1.${BITBUCKET_BUILD_NUMBER}"
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- docker tag "${IMAGE_NAME}" "gcr.io/${GCLOUD_PROJECT}/${IMAGE_NAME}:${VERSION}"
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# Login to google docker hub
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- cat ./gcloud-api-key.json | docker login -u _json_key --password-stdin https://gcr.io
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- docker push "gcr.io/${GCLOUD_PROJECT}/${IMAGE_NAME}:${VERSION}"
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services:
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- docker
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- step:
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name: Deploy to K8s
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deployment: production
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script:
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- IMAGE="gcr.io/$GCLOUD_PROJECT/$BITBUCKET_REPO_SLUG:prod-0.1.$BITBUCKET_BUILD_NUMBER"
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- sed -i "s|{{linode_autoscaler_image}}|$IMAGE|g" k8s/webrtc-be-daemonset.yaml
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- pipe: atlassian/kubectl-run:1.1.2
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variables:
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KUBE_CONFIG: $KUBE_CONFIG
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KUBECTL_COMMAND: 'apply'
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RESOURCE_PATH: 'k8s/'
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