@Marvin is it possible to use cloud storage buckets for caching task results?

Yes, it is possible to use cloud storage buckets for caching task results in Prefect. You can use a storage block from a Prefect cloud integration library, such as

prefect-aws

, to save your configuration information. For example, if you want to use AWS S3, you would install the

prefect-aws

library, register the S3 storage block with the server, and then create your storage block. Afterwards, you can reference the storage block in your flow as follows:

python
from prefect_aws.s3 import S3Bucket
my_s3_block = S3Bucket.load("MY_BLOCK_NAME")

@task(result_storage=my_s3_block)
def my_task():
    # Task implementation

To learn more about configuring cloud-provider storage, such as AWS S3, Azure Blob Storage, and GCP GCS, you can refer to the Prefect Storage Guide. Storage in Prefect uses blocks, which are a technology that enables much more than just storing task results. Storage blocks and other configuration objects can be created via the Prefect UI and Prefect Cloud. For more detailed information, you can refer to the following Prefect documentation pages: - Data Engineering with Prefect - Configuration Storage for Deployment

@Marvin can i use this for the feature where tasks with identical inputs are skipped and the cached result is used when running similar flows

Certainly, Prefect supports task caching with identical inputs, often referred to as memoization or task result reuse. A task can reuse cached results if the inputs to that task have not changed between flow runs. Prefect uses "cache keys" to determine whether a task run should retrieve a cached state. A cache key is a string value that indicates whether one run should be considered identical to another. When a task run with a cache key finishes, the cache key is attached to the state. Subsequent task runs will check for states with matching cache keys, and if one is found with the same inputs and within the cache expiration time, the task will not rerun and the cached value will be returned. Here is an example of how memoization is implemented:

python
from datetime import timedelta
from prefect import flow, task

@task(cache_key_fn=task_input_hash, cache_expiration=timedelta(days=1))
def hello_task(name):
    # Task implementation

@flow
def my_flow(name):
    hello_task(name)

In this example, as long as the input to

hello_task()

remains the same and the

cache_expiration

time has not passed, the cached return value will be used and the task will not be rerun. To learn more about task caching, refer to the Prefect Tasks Concept. For information on how to configure caching with cloud storage blocks like AWS S3, refer back to the earlier response Data Engineering with Prefect. Please note that setting up a storage block for result caching is separate from task caching. The storage block defines where the task outputs will be stored, while task caching logic defines when a task should be skipped based on cache keys and cache expiration. Both functionalities can be used together to optimize your flows.

@Marvin can i use remote storage bocks for task caching

Yes, you can use remote storage blocks, such as AWS S3, for task caching in Prefect. Here's a broad outline of the steps you would take: 1. Install the relevant cloud integration library (e.g.,

prefect-aws

for AWS S3). 2. Register the storage block with your Prefect server. 3. Create your storage block. 4. Reference the storage block in your flow. You can define your task to use this remote storage block for result storage, which is an essential part of task caching:

python
from prefect_aws.s3 import S3Bucket
from prefect import task

my_s3_block = S3Bucket.load("MY_BLOCK_NAME")

@task(result_storage=my_s3_block)
def my_task():
    # Task implementation

By configuring a task to use a remote storage block for its result storage, you enable Prefect to save the task's results to that storage. When a task is configured with caching settings (e.g.,

cache_key_fn

,

cache_expiration

), and it checks for a cached result, it can leverage these remote storage configurations to fetch the cached data. For further details, please refer to the guide on Big Data with Prefect. Keep in mind that the result storage configuration is crucial for task caching, as it defines where the task outputs are cached, whilst the task’s caching configuration determines when to use or bypass the cache.