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deploy/helm/spark-k8s-operator/Chart.yaml

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---
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apiVersion: v2
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name: spark-k8s-operator
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version: "0.1.0"
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appVersion: "0.1.0"
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version: "0.2.0-nightly"
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appVersion: "0.2.0-nightly"
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description: The Stackable Operator for Apache Spark-on-Kubernetes
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home: https://github.com/stackabletech/spark-k8s-operator
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maintainers:

deploy/manifests/configmap.yaml

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labels:
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app.kubernetes.io/name: spark-k8s-operator
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app.kubernetes.io/instance: spark-k8s-operator
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app.kubernetes.io/version: "0.1.0"
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app.kubernetes.io/version: "0.2.0-nightly"

deploy/manifests/deployment.yaml

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labels:
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app.kubernetes.io/name: spark-k8s-operator
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app.kubernetes.io/instance: spark-k8s-operator
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app.kubernetes.io/version: "0.1.0"
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app.kubernetes.io/version: "0.2.0-nightly"
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spec:
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replicas: 1
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strategy:
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containers:
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- name: spark-k8s-operator
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securityContext: {}
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image: "docker.stackable.tech/stackable/spark-k8s-operator:0.1.0"
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image: "docker.stackable.tech/stackable/spark-k8s-operator:0.2.0-nightly"
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imagePullPolicy: IfNotPresent
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resources: {}
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volumeMounts:

deploy/manifests/serviceaccount.yaml

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labels:
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app.kubernetes.io/name: spark-k8s-operator
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app.kubernetes.io/instance: spark-k8s-operator
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app.kubernetes.io/version: "0.1.0"
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app.kubernetes.io/version: "0.2.0-nightly"
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---
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apiVersion: rbac.authorization.k8s.io/v1
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# This cluster role binding allows anyone in the "manager" group to read secrets in any namespace.
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labels:
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app.kubernetes.io/name: spark-k8s-operator
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app.kubernetes.io/instance: spark-k8s-operator
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app.kubernetes.io/version: "0.1.0"
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app.kubernetes.io/version: "0.2.0-nightly"
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subjects:
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- kind: ServiceAccount
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name: spark-k8s-operator-serviceaccount

docs/antora.yml

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name: spark-k8s
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version: "0.1"
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version: "nightly"
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title: Stackable Operator for Apache Spark on Kubernetes
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nav:
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- modules/ROOT/nav.adoc
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prerelease: false
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prerelease: true

docs/modules/ROOT/pages/usage.adoc

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Job-specific settings are annotated below.
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=== Pyspark: externally located artifact and dataset
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=== Pyspark: externally located artifact and dataset
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[source,yaml]
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----
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<6> the name of the volume mount backed by a `PersistentVolumeClaim` that must be pre-existing
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<7> the path on the volume mount: this is referenced in the `sparkConf` section where the extra class path is defined for the driver and executors
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=== JVM (Scala): externally located artifact and dataset
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=== JVM (Scala): externally located artifact and dataset
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[source,yaml]
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----
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|Volume mount path
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|`spec.driver.nodeSelector`
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|A dictionary of labels to use for node selection when scheduling the driver.
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|A dictionary of labels to use for node selection when scheduling the driver N.B. this assumes there are no implicit node dependencies (e.g. `PVC`, `VolumeMount`) defined elsewhere.
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|`spec.executor.cores`
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|Number of cores for each executor
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|Volume mount path
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|`spec.executor.nodeSelector`
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|A dictionary of labels to use for node selection when scheduling the executors.
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|A dictionary of labels to use for node selection when scheduling the executors N.B. this assumes there are no implicit node dependencies (e.g. `PVC`, `VolumeMount`) defined elsewhere.
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|===
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