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#ask-ai
Title
# ask-ai
j

Justin Bricker

03/06/2024, 5:44 PM
@Scout How do I pass resources to an IOManager?
s

Scout

03/06/2024, 5:44 PM
To pass resources to an IOManager in Dagster, you would typically define the resources that the IOManager depends on and then specify these dependencies using the
required_resource_keys
parameter when you define your IOManager. Here's a step-by-step guide based on the documentation: 1. Define the required resources: First, you need to define the resources that your IOManager will depend on. Resources in Dagster are reusable components that ops can use to interact with the outside world, like databases or filesystems. 2. Create the IOManager: Define your IOManager using the
@io_manager
decorator. Within the definition, you can specify the
required_resource_keys
parameter to indicate which resources your IOManager depends on. 3. Implement the IOManager methods: Your IOManager should implement the
handle_output
method to store an object and the
load_input
method to retrieve an object. These methods will have access to the resources specified in
required_resource_keys
. 4. Bind the resources to the IOManager: When you define your repository or job, you will bind the actual resource instances to the IOManager by including them in the
resources
argument. Here's an example of how you might define an IOManager with resource dependencies:
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python from dagster import io_manager, ResourceDefinition  # Define your resources def my_resource_fn(_):     # Resource initialization logic here     return MyResource()  my_resource = ResourceDefinition(resource_fn=my_resource_fn)  # Define your IOManager with required resource keys @io_manager(required_resource_keys={"my_resource"}) def my_io_manager(init_context):     resource = init_context.resources.my_resource     # Use the resource in your IOManager     return MyIOManager(resource)  # Define your repository or job with the IOManager and resources from dagster import job  @job(resource_defs={"my_io_manager": my_io_manager, "my_resource": my_resource}) def my_job():     # Job definition here     pass
In this example,
MyIOManager
is an IOManager that depends on a resource named
my_resource
. When defining the job, both the IOManager and the resource it depends on are included in the
resource_defs
dictionary. Remember to replace
MyResource
and
MyIOManager
with your actual resource and IOManager classes, and implement the necessary logic within them.
j

Justin Bricker

03/06/2024, 5:49 PM
@Scout Give me a full example with an asset materialization and iomanger that uses a resource
s

Scout

03/06/2024, 5:49 PM
Certainly! Below is an example of how you might define an asset with an IOManager that uses a resource for materialization. In this example, we'll create a simple IOManager that writes data to a file, and the IOManager will use a filesystem resource to determine the directory where files should be written. First, let's define the filesystem resource:
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python
from dagster import resource

@resource
def filesystem_resource(_):
    return {"base_dir": "/path/to/base/directory"}
Next, we'll define the IOManager that uses this filesystem resource:
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python
from dagster import IOManager, io_manager
import os
import pickle

@io_manager(required_resource_keys={"filesystem"})
class PickleIOManager(IOManager):
    def handle_output(self, context, obj):
        # Use the filesystem resource to get the base directory
        base_dir = context.resources.filesystem["base_dir"]
        filepath = os.path.join(base_dir, context.asset_key.path[-1])

        # Write the object to a file in pickle format
        with open(filepath, "wb") as file:
            pickle.dump(obj, file)

    def load_input(self, context):
        # Use the filesystem resource to get the base directory
        base_dir = context.resources.filesystem["base_dir"]
        filepath = os.path.join(base_dir, context.upstream_output.asset_key.path[-1])

        # Read the object from the file in pickle format
        with open(filepath, "rb") as file:
            return pickle.load(file)
Now, let's define an asset that uses this IOManager to materialize its output:
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python
from dagster import asset, AssetMaterialization, Output

@asset(io_manager_key="pickle_io_manager")
def my_asset():
    # Your asset computation logic here
    data = [1, 2, 3]  # Example data

    # Yield an AssetMaterialization to indicate where the data will be written
    yield AssetMaterialization(
        asset_key="my_asset",
        description="Materializing list as a pickle file."
    )
    yield Output(data)
Finally, we'll define the job that includes the asset and the resource definitions:
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python
from dagster import job, repository

@job(resource_defs={"filesystem": filesystem_resource, "pickle_io_manager": PickleIOManager})
def my_job():
    my_asset()

@repository
def my_repository():
    return [my_job]
In this example,
my_asset
is an asset that, when materialized, will write its data to a file in pickle format. The
PickleIOManager
is responsible for handling the output and input of the asset, and it uses the
filesystem_resource
to determine where to write the file. The
my_job
job includes both the asset and the resource definitions, and the
my_repository
repository includes the job. Please replace
"/path/to/base/directory"
with the actual path where you want the files
to be written. This example assumes that the directory exists and that you have write permissions to it.
j

Justin Bricker

03/06/2024, 5:58 PM
@Scout 2 questions: 1. Can the
@io_manager
decorator be applied directly to the class that is inheriting from IOManager? 2. Why do jobs need their own
resource_defs
when assets can just have resources injected as parameters?
s

Scout

03/06/2024, 5:58 PM
1. The
@io_manager
decorator is not typically applied directly to a class that inherits from
IOManager
. Instead, it is used to decorate a function that returns an instance of the IOManager. This function is known as the "resource function" and it is where you can access the
init_context
to get any required resources or config. Here's how you would typically use the
@io_manager
decorator:
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python from dagster import io_manager, IOManager  class MyIOManager(IOManager):     def __init__(self, resource):         self.resource = resource      def handle_output(self, context, obj):         # handle output logic      def load_input(self, context):         # load input logic  @io_manager(required_resource_keys={"my_resource"}) def my_io_manager(init_context):     resource = init_context.resources.my_resource     return MyIOManager(resource)
In this example,
my_io_manager
is the resource function that the
@io_manager
decorator is applied to, and it returns an instance of
MyIOManager
. 2. Jobs need their own
resource_defs
because resources in Dagster are scoped to jobs. Each job may require different resources or different configurations of the same resource. By specifying
resource_defs
at the job level, you can control the resources and their configurations that are available to the assets and ops within that job. This allows for greater flexibility and isolation between different jobs in the same repository. Assets can have resources injected as parameters when they are defined as functions with parameters that are annotated with
@asset
. However, when you run a job, the resources that the assets within that job will use are determined by the
resource_defs
specified in the job definition. This is because assets are a higher-level abstraction that can be included in multiple jobs, and the specific resources they use can vary depending on the job they are a part of. Here's an example of how resources are specified in a job:
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python from dagster import job  @job(resource_defs={"my_resource": my_resource, "my_io_manager": my_io_manager}) def my_job():     my_asset()
In this example,
my_job
specifies its required resources through
resource_defs
, which includes both
my_resource
and
my_io_manager
. This ensures that when
my_job
is executed, it has access to the resources it needs.
j

Justin Bricker

03/07/2024, 1:06 AM
@Scout How do I reference a table created from a dagster asset from within dbt so that the relationship between the two are seen in the dagster asset graph?
s

Scout

03/07/2024, 1:06 AM
To reference a table created from a Dagster asset within dbt and have the relationship between the two reflected in the Dagster asset graph, you need to set up dbt to recognize the Dagster asset as a source and then use that source in your dbt models. Here's how you can do it: 1. Create a dbt source: In your dbt project, define a source in the
sources.yml
file that corresponds to the table created by the Dagster asset. The source should match the schema and table name where the Dagster asset is materialized.
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yaml # sources.yml version: 2  sources:   - name: my_source_schema     tables:       - name: my_table         description: "A table created by a Dagster asset."
2. Reference the dbt source in your dbt model: In your dbt model SQL file, use the
source
function to reference the table created by the Dagster asset.
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sql -- models/my_model.sql with source_data as (    select * from {{ source('my_source_schema', 'my_table') }}  )  select * from source_data
3. Load dbt models as Dagster assets: Use Dagster's dbt integration to load your dbt models as assets. This will automatically create assets in Dagster for each dbt model, and the dependencies between dbt models and sources will be inferred from your dbt project.
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python from dagster_dbt import dbt_cli_resource, dbt_run_op  @job(resource_defs={"dbt": dbt_cli_resource.configured({"project_dir": "path/to/dbt/project"})}) def my_dbt_job():     dbt_run_op()
4. Ensure Dagster recognizes the source as an asset: To make sure that Dagster recognizes the source table as an asset and shows the relationship in the asset graph, you can define a corresponding Dagster asset for the source table. This can be done by using the
SourceAsset
class or by defining an asset with the same name as the dbt source.
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python from dagster import SourceAsset, AssetKey  my_table_source_asset = SourceAsset(     key=AssetKey(["my_source_schema", "my_table"]),     description="A table created by a Dagster asset." )
By following these steps, you create a link between the Dagster asset that creates the table and the dbt model that references it. When you view the asset graph in the Dagster UI, you should see an edge from the source asset to the dbt model asset, indicating the dependency relationship. Please note that the exact steps may vary depending on your specific setup and the versions of Dagster and dbt that you are using. Always refer to the latest documentation for both Dagster and dbt for the most accurate and up-to-date instructions.
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