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#ask-ai
Title
# ask-ai
g

Greg H

03/06/2024, 12:33 PM
How can I test an asset that uses an I/O Manager when I'd like to provide the input to the asset from another source?
s

Scout

03/06/2024, 12:33 PM
To test an asset that uses an I/O Manager, you can use the
build_input_context
function provided by Dagster to construct an
InputContext
for testing the
load_input
method of your
IOManager
. This allows you to simulate the loading of input data from a different source during testing. Here's an example from the documentation on how you might use
build_input_context
to test an `IOManager`:
Copy code
python from dagster import IOManager, InputContext, build_input_context  class MyIOManager(IOManager):     def load_input(self, context: InputContext):         # Your custom loading logic here         ...  # Example usage for testing input_context = build_input_context() my_io_manager = MyIOManager() result = my_io_manager.load_input(input_context)
In this example,
MyIOManager
is your custom I/O Manager that you want to test. The
load_input
method is where you define how data is loaded into your asset. By using
build_input_context
, you can create a context that mimics the runtime environment and pass it to your
load_input
method to test how your asset handles the input data. Remember that the
build_input_context
function allows you to specify various parameters such as
name
,
config
,
metadata
,
upstream_output
, and others, which can be used to simulate different testing scenarios. When testing assets, it's also common to use in-memory I/O managers like
mem_io_manager
to avoid writing to disk or external systems. This can simplify testing and make it faster and more isolated. Keep in mind that the actual implementation details for your test setup may vary based on the specific requirements of your asset and the I/O Manager you are using. The provided example is a general guide, and you should refer to the documentation for more detailed information on testing assets with I/O Managers.
g

Greg H

03/06/2024, 12:36 PM
@Scout My asset uses an I/O Manager to load data from a Postgres database into a Polars DataFrame. Can I provide the DataFrame directly when testing the asset?
s

Scout

03/06/2024, 12:37 PM
Yes, you can provide a DataFrame directly when testing an asset that typically loads data from a Postgres database using an I/O Manager. To do this, you can create a test I/O Manager that returns the DataFrame you want to use for testing instead of actually loading data from the database. Here's a general approach to achieve this: 1. Define a test I/O Manager that returns the DataFrame you want to use in your tests. 2. Use this test I/O Manager in your asset's test environment instead of the actual Postgres I/O Manager. Here's an example of how you might implement this:
Copy code
python from dagster import IOManager, InputContext, build_input_context, asset import polars as pl  class TestIOManager(IOManager):     def load_input(self, context: InputContext):         # Return the DataFrame you want to use for testing         return pl.DataFrame({"column": [1, 2, 3]})  # Use the test I/O Manager in your test environment @asset(io_manager_key="test_io_manager") def my_asset(input_df: pl.DataFrame):     # Your asset logic here     ...  # In your test setup def test_my_asset():     test_io_manager = TestIOManager()     input_context = build_input_context()     test_df = test_io_manager.load_input(input_context)          # Now you can call my_asset with the test DataFrame     result = my_asset(test_df)     # Perform your test assertions here
In this example,
TestIOManager
is a custom I/O Manager that you would use only for testing purposes. It overrides the
load_input
method to return a predefined Polars DataFrame instead of querying a Postgres database. When setting up your test, you instantiate
TestIOManager
and use
build_input_context
to create an
InputContext
. You then call
load_input
on your test I/O Manager to get the DataFrame, which you can pass to your asset function
my_asset
for testing. This approach allows you to test the logic of your asset without relying on an actual Postgres database, making your tests faster and more reliable. Remember to adjust the DataFrame and the asset logic to match your specific use case.
g

Greg H

03/06/2024, 1:44 PM
@Scout From my understanding I would have to overwrite the Ins Attribute on the asset. Is there a way to do so?
s

Scout

03/06/2024, 3:00 PM
In Dagster, the
@asset
decorator is used to define assets, and the inputs to an asset are typically inferred from the parameters of the decorated function. However, if you want to provide inputs to an asset in a test environment, you don't need to overwrite the
Ins
attribute directly. Instead, you can use the
build_input_context
function to create an
InputContext
with the necessary data for testing. Here's an example of how you might use
build_input_context
to provide a test input to an asset:
Copy code
python from dagster import asset, build_input_context import polars as pl  @asset def my_asset(input_df: pl.DataFrame):     # Your asset logic here     ...  # In your test setup def test_my_asset():     # Create a test DataFrame     test_df = pl.DataFrame({"column": [1, 2, 3]})          # Build an InputContext with the test DataFrame     input_context = build_input_context(upstream_output=test_df)          # Call the asset with the input context     result = my_asset(input_context)          # Perform your test assertions here
In this example,
build_input_context
is used to create an
InputContext
that includes the
upstream_output
parameter, which is set to the test DataFrame
test_df
. This
InputContext
is then passed to the
my_asset
function during the test. This approach allows you to simulate the presence of an input without having to modify the asset definition itself. It's a common pattern for testing assets in isolation, especially when you want to control or mock the inputs to the asset. If you need to test the asset within the context of a Dagster job or graph, you can use the
execute_in_process
method to run the job or graph with a specific configuration that includes your test inputs or test I/O managers. Remember that the actual implementation details for your test setup may vary based on the specific requirements of your asset and the I/O Manager you are using. The provided example is a general guide, and you should refer to the documentation for more detailed information on testing assets with I/O Managers.
g

Greg H

03/06/2024, 3:08 PM
@Scout upstream_output expects an OutputContext and not the DataFrame itself, though.
s

Scout

03/06/2024, 3:08 PM
You are correct; the
upstream_output
parameter of
build_input_context
expects an
OutputContext
, not the data itself. My apologies for the confusion. To provide a DataFrame directly when testing an asset, you would typically mock the I/O Manager's
load_input
method to return the DataFrame you want to use for testing. Here's an example of how you might do this:
Copy code
python from dagster import IOManager, InputContext, asset, build_input_context import polars as pl  # Define a test IOManager that returns the DataFrame you want to use for testing class TestIOManager(IOManager):     def load_input(self, context: InputContext):         # Return the DataFrame you want to use for testing         return pl.DataFrame({"column": [1, 2, 3]})  # Define your asset @asset def my_asset(input_df: pl.DataFrame):     # Your asset logic here     ...  # In your test setup def test_my_asset():     # Instantiate your test IOManager     test_io_manager = TestIOManager()          # Build an InputContext with the test IOManager     input_context = build_input_context(io_manager=test_io_manager)          # Use the test IOManager to load the input DataFrame     test_df = test_io_manager.load_input(input_context)          # Call the asset with the test DataFrame     result = my_asset(test_df)          # Perform your test assertions here
In this example,
TestIOManager
is a custom IOManager that you would use only for testing purposes. It overrides the
load_input
method to return a predefined Polars DataFrame instead of querying a Postgres database. When setting up your test, you instantiate
TestIOManager
and use
build_input_context
to create an
InputContext
with your test IOManager. You then call
load_input
on your test IOManager to get the DataFrame, which you can pass to your asset function
my_asset
for testing. This approach allows you to test the logic of your asset without relying on an actual Postgres database, making your tests faster and more reliable. Remember to adjust the DataFrame and the asset logic to match your specific use case.
It seems like your message got cut off before you could provide the details of your asset. If you could provide the code for your asset, I can give you more specific guidance on how to test it with a mock I/O Manager or another method that suits your needs. Generally, if you have an asset that uses an I/O Manager to load data from a Postgres database into a Polars DataFrame, you would create a test I/O Manager that returns a Polars DataFrame directly. Then, in your test, you would use this test I/O Manager to provide the input to the asset. Please provide the code for your asset, and I'll assist you further with the testing approach.
g

Greg H

03/06/2024, 3:22 PM
@Scout my asset looks like this:
@asset(
ins={
"my_input": AssetIn(
key=AssetKey([_prefix, "schema", "my_input"]),
input_manager_key="input_io_manager",
),
},
)
def trigger_asset(
context: OpExecutionContext,
input_io_manager: pl.DataFrame,
) -> int:
# asset logic here
How can I test the asset's implementation? I'll need to pass a Polars DataFrame that is loaded using an I/O Manager into the asset.
s

Scout

03/06/2024, 3:22 PM
To test the implementation of your asset, you can create a test I/O Manager that returns a Polars DataFrame. You would then configure your test environment to use this test I/O Manager instead of the actual one that interacts with the Postgres database. Here's a step-by-step guide on how to set up your test: 1. Create a test I/O Manager that returns a Polars DataFrame.
Copy code
python from dagster import IOManager, InputContext import polars as pl  class TestIOManager(IOManager):     def load_input(self, context: InputContext):         # Return the DataFrame you want to use for testing         return pl.DataFrame({"column": [1, 2, 3]})
2. Define your asset with the test I/O Manager in your test environment.
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python from dagster import asset, AssetIn, AssetKey, OpExecutionContext import polars as pl  # Assuming _prefix is defined somewhere in your code _prefix = "your_prefix"  @asset(     ins={         "my_input": AssetIn(             key=AssetKey([_prefix, "schema", "my_input"]),             input_manager_key="input_io_manager",         ),     }, ) def trigger_asset(context: OpExecutionContext, my_input: pl.DataFrame) -> int:     # asset logic here     return len(my_input)  # Example logic
3. Configure your test environment to use the test I/O Manager.
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python from dagster import build_input_context, ResourceDefinition  # Create an instance of your test IOManager test_io_manager = TestIOManager()  # Build an InputContext with the test IOManager for "my_input" input_context = build_input_context(resources={"input_io_manager": test_io_manager})  # Use the test IOManager to load the input DataFrame test_df = test_io_manager.load_input(input_context)  # Now you can call trigger_asset with the test DataFrame result = trigger_asset(None, test_df)  # Pass None for context if it's not used  # Perform your test assertions here assert result == 3  # Example assertion
In this example, we're using
build_input_context
to create an
InputContext
that includes the test I/O Manager. We then call
load_input
on the test I/O Manager to get the DataFrame, which we pass to the
trigger_asset
function for testing. Please note that in the actual test, you might need to adjust the
build_input_context
and the way you call the asset function based on how your asset uses the context and the input DataFrame. This approach allows you to test the logic of your asset without relying on an actual Postgres database, making your tests faster and more isolated. Remember to adjust the DataFrame and the asset logic to match your specific use case.
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