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#ask-ai
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# ask-ai
s

Selim Raboudi

02/22/2024, 10:32 PM
Hello community, I am trying to load my airbyte connections as assets in my dagster project. I have followed the official doc, Using Airbyte Cloud with Dagster, but keep running in the following error:
dagster._check.CheckError: Member of iterable mismatches type. Expected (<class 'dagster._core.definitions.assets.AssetsDefinition'>, <class 'dagster._core.definitions.source_asset.SourceAsset'>, <class 'dagster._core.definitions.cacheable_assets.CacheableAssetsDefinition'>). Got [<dagster._core.definitions.assets.AssetsDefinition object at 0x111f27e50>] of type <class 'list'>.
Has anyone faced the same issue ? Here is my code:
Copy code
from dagster_airbyte import build_airbyte_assets
from dagster_airbyte import AirbyteCloudResource
from dagster import EnvVar
from dagster import Definitions

airbyte_assets = build_airbyte_assets(
    connection_id="4842929b-7169-4024-be39-fd6ff12fZHE4",
    destination_tables=["deals", "contacts"],
)

airbyte_instance = AirbyteCloudResource(api_key=EnvVar("AIRBYTE_API_KEY"))

airbyte_definitions = Definitions(
    assets=[airbyte_assets], resources={"airbyte": airbyte_instance}
)
s

Scout

02/22/2024, 10:32 PM
It looks like you're encountering a type mismatch error when trying to load your Airbyte connections as assets in your Dagster project. This error suggests that the
assets
parameter is expecting an instance of
AssetsDefinition
,
SourceAsset
, or
CacheableAssetsDefinition
, but it received a list instead. Based on the documentation, when using
build_airbyte_assets
, you should not wrap the result in a list when passing it to the
Definitions
object. The
build_airbyte_assets
function already returns an
AssetsDefinition
object, which is directly usable by the
Definitions
object. Here is the corrected code snippet based on the documentation:
Copy code
python
from dagster_airbyte import build_airbyte_assets
from dagster_airbyte import AirbyteCloudResource
from dagster import EnvVar
from dagster import Definitions

# Build Airbyte assets
airbyte_assets = build_airbyte_assets(
    connection_id="4842929b-7169-4024-be39-fd6ff12fZHE4",
    destination_tables=["deals", "contacts"],
)

# Create an AirbyteCloudResource instance
airbyte_instance = AirbyteCloudResource(api_key=EnvVar("AIRBYTE_API_KEY"))

# Define Dagster Definitions
airbyte_definitions = Definitions(
    assets=airbyte_assets,  # Pass the AssetsDefinition object directly, without wrapping it in a list
    resources={"airbyte": airbyte_instance}
)
Make sure to pass
airbyte_assets
directly to the
Definitions
object without wrapping it in a list. This should resolve the type mismatch error you are encountering. If you continue to face issues, please refer to the documentation or consider reaching out to the community for further assistance. issue with build_airbyte_assets using ai... button DagsterInvalidDefinitionError when creat... button [dagster-airbyte] TypeError with Airbyte... button Dagster Docs button Thank you for your feedback!
s

Selim Raboudi

02/22/2024, 10:36 PM
That solved the problem 👍 thanks, AI.
@Scout how can I test that these assets are correctly configured ? When I change the connection ID to something false, or add bogus table names, no error is thrown.
s

Scout

02/22/2024, 10:42 PM
Testing assets in Dagster typically involves verifying that they are correctly configured and that they behave as expected under various conditions. For your Airbyte assets, you want to ensure that they are correctly set up to interact with Airbyte connections and that they handle errors appropriately, such as when provided with an incorrect connection ID or table names. Here are some general steps you can take to test your assets: 1. Unit Testing: Write unit tests for your assets to ensure they behave correctly with valid configurations. You can mock the Airbyte API responses to simulate different scenarios, including successful connections and errors. 2. Integration Testing: Set up integration tests that actually run the assets against a real or staged Airbyte instance. This can help catch issues that unit tests might miss, such as connectivity problems or unexpected API behavior. 3. Error Handling: Specifically test for error conditions by providing incorrect connection IDs or table names. Your assets should handle these errors gracefully, either by logging appropriate error messages or by raising exceptions that can be caught and handled by the surrounding infrastructure. 4. Dry Runs: Perform dry runs of your assets where possible. This can help identify configuration issues before running the full asset computations. 5. Monitoring and Logging: Ensure that your assets have proper monitoring and logging in place. This can help you quickly identify when and why an asset fails in a production environment. 6. Asset Materialization: Use Dagster's asset materialization capabilities to check that the assets are producing the expected outputs when run. 7. Smoke Testing: Perform smoke tests by running your assets in a minimal configuration to ensure that the basic functionality works as expected. 8. Continuous Integration (CI): Incorporate these tests into your CI pipeline to automatically run them on code changes. This helps catch issues early in the development process. For your specific case, if changing the connection ID to an incorrect value or adding bogus table names does not throw an error, it could be due to the assets not being executed or the error not being propagated correctly. You may need to ensure that the assets are actually being run and that any errors from the Airbyte API are being handled and reported by your asset definitions. Remember that testing external integrations can be challenging due to the dependency on the external service's behavior. Mocking or stubbing the external service can provide a controlled environment for testing your assets' behavior in response to various scenarios. If you find that errors are not being thrown as expected, you may need to add explicit checks in your a
sset code to validate the connection ID and table names before attempting to use them with the Airbyte API. This could involve calling an endpoint that validates the connection or checking the existence of the specified tables. Since the documentation does not provide a specific example for testing Airbyte assets, these general testing strategies should guide you in setting up your tests. If you encounter difficulties, consider reaching out to the Dagster community for support or consulting the documentation for any updates on testing practices related to Airbyte assets. DBT tests not run when materializing button DBIOManager doesn't work with self-depen... button How do I add data contracts to my assets... button RedshiftClientResource Does Not Create T... button Thank you for your feedback!
s

Selim Raboudi

02/22/2024, 10:55 PM
@Scout when I “materialize all” from the Dagster UI, I can see the job getting triggered in my airbyte cloud UI. But when I trigger the sync from airbyte cloud, my assets in Dagster UI don’t appear as “materializing”. Is this normal ?
s

Scout

02/22/2024, 10:59 PM
When you trigger a job from the Dagster UI using "materialize all," Dagster is aware of the job and can track its progress, including the materialization status of the assets. This is because Dagster initiates the process and has the context needed to monitor the job's lifecycle. On the other hand, when you trigger a sync directly from the Airbyte Cloud UI, Dagster does not initiate the process and may not be aware that the sync is occurring. As a result, Dagster's UI may not automatically update the materialization status of the assets because it does not have the context of the job that was started outside of its control. For Dagster to reflect the materialization status of assets triggered from external systems like Airbyte Cloud, there needs to be a mechanism in place to report the job status back to Dagster. This typically involves using hooks or callbacks that can notify Dagster when a job starts, progresses, and completes. The documentation mentions that as the sync completes, Dagster will automatically parse the metadata that Airbyte provides, recording an event for each table that was updated during the sync. However, this assumes that Dagster is aware of the sync operation. If the sync is triggered outside of Dagster's context, such as directly from the Airbyte Cloud UI, this automatic parsing and recording may not occur. To ensure that Dagster reflects the materialization status of assets triggered from Airbyte Cloud, you might need to set up additional integration points between Airbyte and Dagster. This could involve: - Configuring Airbyte to send notifications or webhooks to Dagster upon job completion. - Using Dagster's APIs to programmatically update the asset status based on external triggers. - Implementing custom logic within your Dagster assets to check the status of Airbyte syncs and update the materialization status accordingly. If this functionality is critical for your workflow and you are not able to achieve the desired behavior with the current setup, it may be worth reaching out to the Dagster community or checking the documentation for any updates or recommended practices for integrating Airbyte Cloud with Dagster. Just-computed software-defined assets sh... button Auto-materialize is skipped and does not... button dagster-dbt assets can't be materialized... button DBT Cloud Assets incorrectly marked as m... button Thank you for your feedback!
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