How to set a hook on a software defined asset

As of the information available, hooks are not directly applicable to software-defined assets in the same way they are used with ops within jobs. However, there is a discussion in the Dagster community about enabling hooks with software-defined assets. A workaround mentioned involves setting the hook in

define_asset_job

and using

op_tags

to dictate the behavior of the hook. Here's an example provided in the GitHub issue:

python @success_hook def slack_message_on_success(context: HookContext):     if context.op.tags.get("success_message"):         message = (             f"{context.op.name} with tags {context.op.tags} in job {context.job_name} succeeded."         )         <http://context.log.info|context.log.info>(message)  @asset(       op_tags={"success_message": True}, ) def my_asset():     ...

This example shows a custom hook

slack_message_on_success

that sends a Slack message when an op succeeds, and it's being used in conjunction with a software-defined asset by setting

op_tags

on the asset. This is a workaround and not the direct way to set hooks on assets, as you would with ops in jobs. Please note that this is a workaround and might not be the ideal or recommended way to use hooks with software-defined assets. The Dagster team might introduce more direct support for this functionality in the future, and it's always a good idea to check the latest documentation or GitHub discussions for updates on this topic.

how would you set the hook on the define_asset_job call

In Dagster, resources are typically associated with assets or ops rather than directly with jobs. When you define a job using

define_asset_job

, the resources are supplied to the assets, not the job itself. Each asset can specify its required resources using the

required_resource_keys

parameter in the

@asset

decorator. Here's an example of how you might define a resource and supply it to an asset, which is then included in a job defined by `define_asset_job`:

python from dagster import asset, define_asset_job, ResourceDefinition, resource  # Define a resource @resource def my_resource(context):     return "my_resource_value"  # Define an asset that requires the 'my_resource' resource @asset(required_resource_keys={"my_resource"}) def my_asset(context):     # Access the resource via context.resources     resource_value = context.resources.my_resource     # ... use the resource_value ...  # Define a job that includes the 'my_asset' asset my_asset_job = define_asset_job(     name="my_asset_job",     selection=[my_asset],     resource_defs={"my_resource": my_resource}  # Supply the resource to the job )

In this example,

my_resource

is a resource that's being defined and then supplied to

my_asset

via the

required_resource_keys

parameter. When defining the job with

define_asset_job

, you can pass the

resource_defs

argument to supply the resource definitions that the assets within the job require. Please note that the example above is based on the typical usage pattern in Dagster as of the knowledge cutoff date. Always refer to the latest Dagster documentation for the most up-to-date practices and API usage.

@Scout how to set a retry policy on an asset

To set a retry policy on an asset in Dagster, you can use the

retry_policy

argument within the

@asset

decorator. This allows you to specify how the asset should be retried in case of failure during computation. You can define the number of retries, the delay between retries, and other parameters such as backoff and jitter. Here's an example of how to set a retry policy on an asset:

python
from dagster import asset, RetryPolicy, Backoff, Jitter

@asset(
    retry_policy=RetryPolicy(
        max_retries=3,
        delay=0.2,  # 200ms
        backoff=Backoff.EXPONENTIAL,
        jitter=Jitter.PLUS_MINUS,
    )
)
def my_asset():
    # Asset computation logic here
    ...

In this example, the

RetryPolicy

is configured to retry the asset computation up to three times with an initial delay of 200 milliseconds between retries. The delay will increase exponentially due to the

Backoff.EXPONENTIAL

setting, and the

Jitter.PLUS_MINUS

setting will add a random amount of jitter to the delay to prevent thundering herd problems. Please ensure that you refer to the latest Dagster documentation to confirm the current API and best practices for setting retry policies on assets, as the platform is actively developed and features may evolve. How can i ensure that my assets and ops ... button Thank you for your feedback!