Crea un cluster Dataproc JupyterLab da Dataproc Hub
Seleziona la scheda Blocchi note gestiti dall'utente nella pagina
Dataproc→Workbench
nella console Google Cloud .
Fai clic su Apri JupyterLab nella riga che
elenca l'istanza di Dataproc Hub creata dall'amministratore.
Se non hai accesso alla console Google Cloud , inserisci l'URL dell'istanza Dataproc Hub che un amministratore ha condiviso con te nel browser web.
Nella pagina Jupyterhub→Opzioni Dataproc, seleziona
una configurazione del cluster e una zona. Se è abilitata, specifica le personalizzazioni, poi
fai clic su Crea.
Dopo aver creato il cluster Dataproc, viene visualizzata
l'interfaccia JupyterLab in esecuzione sul cluster.
Crea un notebook ed esegui un job Spark
Nel riquadro a sinistra dell'interfaccia di JupyterLab, fai clic su GCS (Cloud Storage).
Crea un notebook PySpark dal launcher JupyterLab.
Il kernel PySpark inizializza uno SparkContext (utilizzando la variabile sc).
Puoi esaminare SparkContext ed eseguire un job Spark dal blocco note.
rdd = (sc.parallelize(['lorem', 'ipsum', 'dolor', 'sit', 'amet', 'lorem'])
.map(lambda word: (word, 1))
.reduceByKey(lambda a, b: a + b))
print(rdd.collect())
Assegna un nome e salva il notebook. Il blocco note viene salvato e rimane in
Cloud Storage dopo l'eliminazione del cluster Dataproc.
Arresta il cluster Dataproc
Dall'interfaccia di JupyterLab, seleziona File→Pannello di controllo hub per
aprire la pagina JupyterHub.
Fai clic su Arresta il mio cluster per arrestare (eliminare) il server JupyterLab, che
elimina il cluster Dataproc.
[[["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 2025-09-08 UTC."],[[["\u003cp\u003eDataproc Hub and Vertex AI Workbench user-managed notebooks are deprecated and will no longer be supported after January 30, 2025.\u003c/p\u003e\n"],["\u003cp\u003eYou can use Dataproc Hub to create a single-user JupyterLab notebook environment running on a Dataproc cluster, utilizing a configured cluster and zone from the Dataproc Options page.\u003c/p\u003e\n"],["\u003cp\u003eUsers can create a PySpark notebook within the JupyterLab interface, allowing them to run Spark jobs, and the notebook is saved in Cloud Storage even after the Dataproc cluster is deleted.\u003c/p\u003e\n"],["\u003cp\u003eTo shut down the Dataproc cluster, users must navigate to the Jupyterhub page and click "Stop My Cluster," which deletes the JupyterLab server and the Dataproc cluster, but not the Dataproc Hub instance itself.\u003c/p\u003e\n"],["\u003cp\u003eThe admin user must grant the \u003ccode\u003enotebooks.instances.use\u003c/code\u003e permission for a user to be able to utilize Dataproc Hub.\u003c/p\u003e\n"]]],[],null,["# Use Dataproc Hub\n\n*** ** * ** ***\n\n|\n| Dataproc Hub and\n| Vertex AI Workbench user-managed notebooks are\n| deprecated. On January 30, 2025, support for user-managed notebooks\n| will end and the ability to create user-managed notebooks instances\n| will be removed. For alternative notebook solutions\n| on Google Cloud, see:\n|\n| - [Install\n| the Jupyter component on your Dataproc cluster](/dataproc/docs/concepts/components/jupyter#install_jupyter).\n| - [Create\n| a Dataproc-enabled\n| Vertex AI Workbench instance](/vertex-ai/docs/workbench/instances/create-dataproc-enabled).\n\nObjectives\n----------\n\n1. Use Dataproc Hub to create a single-user\n JupyterLab notebook environment running on a Dataproc cluster.\n\n2. Create a notebook and run a Spark job on the Dataproc cluster.\n\n3. Delete your cluster and preserve your notebook in Cloud Storage.\n\nBefore you begin\n----------------\n\n1. The administrator must grant you `notebooks.instances.use` permission (see [Set Identity and Access Management (IAM) roles](/dataproc/docs/tutorials/dataproc-hub-admins#set_identity_and_access_management_iam_roles)). \n\nCreate a Dataproc JupyterLab cluster from Dataproc Hub\n------------------------------------------------------\n\n1. Select the **User-Managed Notebooks** tab on the\n **[Dataproc→Workbench](https://console.cloud.google.com/dataproc/workbench)**\n page in the Google Cloud console.\n\n2. Click **Open JupyterLab** in the row that\n lists the Dataproc Hub instance created by the administrator.\n\n 1. If you do not have access to the Google Cloud console, enter the Dataproc Hub instance URL that an administrator shared with you in your web browser.\n3. On the **Jupyterhub→Dataproc Options** page, select\n a cluster configuration and zone. If enabled, specify any customizations, then\n click **Create**.\n\n After the Dataproc cluster is created, you are redirected\n to the JupyterLab interface running on the cluster.\n\nCreate a notebook and run a Spark job\n-------------------------------------\n\n1. On the left panel of the JupyterLab interface, click on `GCS` (Cloud Storage).\n\n2. Create a PySpark notebook from the JupyterLab launcher.\n\n3. The PySpark kernel initializes a SparkContext (using the `sc` variable).\n You can examine the SparkContext and run a Spark job from the notebook.\n\n ```\n rdd = (sc.parallelize(['lorem', 'ipsum', 'dolor', 'sit', 'amet', 'lorem'])\n .map(lambda word: (word, 1))\n .reduceByKey(lambda a, b: a + b))\n print(rdd.collect())\n ```\n4. Name and save the notebook. The notebook is saved and remains in\n Cloud Storage after the Dataproc cluster is deleted.\n\nShut down the Dataproc cluster\n------------------------------\n\n1. From the JupyterLab interface, select **File→Hub Control Panel** to\n open the **Jupyterhub** page.\n\n | When using Dataproc image versions 1.4 or earlier, navigate to `/hub/home` to access the **Jupyterhub** page.\n2. Click **Stop My Cluster** to shut down (delete) the JupyterLab server, which\n deletes the Dataproc cluster.\n\n | Stopping the server and deleting the cluster **does not delete the Dataproc Hub instance** . You can click **Start my server** on the **Jupyterhub** (Hub Control Panel) page or select the **Open JupyterLab** link for your Dataproc Hub instance on the **Dataproc→Workbench→User-Managed Notebooks** page in the Google Cloud console to open configure and create another Dataproc JupyterLab cluster.\n\nWhat's next\n-----------\n\n- Explore [Spark and Jupyter Notebooks on Dataproc](https://github.com/GoogleCloudDataproc/cloud-dataproc/tree/master/notebooks) on GitHub."]]
