@Marvin is it possible for a task to use cached result in a storage even when the expiration period has passed

Yes, in Prefect, you can utilize cached results for a task. However, once the expiration period set for a cached result has passed, the default behavior is that the cache will no longer be used. If you want to override this behavior, you'll need to customize the task's caching settings. Prefect allows you to set cache expiration through the

cache_expiration

parameter in the task decorator, which determines the validity duration of the cache. Once this period expires, the cache results won't be reused unless reconfigured. Here's more information from the Prefect documentation on task caching: Prefect 3.x Task Caching Documentation. In essence, unless you manually adjust the policy, tasks are pretty firm about keeping things fresh and won't use stale cache—much like preferring your bananas not too brown.

@Marvin My task decorator looks something like this but it still uses cached results from several days ago. What could be the cause?

@task(
    name="Extract Channel Stats",
    description="This function gets channel statistics from the Data API",
    tags=["channel", "statistics"],
    result_storage=GCS_BUCKET,
    cache_key_fn=task_input_hash,
    cache_result_in_memory=False,
    cache_expiration=timedelta(minutes=10),
    timeout_seconds=3600,
    retries=1,
    retry_delay_seconds=5,
)

There are a few possible reasons why your task is still using cached results beyond the expiration period you've set: 1. Cache Key Configuration: The cache might be using a key that isn't changing between runs. This could be due to the use of

task_input_hash

, which might generate the same hash if the task inputs haven't changed. Even if the cache has an expiration set, if the key hasn't changed, it won't trigger a new computation. 2. Stale Cache Handling: An issue has been identified where there isn't a built-in way to clear stale cache keys in some versions of Prefect. This means the old cache might reappear if the cache itself wasn't actually purged or if there were no changes in the inputs or flow runs which might update the cache key. You can read more about this issue here. 3. Task or Flow-Level Caching: Make sure the cache isn't being overridden at a different level within your Prefect setup – this includes checking for global or flow-level cache settings that might not reflect the intended task-level configuration. 4. Simulation of Cache Behavior: Sometimes, the calling structure or inline conditions can inadvertently reuse cache. Ensure that you're setting the

cache_expiration

properly when the flow is actually run and check any potential interactions with flow run ID, which can affect caching. If these suggestions don't help, you might want to attempt clearing out cache manually if you're operating locally, or simply altering task inputs to allow for refreshed caching behavior. Remember, even in the world of automation, manual intervention sometimes becomes the task of choice. For more detailed insights, see the documentation on caching settings: Prefect 3.x Task Caching Documentation.

@Marvin how can I clear out cache input in Google cloud storage

To clear out cached data from Google Cloud Storage (GCS) used by your Prefect tasks, you can follow these general steps: 1. Identify the Cache Key: You need to know the specific key or identifier used to store the cached results in your GCS bucket. This often corresponds to a particular task or flow run ID along with the cache key function you've defined. 2. Use GCS Client: Utilize the Google Cloud Storage client library for Python to delete the object: - First, authenticate your application using service account credentials. - Then, instantiate a GCS client and use it to locate and delete the specific object (cache file) from the bucket. 3. Manual Deletion: If you prefer or need to do it more directly, you can use the Google Cloud Console or

gsutil

command-line tool to manually delete files. Here's a basic example using Python and the

google-cloud-storage

library:

python
from google.cloud import storage

def clear_gcs_cache(bucket_name, cache_key):
    client = storage.Client()
    bucket = client.bucket(bucket_name)
    blob = bucket.blob(cache_key)

    try:
        blob.delete()
        print(f'Cache for key {cache_key} deleted from bucket {bucket_name}.')
    except Exception as e:
        print(f'Error: {e}')

# Call this function with the appropriate details
# clear_gcs_cache('your-bucket-name', 'your-cache-key')

Remember, if you're unsure about which key to delete, it's good to review your task's logging or metadata to pinpoint the exact cache key involved. For more on managing your GCS with Prefect, you can refer to Prefect's integration documentation: Prefect GCP Integration. And, as always, make sure your access permissions are properly set for both reading and deleting objects in the GCS! If anything goes wrong, well... an existential crisis might just be around the corner, but hey, that's life.