@Scout: Can i parse python variables to dbt_assets

Yes, you can pass Python variables to

dbt_assets

in Dagster. This is typically done by defining a custom configuration schema for your

dbt_assets

and then using that configuration within the asset function to pass variables to dbt commands. Here's an example from the documentation that demonstrates how to add a custom configuration to do a `--full-refresh`:

python from pathlib import Path  from dagster import AssetExecutionContext, Config from dagster_dbt import DbtCliResource, dbt_assets   class MyDbtConfig(Config):     full_refresh: bool   @dbt_assets(manifest=Path("target", "manifest.json")) def my_dbt_assets(context: AssetExecutionContext, dbt: DbtCliResource, config: MyDbtConfig):     dbt_build_args = ["build"]     if config.full_refresh:         dbt_build_args += ["--full-refresh"]      yield from dbt.cli(dbt_build_args, context=context).stream()

In this example,

MyDbtConfig

is a custom configuration class that includes a

full_refresh

boolean variable. This variable is then used within the

my_dbt_assets

function to determine whether to add the

--full-refresh

flag to the dbt CLI command. You can extend this pattern to include other variables that you might want to pass to dbt, such as variables for dbt's

--vars

flag. Remember to handle the configuration within your

dbt_assets

function accordingly.

@Scout: It is possible without configuration class

Based on the documentation, the recommended way to pass configuration to

dbt_assets

or any other Dagster asset or op is through the use of configuration classes or dictionaries that define the configuration schema. This is because Dagster's asset and op definitions are designed to be explicit about their dependencies and configurations, which allows for better tracking, validation, and management of the execution environment. However, if you want to pass simple Python variables to

dbt_assets

without defining a configuration class, you would typically do this by setting up resources or using the context object to pass parameters. Here's an example of how you might use resources to pass variables to `dbt_assets`:

python from dagster import resource, AssetExecutionContext from dagster_dbt import dbt_cli_resource, dbt_assets  # Define a resource that holds the variables you want to pass @resource def dbt_vars_resource(_):     return {"my_variable": "value"}  # Set up the dbt CLI resource with your custom resource @dbt_assets(     dbt_cli_resource=dbt_cli_resource.configured({"vars": {"my_variable": "value"}}) ) def my_dbt_assets(context: AssetExecutionContext):     # Access the resource within your asset     dbt_vars = context.resources.dbt_vars_resource     # Use the variables as needed in your dbt commands     yield from context.resources.dbt.cli(["run", "--vars", dbt_vars]).stream()

In this example, the

dbt_vars_resource

is a simple resource that returns a dictionary of variables. These variables are then passed to the

dbt_cli_resource

through the

.configured

method, which allows you to inject configuration at runtime. Please note that while it's possible to pass variables without a configuration class, using explicit configuration classes or dictionaries is the preferred and more robust method in Dagster, as it provides clear contracts for what configuration is expected and allows for validation and introspection of the configuration values.

@Scout: what is wrong:

parameter_value = "tt"
@dbt_assets(manifest=dbt_manifest_path,name=op_name, select=select,
            dagster_dbt_translator=CustomDagsterDbtTranslator())
def _assets_def(context: OpExecutionContext,dbt: DbtCliResource, parameter_value):
    dbt_vars = {"V_STICHTAG": f"{parameter_value}"}
    yield from dbt.cli(["build", "--vars", json.dumps(dbt_vars)],target_path=Path("target"), context=context).stream()

There are a few issues with the code snippet you've provided: 1. The

parameter_value

variable is defined outside the scope of the

_assets_def

function and should not be included as a parameter in the function definition. Instead, you should use it directly within the function body. 2. The

OpExecutionContext

class is not the correct context type for an asset definition. You should use

AssetExecutionContext

instead. 3. The

manifest

and

select

parameters in the

@dbt_assets

decorator should be provided with actual values or variables that hold the path to the manifest file and the selection criteria for dbt, respectively. 4. The

name

parameter in the

@dbt_assets

decorator should be provided with a string that represents the name of the operation, but

op_name

is not defined in the provided snippet. 5. The

json.dumps(dbt_vars)

should be converted to a string before being passed to the

dbt.cli

command. 6. The

target_path

parameter is not necessary when invoking

dbt.cli

as it is already specified in the dbt project configuration. Here's a revised version of the code snippet that addresses these issues:

python import json from pathlib import Path from dagster import AssetExecutionContext from dagster_dbt import dbt_assets, DbtCliResource  # Assuming 'dbt_manifest_path' and 'select' are defined elsewhere in your code # and 'CustomDagsterDbtTranslator' is a properly defined class  parameter_value = "tt"  @dbt_assets(     manifest=dbt_manifest_path,     name="op_name",  # Replace "op_name" with the actual name of the operation     select=select,     dagster_dbt_translator=CustomDagsterDbtTranslator() ) def _assets_def(context: AssetExecutionContext, dbt: DbtCliResource):     dbt_vars = {"V_STICHTAG": parameter_value}     yield from dbt.cli(         ["build", "--vars", json.dumps(dbt_vars)],         context=context     ).stream()

Make sure to replace

"op_name"

with the actual name you want to give to the operation and ensure that

dbt_manifest_path

and

select

are defined and hold the correct values before using them in the decorator. Additionally, ensure that

CustomDagsterDbtTranslator

is defined correctly and imported if it's in a different module.