If I'm using a brach deployment and cloning dbt models to my database clone using the

dbt clone

command, how can i ensure the asset key of the materialized model from the prod manifest matches the one from the current manifest

When using branch deployments with Dagster and dbt, ensuring that the asset keys of materialized models match between the production manifest and the current manifest is important for consistency and traceability. The documentation mentions that for each new pull request (PR), we automatically make a zero-copy clone of our production databases and name that clone based on the PR number. When running pipelines within a Branch Deployment, the assets materialized will pull data from the zero-copy clones and write new data into that database clone. After the PR gets merged, the database is deleted. To match dbt sources to their asset keys, you may use the

@dbt_assets

decorator and its companion

DagsterDbtTranslator

class. We configure our

DagsterDbtTranslator

to define the asset keys and map our dbt sources to upstream models. Asset keys typically convey the physical location of where the asset is stored. In the code snippet provided in the documentation, the asset key is set based on the namespace of the source. However, there was an issue raised regarding the asset key produced by

load_assets_from_dbt_project

for a dbt model, which was problematic because the schema depends on the target, making the asset key depend on the environment. An alternative suggested was to take the schema that's configured for the model, which can be found underneath "config". To ensure that the asset key of the materialized model from the prod manifest matches the one from the current manifest, you would need to ensure that the asset key generation logic is consistent across different environments and manifests. This might involve customizing the

DagsterDbtTranslator

or the logic you use to generate asset keys to be independent of the target environment. If you encounter issues with asset keys not matching or need to handle environment-specific schemas, you may need to adjust your dbt and Dagster configuration accordingly. The documentation does not provide a specific example of how to ensure asset key consistency across manifests in the context of branch deployments using the

dbt clone

command, so you may need to consider the structure of your dbt project and how you are integrating it with Dagster. For more detailed guidance on handling asset keys and ensuring consistency across environments, you may need to refer to the latest Dagster and dbt integration documentation or seek advice from the community or support channels.

@Scout Can you provide a link to the issue raised about

load_assets_from_dbt_project

? Can you also provide example code for a DagsterDbtTranslator that would ensure the asset keys match across environments

Certainly! The issue raised about

load_assets_from_dbt_project

and the asset keys being environment-dependent is documented in the following GitHub issue: - Issue Title: finalize dbt asset keys - Issue URL: [https://github.com/dagster-io/dagster/issues/8111](https://github.com/dagster-io/dagster/issues/8111) Regarding the

DagsterDbtTranslator

, the documentation does not provide a specific example of a

DagsterDbtTranslator

that ensures asset keys match across environments. However, I can guide you on how you might implement one based on the principles outlined in the documentation. The

DagsterDbtTranslator

class allows you to customize how dbt models, seeds, etc., are mapped to asset keys and asset metadata. You would typically subclass

DagsterDbtTranslator

and override the

get_asset_key

method to define your custom logic for generating asset keys. Here's a conceptual example of how you might implement a

DagsterDbtTranslator

to ensure consistent asset keys across environments:

python from dagster_dbt import DagsterDbtTranslator from dagster import AssetKey  class ConsistentAssetKeyTranslator(DagsterDbtTranslator):     def get_asset_key(self, dbt_resource_props):         # Example logic to generate a consistent asset key         # This assumes that 'name' is a property of dbt_resource_props that         # uniquely identifies the model across environments.         model_name = dbt_resource_props['name']         return AssetKey(['my_project', model_name])  # Usage in your Dagster code @dbt_assets(     manifest=manifest_path,     dagster_dbt_translator=ConsistentAssetKeyTranslator(), ) def my_dbt_assets(context, dbt):     # Your dbt execution logic here     ...

In this example,

ConsistentAssetKeyTranslator

generates an asset key that includes a fixed namespace ('my_project') and the model name, which should be consistent across environments. You would need to adjust the logic in

get_asset_key

to fit the specifics of your dbt project and how you want to handle asset keys. Please note that this is a conceptual example and may require adjustments to work with your specific setup. You should refer to the latest documentation and consider your dbt project's structure and naming conventions when implementing your custom translator.