@Scout orchestrate a graph after the asset "load_all" is materialized

To orchestrate a graph after the materialization of an asset named "load_all," you would typically define dependencies within your asset graph to ensure that the graph's execution is triggered upon the completion of the "load_all" asset. In Dagster, you can define these dependencies explicitly in your asset definitions. Here's a step-by-step approach based on the documentation: 1. Define the "load_all" asset using the

@asset

decorator. 2. Define the graph that should be orchestrated after "load_all" is materialized. This graph will consist of ops, which are the building blocks of computation in Dagster. 3. Use the

@graph

decorator to define the graph. 4. Create an asset from the graph using

AssetsDefinition.from_graph

if you want the graph's execution to result in the creation of new assets. 5. Set up the dependency such that the graph is dependent on the "load_all" asset. This means that the graph will only execute after "load_all" has been materialized. Here is an example code snippet that illustrates how you might set this up, using the

@asset

and

@graph

decorators, as well as the

AssetsDefinition.from_graph

method:

python from dagster import asset, graph, op, AssetsDefinition  @asset def load_all():     # Logic to load data     pass  @op def process_data(data):     # Logic to process data     pass  @graph def process_graph(load_all_data):     return process_data(load_all_data)  # Create an asset from the graph, with load_all as an upstream dependency process_assets = AssetsDefinition.from_graph(     graph_def=process_graph,     keys_by_input_name={"load_all_data": AssetKey("load_all")}, )  # Now, when you materialize the 'load_all' asset, the 'process_graph' will be orchestrated after it.

Please note that the actual implementation may vary based on your specific use case and the structure of your data pipeline. The provided example is a simplified illustration and should be adapted to fit the context of your application. If you need more detailed guidance or have a more complex scenario, please refer to the Dagster documentation or reach out with specific questions.

@Scout I'm using the annotation

@graph_asset

If you're using the

@graph_asset

decorator, you can define a graph-backed asset that will be computed after another asset, such as "load_all", is materialized. The

@graph_asset

decorator allows you to define an asset that's computed using a graph of ops, with dependencies between the ops specified inside the body of the decorated function. Here's how you can orchestrate a graph after the "load_all" asset is materialized using `@graph_asset`: 1. Define your "load_all" asset using the

@asset

decorator. 2. Define the ops that will be part of your graph. 3. Use the

@graph_asset

decorator to define a new asset that depends on "load_all". Inside the

@graph_asset

function, you will invoke the ops that make up your graph. Here's an example:

python from dagster import asset, op, graph_asset  @asset def load_all():     # Logic to load data     pass  @op def process_data(data):     # Logic to process data     pass  @graph_asset def my_graph_asset(load_all_data):     # 'load_all_data' is the materialized asset 'load_all'     return process_data(load_all_data)  # When you materialize the 'load_all' asset, the 'my_graph_asset' will be orchestrated after it.

In this example,

my_graph_asset

is a graph-backed asset that depends on the

load_all

asset. When

load_all

is materialized,

my_graph_asset

will be triggered, and the

process_data

op will be executed with the data from

load_all

. Remember to define the dependencies correctly so that Dagster knows to execute

my_graph_asset

after

load_all

has been materialized. The documentation provides more details and examples on how to use

@graph_asset

and set up dependencies between assets.