Josh
02/22/2024, 12:59 PMScout
02/22/2024, 12:59 PMis_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: 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: 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.Josh
02/22/2024, 1:08 PMAssetIn
does not take any parameter is_required
. I get the following error when I tried the example you provided:
TypeError: AssetIn.__new__() got an unexpected keyword argument 'is_required'
Scout
02/22/2024, 1:08 PMAssetIn
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`: 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.Josh
02/22/2024, 1:15 PMScout
02/22/2024, 1:15 PMpython 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.Josh
02/22/2024, 1:39 PMScout
02/22/2024, 1:39 PMpython 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.Josh
02/22/2024, 1:55 PMScout
02/22/2024, 1:55 PMpython
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 approachScout
02/22/2024, 1:56 PM