juana
10/17/2024, 4:31 AMMarvin
10/17/2024, 4:31 AMpython
prefect config set PREFECT_RESULTS_PERSIST_BY_DEFAULT=true
- This will persist all task and flow results by default, but you need to configure the storage where the results will be saved.
2. Task and Flow Configuration:
- You can specify persistence for individual tasks or flows using the persist_result
and result_storage
parameters.
- Here's an example that uses Azure for storage:
python
from prefect import task, flow
from prefect.filesystems import Azure
import pandas as pd
@task(persist_result=True)
def my_task():
return pd.DataFrame({"a": [1, 2, 3]})
credential = ... # Set up your Azure credentials
storage = Azure(
azure_storage_account_name="myaccount",
bucket_path="mybucket/",
azure_storage_credential=credential,
azure_storage_anon=False,
)
@flow(result_storage=storage)
def my_flow():
my_task()
my_flow()
- This script configures persistence at the task level, and explicitly sets the storage at the flow level using an Azure storage block.
3. Result Storage and Serialization:
- Prefect allows you to configure the filesystem to persist results and set the serializer to serialize the pandas DataFrame.
- The settings can include filesystems like S3, Azure, GCS, etc., and serializers like JSON, pickle, etc.
For the complete documentation on managing and persisting results, check out the Prefect 3.x documentation on results. If you need deeper integration with specific storage solutions, Prefect's flexibility allows for custom solutions or integrations.
There have been related discussions in GitHub issues, such as enabling credentials to Azure storage or effectively managing the URIs for persisted results. These can provide further insights if you need to customize beyond the standard configurations.