base on Kubernetes operator for managing the lifecycle of Apache Spark applications on Kubernetes. # Kubeflow Spark Operator [](https://github.com/kubeflow/spark-operator/actions/workflows/integration.yaml) [](https://goreportcard.com/report/github.com/kubeflow/spark-operator) [](https://github.com/kubeflow/spark-operator/releases) ## What is Spark Operator? The Kubernetes Operator for Apache Spark aims to make specifying and running [Spark](https://github.com/apache/spark) applications as easy and idiomatic as running other workloads on Kubernetes. It uses [Kubernetes custom resources](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) for specifying, running, and surfacing status of Spark applications. ## Quick Start For a more detailed guide, please refer to the [Getting Started guide](https://www.kubeflow.org/docs/components/spark-operator/getting-started/). ```bash # Add the Helm repository helm repo add spark-operator https://kubeflow.github.io/spark-operator helm repo update # Install the operator into the spark-operator namespace and wait for deployments to be ready helm install spark-operator spark-operator/spark-operator \ --namespace spark-operator --create-namespace --wait # Create an example application in the default namespace kubectl apply -f https://raw.githubusercontent.com/kubeflow/spark-operator/refs/heads/master/examples/spark-pi.yaml # Get the status of the application kubectl get sparkapp spark-pi ``` ## Overview For a complete reference of the custom resource definitions, please refer to the [API Definition](docs/api-docs.md). For details on its design, please refer to the [Architecture](https://www.kubeflow.org/docs/components/spark-operator/overview/#architecture). It requires Spark 2.3 and above that supports Kubernetes as a native scheduler backend. The Kubernetes Operator for Apache Spark currently supports the following list of features: * Supports Spark 2.3 and up. * Enables declarative application specification and management of applications through custom resources. * Automatically runs `spark-submit` on behalf of users for each `SparkApplication` eligible for submission. * Provides native [cron](https://en.wikipedia.org/wiki/Cron) support for running scheduled applications. * Supports customization of Spark pods beyond what Spark natively is able to do through the mutating admission webhook, e.g., mounting ConfigMaps and volumes, and setting pod affinity/anti-affinity. * Supports automatic application re-submission for updated `SparkApplication` objects with updated specification. * Supports automatic application restart with a configurable restart policy. * Supports automatic retries of failed submissions with optional linear back-off. * Supports mounting local Hadoop configuration as a Kubernetes ConfigMap automatically via `sparkctl`. * Supports automatically staging local application dependencies to Google Cloud Storage (GCS) via `sparkctl`. * Supports collecting and exporting application-level metrics and driver/executor metrics to Prometheus. ## Project Status **Project status:** *beta* **Current API version:** *`v1beta2`* **If you are currently using the `v1beta1` version of the APIs in your manifests, please update them to use the `v1beta2` version by changing `apiVersion: "sparkoperator.k8s.io/<version>"` to `apiVersion: "sparkoperator.k8s.io/v1beta2"`. You will also need to delete the `previous` version of the CustomResourceDefinitions named `sparkapplications.sparkoperator.k8s.io` and `scheduledsparkapplications.sparkoperator.k8s.io`, and replace them with the `v1beta2` version either by installing the latest version of the operator or by running `kubectl create -f config/crd/bases`.** ## Prerequisites * Version >= 1.13 of Kubernetes to use the [`subresource` support for CustomResourceDefinitions](https://kubernetes.io/docs/tasks/access-kubernetes-api/custom-resources/custom-resource-definitions/#subresources), which became beta in 1.13 and is enabled by default in 1.13 and higher. * Version >= 1.16 of Kubernetes to use the `MutatingWebhook` and `ValidatingWebhook` of `apiVersion: admissionregistration.k8s.io/v1`. ## Getting Started For getting started with Spark operator, please refer to [Getting Started](https://www.kubeflow.org/docs/components/spark-operator/getting-started/). ## User Guide For detailed user guide and API documentation, please refer to [User Guide](https://www.kubeflow.org/docs/components/spark-operator/user-guide/) and [API Specification](docs/api-docs.md). If you are running Spark operator on Google Kubernetes Engine (GKE) and want to use Google Cloud Storage (GCS) and/or BigQuery for reading/writing data, also refer to the [GCP guide](https://www.kubeflow.org/docs/components/spark-operator/user-guide/gcp/). ## Version Matrix The following table lists the most recent few versions of the operator. | Operator Version | API Version | Kubernetes Version | Base Spark Version | |-----------------------|-------------|--------------------|--------------------| | `v2.0.x` | `v1beta2` | 1.16+ | `3.5.2` | | `v1beta2-1.6.x-3.5.0` | `v1beta2` | 1.16+ | `3.5.0` | | `v1beta2-1.5.x-3.5.0` | `v1beta2` | 1.16+ | `3.5.0` | | `v1beta2-1.4.x-3.5.0` | `v1beta2` | 1.16+ | `3.5.0` | | `v1beta2-1.3.x-3.1.1` | `v1beta2` | 1.16+ | `3.1.1` | | `v1beta2-1.2.3-3.1.1` | `v1beta2` | 1.13+ | `3.1.1` | | `v1beta2-1.2.2-3.0.0` | `v1beta2` | 1.13+ | `3.0.0` | | `v1beta2-1.2.1-3.0.0` | `v1beta2` | 1.13+ | `3.0.0` | | `v1beta2-1.2.0-3.0.0` | `v1beta2` | 1.13+ | `3.0.0` | | `v1beta2-1.1.x-2.4.5` | `v1beta2` | 1.13+ | `2.4.5` | | `v1beta2-1.0.x-2.4.4` | `v1beta2` | 1.13+ | `2.4.4` | ## Developer Guide For developing with Spark Operator, please refer to [Developer Guide](https://www.kubeflow.org/docs/components/spark-operator/developer-guide/). ## Contributor Guide For contributing to Spark Operator, please refer to [Contributor Guide](CONTRIBUTING.md). ## Community * Join the [CNCF Slack Channel](https://www.kubeflow.org/docs/about/community/#kubeflow-slack-channels) and then join `#kubeflow-spark-operator` Channel. * Check out our blog post [Announcing the Kubeflow Spark Operator: Building a Stronger Spark on Kubernetes Community](https://blog.kubeflow.org/operators/2024/04/15/kubeflow-spark-operator.html). * Join our monthly community meeting [Kubeflow Spark Operator Meeting Notes](https://bit.ly/3VGzP4n). ## Adopters Check out [adopters of Spark Operator](ADOPTERS.md). ", Assign "at most 3 tags" to the expected json: {"id":"10046","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"