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#ask-ai
Title
# ask-ai
j

Josh

02/22/2024, 12:59 PM
@Scout How to make an asset dependency optional? if the partition is not materialized, use default data.
s

Scout

02/22/2024, 12:59 PM
To make an asset dependency optional and use default data if the partition is not materialized, you can leverage the
is_required
parameter in the
AssetOut
definition. By setting
is_required
to
False
, you can control the behavior of asset materialization based on the presence of upstream data. Here's an example from the documentation that demonstrates how to use the
is_required
parameter with
yield
syntax to implement conditional materialization:
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python import random from dagster import AssetOut, Output, asset, multi_asset  @multi_asset(     outs={"asset1": AssetOut(is_required=False), "asset2": AssetOut(is_required=False)} ) def assets_1_and_2():     if random.randint(1, 10) < 5:         yield Output([1, 2, 3, 4], output_name="asset1")          if random.randint(1, 10) < 5:         yield Output([6, 7, 8, 9], output_name="asset2")  @asset def downstream1(asset1):     # will not run when assets_1_and_2 doesn't materialize the asset1     return asset1 + [5]  @asset def downstream2(asset2):     # will not run when assets_1_and_2 doesn't materialize the asset2     return asset2 + [10]
In this example,
assets_1_and_2
is a multi-asset function that may yield outputs for
asset1
and
asset2
based on a random condition. Both
AssetOut
definitions have
is_required
set to
False
, which means that if the function does not yield an
Output
object for a particular output, no asset materialization event will be created for that output, and downstream assets will not be materialized. For your specific use case, where you want to use default data if a partition is not materialized, you would need to implement logic within your asset function to check if the upstream data is available. If it is not, you can provide default data instead. Here's a conceptual example:
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python from dagster import asset, AssetIn, Output  @asset def upstream_asset():     # This asset may or may not materialize data     pass  @asset(ins={"upstream": AssetIn(is_required=False)}) def downstream_asset_with_default(upstream):     if upstream is None:         # No data was materialized for the upstream asset, use default data         default_data = [...]  # Define your default data         return default_data     else:         # Upstream data is available, use it         return transform_upstream_data(upstream)
In this example,
downstream_asset_with_default
has an optional input
upstream
with
is_required
set to
False
. If
upstream
is not materialized, the function uses default data; otherwise, it processes the available upstream data. Please note that the exact implementation details may vary based on your specific requirements and the structure of your Dagster assets.
j

Josh

02/22/2024, 1:08 PM
@Scout It appears that the
AssetIn
does not take any parameter
is_required
. I get the following error when I tried the example you provided:
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TypeError: AssetIn.__new__() got an unexpected keyword argument 'is_required'
s

Scout

02/22/2024, 1:08 PM
I apologize for the confusion. You are correct that
AssetIn
does not take an
is_required
parameter. The
is_required
parameter is used with
AssetOut
to indicate whether an output is required for a multi-asset. For making an asset dependency optional and providing a default value if the upstream asset is not materialized, you would typically handle this within the asset function itself. You can check if the upstream data is available and, if not, provide a default value. Here's a revised conceptual example that does not use
is_required
with `AssetIn`:
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python from dagster import asset  @asset def upstream_asset():     # This asset may or may not materialize data     pass  @asset def downstream_asset_with_default(upstream=None):     if upstream is None:         # No data was materialized for the upstream asset, use default data         default_data = [...]  # Define your default data         return default_data     else:         # Upstream data is available, use it         return transform_upstream_data(upstream)
In this example,
downstream_asset_with_default
takes an optional parameter
upstream
with a default value of
None
. If
upstream
is not provided (i.e., the upstream asset did not materialize data), the function uses the default data. Please adjust the logic within your asset function to handle the presence or absence of upstream data according to your specific use case.
j

Josh

02/22/2024, 1:15 PM
@Scout that still doesn't work, dagster tries to read the file corresponding to the dependency and since the upstream asset is not materialized, it fails to open the file and throws an error.
s

Scout

02/22/2024, 1:15 PM
I apologize for the inconvenience. In Dagster, if an asset depends on an upstream asset that has not been materialized, and you attempt to materialize the downstream asset, Dagster will indeed try to load the upstream asset's data and fail if it is not available. To work around this issue, you can use a combination of Dagster's execution logic and custom code to check for the existence of the upstream asset's materialization before attempting to load it. If the upstream asset's materialization does not exist, you can then use default data for the downstream asset. Here's a conceptual example of how you might implement this:
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python from dagster import asset, AssetMaterialization, AssetKey, OpExecutionContext, IOManager, io_manager from typing import Optional  class MyIOManager(IOManager):     def has_asset(self, context, asset_key: AssetKey) -> bool:         # Implement logic to check if the asset has been materialized         # This could involve checking a database, filesystem, etc.         pass      def load_input(self, context) -> Optional[object]:         asset_key = context.upstream_output.asset_key         if self.has_asset(context, asset_key):             # Load the asset as usual             return super().load_input(context)         else:             # Return default data if the asset is not materialized             return default_data      def handle_output(self, context, obj):         # Handle output as usual         super().handle_output(context, obj)  @io_manager def my_io_manager(_):     return MyIOManager()  @asset(io_manager_key="my_io_manager") def upstream_asset():     # This asset may or may not materialize data     pass  @asset(io_manager_key="my_io_manager") def downstream_asset(upstream):     # The logic for handling default data is now within the IOManager     # If the upstream asset is not materialized, `upstream` will be the default data     return transform_upstream_data(upstream)
In this example,
MyIOManager
is a custom IOManager that overrides the
load_input
method to check if the upstream asset has been materialized. If it has not, it returns default data instead of attempting to load the asset. The
has_asset
method should contain the logic to determine if the asset has been materialized, which could involve checking a database, filesystem, or other storage backend. Please note that this is a conceptual example, and the actual implementation of
has_asset
and other IOManager methods will depend on your specific storage backend and how you track materializations. By using a custom IOManager, you can control the behavior of loading inputs and provide default data when necessary, allowing you to handle optional asset dependencies in a more granular way.
j

Josh

02/22/2024, 1:39 PM
@Scout is it necessary for the downstream asset to use the iomanager?
s

Scout

02/22/2024, 1:39 PM
No, it is not strictly necessary for the downstream asset to use an IOManager if you are handling the loading of upstream data manually within the asset's computation function. The IOManager is typically used to abstract away the details of how data is loaded and stored, but if you have a specific use case where you need to conditionally load data or provide a default value, you can implement that logic directly in your asset function. Here's an example of how you might handle this without using an IOManager:
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python from dagster import asset, AssetKey, build_input_context import os  # Define a function to check if an asset has been materialized def check_asset_materialized(asset_key: AssetKey, base_path: str) -> bool:     # This is a simple example using the filesystem.     # You would replace this with the appropriate logic for your storage backend.     asset_path = os.path.join(base_path, *asset_key.path)     return os.path.exists(asset_path)  # Define your upstream asset @asset def upstream_asset():     # This asset may or may not materialize data     pass  # Define your downstream asset @asset def downstream_asset_with_default(context):     # Construct an AssetKey for the upstream asset     upstream_asset_key = AssetKey(["upstream_asset"])          # Check if the upstream asset has been materialized     if check_asset_materialized(upstream_asset_key, context.instance.storage_directory()):         # Load the upstream data because it has been materialized         input_context = build_input_context(upstream_output=upstream_asset_key)         upstream_data = context.instance.get_io_manager().load_input(input_context)     else:         # Provide default data because the upstream asset has not been materialized         upstream_data = [...]  # Define your default data          # Continue with your computation using the upstream data     return transform_upstream_data(upstream_data)
In this example, the
downstream_asset_with_default
function manually checks if the upstream asset has been materialized by calling the
check_asset_materialized
function. If the upstream asset is not materialized, it uses default data. Otherwise, it loads the upstream data using the IOManager associated with the context. This approach gives you full control over the loading of upstream data within the asset computation function, without relying on an IOManager to handle optional dependencies. However, it does require you to manage the details of data loading and checking for materializations yourself.
j

Josh

02/22/2024, 1:55 PM
@Scout is there any way to do it without iomanagers?
s

Scout

02/22/2024, 1:55 PM
As of my knowledge cutoff in early 2023, Dagster's asset model typically expects that assets will be materialized when they are computed, and dependencies between assets are resolved by the framework using IOManagers to pass data between them. If an asset is not materialized, the framework's default behavior is to attempt to load it, which will fail if the data does not exist. However, if you want to implement a pattern where an asset can have an optional dependency on another asset, you would need to handle the existence check and the default data provision within the asset's computation function. This would involve custom logic to determine whether the upstream asset's data is available and, if not, to use a default value instead. Here's a conceptual example of how you might implement this pattern without using IOManagers:
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python
from dagster import asset, AssetKey, build_op_context
import os

# Define a function to check if an asset has been materialized
def check_asset_materialized(asset_key: AssetKey, base_path: str) -> bool:
    # This is a simple example using the filesystem.
    # You would replace this with the appropriate logic for your storage backend.
    asset_path = os.path.join(base_path, *asset_key.path)
    return os.path.exists(asset_path)

# Define your upstream asset
@asset
def upstream_asset():
    # This asset may or may not materialize data
    pass

# Define your downstream asset
@asset
def downstream_asset_with_default(context):
    # Construct an AssetKey for the upstream asset
    upstream_asset_key = AssetKey(["upstream_asset"])
    
    # Check if the upstream asset has been materialized
    if check_asset_materialized(upstream_asset_key, context.instance.storage_directory()):
        # Load the upstream data because it has been materialized
        # You would replace this with your own logic to load the data
        upstream_data = load_upstream_data(upstream_asset_key)
    else:
        # Provide default data because the upstream asset has not been materialized
        upstream_data = [...]  # Define your default data
    
    # Continue with your computation using the upstream data
    return transform_upstream_data(upstream_data)

# Define a function to load the upstream data
def load_upstream_data(asset_key: AssetKey):
    # Implement your logic to load the data for the given asset key
    # This could involve reading from a file, database, or other storage system
    pass
In this example, the
downstream_asset_with_default
function manually checks if the upstream asset has been materialized by calling the
check_asset_materialized
function. If the upstream asset is not materialized, it uses default data. Otherwise, it loads the upstream data using a custom
load_upstream_data
function. This approach
allows you to bypass the IOManager system and handle the conditional loading of upstream data within the asset computation function. However, it requires you to manage the details of data checking and loading yourself, which can be more complex and error-prone than using the built-in IOManager system. Please note that the example above is conceptual and would need to be adapted to fit the specifics of your storage backend and data access patterns. Additionally, Dagster's features and best practices may have evolved since my last update, so I recommend checking the latest Dagster documentation or community discussions for any new patterns or features that might help with this use case.
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