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<@U0667DNC02Y> How do I mock upstream dependencies...
# ask-ai
j
@Scout How do I mock upstream dependencies when testing asset materialization?
s
When testing asset materialization and you need to mock upstream dependencies, the documentation provides guidance on how to approach this. Here's a step-by-step explanation based on the documentation: 1. Create Mock Asset Definitions: Instead of using the actual upstream assets, you can create mock asset definitions that return the values you want to use in your test. This allows you to control the input data and isolate the asset under test. 2. Use 
materialize_to_memory
: The 
materialize_to_memory
function can be used to materialize assets in memory for testing purposes. This function can work with the mock asset definitions you've created. Here's an example from the documentation that illustrates how to create mock assets and use them in testing:
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python from dagster import asset, materialize_to_memory  @asset def upstream():     # ... actual implementation ...  @asset def downstream(upstream):     # ... actual implementation ...  # Create a mock asset to replace the actual upstream asset @asset(name="upstream") def mock_upstream():     return some_hardcoded_dataframe_for_use_in_my_test  # Materialize the downstream asset using the mock upstream asset materialize_to_memory([downstream, mock_upstream])
In this example, 
mock_upstream
is a mock asset that replaces the actual 
upstream
asset. When calling 
materialize_to_memory
, both the 
downstream
and 
mock_upstream
assets are passed, allowing the 
downstream
asset to be tested with controlled input data. Please note that the error message mentioned in the documentation's discussion about using 
materialize_to_memory
with dependencies indicates that Dagster doesn't know how to load the value of the source asset. The provided example shows how to overcome this by creating mock asset definitions. Keep in mind that the specifics of how you mock the upstream dependencies may vary depending on the complexity of your assets and the nature of the tests you are conducting. The general approach, however, is to create mock assets that simulate the behavior of the actual upstream assets.
j
@Scout For context, this is my test currently:
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class MockIOManager(IOManager):
    def handle_output(self, context, obj):
        pass

    def load_input(self, context):
        pass

def test_assets_my_downstream_asset():
    mock_io_manager = MockIOManager()
    mock_io_manager.handle_output = MagicMock()
    mock_db_manager = ResourceDefinition.mock_resource()
    mock_service = ResourceDefinition.mock_resource()
    resources={
        "my_downstram_asset_io_manager": mock_io_manager,
        "db_manager": mock_db_manager,
        "service": mock_service,
    }

    instance = DagsterInstance.ephemeral()
    result = (
        Definitions(
            assets=[my_downstream_asset],
            jobs=[define_asset_job("asset_job", [my_downstream_asset])],
            resources=resources,
        )
        .get_job_def("asset_job")
        .execute_in_process(instance=instance)
    )

    assert result.success
my_downstream_asset
has one upstream dependency 
my_upstream_asset
that needs to be loaded into 
my_downstream_asset
as a pandas dataframe
s
To mock the upstream dependency 
my_upstream_asset
for your 
my_downstream_asset
in a test, you can follow these steps: 1. Create a Mock IOManager: You've already created a 
MockIOManager
class, which is good. You'll need to implement the 
load_input
method to return the mock data that you want to use as the upstream asset. 2. Implement the Mock Data Return: In the 
load_input
method of your 
MockIOManager
, you should return the mock data that represents the output of 
my_upstream_asset
. This is typically a pandas DataFrame since you mentioned that 
my_downstream_asset
expects a DataFrame as input. 3. Assign the Mock IOManager: Assign the 
MockIOManager
to the upstream asset within your test's resource definitions. This will ensure that when 
my_downstream_asset
tries to load its upstream dependency, it will receive the mock data. Here's how you can modify your test to include these steps:
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python from unittest.mock import MagicMock from dagster import IOManager, ResourceDefinition, Definitions, define_asset_job, DagsterInstance import pandas as pd  class MockIOManager(IOManager):     def handle_output(self, context, obj):         pass      def load_input(self, context):         # Return the mock DataFrame that `my_downstream_asset` expects         return pd.DataFrame({"column1": [1, 2, 3], "column2": [4, 5, 6]})  def test_assets_my_downstream_asset():     mock_io_manager = MockIOManager()     mock_io_manager.handle_output = MagicMock()     mock_db_manager = ResourceDefinition.mock_resource()     mock_service = ResourceDefinition.mock_resource()     resources = {         "io_manager": mock_io_manager,  # Use the same key as the upstream asset's IO manager         "db_manager": mock_db_manager,         "service": mock_service,     }      instance = DagsterInstance.ephemeral()     result = (         Definitions(             assets=[my_downstream_asset],             jobs=[define_asset_job("asset_job", [my_downstream_asset])],             resources=resources,         )         .get_job_def("asset_job")         .execute_in_process(instance=instance)     )      assert result.success
In this example, the 
load_input
method of 
MockIOManager
returns a mock pandas DataFrame. This DataFrame should match the structure and data that 
my_downstream_asset
expects to receive from 
my_upstream_asset
. Make sure that the key used in the 
resources
dictionary for the 
MockIOManager
matches the key used by 
my_upstream_asset
to access its IO manager. If 
my_upstream_asset
uses a different IO manager key, you should adjust the 
resources
dictionary accordingly. By following these steps, you can effectively mock the upstream dependency and test 
my_downstream_asset
in isolation.
j
@Scout The test still does not have knowledge of the upstream asset so I get the error:
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E                   dagster._core.errors.DagsterInvalidDefinitionError: Input asset '["my_upstream_asset"]' for asset '["my_downstream_asset"]' is not produced by any of the provided asset ops and is not one of the provided sources
s
The error you're encountering indicates that Dagster is expecting an upstream asset definition for 
my_upstream_asset
but isn't finding it in the provided definitions. To resolve this, you need to include a mock definition for 
my_upstream_asset
in your test setup. Here's how you can do it: 1. Create a Mock Asset: Define a mock asset that will serve as 
my_upstream_asset
. This mock asset should produce the same type of output that 
my_downstream_asset
expects as input. 2. Include the Mock Asset in Definitions: Add the mock asset to the 
Definitions
object so that Dagster knows about the upstream asset when constructing the job graph. Here's an updated version of your test that includes a mock asset definition:
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python
from unittest.mock import MagicMock
from dagster import IOManager, ResourceDefinition, Definitions, define_asset_job, DagsterInstance, asset
import pandas as pd

class MockIOManager(IOManager):
    def handle_output(self, context, obj):
        pass

    def load_input(self, context):
        # Return the mock DataFrame that `my_downstream_asset` expects
        return pd.DataFrame({"column1": [1, 2, 3], "column2": [4, 5, 6]})

# Create a mock asset to simulate `my_upstream_asset`
@asset
def mock_my_upstream_asset():
    # Return the same data structure that the real `my_upstream_asset` would produce
    return pd.DataFrame({"column1": [1, 2, 3], "column2": [4, 5, 6]})

def test_assets_my_downstream_asset():
    mock_io_manager = MockIOManager()
    mock_io_manager.handle_output = MagicMock()
    mock_db_manager = ResourceDefinition.mock_resource()
    mock_service = ResourceDefinition.mock_resource()
    resources = {
        "io_manager": mock_io_manager,  # Use the same key as the upstream asset's IO manager
        "db_manager": mock_db_manager,
        "service": mock_service,
    }

    instance = DagsterInstance.ephemeral()
    result = (
        Definitions(
            assets=[mock_my_upstream_asset, my_downstream_asset],
            jobs=[define_asset_job("asset_job", [my_downstream_asset])],
            resources=resources,
        )
        .get_job_def("asset_job")
        .execute_in_process(instance=instance)
    )

    assert result.success
In this updated test: - 
mock_my_upstream_asset
is a mock asset that produces a DataFrame with the same structure expected by 
my_downstream_asset
. - The 
Definitions
object now includes both 
mock_my_upstream_asset
and 
my_downstream_asset
, so the job graph includes the necessary upstream asset definition. This should resolve the 
DagsterInvalidDefinitionError
by providing a mock definition for the upstream asset. When 
my_downstream_asset
is executed, it will use the mock data provided by 
mock_my_upstream_asset
instead of trying to load data from a
real upstream asset.
j
@Scout For the test above, how can I access the object returned in the asset body?
s
The 
handle_output
function of an 
IOManager
in Dagster is not expected to return anything. Its purpose is to handle the storage of an output from an asset or op, and it does not provide a return value to the framework. Here's the typical signature of the 
handle_output
method:
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python def handle_output(self, context, obj):     # logic to store the object `obj` somewhere, e.g., a database, file system, etc.
The 
context
parameter provides information about the output, such as the asset or op's name, the run ID, and any other metadata associated with the output. The 
obj
parameter is the actual data object that needs to be handled (stored, written, etc.). If you need to perform additional actions based on the result of storing the data, such as logging or emitting events, you would do that within the 
handle_output
method itself, but you wouldn't return a value from this method. Here's an example from the documentation:
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python from dagster import IOManager, OutputContext  class MyIOManager(IOManager):     def handle_output(self, context: OutputContext, obj):         # logic to store the object         pass  # replace with actual storage logic
In this example, the 
handle_output
method includes logic to store the output object but does not return anything. If you need to record metadata or perform other side effects, you can do so within the body of the 
handle_output
method.
To access the object returned in the asset body during a test, you can use the 
result_for_asset
method on the 
ExecuteInProcessResult
object returned by the 
execute_in_process
method. This allows you to retrieve the output of a specific asset from the test result. Here's how you can modify your test to access the object returned by `my_downstream_asset`:
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python from dagster import (     asset,     build_assets_job,     DagsterInstance,     IOManager,     ResourceDefinition,     OutputContext, ) from unittest.mock import MagicMock import pandas as pd  # Assuming `my_downstream_asset` is defined somewhere in your codebase # and it has an upstream dependency `my_upstream_asset`  class MockIOManager(IOManager):     def handle_output(self, context: OutputContext, obj):         pass  # Replace with logic to store the object if needed      def load_input(self, context):         # Return the mock DataFrame that `my_downstream_asset` expects         return pd.DataFrame({"column1": [1, 2, 3], "column2": [4, 5, 6]})  @asset def my_downstream_asset(upstream):     # Your asset logic here     return pd.DataFrame({"column1": [1, 2, 3], "column2": [4, 5, 6]})  def test_assets_my_downstream_asset():     mock_io_manager = MockIOManager()     mock_io_manager.handle_output = MagicMock()     mock_db_manager = ResourceDefinition.mock_resource()     mock_service = ResourceDefinition.mock_resource()     resources = {         "io_manager": mock_io_manager,         "db_manager": mock_db_manager,         "service": mock_service,     }      instance = DagsterInstance.ephemeral()     result = build_assets_job(         "asset_job",         assets=[my_downstream_asset],         resource_defs=resources     ).execute_in_process(instance=instance)      assert result.success     # Access the object returned by `my_downstream_asset`     downstream_asset_result = result.result_for_asset(my_downstream_asset)     # Now you can perform assertions or other checks on `downstream_asset_result`     assert isinstance(downstream_asset_result, pd.DataFrame)     assert not downstream_asset_result.empty
In this example, after asserting that the job execution was successful, we use 
result.result_for_asset(my_downstream_asset)
to retrieve the output of 
my_downstream_asset
. You can then perform further assertions or checks on this object to validate that your asset is behaving as expected during the test.
j
@Scout Traceback (most recent call last): File "<string>", line 1, in <module> _AttributeError: 'ExecuteInProcessResult' object has no attribute 'result_for_asset'_
s
I apologize for the confusion. The 
ExecuteInProcessResult
object does not have a 
result_for_asset
method. To access the output of an asset during testing, you should instead use the 
output_for_node
method, which allows you to retrieve the output of a specific asset or op from the test result. Here's how you can modify your test to access the object returned by `my_downstream_asset`:
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python from dagster import (     asset,     build_assets_job,     DagsterInstance,     IOManager,     ResourceDefinition,     OutputContext, ) from unittest.mock import MagicMock import pandas as pd  # Assuming `my_downstream_asset` is defined somewhere in your codebase # and it has an upstream dependency `my_upstream_asset`  class MockIOManager(IOManager):     def handle_output(self, context: OutputContext, obj):         pass  # Replace with logic to store the object if needed      def load_input(self, context):         # Return the mock DataFrame that `my_downstream_asset` expects         return pd.DataFrame({"column1": [1, 2, 3], "column2": [4, 5, 6]})  @asset def my_downstream_asset(upstream):     # Your asset logic here     return pd.DataFrame({"column1": [1, 2, 3], "column2": [4, 5, 6]})  def test_assets_my_downstream_asset():     mock_io_manager = MockIOManager()     mock_io_manager.handle_output = MagicMock()     mock_db_manager = ResourceDefinition.mock_resource()     mock_service = ResourceDefinition.mock_resource()     resources = {         "io_manager": mock_io_manager,         "db_manager": mock_db_manager,         "service": mock_service,     }      instance = DagsterInstance.ephemeral()     result = build_assets_job(         "asset_job",         assets=[my_downstream_asset],         resource_defs=resources     ).execute_in_process(instance=instance)      assert result.success     # Access the object returned by `my_downstream_asset`     downstream_asset_result = result.output_for_node("my_downstream_asset")     # Now you can perform assertions or other checks on `downstream_asset_result`     assert isinstance(downstream_asset_result, pd.DataFrame)     assert not downstream_asset_result.empty
In this updated example, after asserting that the job execution was successful, we use 
result.output_for_node("my_downstream_asset")
to retrieve the output of 
my_downstream_asset
. You can then perform further assertions or checks on this object to validate that your asset is behaving as expected during the test.
j
@Scout How can I access metadata added through context.add_output_metadata ?
s
To access metadata that was added through 
context.add_output_metadata
within an 
IOManager
, you can use the 
OutputContext
object that is passed to the 
handle_output
method. The 
OutputContext
contains a 
metadata
property that holds the metadata dictionary added via 
context.add_output_metadata
. Here's an example of how you might use 
context.add_output_metadata
within an asset and then access that metadata within an `IOManager`:
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python from dagster import IOManager, OutputContext, asset, io_manager, build_output_context  class MyIOManager(IOManager):     def handle_output(self, context: OutputContext, obj):         # Access the metadata added via `context.add_output_metadata`         metadata = context.metadata         # Now you can use the metadata as needed, for example, logging it         print(f"Metadata: {metadata}")  # Define a custom IO manager that will be used by the asset @io_manager def my_io_manager():     return MyIOManager()  # Define an asset that adds metadata using `context.add_output_metadata` @asset(io_manager_key="my_io_manager") def my_asset(context):     # Add metadata using `context.add_output_metadata`     context.add_output_metadata({"key": "value"})     # Return some data     return "data"  # Example test function to execute the asset and IOManager def test_my_asset():     result = build_assets_job("my_asset_job", assets=[my_asset], resource_defs={"my_io_manager": my_io_manager}).execute_in_process()     assert result.success  # Run the test test_my_asset()
In this example, the 
my_asset
asset adds metadata using 
context.add_output_metadata
. The 
MyIOManager
class, which is used as the IO manager for 
my_asset
, accesses this metadata in its 
handle_output
method through the 
context.metadata
property. When you run the test, the 
handle_output
method of 
MyIOManager
will print the metadata to the console, demonstrating how the metadata can be accessed. Please note that the metadata added via 
context.add_output_metadata
is available in the 
handle_output
method of the IO manager and is also recorded in the Dagster event log. If you want to access this metadata outside of the 
handle_output
method, such as in downstream assets or elsewhere in your application, you would typically need to query the event log or use other mechanisms provided by Dagster to retrieve the metadata associated with a particular asset or run.
j
@Scout That is incorrect. The context.add_output_metadata method does not add metadata to the context.metadata dictionary
s
I apologize for the confusion. You are correct that 
context.add_output_metadata
does not add metadata to the 
context.metadata
dictionary within the 
IOManager
. The 
context.metadata
dictionary within the `IOManager`'s 
handle_output
method contains metadata from the output definition, not the runtime metadata added via 
context.add_output_metadata
. As of my knowledge cutoff date, there is an open issue in the Dagster repository discussing the need to make output metadata available on the 
InputContext
, which suggests that accessing runtime metadata added via 
context.add_output_metadata
in downstream components like IO managers is not directly supported. The metadata added via 
context.add_output_metadata
is intended to be recorded in the Dagster event log and is visible in the Dagster UI associated with the asset materialization or op's output event. If you need to access this metadata programmatically, you may need to query the event log to retrieve it. If you need to pass metadata between assets or ops during execution and access it within an 
IOManager
, you might consider alternative approaches, such as: - Storing the metadata in an intermediate storage system (like a database or a file) that both the asset and the 
IOManager
can access. - Passing the metadata as part of the data object itself, if appropriate for your use case. For the most up-to-date information and potential new features that may support this use case, please refer to the latest Dagster documentation or the Dagster GitHub repository.
j
@Scout What about 
context._user_generated_metadata
?_
s
As of my knowledge cutoff date, the 
context._user_generated_metadata
is not part of the public API in Dagster. The leading underscore in 
_user_generated_metadata
suggests that it is intended for internal use and not for public consumption. Accessing or relying on internal attributes (those prefixed with an underscore) is generally discouraged because they are not guaranteed to be stable and may change without notice in future releases. The recommended way to handle metadata in Dagster is through the mechanisms provided by the public API, such as 
context.add_output_metadata
for adding metadata to asset materializations and events, and accessing metadata through the event log for historical data. If you are looking to access metadata within an 
IOManager
or in downstream assets, you should use the supported methods and properties provided by Dagster's public API. If the current API does not support your use case, you may want to follow the open issues on the Dagster GitHub repository or reach out to the Dagster community for guidance on best practices or potential workarounds. For the most accurate and up-to-date information, please refer to the latest Dagster documentation or the Dagster GitHub repository. If there have been updates or new features added after my knowledge cutoff date that provide additional support for accessing user-generated metadata, those resources will have the latest details.
j
@Scout When calling 
build_input_context
for a test, I want to pass in 
pandas.DataFrame
, but I get error:
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dagster._check.ParameterCheckError: Param "dagster_type" is not a DagsterType. Got <class 'pandas.core.frame.DataFrame'> which is type <class 'type'>.
s
The error you're encountering suggests that you're passing a 
pandas.DataFrame
class directly to a parameter that expects a 
DagsterType
. When using 
build_input_context
to create an 
InputContext
for testing, you should pass a 
DagsterType
that corresponds to the type of data you're working with, not the actual Python type or class. Dagster provides built-in types for common data structures, including pandas DataFrames. You can use 
dagster_pandas.DataFrame
as the 
dagster_type
when building the input context for a pandas DataFrame. Here's an example of how you might use 
build_input_context
with a pandas DataFrame:
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python from dagster import build_input_context from dagster_pandas import DataFrame as DagsterDataFrame import pandas as pd  # Create a pandas DataFrame to use as input for the context df = pd.DataFrame({     'column1': [1, 2, 3],     'column2': [4, 5, 6] })  # Build the input context, specifying the Dagster type for a pandas DataFrame input_context = build_input_context(     upstream_output={'result': df},     dagster_type=DagsterDataFrame, )  # Now you can use input_context in your tests
In this example, 
DagsterDataFrame
is used as the 
dagster_type
when calling 
build_input_context
. This tells Dagster that the input data is expected to be a pandas DataFrame. The actual DataFrame 
df
is passed as part of the 
upstream_output
dictionary. Make sure you have the 
dagster-pandas
library installed to use 
DagsterDataFrame
. If it's not already installed, you can add it to your project with:
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shell pip install dagster-pandas
This should resolve the 
ParameterCheckError
you're encountering in your tests.
j
@Scout how do you make your own dagster type?
s
To create your own Dagster type, you can use the 
DagsterType
class and define a custom type check function that specifies the validation logic for your type. This function is called whenever a value of this type is passed between ops (now referred to as assets in Dagster's asset-oriented approach) or when the value is an input or output of an asset. Here's a step-by-step guide on how to create a custom Dagster type: 1. Define the Type Check Function: This function takes two arguments: a 
context
and the 
value
to be checked. It should return a 
TypeCheck
object that indicates whether the value is valid and can include a description and metadata. 2. Create the DagsterType: Use the 
DagsterType
class to define your custom type, providing the type check function and other optional parameters such as name and description. Here's an example of a custom Dagster type that checks if a value is a non-empty pandas DataFrame:
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python from dagster import DagsterType, TypeCheck, Failure import pandas as pd  # Define the type check function def non_empty_dataframe_check(_, value):     if not isinstance(value, pd.DataFrame):         return TypeCheck(             success=False,             description="Value must be a pandas.DataFrame."         )     if value.empty:         return TypeCheck(             success=False,             description="DataFrame must not be empty."         )     return TypeCheck(success=True)  # Create the custom Dagster type NonEmptyDataFrame = DagsterType(     name="NonEmptyDataFrame",     type_check_fn=non_empty_dataframe_check,     description="A non-empty pandas DataFrame." )  # Use the custom type in an asset @asset(out={'result': Out(dagster_type=NonEmptyDataFrame)}) def my_asset():     # Asset logic that produces a DataFrame     df = pd.DataFrame({"column1": [1, 2, 3], "column2": [4, 5, 6]})     return df
In this example, 
NonEmptyDataFrame
is a custom Dagster type that ensures the value is a non-empty pandas DataFrame. The 
my_asset
asset uses this custom type for its output, and Dagster will run the 
non_empty_dataframe_check
function to validate the DataFrame produced by the asset. Remember to handle the case where the type check fails by returning a 
TypeCheck
object with 
success=False
and an appropriate description. If the check passes, return a 
TypeCheck
object with 
success=True
. By creating custom Dagster types, you can enforce specific data quality constraints and provide clear error messages when data does not meet those constraints.
j
@Scout Is there anything that can be configured in my dagster project so that the UI looks different in different environments (e.g. a banner, or a change in color?)
s
As of my knowledge cutoff date, there isn't a built-in feature in Dagster that allows you to configure the UI to look different in different environments directly through a banner or color change. However, there have been discussions and feature requests in the Dagster community about the ability to distinguish between different Dagster instances or environments within the Dagit UI, such as through visual indicators or customizable UI elements. While these features are not part of the core Dagster product, there are a few workarounds that you might consider: 1. Custom Favicon: You can replace the favicon for Dagit in different environments. This would require you to serve a different favicon file based on the environment where Dagit is running. 2. Custom Title: You can set a custom title for the Dagit browser tab by modifying the HTML title tag in the Dagit's 
index.html
file. This would help users identify which environment they are working in based on the browser tab title. 3. Environment Variables: You can use environment variables to display environment-specific information in your assets or pipelines. For example, you could have an asset that reads an environment variable and logs a message indicating the environment. This message would then appear in the Dagit logs when the asset is executed. 4. Documentation and Training: Ensure that team members are aware of the different environments and train them to check the URL or other environment-specific configurations before performing actions in Dagit. 5. Monitoring and Alerts: Implement monitoring and alerting mechanisms that notify you if actions are performed in the wrong environment. It's important to note that these workarounds are not official features for environment differentiation in the Dagit UI and may require additional setup or maintenance. For the most up-to-date information on this topic, please refer to the latest Dagster documentation or check the Dagster GitHub repository for any updates or new features that may have been introduced after my knowledge cutoff date. If this is a critical feature for your team, you might also consider contributing to the Dagster project or discussing it further with the Dagster community.
j
@Scout How can I get Dagster to check data_types on dbt models using asset checks?
s
As of my knowledge cutoff date, Dagster's integration with dbt allows you to define asset checks that can verify properties of your dbt models, including data types. Asset checks in Dagster are a way to define and execute data quality checks on your software-defined assets. Each asset check verifies some property of a data asset, such as the presence of null values in a column or the data type of a column. To check data types on dbt models using asset checks, you would typically follow these steps: 1. Define dbt Models as Dagster Assets: Use the 
dbt_assets
decorator to define your dbt models as Dagster assets. This allows you to integrate dbt models into the Dagster asset graph. 2. Create Asset Checks: Define asset checks using the 
@asset_check
decorator. These checks can include custom Python code to validate the data types of columns in your dbt models. 3. Use dbt's 
run_results.json
: After running your dbt models, dbt generates a 
run_results.json
file that contains information about the executed models, including their schema and data types. You can write an asset check that reads this file and validates the data types of the columns against your expectations. Here's an example of how you might set up an asset check to validate data types in a dbt model: ```python from dagster import asset, asset_check, AssetCheckResult from dagster_dbt import dbt_assets import json # Define your dbt models as Dagster assets @dbt_assets def my_dbt_models(): # dbt run logic here pass # Define an asset check to validate data types @asset_check(asset=my_dbt_models) def check_dbt_model_data_types(context): # Load the dbt run_results.json file with open('path/to/run_results.json') as f: run_results = json.load(f) # Extract the schema information for your model # This is a simplified example; you would need to navigate the JSON structure # based on your specific dbt project and model names model_schema_info = run_results['results'][0]['columns'] # Define the expected data types for your columns expected_data_types = { 'column_name_1': 'varchar', 'column_name_2': 'integer', # Add more columns and their expected data types here } # Check if the data types match your expectations for column_name, expected_type in expected_data_types.items(): actual_type = model_schema_info[column_name]['data_type'] if actual_type != expected_type: # If a data type does not match, return a failed check return AssetCheckResult( success=False, description=f"Data type for column '{column_name}' is '{actual_type}', expected '{expected_type}'." ) # If all data types match, return
a successful check return AssetCheckResult(success=True) # Use the asset and asset check in your repository definition ``` In this example, 
my_dbt_models
represents your dbt models defined as Dagster assets. The 
check_dbt_model_data_types
function is an asset check that validates the data types of the columns in your dbt model against the expected data types defined in the 
expected_data_types
dictionary. Please note that the actual implementation may vary based on the structure of your dbt project and the specific details of your 
run_results.json
file. Additionally, the paths and methods for accessing dbt artifacts like 
run_results.json
may differ depending on how you have configured your dbt and Dagster setup. For the most accurate and up-to-date information on integrating dbt tests with asset checks in Dagster, please refer to the latest Dagster documentation or the Dagster GitHub repository.
j
@Scout When materializing multiple dbt assets, it groups all dbt assets together into a sing 
dbt_<code_location>_assets
. Is there a way to see indiviual entries for each table being materialized with dbt?
s
As of my knowledge cutoff date, when you materialize multiple dbt assets using Dagster's dbt integration, Dagster groups all dbt assets from the same project into a single entry in the asset catalog, typically named 
dbt_<code_location>_assets
. This is because Dagster treats the entire dbt project as a single unit of computation, and the dbt CLI, when invoked, runs the models as a batch. However, if you want to see individual entries for each table being materialized with dbt, you can use the 
DagsterDbtCliOutput
object that is returned when you execute a dbt CLI command within Dagster. This object contains detailed information about each dbt model that was run, including their materialization status. To get individual entries for each dbt model in the asset catalog, you can yield 
AssetMaterialization
events for each model within a Dagster op (formerly known as a solid). Here's an example of how you might do this:
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python from dagster import op, AssetMaterialization, Out from dagster_dbt import dbt_cli_resource, DbtCliOutput  @op(required_resource_keys={"dbt"}, out=Out(dagster_type=DbtCliOutput)) def run_dbt_models(context):     # Run the dbt project using the dbt CLI resource     output: DbtCliOutput = context.resources.dbt.run()          # Iterate over each model in the dbt run output     for model in output.result['results']:         # Yield an AssetMaterialization for each individual dbt model         yield AssetMaterialization(             asset_key=model['unique_id'],             description=f"Materialized dbt model: {model['unique_id']}",             metadata={                 "materialization": model['status'],                 # Include additional metadata as needed             }         )          # Return the dbt CLI output for downstream ops or checks     return output  # Include the op in your job definition
In this example, the 
run_dbt_models
op runs the dbt project using the 
dbt
resource and then iterates over the results, yielding an 
AssetMaterialization
for each individual dbt model. This will result in individual entries for each table being materialized with dbt appearing in the asset catalog. Please note that this is a simplified example, and the actual implementation may vary based on your specific dbt project configuration and the structure of the 
DbtCliOutput
object. Additionally, this approach may require additional logic to handle incremental models, tests, and other dbt-specific behaviors. For the most accurate and up-to-date information on how to achieve individual asset materializations for dbt models in Dagster, please refer to the latest Dagster documentation or the Dagster GitHub repository.
j
@Scout dbt has a concept of 
datatype
and a config:
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contract:
        enforced: true
That adds a test to make sure that the output table has columns that match the model. Even though I am enabling `enable_asset_checks`:
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@dbt_assets(
    manifest=DBT_MANIFEST,
    dagster_dbt_translator=CustomDwhDagsterDbtTranslator(settings=DagsterDbtTranslatorSettings(enable_asset_checks=True)),
    io_manager_key="bq_table_io_manager",
)
I am not seeing these data type checks appear as an asset check. I see other tests defined in the models.yml file, but not these even though it appears to be running (I get errors if I have the wrong datatypes of column names, etc...)
s
The 
contract
configuration and the 
enforced: true
setting you're referring to are part of dbt's schema tests, which are used to ensure that the columns in your output table match the expected schema defined in your dbt model's 
schema.yml
file. When 
enforced
is set to 
true
, dbt will automatically generate and run a test for each column to check its data type against the one specified in the schema file. When you enable 
enable_asset_checks
in Dagster's dbt integration, Dagster should load dbt tests as asset checks, which allows you to visualize the success and failure of those tests in the Dagster UI. However, as of my knowledge cutoff date, there may be some nuances or limitations regarding which dbt tests are automatically converted into Dagster asset checks. If you are not seeing the data type checks appear as an asset check in Dagster, even though other tests from the 
models.yml
file are appearing, it could be due to one of the following reasons: 1. Version Compatibility: Ensure that you are using a version of Dagster and dagster-dbt that supports the automatic conversion of dbt tests into asset checks. This feature was experimental and may have evolved over time. 2. Test Recognition: Dagster may not recognize certain dbt-generated tests as asset checks. This could be the case for tests that dbt automatically generates based on the 
contract
configuration, as opposed to explicitly defined tests in the 
schema.yml
file. 3. Configuration: There might be additional configuration required to ensure that all dbt tests, including those generated by the 
contract
configuration, are recognized as asset checks by Dagster. 4. Limitations or Bugs: There could be limitations or bugs in the Dagster dbt integration that prevent certain types of dbt tests from being loaded as asset checks. To troubleshoot this issue, you can: - Check the Dagster and dagster-dbt release notes and documentation for any updates or changes related to dbt test integration as asset checks. - Review the dbt 
run_results.json
file to confirm that the data type tests are being executed by dbt and to understand how they are represented in the dbt artifacts. - Look for any open issues or discussions in the Dagster GitHub repository that might be related to this behavior. If the issue persists and you believe it is a bug or a missing feature in Dagster's dbt integration, you may consider filing an issue in the Dagster GitHub repository to bring it to the attention of the maintainers. For the most accurate and up-to-date information on integrating dbt tests with asset checks in Dagster, please refer to the latest Dagster documentation or the Dagster GitHub repository.
j
@Scout Is there a way to delay a scheduled job
s
Yes, there are ways to delay the execution of a scheduled job in Dagster. While the schedule itself triggers at specified intervals defined by the cron schedule, you can implement custom logic within the schedule to delay the actual execution of the job. Here are a couple of methods to achieve this: 1. Custom Schedule Logic: You can define a custom schedule that includes logic to delay the execution. This can be done by checking the current time and comparing it to the desired execution time, then using a 
time.sleep()
to wait for the remaining time before returning a 
RunRequest
.
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python from dagster import schedule, RunRequest import time from datetime import datetime, timedelta  @schedule(job=my_job, cron_schedule="0 * * * *")  # This schedule triggers every hour def my_delayed_schedule(context):     # Define the desired delay in minutes     delay_minutes = 10     # Calculate the desired execution time (10 minutes after the hour)     desired_execution_time = context.scheduled_execution_time.replace(minute=delay_minutes)     # Calculate the current time     current_time = datetime.now(context.scheduled_execution_time.tzinfo)     # Calculate the remaining time to wait     remaining_time_to_wait = (desired_execution_time - current_time).total_seconds()          # If the remaining time to wait is positive, delay the execution     if remaining_time_to_wait > 0:         time.sleep(remaining_time_to_wait)          # Return the run request to execute the job     return RunRequest(run_key=None, run_config={})
2. Offset in Cron Schedule: If you want to consistently delay the job execution by a fixed amount of time, you can adjust the cron schedule to include the delay. For example, if you want to run the job 10 minutes past every hour, you can set the cron schedule to 
"10 * * * *"
. Please note that using 
time.sleep()
to delay execution within the schedule evaluation function is generally not recommended because it can block the scheduler thread. It's better to adjust the cron schedule or use other scheduling mechanisms to achieve the desired delay. For more complex scheduling requirements, you might consider using sensors instead of schedules. Sensors can be used to trigger jobs based on certain conditions or events, which can provide more flexibility than cron-based schedules. Always refer to the latest Dagster documentation for the most up-to-date methods and best practices for scheduling jobs.
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