during an asset execution, how do I get the path where the pickled output will be written

During asset execution, the path where the pickled output will be written is determined by the configuration of the IO manager that you are using. If you are using a built-in IO manager like the

FilesystemIOManager

or a custom IO manager from a package such as

dagster-aws

or

dagster-azure

, the path is typically constructed using the base directory, asset key, and possibly other components such as the run ID, step key, and output name. For example, with the

FilesystemIOManager

, the documentation provides guidance on how the base directory is determined and how the path is constructed: - The base directory can be specified in the IO manager’s “base_dir” configuration value. - If not specified, it may fall back to a “storage/” directory under the “local_artifact_storage” in your

dagster.yaml

file, or the

DAGSTER_HOME

environment variable, or a temporary directory. - The path for an asset's output is then constructed as

<base_dir>/<asset_key>

. If the asset key has multiple components, the final component is used as the name of the file, and the preceding components as parent directories under the

base_dir

. For example, if you have an asset with key

AssetKey(["one", "two", "three"])

and a base directory of

/my/base/path

, the output would be stored in a file called

three

in the directory

/my/base/path/one/two/

. If you need to access the path programmatically during asset execution, you would typically rely on the context provided to your asset's function. However, the exact method to retrieve the path depends on the IO manager's implementation and is not explicitly detailed in the documentation. If you are using a custom IO manager or need to access this path, you may need to refer to the specific IO manager's documentation or source code to understand how to retrieve the path within the asset's execution context.

@Scout how do I access the IO manager object using the asset's context?

To access the IO manager object using the asset's context, you can use the

context.resources

attribute within your asset's computation function. The

context.resources

is a dictionary-like object that contains all the resources available to the asset, including the IO manager. Here's a step-by-step guide on how to access the IO manager from the asset's context: 1. Define your IO manager and make sure it is configured in your Dagster repository. 2. When defining your asset, specify the IO manager key if it's not the default IO manager. 3. Inside your asset's computation function, you can access the IO manager using the

context.resources

attribute followed by the key of the IO manager. Here's an example of how you might do this:

python from dagster import asset, IOManager, ResourceDefinition  class MyIOManager(IOManager):     # Implementation of your IO manager     ...  # Define your IO manager resource my_io_manager_resource = ResourceDefinition.resource(MyIOManager())  # Define your asset and specify the IO manager key if necessary @asset(required_resource_keys={'my_io_manager_key': my_io_manager_resource}) def my_asset(context):     # Access the IO manager from the context     io_manager = context.resources.my_io_manager_key     # Now you can use the io_manager object as needed     ...

In this example,

my_io_manager_key

is the key that you use to access the IO manager from the

context.resources

. If you are using the default IO manager, you can access it without specifying a key, as it will be available under

context.resources.io_manager

. Please note that the exact key to use when accessing the IO manager from

context.resources

depends on how you have configured your resources and what key you have associated with your IO manager in the resource definitions.

@Scout It seems the IO manager needs a OutputContext to compute the output path. Is it possible to provide one from an AssetExecutionContext?

In Dagster, the

OutputContext

is typically constructed internally by the Dagster framework during the execution of an asset or op. It is not common practice to manually construct an

OutputContext

within an

AssetExecutionContext

because the

OutputContext

is meant to be used within the IO manager's

handle_output

method, which is called by the framework when an output is being handled. However, if you need to construct an

OutputContext

for testing purposes or to manually invoke an IO manager's methods, you can use the

build_output_context

utility function provided by Dagster. This function allows you to create an

OutputContext

with the necessary parameters for testing or other non-standard use cases. Here's an example of how you might use `build_output_context`:

python from dagster import build_output_context  # Construct an OutputContext for testing or other purposes output_context = build_output_context(     # You can specify various parameters here as needed     asset_key=AssetKey(["my_asset"]),     # ... other parameters )  # Now you can use the output_context with your IO manager io_manager = MyIOManager() io_manager.handle_output(output_context, value)

Please note that this is not a typical pattern for asset execution in Dagster. The

build_output_context

function is primarily intended for testing IO managers outside of the normal execution flow. In a standard Dagster asset, you would not manually create an

OutputContext

or directly call

handle_output

on an IO manager. Instead, you would let the Dagster framework manage the execution and IO management process. If you find yourself needing to manually construct an

OutputContext

within an asset's execution context, it might be worth reconsidering the design of your pipeline to ensure it aligns with Dagster's execution model. If you have a specific use case that requires this pattern, it would be helpful to provide more context so that we can offer more targeted advice.