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committedMay 24, 2025·
[SPARK-52292] Use super-linter for markdown files
### What changes were proposed in this pull request? This PR aims to apply `super-linter` for markdown files. ### Why are the changes needed? For consistency. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Pass the CIs with the newly added `super-linter` test. ### Was this patch authored or co-authored using generative AI tooling? No. Closes #224 from dongjoon-hyun/SPARK-52292. Authored-by: Dongjoon Hyun <[email protected]> Signed-off-by: Dongjoon Hyun <[email protected]>
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‎.github/workflows/build_and_test.yml

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steps:
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- name: Checkout repository
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uses: actions/checkout@v4
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with:
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fetch-depth: 0
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- name: Super-Linter
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uses: super-linter/super-linter@12150456a73e248bdc94d0794898f94e23127c88
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env:
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DEFAULT_BRANCH: main
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VALIDATE_MARKDOWN: true
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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- name: Set up JDK 17
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uses: actions/setup-java@v4
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with:

‎.markdownlint.yaml

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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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MD013: false

‎.markdownlintignore

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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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docs/config_properties.md

‎README.md

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## Install Helm Chart
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Apache Spark provides a Helm Chart.
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- <https://apache.github.io/spark-kubernetes-operator/>
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- <https://artifacthub.io/packages/helm/spark-kubernetes-operator/spark-kubernetes-operator/>
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```
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$ helm repo add spark-kubernetes-operator https://apache.github.io/spark-kubernetes-operator
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$ helm repo update
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$ helm install spark-kubernetes-operator spark-kubernetes-operator/spark-kubernetes-operator
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```bash
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helm repo add spark-kubernetes-operator https://apache.github.io/spark-kubernetes-operator
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helm repo update
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helm install spark-kubernetes-operator spark-kubernetes-operator/spark-kubernetes-operator
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```
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## Building Spark K8s Operator
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To build, run:
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```bash
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$ ./gradlew build -x test
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./gradlew build -x test
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```
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## Running Tests
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```bash
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$ ./gradlew build
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./gradlew build
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```
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## Build Docker Image
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```bash
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$ ./gradlew buildDockerImage
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./gradlew buildDockerImage
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```
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## Install Helm Chart
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## Install Helm Chart from the source code
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```bash
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$ helm install spark -f build-tools/helm/spark-kubernetes-operator/values.yaml build-tools/helm/spark-kubernetes-operator/
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helm install spark -f build-tools/helm/spark-kubernetes-operator/values.yaml build-tools/helm/spark-kubernetes-operator/
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```
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## Run Spark Pi App
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## Run Spark Pi App on Apache YuniKorn scheduler
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If you have not yet done so, follow [YuniKorn docs](https://yunikorn.apache.org/docs/#install) to install the latest version:
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If you have not yet done so, follow [YuniKorn docs](https://yunikorn.apache.org/docs/#install) to install the latest version:
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```bash
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$ helm repo add yunikorn https://apache.github.io/yunikorn-release
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helm repo add yunikorn https://apache.github.io/yunikorn-release
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$ helm repo update
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helm repo update
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$ helm install yunikorn yunikorn/yunikorn --namespace yunikorn --version 1.6.3 --create-namespace --set embedAdmissionController=false
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helm install yunikorn yunikorn/yunikorn --namespace yunikorn --version 1.6.3 --create-namespace --set embedAdmissionController=false
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```
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Submit a Spark app to YuniKorn enabled cluster:
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Check the existing Spark applications and clusters. If exists, delete them.
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```
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```bash
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$ kubectl get sparkapp
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No resources found in default namespace.
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Remove HelmChart and CRDs.
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```
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$ helm uninstall spark-kubernetes-operator
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```bash
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helm uninstall spark-kubernetes-operator
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$ kubectl delete crd sparkapplications.spark.apache.org
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kubectl delete crd sparkapplications.spark.apache.org
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$ kubectl delete crd sparkclusters.spark.apache.org
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kubectl delete crd sparkclusters.spark.apache.org
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```
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## Contributing

‎docs/architecture.md

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deployment lifecycle of Spark applications and clusters. The Operator can be installed on Kubernetes
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cluster(s) using Helm. In most production environments it is typically deployed in a designated
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namespace and controls Spark workload in one or more managed namespaces.
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Spark Operator enables user to describe Spark application(s) or cluster(s) as
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[Custom Resources](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/).
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Spark Operator enables user to describe Spark application(s) or cluster(s) as
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[Custom Resources](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/).
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The Operator continuously tracks events related to the Spark custom resources in its reconciliation
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The Operator continuously tracks events related to the Spark custom resources in its reconciliation
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loops:
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For SparkApplications:
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* Operator releases all Spark-cluster owned resources to cluster upon failure
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The Operator is built with the [Java Operator SDK](https://javaoperatorsdk.io/) for
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launching Spark deployments and submitting jobs under the hood. It also uses
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launching Spark deployments and submitting jobs under the hood. It also uses
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[fabric8](https://fabric8.io/) client to interact with Kubernetes API Server.
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## Application State Transition
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[<img src="resources/application_state_machine.png">](resources/application_state_machine.png)
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[![Application State Transition](resources/application_state_machine.png)](resources/application_state_machine.png)
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* Spark applications are expected to run from submitted to succeeded before releasing resources
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* User may configure the app CR to time-out after given threshold of time if it cannot reach healthy
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state after given threshold. The timeout can be configured for different lifecycle stages,
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state after given threshold. The timeout can be configured for different lifecycle stages,
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when driver starting and when requesting executor pods. To update the default threshold,
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configure `.spec.applicationTolerations.applicationTimeoutConfig` for the application.
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* K8s resources created for an application would be deleted as the final stage of the application
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configure `.spec.applicationTolerations.applicationTimeoutConfig` for the application.
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* K8s resources created for an application would be deleted as the final stage of the application
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lifecycle by default. This is to ensure resource quota release for completed applications.
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* It is also possible to retain the created k8s resources for debug or audit purpose. To do so,
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user may set `.spec.applicationTolerations.resourceRetainPolicy` to `OnFailure` to retain
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resources upon application failure, or set to `Always` to retain resources regardless of
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* It is also possible to retain the created k8s resources for debug or audit purpose. To do so,
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user may set `.spec.applicationTolerations.resourceRetainPolicy` to `OnFailure` to retain
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resources upon application failure, or set to `Always` to retain resources regardless of
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application final state.
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- This controls the behavior of k8s resources created by Operator for the application, including
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driver pod, config map, service, and PVC(if enabled). This does not apply to resources created
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* This controls the behavior of k8s resources created by Operator for the application, including
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driver pod, config map, service, and PVC(if enabled). This does not apply to resources created
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by driver (for example, executor pods). User may configure SparkConf to
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include `spark.kubernetes.executor.deleteOnTermination` for executor retention. Please refer
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include `spark.kubernetes.executor.deleteOnTermination` for executor retention. Please refer
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[Spark docs](https://spark.apache.org/docs/latest/running-on-kubernetes.html) for details.
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- The created k8s resources have `ownerReference` to their related `SparkApplication` custom
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* The created k8s resources have `ownerReference` to their related `SparkApplication` custom
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resource, such that they could be garbage collected when the `SparkApplication` is deleted.
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- Please be advised that k8s resources would not be retained if the application is configured to
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restart. This is to avoid resource quota usage increase unexpectedly or resource conflicts
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* Please be advised that k8s resources would not be retained if the application is configured to
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restart. This is to avoid resource quota usage increase unexpectedly or resource conflicts
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among multiple attempts.
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## Cluster State Transition
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[<img src="resources/cluster_state_machine.png">](resources/application_state_machine.png)
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[![Cluster State Transition](resources/application_state_machine.png)](resources/application_state_machine.png)
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* Spark clusters are expected to be always running after submitted.
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* Similar to Spark applications, K8s resources created for a cluster would be deleted as the final
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* Similar to Spark applications, K8s resources created for a cluster would be deleted as the final
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stage of the cluster lifecycle by default.

‎docs/configuration.md

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files](../build-tools/helm/spark-kubernetes-operator/values.yaml).
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* **System Properties** : when provided as system properties (e.g. via -D options to the
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operator JVM), it overrides the values provided in property file.
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* **Hot property loading** : when enabled, a
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[configmap](https://kubernetes.io/docs/concepts/configuration/configmap/) would be created with
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the operator in the same namespace. Operator can monitor updates performed on the configmap. Hot
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* **Hot property loading** : when enabled, a
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[configmap](https://kubernetes.io/docs/concepts/configuration/configmap/) would be created with
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the operator in the same namespace. Operator can monitor updates performed on the configmap. Hot
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properties reloading takes higher precedence comparing with default properties override.
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- An example use case: operator use hot properties to figure the list of namespace(s) to
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* An example use case: operator use hot properties to figure the list of namespace(s) to
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operate Spark applications. The hot properties config map can be updated and
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maintained by user or additional microservice to tune the operator behavior without
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rebooting it.
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- Please be advised that not all properties can be hot-loaded and honored at runtime.
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* Please be advised that not all properties can be hot-loaded and honored at runtime.
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Refer the list of [supported properties](./config_properties.md) for more details.
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To enable hot properties loading, update the **helm chart values file** with
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```
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```yaml
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operatorConfiguration:
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spark-operator.properties: |+
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spark.operator.dynamic.config.enabled=true
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does not have Spark UI, MetricsServlet
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and PrometheusServlet from org.apache.spark.metrics.sink package are not supported. If you are
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interested in Prometheus metrics exporting, please take a look at below
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section [Forward Metrics to Prometheus](#Forward-Metrics-to-Prometheus)
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section [Forward Metrics to Prometheus](#forward-metrics-to-prometheus)
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### JVM Metrics
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Spark Operator collects JVM metrics
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via [Codahale JVM Metrics](https://javadoc.io/doc/com.codahale.metrics/metrics-jvm/latest/index.html)
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- BufferPoolMetricSet
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- FileDescriptorRatioGauge
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- GarbageCollectorMetricSet
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- MemoryUsageGaugeSet
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- ThreadStatesGaugeSet
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* BufferPoolMetricSet
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* FileDescriptorRatioGauge
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* GarbageCollectorMetricSet
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* MemoryUsageGaugeSet
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* ThreadStatesGaugeSet
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### Kubernetes Client Metrics
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| kubernetes.client.http.response | Meter | Tracking the rates of HTTP response from the Kubernetes API Server |
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| kubernetes.client.http.response.failed | Meter | Tracking the rates of HTTP requests which have no response from the Kubernetes API Server |
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| kubernetes.client.http.response.latency.nanos | Histograms | Measures the statistical distribution of HTTP response latency from the Kubernetes API Server |
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| kubernetes.client.http.response.<ResponseCode> | Meter | Tracking the rates of HTTP response based on response code from the Kubernetes API Server |
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| kubernetes.client.http.request.<RequestMethod> | Meter | Tracking the rates of HTTP request based type of method to the Kubernetes API Server |
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| kubernetes.client.http.response.`ResponseCode` | Meter | Tracking the rates of HTTP response based on response code from the Kubernetes API Server |
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| kubernetes.client.http.request.`RequestMethod` | Meter | Tracking the rates of HTTP request based type of method to the Kubernetes API Server |
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| kubernetes.client.http.response.1xx | Meter | Tracking the rates of HTTP Code 1xx responses (informational) received from the Kubernetes API Server per response code. |
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| kubernetes.client.http.response.2xx | Meter | Tracking the rates of HTTP Code 2xx responses (success) received from the Kubernetes API Server per response code. |
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| kubernetes.client.http.response.3xx | Meter | Tracking the rates of HTTP Code 3xx responses (redirection) received from the Kubernetes API Server per response code. |
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| kubernetes.client.http.response.4xx | Meter | Tracking the rates of HTTP Code 4xx responses (client error) received from the Kubernetes API Server per response code. |
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| kubernetes.client.http.response.5xx | Meter | Tracking the rates of HTTP Code 5xx responses (server error) received from the Kubernetes API Server per response code. |
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| kubernetes.client.<ResourceName>.<Method> | Meter | Tracking the rates of HTTP request for a combination of one Kubernetes resource and one http method |
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| kubernetes.client.<NamespaceName>.<ResourceName>.<Method> | Meter | Tracking the rates of HTTP request for a combination of one namespace-scoped Kubernetes resource and one http method |
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| kubernetes.client.`ResourceName`.`Method` | Meter | Tracking the rates of HTTP request for a combination of one Kubernetes resource and one http method |
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| kubernetes.client.`NamespaceName`.`ResourceName`.`Method` | Meter | Tracking the rates of HTTP request for a combination of one namespace-scoped Kubernetes resource and one http method |
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### Forward Metrics to Prometheus
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open your browser with address `localhost:8080`. Click on Status Targets tab, you should be able
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to find target as below.
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[<img src="resources/prometheus.png">](resources/prometheus.png)
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[![Prometheus](resources/prometheus.png)](resources/prometheus.png)

‎docs/operations.md

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under the License.
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-->
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### Compatibility
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# Operations
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## Compatibility
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- Java 17, 21 and 24
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- Kubernetes version compatibility:
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+ k8s version >= 1.30 is recommended. Operator attempts to be API compatible as possible, but
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- k8s version >= 1.30 is recommended. Operator attempts to be API compatible as possible, but
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patch support will not be performed on k8s versions that reached EOL.
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- Spark versions 3.5 or above.
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### Spark Application Namespaces
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## Spark Application Namespaces
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By default, Spark applications are created in the same namespace as the operator deployment.
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You many also configure the chart deployment to add necessary RBAC resources for
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To override single parameters you can use `--set`, for example:
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```
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```bash
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helm install --set image.repository=<my_registory>/spark-kubernetes-operator \
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-f build-tools/helm/spark-kubernetes-operator/values.yaml \
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build-tools/helm/spark-kubernetes-operator/
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You can also provide multiple custom values file by using the `-f` flag, the latest takes
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higher precedence:
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```
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```bash
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helm install spark-kubernetes-operator \
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-f build-tools/helm/spark-kubernetes-operator/values.yaml \
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-f my_values.yaml \

‎docs/spark_custom_resources.md

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under the License.
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-->
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## Spark Operator API
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# Spark Operator API
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The core user facing API of the Spark Kubernetes Operator is the `SparkApplication` and
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`SparkCluster` Custom Resources Definition (CRD). Spark custom resource extends
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The core user facing API of the Spark Kubernetes Operator is the `SparkApplication` and
23+
`SparkCluster` Custom Resources Definition (CRD). Spark custom resource extends
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standard k8s API, defines Spark Application spec and tracks status.
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Once the Spark Operator is installed and running in your Kubernetes environment, it will
27-
continuously watch SparkApplication(s) and SparkCluster(s) submitted, via k8s API client or
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continuously watch SparkApplication(s) and SparkCluster(s) submitted, via k8s API client or
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kubectl by the user, orchestrate secondary resources (pods, configmaps .etc).
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Please check out the [quickstart](../README.md) as well for installing operator.
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## SparkApplication
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34-
SparkApplication can be defined in YAML format. User may configure the application entrypoint
34+
SparkApplication can be defined in YAML format. User may configure the application entrypoint
3535
and configurations. Let's start with the [Spark-Pi example](../examples/pi.yaml):
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```yaml
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After application is submitted, Operator will add status information to your application based on
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the observed state:
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```
62+
```bash
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kubectl get sparkapp pi -o yaml
6464
```
6565

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## Enable Additional Ingress for Driver
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Operator may create [Ingress](https://kubernetes.io/docs/concepts/services-networking/ingress/) for
104-
Spark driver of running applications on demand. For example, to expose Spark UI - which is by
105-
default enabled on driver port 4040, you may configure
104+
Spark driver of running applications on demand. For example, to expose Spark UI - which is by
105+
default enabled on driver port 4040, you may configure
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```yaml
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spec:
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number: 80
133133
```
134134
135-
Spark Operator by default would populate the `.spec.selector` field of the created Service to match
135+
Spark Operator by default would populate the `.spec.selector` field of the created Service to match
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the driver labels. If `.ingressSpec.rules` is not provided, Spark Operator would also populate one
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default rule backed by the associated Service. It's recommended to always provide the ingress spec
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to make sure it's compatible with your
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default rule backed by the associated Service. It's recommended to always provide the ingress spec
138+
to make sure it's compatible with your
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[IngressController](https://kubernetes.io/docs/concepts/services-networking/ingress-controllers/).
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## Create and Mount ConfigMap
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It is possible to ask operator to create configmap so they can be used by driver and/or executor
144-
pods on the fly. `configMapSpecs` allows you to specify the desired metadata and data as string
143+
It is possible to ask operator to create configmap so they can be used by driver and/or executor
144+
pods on the fly. `configMapSpecs` allows you to specify the desired metadata and data as string
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literals for the configmap(s) to be created.
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```yaml
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Like other app-specific resources, the created configmap has owner reference to Spark driver and
156156
therefore shares the same lifecycle and garbage collection mechanism with the associated app.
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158-
This feature can be used to create lightweight override config files for given Spark app. For
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This feature can be used to create lightweight override config files for given Spark app. For
159159
example, below snippet would create and mount a configmap with metrics property file, then use it
160-
in SparkConf:
160+
in SparkConf:
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162162
```yaml
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spec:
@@ -201,17 +201,11 @@ with non-zero code), Spark Operator introduces a few different failure state for
201201
app status monitoring at high level, and for ease of setting up different handlers if users
202202
are creating / managing SparkApplications with external microservices or workflow engines.
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205204
Spark Operator recognizes "infrastructure failure" in the best effort way. It is possible to
206205
configure different restart policy on general failure(s) vs. on potential infrastructure
207206
failure(s). For example, you may configure the app to restart only upon infrastructure
208-
failures. If Spark application fails as a result of
209-
210-
```
211-
DriverStartTimedOut
212-
ExecutorsStartTimedOut
213-
SchedulingFailure
214-
```
207+
failures. If Spark application fails as a result of `DriverStartTimedOut`,
208+
`ExecutorsStartTimedOut`, `SchedulingFailure`.
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216210
It is more likely that the app failed as a result of infrastructure reason(s), including
217211
scenarios like driver or executors cannot be scheduled or cannot initialize in configured
@@ -242,9 +236,8 @@ restartConfig:
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243237
### Timeouts
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245-
It's possible to configure applications to be proactively terminated and resubmitted in particular
246-
cases to avoid resource deadlock.
247-
239+
It's possible to configure applications to be proactively terminated and resubmitted in particular
240+
cases to avoid resource deadlock.
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| Field | Type | Default Value | Descritpion |
250243
|-----------------------------------------------------------------------------------------|---------|---------------|--------------------------------------------------------------------------------------------------------------------|
@@ -254,7 +247,6 @@ cases to avoid resource deadlock.
254247
| .spec.applicationTolerations.applicationTimeoutConfig.driverReadyTimeoutMillis | integer | 300000 | Time to wait for driver reaches ready state. |
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| .spec.applicationTolerations.applicationTimeoutConfig.terminationRequeuePeriodMillis | integer | 2000 | Back-off time when releasing resource need to be re-attempted for application. |
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### Instance Config
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Instance Config helps operator to decide whether an application is running healthy. When
@@ -318,5 +310,5 @@ worker instances would be deployed as [StatefulSets](https://kubernetes.io/docs/
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and exposed via k8s [service(s)](https://kubernetes.io/docs/concepts/services-networking/service/).
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Like Pod Template Support for Applications, it's also possible to submit template(s) for the Spark
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instances for `SparkCluster` to configure spec that's not supported via SparkConf. It's worth notice
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instances for `SparkCluster` to configure spec that's not supported via SparkConf. It's worth notice
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that Spark may overwrite certain fields.

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