@Marvin If I add to the flow decorator the argument persis_result=true, will it persist the results of all the tasks? and also what would happen with the tasks that fail?

The

persist_result

parameter in the

Flow

decorator in Prefect does indeed persist the results, but let's be precise: 1. Persistence of Task Results: When you set

persist_result=True

in the

Flow

decorator, Prefect will persist the state and result of each task. This means that the results of completed tasks will be stored in a location like a database or file system, depending on your storage configuration. 2. Handling of Failed Tasks: For tasks that fail, Prefect will also persist their states. More specifically, the failed states and the associated tracebacks or error messages will be stored. This allows you to inspect the reason for failure and potentially retry or resume your flow from the point it failed. To ensure all your bases are covered, I will look into the Prefect documentation for more detailed information. ### Persisting Task Results in Prefect 1. Summary of

persist_result=True

: When set on a flow,

persist_result=True

will ensure that the results of all tasks within the flow are persisted to a specified storage backend. Prefect will store references to these results, which you can retrieve later. 2. Handling of Failed Tasks: Even if a task fails, Prefect will persist its state, including error messages and tracebacks. This enables easier debugging and allows you to manage retries or resume from specific points of failure. ### Documentation Links and Code Example - Persisting Results - Flow Configuration Here’s an example illustrating the use of `persist_result`:

python
from prefect import flow, task

@task
def my_task():
    return 1

@flow(persist_result=True)
def my_flow():
    result = my_task()
    return result + 1

if __name__ == "__main__":
    state = my_flow()
    print(state)

The documentation and code example should give you a clear understanding of how to use

persist_result

to manage task results effectively.

@Marvin And how would it behave if in addition to setting persist_results=true, I also add result_storage=S3(bucket_path=s3_bucket_path)?

When you set

persist_result=True

and specify

result_storage=S3(bucket_path=s3_bucket_path)

, Prefect will persist task and flow results to an Amazon S3 bucket. This allows you to store results remotely and access them whenever needed. ### Details 1. Persisting Results: -

persist_result=True

: Ensures all task results within the flow are persisted. -

result_storage=S3(bucket_path=s3_bucket_path)

: Specifies that the storage backend for these results is an S3 bucket located at the given

s3_bucket_path

. ### Code Example Here's how you can set this up in your flow:

python
from prefect import flow, task
from prefect.filesystems import S3

s3_storage = S3(bucket_path="your-s3-bucket-path")

@task
def my_task():
    return 1

@flow(persist_result=True, result_storage=s3_storage)
def my_flow():
    result = my_task()
    return result + 1

if __name__ == "__main__":
    state = my_flow()
    print(state)

### Documentation Links - Persisting Results - Flow Configuration - S3 Storage Options By configuring both

persist_result

and

result_storage

, you ensure that your flow's results are not only persisted but also stored in a highly available and durable S3 bucket. Tasks that fail will have their error states and messages persisted in the same manner, enabling you to debug or resume flow execution seamlessly.