Configurar o Dataproc Hub

O Dataproc Hub é um servidor JupyterHub personalizado. Os administradores configuram e criam instâncias do Dataproc Hub que podem gerar clusters do Dataproc de usuário único para hospedar ambientes de notebook Jupyter e JupyterLab (consulte Usar o Dataproc Hub).

Iniciar notebooks para vários usuários. É possível criar uma instância do Vertex AI Workbench ativada para o Dataproc ou instalar o plug-in JupyterLab do Dataproc em uma VM para disponibilizar notebooks a vários usuários.

Objetivos

1. Defina uma configuração de cluster do Dataproc ou use um dos arquivos de configuração predefinidos.

2. Definir variáveis de ambiente da instância do Dataproc Hub.

3. Crie uma instância do Dataproc Hub

Antes de começar

Se ainda não tiver feito isso, crie um projeto do Google Cloud e um bucket do Cloud Storage.

1. Como configurar o projeto

   Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

   In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Go to project selector

   Make sure that billing is enabled for your Google Cloud project.

   Enable the Dataproc, Compute Engine, and Cloud Storage APIs.

   Enable the APIs

   Install the Google Cloud CLI.

   If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity.

   To initialize the gcloud CLI, run the following command:

   gcloud init

   In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Go to project selector

   Make sure that billing is enabled for your Google Cloud project.

   Enable the Dataproc, Compute Engine, and Cloud Storage APIs.

   Enable the APIs

   Install the Google Cloud CLI.

   If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity.

   To initialize the gcloud CLI, run the following command:

   gcloud init

   1. Como criar um bucket do Cloud Storage em seu projeto para armazenar os dados usados neste tutorial.

      1. In the Google Cloud console, go to the Cloud Storage Buckets page.

         Go to Buckets

      2. Click add_box Create.
      3. On the Create a bucket page, enter your bucket information. To go to the next step, click Continue.
         1. In the Get started section, do the following:
            • Enter a globally unique name that meets the bucket naming requirements.
            • To add a bucket label, expand the Labels section (expand_more), click add_box Add label, and specify a key and a value for your label.
         2. In the Choose where to store your data section, do the following:
            1. Select a Location type.
            2. Choose a location where your bucket's data is permanently stored from the Location type drop-down menu.
               • If you select the dual-region location type, you can also choose to enable turbo replication by using the relevant checkbox.
            3. To set up cross-bucket replication, select Add cross-bucket replication via Storage Transfer Service and follow these steps:

               Set up cross-bucket replication

               1. In the Bucket menu, select a bucket.

               2. In the Replication settings section, click Configure to configure settings for the replication job.

                  The Configure cross-bucket replication pane appears.

                  • To filter objects to replicate by object name prefix, enter a prefix that you want to include or exclude objects from, then click add Add a prefix.
                  • To set a storage class for the replicated objects, select a storage class from the Storage class menu. If you skip this step, the replicated objects will use the destination bucket's storage class by default.
                  • Click Done.
         3. In the Choose how to store your data section, do the following:
            1. Select a default storage class for the bucket or Autoclass for automatic storage class management of your bucket's data.
            2. To enable hierarchical namespace, in the Optimize storage for data-intensive workloads section, select Enable hierarchical namespace on this bucket.
         4. In the Choose how to control access to objects section, select whether or not your bucket enforces public access prevention, and select an access control method for your bucket's objects.
         5. In the Choose how to protect object data section, do the following:
            • Select any of the options under Data protection that you want to set for your bucket.
              • To enable soft delete, click the Soft delete policy (For data recovery) checkbox, and specify the number of days you want to retain objects after deletion.
              • To set Object Versioning, click the Object versioning (For version control) checkbox, and specify the maximum number of versions per object and the number of days after which the noncurrent versions expire.
              • To enable the retention policy on objects and buckets, click the Retention (For compliance) checkbox, and then do the following:
                • To enable Object Retention Lock, click the Enable object retention checkbox.
                • To enable Bucket Lock, click the Set bucket retention policy checkbox, and choose a unit of time and a length of time for your retention period.
            • To choose how your object data will be encrypted, expand the Data encryption section (expand_more), and select a Data encryption method.
      4. Click Create.

Limpar

Excluir a instância do Dataproc Hub

• Para excluir sua instância do Dataproc Hub:

  gcloud compute instances delete --project=${PROJECT} ${INSTANCE_NAME}
  

Excluir o bucket

• Para excluir o bucket do Cloud Storage criado em Antes de começar, incluindo os arquivos de dados armazenados no bucket:

  gcloud storage rm gs://${BUCKET_NAME} --recursive
  

A seguir

• Usar o Dataproc Hub