Componenti di Google Cloud Serverless per Apache Spark
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I componenti Serverless for Apache Spark ti consentono di eseguire batch Apache Spark
workload da una pipeline all'interno di Vertex AI Pipelines.
Serverless for Apache Spark esegue i workload batch su un'infrastruttura di calcolo gestita, scalando automaticamente le risorse in base alle esigenze.
In Serverless per Apache Spark, una risorsa Batch rappresenta un carico di lavoro batch.
L'SDK Google Cloud include i seguenti operatori per
creare risorse Batch e monitorarne l'esecuzione:
[[["Facile da capire","easyToUnderstand","thumb-up"],["Il problema รจ stato risolto","solvedMyProblem","thumb-up"],["Altra","otherUp","thumb-up"]],[["Difficile da capire","hardToUnderstand","thumb-down"],["Informazioni o codice di esempio errati","incorrectInformationOrSampleCode","thumb-down"],["Mancano le informazioni o gli esempi di cui ho bisogno","missingTheInformationSamplesINeed","thumb-down"],["Problema di traduzione","translationIssue","thumb-down"],["Altra","otherDown","thumb-down"]],["Ultimo aggiornamento 2025-09-04 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)."]]
