Componentes de Google Cloud sin servidores para Apache Spark
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Los componentes de Serverless para Apache Spark te permiten ejecutar cargas de trabajo por lotes de Apache Spark desde una canalización dentro de Vertex AI Pipelines.
Serverless para Apache Spark ejecuta las cargas de trabajo por lotes en una infraestructura de procesamiento administrada, con ajuste de escala automático de los recursos según sea necesario.
En Serverless for Apache Spark, un recurso Batch representa una carga de trabajo por lotes.
El SDK de Google Cloud incluye los siguientes operadores para crear recursos Batch y supervisar su ejecución:
[[["Fácil de comprender","easyToUnderstand","thumb-up"],["Resolvió mi problema","solvedMyProblem","thumb-up"],["Otro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Información o código de muestra incorrectos","incorrectInformationOrSampleCode","thumb-down"],["Faltan la información o los ejemplos que necesito","missingTheInformationSamplesINeed","thumb-down"],["Problema de traducción","translationIssue","thumb-down"],["Otro","otherDown","thumb-down"]],["Última actualización: 2025-09-02 (UTC)"],[],[],null,["The Serverless for Apache Spark components let you run Apache Spark batch\nworkloads from a pipeline within Vertex AI Pipelines.\nServerless for Apache Spark runs the batch workloads on a managed compute\ninfrastructure, autoscaling resources as needed.\n\nLearn more about [Google Cloud Serverless for Apache Spark](/dataproc-serverless/docs/overview) and [supported Spark workloads](/dataproc-serverless/docs/overview#for_spark_workload_capabilities).\n\nIn Serverless for Apache Spark, a `Batch` resource represents a batch workload.\nThe Google Cloud SDK includes the following operators to\ncreate `Batch` resources and monitor their execution:\n\n\n- [`DataprocPySparkBatchOp`](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.19.0/api/v1/dataproc.html#v1.dataproc.DataprocPySparkBatchOp)\n- [`DataprocSparkBatchOp`](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.19.0/api/v1/dataproc.html#v1.dataproc.DataprocSparkBatchOp)\n- [`DataprocSparkRBatchOp`](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.19.0/api/v1/dataproc.html#v1.dataproc.DataprocSparkRBatchOp)\n- [`DataprocSparkSqlBatchOp`](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.19.0/api/v1/dataproc.html#v1.dataproc.DataprocSparkSqlBatchOp)\n\n\u003cbr /\u003e\n\nAPI reference\n\n- For component reference, see the\n [Google Cloud SDK reference for Google Cloud Serverless for Apache Spark components](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.19.0/api/v1/dataproc.html) .\n\n- For Serverless for Apache Spark resource reference, see the following API\n reference page:\n\n - [`Batch`](/dataproc-serverless/docs/reference/rest/v1/projects.locations.batches#resource:-batch) resource\n\nTutorials\n\n- [Get started with Google Cloud Serverless for Apache Spark pipeline components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_with_dataproc_serverless_pipeline_components.ipynb)\n\nVersion history and release notes\n\nTo learn more about the version history and changes to the Google Cloud Pipeline Components SDK, see the [Google Cloud Pipeline Components SDK Release Notes](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.19.0/release.html).\n\nTechnical support contacts\n\nIf you have any questions, reach out to\n[kfp-dataproc-components@google.com](mailto: kfp-dataproc-components@google.com)."]]
