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Questo documento fornisce informazioni sui bucket di staging di Serverless per Apache Spark.
Serverless per Apache Spark crea un bucket gestione temporanea Cloud Storage nel tuo progetto
o riutilizza un bucket gestione temporanea esistente dalle richieste di creazione batch precedenti. Questo è il bucket predefinito creato dai
cluster Dataproc su Compute Engine. Per ulteriori
informazioni, vedi
Bucket temporanei e di gestione temporanea di Dataproc.
Serverless for Apache Spark archivia le dipendenze dei workload, i file di configurazione e
l'output della console del driver dei job nel bucket gestione temporanea.
Serverless for Apache Spark imposta i bucket di staging regionali nelle
posizioni Cloud Storage
in base alla zona Compute Engine in cui viene eseguito il deployment del workload,
quindi crea e gestisce questi bucket a livello di progetto per località.
I bucket di staging creati da Serverless per Apache Spark sono condivisi tra i workload nella stessa regione e vengono creati con una durata di conservazione dell'eliminazione temporanea di Cloud Storage impostata su 0 secondi.
[[["Facile da capire","easyToUnderstand","thumb-up"],["Il problema è 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."],[[["\u003cp\u003eDataproc Serverless utilizes Cloud Storage staging buckets to store workload dependencies, config files, and job driver console output.\u003c/p\u003e\n"],["\u003cp\u003eThese staging buckets are created by Dataproc Serverless within your project, or an existing one is reused, similar to the default bucket used with Dataproc on Compute Engine clusters.\u003c/p\u003e\n"],["\u003cp\u003eDataproc Serverless creates regional staging buckets, which are shared across workloads within the same region, based on the Compute Engine zone where the workload is deployed.\u003c/p\u003e\n"],["\u003cp\u003eThe staging buckets created by Dataproc Serverless can be identified by filtering for the \u003ccode\u003edataproc-staging-\u003c/code\u003e prefix in Cloud Storage, and they are created with a 0-second soft delete retention.\u003c/p\u003e\n"]]],[],null,["# Serverless for Apache Spark staging buckets\n\nThis document provides information about Serverless for Apache Spark staging buckets.\nServerless for Apache Spark creates a Cloud Storage staging bucket in your project\nor reuses an existing staging bucket from previous batch\ncreation requests. This is the default bucket created by\nDataproc on Compute Engine clusters. For more\ninformation, see\n[Dataproc staging and temp buckets](/dataproc/docs/concepts/configuring-clusters/staging-bucket).\n\nServerless for Apache Spark stores workload dependencies, config files, and\njob driver console output in the staging bucket.\n\nServerless for Apache Spark sets regional staging buckets in\n[Cloud Storage locations](/storage/docs/locations#location-r)\naccording to the Compute Engine zone where your workload is deployed,\nand then creates and manages these project-level, per-location buckets.\nServerless for Apache Spark-created staging buckets are shared among\nworkloads in the same region, and are created with a\nCloud Storage [soft delete retention](/storage/docs/soft-delete#retention-duration)\nduration set to 0 seconds.\n| To locate the Dataproc default staging bucket, in the Google Cloud console, go to **[Cloud Storage](https://console.cloud.google.com/storage/browser)** and filter the results using the `dataproc-staging-` prefix."]]
