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#ask-ai
Title
# ask-ai
t

TruffleOil

03/08/2024, 3:34 PM
I have an asset_b that depends on asset_a. asset_a passes data to asset_b as a dataframe, and yet when I run both together, asset_b materialization fails and the error states that "No such file or directory" because asset_b is looking for a local storage copy of the data. Why does this happen?
s

Scout

03/08/2024, 3:34 PM
The error you're encountering, where
asset_b
fails to materialize because it cannot find a local storage copy of the data from
asset_a
, suggests that there might be an issue with the Input/Output (I/O) management in your asset pipeline. Here are a few potential reasons why this could be happening: 1. Missing or Misconfigured I/O Manager: Each asset in Dagster can specify an I/O manager that handles the storage and retrieval of its outputs and inputs. If
asset_a
is supposed to pass a dataframe to
asset_b
, you need to ensure that there is an I/O manager configured to handle the dataframe between the two assets. If the I/O manager is not set up correctly,
asset_b
might not be able to find the output from
asset_a
. 2. Incorrect Asset Dependencies: Ensure that
asset_b
correctly declares
asset_a
as a dependency. If the dependency is not set up correctly,
asset_b
might not know where to look for the output of
asset_a
. 3. Filesystem Path Issues: If you are using a local filesystem I/O manager, it's possible that the path where
asset_a
writes its output and where
asset_b
expects to read its input do not match. This could lead to a "No such file or directory" error. 4. Execution Order: Make sure that
asset_a
is executed and its output is materialized before
asset_b
runs. If
asset_b
runs before
asset_a
has produced its output, it will not find the expected data. To resolve this issue, you should: - Verify that the I/O manager is correctly configured for both assets. The I/O manager should know how to handle the specific data types being passed between assets. - Check the asset definitions to ensure that dependencies are correctly declared. - Review the storage configuration and paths used by the I/O manager to ensure they are consistent and accessible. If you need further assistance, please provide more details about how your assets are defined and how the I/O manager is configured, so we can help you troubleshoot the issue more effectively.
t

TruffleOil

03/08/2024, 3:38 PM
@Scout assuming asset_a is a dbt asset, and asset_b is an asset that runs some calculation on top of the data passed in by asset_a, how would the code structure look like?
s

Scout

03/08/2024, 3:38 PM
When working with dbt assets (
asset_a
) and downstream Dagster assets that perform calculations on the data produced by dbt (
asset_b
), you would typically structure your code to ensure that
asset_b
depends on
asset_a
and that the data is passed correctly between them. Here's a high-level example of how you might structure your code:
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python from dagster import asset, AssetIn, AssetOut, AssetKey from dagster_dbt import dbt_cli_resource, dbt_run_op  # Define your dbt asset using the dbt_cli_resource and dbt_run_op @asset(     required_resource_keys={"dbt"},     outs={"result": AssetOut(asset_key=AssetKey("dbt_result"))} ) def asset_a(context):     # Run your dbt project, which materializes your dbt models     result = dbt_run_op(context.resources.dbt)     return result  # Define your downstream asset that performs calculations on the dbt asset's data @asset(     ins={"dbt_data": AssetIn(asset_key=AssetKey("dbt_result"))} ) def asset_b(dbt_data):     # Perform your calculations on the dbt_data     calculated_data = some_calculation_function(dbt_data)     return calculated_data  # Define your Dagster job to orchestrate the assets from dagster import job  @job(resource_defs={"dbt": dbt_cli_resource.configured({"project_dir": "path/to/your/dbt/project"})}) def my_pipeline():     dbt_data = asset_a()     asset_b(dbt_data)
In this example: -
asset_a
is defined as a dbt asset. It uses
dbt_cli_resource
and
dbt_run_op
to run the dbt project and materialize the dbt models. The output of this asset is given an explicit
AssetKey
to make it identifiable in the asset graph. -
asset_b
is a downstream asset that takes the output of
asset_a
as an input. It is defined with an
AssetIn
that specifies the
AssetKey
of `asset_a`'s output. This establishes the dependency between
asset_b
and
asset_a
. - The
my_pipeline
job orchestrates the execution of the assets. It ensures that
asset_a
runs before
asset_b
, and it passes the output of
asset_a
as input to
asset_b
. Please note that the actual implementation of
some_calculation_function
and the configuration of the dbt CLI resource will depend on your specific use case and dbt project setup. This structure ensures that
asset_b
will only run after
asset_a
has successfully completed and that the data from
asset_a
is available for
asset_b
to use.
t

TruffleOil

03/08/2024, 3:50 PM
@Scout I have an aws athena resource that I could use, so that asset_b looks for the data in aws athena instead of looking locally. How can I specify that asset_b should use this resource?
s

Scout

03/08/2024, 3:50 PM
To specify that
asset_b
should use an AWS Athena resource to look for its data, you would define the Athena resource and then configure
asset_b
to require that resource. Here's how you can do it: 1. Define the AWS Athena resource. This resource will manage the connection to Athena and provide a way to execute queries.
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python from dagster_aws.athena.resources import athena_resource  # Define your AWS Athena resource configuration athena_resource_config = {     "s3_staging_dir": "<s3://your-staging-bucket/path/>",     "region_name": "your-aws-region",     # Add any other necessary configurations for your Athena resource }  # Configure your Athena resource configured_athena_resource = athena_resource.configured(athena_resource_config)
2. Use the Athena resource in
asset_b
by specifying it in the
required_resource_keys
parameter of the
@asset
decorator. This tells Dagster that
asset_b
requires the Athena resource to execute.
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python from dagster import asset  # Define your downstream asset that uses the Athena resource @asset(required_resource_keys={"athena"}) def asset_b(context):     # Use the Athena resource to execute a query     query_result = context.resources.athena.execute_query("SELECT * FROM your_table")     # Perform any additional calculations or transformations on the query_result     calculated_data = some_calculation_function(query_result)     return calculated_data
3. Include the Athena resource in your job definition to make it available to
asset_b
when the job runs.
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python from dagster import job  # Define your Dagster job to orchestrate the assets @job(resource_defs={"athena": configured_athena_resource}) def my_pipeline():     dbt_data = asset_a()     asset_b(dbt_data)
In this setup: - The
athena_resource
is defined with the necessary configuration for connecting to AWS Athena and specifying the S3 staging directory. -
asset_b
is configured to use the Athena resource by including it in the
required_resource_keys
. - The
my_pipeline
job includes the configured Athena resource in its
resource_defs
, making it available to the assets within the job. By following these steps, you ensure that
asset_b
uses the AWS Athena resource to query data from Athena instead of looking for it locally. Make sure to replace
"SELECT * FROM your_table"
with your actual Athena query and adjust the
athena_resource_config
with the appropriate values for your AWS setup.