更新集群

此示例引导用户使用 Python 客户端库更新 Cloud Dataproc 集群。

代码示例

Python

试用此示例之前，请按照使用客户端库的 Managed Service for Apache Spark 快速入门中的 Python 设置说明进行操作。如需了解详情，请参阅 Managed Service for Apache Spark Python API 参考文档。

如需向 Managed Service for Apache Spark 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅 为本地开发环境设置身份验证。

from google.cloud import dataproc_v1 as dataproc


def update_cluster(project_id, region, cluster_name, new_num_instances):
    """This sample walks a user through updating a Cloud Dataproc cluster
    using the Python client library.

    Args:
        project_id (str): Project to use for creating resources.
        region (str): Region where the resources should live.
        cluster_name (str): Name to use for creating a cluster.
    """

    # Create a client with the endpoint set to the desired cluster region.
    client = dataproc.ClusterControllerClient(
        client_options={"api_endpoint": f"{region}-dataproc.googleapis.com:443"}
    )

    # Get cluster you wish to update.
    cluster = client.get_cluster(
        project_id=project_id, region=region, cluster_name=cluster_name
    )

    # Update number of clusters
    mask = {"paths": {"config.worker_config.num_instances": str(new_num_instances)}}

    # Update cluster config
    cluster.config.worker_config.num_instances = new_num_instances

    # Update cluster
    operation = client.update_cluster(
        project_id=project_id,
        region=region,
        cluster=cluster,
        cluster_name=cluster_name,
        update_mask=mask,
    )

    # Output a success message.
    updated_cluster = operation.result()
    print(f"Cluster was updated successfully: {updated_cluster.cluster_name}")

后续步骤

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