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。