what’s a graph asset typically used for?

A graph asset in Dagster is typically used for representing a collection of computations that together produce one or more assets. It allows you to construct software-defined assets using graphs, which are composed of multiple ops. This is particularly useful when you have complex computations that need to be split into separate, reusable components. Graph assets enable you to: 1. Split Computation: Break down complex asset computations into separate ops, which can improve code readability and maintainability. 2. Reuse Code: Reuse ops across different assets and jobs, which can lead to more modular and DRY (Don't Repeat Yourself) codebases. 3. Define Multiple Outputs: Create multiple assets from a single graph, each representing a different piece of data or a different view on the data. Here is an example from the documentation on how you can create asset definitions from graphs:

python @graph(     ins={"new_user_signups": GraphIn()},     out={"signups_today": GraphOut(), "num_signups_today": GraphOut()}, ) def users_filtered_by_date(new_user_signups):     signups_today = filter_for_date(new_user_signups)     return signups_today, num_users(signups_today)  asset_def = AssetsDefinition.from_graph(users_filtered_by_date)

In this example, the

users_filtered_by_date

graph accepts

new_user_signups

as its input asset and outputs two assets,

signups_today

and

num_signups_today

. The

AssetsDefinition.from_graph

method is used to create asset definitions from this graph. Graph assets are a powerful abstraction in Dagster that help you manage and organize your data computations in a way that is clear, maintainable, and scalable.

@Scout Do graph assets allow you to run an asset concurrently in batches?

Graph assets in Dagster allow you to define computations that can be executed in parallel, but the concurrency model depends on how you structure your graph and the execution strategy you choose for your Dagster job. If you want to process data in batches concurrently, you can use dynamic outputs within your graph to create parallelizable work. For example, you can have an op within your graph that yields

DynamicOutput

objects, each representing a batch of data to be processed. Subsequent ops can then map over these dynamic outputs to process each batch in parallel. Here's a simplified example from the documentation that illustrates the concept:

python @op(out=DynamicOut()) def return_dynamic(input_val):     for idx in range(10):         yield DynamicOutput(value=input_val, mapping_key=str(idx))  @op def process_batch(batch):     # Process each batch     pass  @graph def my_graph():     dynamic_result = return_dynamic()     dynamic_result.map(process_batch)

In this example,

return_dynamic

yields multiple dynamic outputs that represent different batches of data. The

process_batch

op is then mapped over these dynamic outputs, allowing each batch to be processed in parallel. However, the actual concurrency behavior—whether these batches are processed simultaneously or not—will depend on the executor configuration for your job. For instance, if you use the multiprocess executor, Dagster can run each batch in a separate process, achieving parallel execution. Keep in mind that while graph assets can facilitate parallel execution, the degree of concurrency and the exact execution behavior are controlled by the job's executor and any concurrency limits you set in your Dagster system configuration.