@Marvin in Prefect 3, what is the difference in caching behavior between flows and tasks? Do flows cache results by default?

In Prefect 3.x, caching behavior differs between tasks and flows: 1. Task Caching: Tasks can cache their results to avoid redundant computations, which can be particularly useful for expensive operations. By default, Prefect uses a caching logic based on the task's inputs, code definition, and the flow run ID to compute a cache key. However, caching requires result persistence, which is off by default. You can enable it by setting

persist_result=True

and configuring result storage. More details on task caching can be found here. 2. Flow Caching: Flows do not cache results by default. However, enabling result persistence on a flow (using

persist_result=True

,

result_storage

, or

result_serializer

) will also enable persistence for all tasks within that flow by default. This means tasks will cache their results if persistence is enabled at the flow level. You can override these settings at the task level if needed. More information on result persistence is available here. In summary, task caching is supported but requires explicit configuration, while flow-level caching isn't a default behavior but can influence task caching through result persistence settings.

@Marvin If I understand correctly, Python code which is inside a flow but not inside a task is always run, regardless of cache setting

Yes, that's correct. In Prefect 3.x, only tasks have caching capabilities. Any Python code that is executed directly within a flow, but not encapsulated within a task, will always run when the flow is executed. Caching settings and result persistence apply specifically to tasks, not to the flow itself. So, if you want to benefit from caching, you should encapsulate the logic you want to cache within tasks.

@Marvin Is it possible to set the result cache of a task to a subfolder in a Aws S3 bucket, where the subfolder is decided at runtime?

Yes, it is possible to set the result cache of a task to a subfolder in an AWS S3 bucket dynamically at runtime. Prefect allows you to configure result storage, including using S3, and you can specify the path dynamically by using a function or logic to determine the subfolder path at runtime. Here's a general approach to achieve this: 1. Configure Result Storage: Use the

result_storage

parameter to specify an S3 bucket for result storage. You can use a function to dynamically generate the subfolder path based on runtime parameters. 2. Dynamic Path Configuration: Create a custom function that computes the desired S3 path during the flow or task execution. This function can use inputs or other runtime information to decide the subfolder structure. 3. Set Cache Key: Use a custom cache key function that includes your dynamic path logic, ensuring that the cache key corresponds to the specific subfolder path. For detailed guidance, you can refer to the task caching documentation. This approach allows you to utilize dynamic S3 paths for caching, enabling flexible and efficient result storage management.

@Marvin can you provide an example of a task setting the result_storage to a dynamically decided subfolder of an S3 bucket?

To set a task's result storage to a dynamically decided subfolder of an S3 bucket in Prefect, you can use the

prefect_aws

integration to configure S3 storage. Here's a basic example of how you might achieve this:

python
from prefect import task, flow
from prefect_aws import S3
import os

# Define a function to determine the subfolder path at runtime
def get_dynamic_subfolder():
    # Logic to determine subfolder, e.g., based on environment variables or task inputs
    return f"subfolder/{os.getenv('RUN_ID', 'default')}"

@task
def my_task():
    # Task logic
    return "some result"

@flow
def my_flow():
    # Determine the dynamic subfolder path
    subfolder_path = get_dynamic_subfolder()

    # Configure S3 result storage with the dynamic path
    s3_storage = S3(bucket="my-bucket", key=f"{subfolder_path}/result.json")

    # Set the result storage for the task
    result = my_task.with_options(result_storage=s3_storage)()

    return result

# Run the flow
my_flow()

### Key Points: - Dynamic Subfolder Path: The

get_dynamic_subfolder

function determines the subfolder path at runtime. - S3 Configuration: The

S3

class from

prefect_aws

is used to configure result storage, with the

key

parameter including the dynamic subfolder path. - Task Options: The

with_options

method is used to set the

result_storage

for the task. This example assumes you have the

prefect_aws

module installed and configured correctly to interact with your AWS S3 bucket. Adjust the logic in

get_dynamic_subfolder

as needed for your specific use case.

@Marvin prefect_aws doesn't have an S3 class

It seems there isn't an

S3

class directly available in the

prefect_aws.s3

module. Let's adjust the approach to use Prefect's built-in capabilities to interact with S3 for result storage. Here's how you can dynamically set the result storage to a subfolder in an S3 bucket using Prefect's capabilities:

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

# Define a function to determine the subfolder path at runtime
def get_dynamic_subfolder():
    # Logic to determine subfolder, e.g., based on environment variables or task inputs
    return f"subfolder/{os.getenv('RUN_ID', 'default')}"

@task
def my_task():
    # Task logic
    return "some result"

@flow
def my_flow():
    # Determine the dynamic subfolder path
    subfolder_path = get_dynamic_subfolder()

    # Configure S3 result storage with the dynamic path
    s3_storage = S3(bucket_path=f"<s3://my-bucket/{subfolder_path}/>")

    # Set the result storage for the task
    result = my_task.with_options(result_storage=s3_storage)()

    return result

# Run the flow
my_flow()

### Key Points: - Dynamic Subfolder Path: The

get_dynamic_subfolder

function determines the subfolder path at runtime. - S3 Configuration: The

S3

class from

prefect.filesystems

is used to configure result storage, with the

bucket_path

parameter including the dynamic subfolder path. - Task Options: The

with_options

method is used to set the

result_storage

for the task. Ensure you have the necessary AWS credentials configured in your environment to access the S3 bucket. Adjust the logic in

get_dynamic_subfolder

as needed for your specific use case.

@Marvin in Prefect 3, is it possible to use temporary Prefect blocks? Meaning Prefect blocks that are created at runtime but that are not saved.

In Prefect 3.x, blocks are typically registered and saved for reuse across different flows and tasks. However, creating a block at runtime without saving it for future use is not the standard workflow that Prefect encourages, as blocks are meant to provide reusable configurations. That said, you can create configurations similar to blocks at runtime within your flow or task logic without registering them as Prefect blocks. This means you can dynamically create and use configurations or credentials as needed without persisting them. However, these will not be managed as Prefect blocks and won't have the benefits of Prefect's block management features like versioning or sharing across projects. For more detailed information on Prefect blocks and their management, you can refer to the Prefect blocks documentation.