@Marvin Can I use an async function for a task’s

cache_key_fn

?

It appears from the Prefect documentation that a

cache_key_fn

function is intended to be a regular function and not an async one. The documentation for Task caching does not specify that

cache_key_fn

can be asynchronous, suggesting that it should be a synchronous function. Here's the general idea of defining a `cache_key_fn`:

python
from prefect import task, flow

def my_cache_key(context, parameters):
    # your logic here
    return "computed-key-for-caching"

@task(cache_key_fn=my_cache_key)
def my_task():
    # task implementation

@flow
def my_flow():
    my_task()

The

cache_key_fn

takes two arguments: 1.

context

, which stores task run metadata. 2.

parameters

, which is a dictionary of the task's input values. Based on this, if you require asynchronous operations for generating a cache key, you'll possibly need to run those operations synchronously within the

cache_key_fn

or pre-compute the necessary data asynchronously before the task runs.

@Marvin Where does prefect cache the task results by defualt?

By default, Prefect caches task results in the local filesystem at the path specified by the

PREFECT_LOCAL_STORAGE_PATH

setting. If this setting is not otherwise configured, it defaults to

~/.prefect/storage

. Here is an excerpt from the Prefect documentation:

If there is no context to load the storage from and results must be persisted, results will be stored in the path specified by the

PREFECT_LOCAL_STORAGE_PATH

setting (defaults to

~/.prefect/storage

).

For more details, you can refer to the Results concept page in the Prefect documentation.

@Marvin How can i configure the task results to be only cached in memory such that if i were to run a new flow it would have to recompute the task results?

To configure task results to be cached only in memory during a flow run, you do not need to make any special configuration changes. By default, task results are cached in memory for the duration of the flow run. The results will not persist between different flow runs unless specifically configured to do so. Here's what you need to know from the documentation excerpt:

Task results are cached in memory during a flow run and persisted to the location specified by the

PREFECT_LOCAL_STORAGE_PATH

setting. As a result, task caching between flow runs is currently limited to flow runs with access to that local storage path.

In other words, if you do nothing, task results are not persisted across flow runs by default and are only kept in memory. If you need to ensure that no disk persistence happens at all (even within a single flow run), you can specifically set

PREFECT_LOCAL_STORAGE_PATH

to a Volatile Storage or avoid setting up a persistent result storage in the flow configuration. For more details, refer to the Tasks concept page in the Prefect documentation.

@Marvin I removed all the cached results from .prefect/storage, and im getting errors now like

.prefect/storage/df396232fbd4403092a3a4dce4e6f3e2 does not exist.

How do i clear prefects data for what cached values exist

If you're encountering errors after manually deleting cached results from

.prefect/storage

, you may need to clear the metadata associated with the results that Prefect is tracking. Unfortunately, the provided documentation excerpts did not include a straightforward way to clear cached task results metadata. However, based on common practices, to clear metadata about cached results, you may need to reset or delete the associated metadata from Prefect's database or the location where this metadata is stored. If you are using Prefect Cloud or Prefect Server, this might involve clearing data related to task runs for your flow from the UI or using an API call. Alternatively, if you're using a local SQLite database, you would need to manipulate the database directly. Be cautious with these approaches as they can affect the integrity of your workflow runs. If these general hints don't solve your issue, you might consider reaching out to the Prefect community or checking Prefect's GitHub issues for similar cases. I see there has been some communication on GitHub, particularly on issues related to caching, but not specifically about clearing cached results metadata. For a more precise solution, it would be best to refer to Prefect's documentation on task results and caching or seek support from the Prefect community. For documentation on task results and caching, you can start with the Results concept documented by Prefect. If you need to bring this issue to the community, you can open a discussion on Prefect's GitHub repository.

@Marvin how do i cache task results only within a single flow run aka i dont want to persist the results anywhere and have them be used when other flow runs call the task

To cache task results only within a single flow run without persisting them anywhere, you should set the

persist_result

parameter to

False

on the task decorator or function. This ensures that task results are not persisted, so they will only be available in-memory for the duration of the flow run, and will not be reused in subsequent flow runs. Here is a code example from the Prefect documentation:

python
from prefect import flow, task

@flow
def my_flow():
    my_task()

@task(persist_result=False)
def my_task():
    # Task implementation
    pass

my_flow()

By setting

persist_result=False

, you indicate that the result of the task should not be persisted. If persistence is needed for a feature and it's turned off, an error will be raised. For more information, you can refer to the documentation about Results and caching.

@Marvin how do i clear all cached task results?

The documentation does not provide a direct method to clear all cached task results. However, based on the standard behavior of Prefect's caching system, you would typically clear the local cache by deleting the files from the directory specified by the

PREFECT_LOCAL_STORAGE_PATH

setting, which defaults to

~/.prefect/storage

. If you have already attempted to remove cached results manually from

.prefect/storage

(the default location) and are experiencing errors, it might indicate that Prefect is still referencing cached Task Run IDs from its internal database. If you cannot find a built-in Prefect command to clear the cache, you might need to manually reset the metadata in Prefect's database that refers to those cached results. This would likely be done by accessing the database Prefect uses to store run data and removing or resetting the relevant entries. If you are using Prefect Server or Prefect Cloud, you might need to perform these operations through the provided UI or API endpoints to clean up or invalidate the caches. For detailed instructions, it would be best to contact Prefect Support or check the community discussions for advice from other users who may have faced similar issues.