要按照createTime筛选GCP Dataproc批次列表,您可以使用Dataproc的API进行操作。以下是一个使用Python和Dataproc API的示例代码:
from google.cloud import dataproc_v1 as dataproc
from google.protobuf.timestamp_pb2 import Timestamp
def filter_dataproc_clusters_by_create_time(project_id, region, start_time, end_time):
# Create a client for the Dataproc API
client = dataproc.ClusterControllerClient()
# Prepare the filter for createTime range
start_timestamp = Timestamp()
start_timestamp.FromDatetime(start_time)
end_timestamp = Timestamp()
end_timestamp.FromDatetime(end_time)
filter_str = f"createTime >= '{start_timestamp}' AND createTime <= '{end_timestamp}'"
# Define the request to list clusters
request = dataproc.ListClustersRequest(
project_id=project_id,
region=region,
filter=filter_str
)
# Send the request and retrieve the response
response = client.list_clusters(request)
# Process the response
for cluster in response.clusters:
print(f"Cluster name: {cluster.cluster_name}")
# Set the project ID, region, and time range for filtering
project_id = "your-project-id"
region = "your-region"
start_time = datetime.datetime(2022, 1, 1) # Replace with your desired start time
end_time = datetime.datetime(2022, 1, 31) # Replace with your desired end time
# Call the function to filter clusters by createTime
filter_dataproc_clusters_by_create_time(project_id, region, start_time, end_time)
请确保已经安装了google-cloud-dataproc
库,并替换示例代码中的“your-project-id”和“your-region”为您自己的项目ID和区域。还需要根据您的需求调整筛选的时间范围。
这个示例代码将通过Dataproc API列出指定时间范围内的符合条件的批次列表。您可以根据自己的需求进一步处理和使用这些批次数据。