Where is the best place to put creation of a cloud resource like a computing cluster in a dagster workflow? It doesn’t feel like this fits in the use of an asset

In a Dagster workflow, the creation of a cloud resource like a computing cluster does not typically fit the concept of an asset, as assets in Dagster are generally data objects that are produced by pipelines. Instead, the creation and management of cloud resources can be handled using resources or ops. According to the documentation, resources in Dagster are logical constructs that provide a way to make external resources like databases, computing clusters, or other services available to your ops during execution. You can define a resource for your computing cluster and configure it in such a way that it sets up the cluster when needed for the execution of your pipeline. If you need to perform an action like creating a computing cluster as part of your workflow, but it doesn't directly produce a data asset, you can use an op to encapsulate this logic. Ops are the building blocks in Dagster that represent computations or actions, and they can be used for tasks such as setting up infrastructure, sending notifications, or other side effects that are not directly related to the creation of data assets. For example, you might have an op that creates a computing cluster, and this op can be part of a job that includes other ops or assets that depend on this cluster. The creation of the cluster would be a step in your workflow, managed by the orchestration layer of Dagster, ensuring that it is available when subsequent steps in the pipeline require it. In summary, for creating a cloud resource like a computing cluster, you would typically use a resource or an op within your Dagster workflow, rather than treating it as an asset.

How do I combine ops with assets?

Combining ops with assets in a Dagster workflow can be achieved in a few ways, depending on the structure and requirements of your pipeline. Here are the main approaches based on the documentation: 1. Graph-backed assets: If you have a sequence of steps where some do not produce assets but are necessary for the computation of subsequent assets, you can use a graph-backed asset. This approach allows you to compose a set of ops into an op graph, which can then be used to compute an asset. Graph-backed assets are useful when you want to execute multiple separate steps, and some of those steps don’t produce assets of their own. 2. Op-backed assets: You can create an asset from an op using the

AssetsDefinition.from_op

method. This is useful when you have an op that performs a computation and you want to represent the output of that computation as an asset. 3. Including ops in asset computations: If you have an op that needs to run before an asset is computed, you can include the op within the body of the asset computation. This way, the op's execution is part of the asset's computation graph. 4. Using resources: Resources can be used to manage external systems or services that are required by your assets. For example, if you have an op that sets up a computing cluster, you can define a resource that represents the cluster and make it available to the assets that require it. 5. Job composition: You can define a job that includes both assets and ops. The job orchestrates the execution of these components, ensuring that the ops run at the appropriate times in relation to the asset computations. Here's a simplified example of how you might define a graph-backed asset that includes an op:

python from dagster import op, graph, Out, Output, graph_asset  @op def prepare_data():     # Logic to prepare data     return "data"  @op def process_data(data):     # Logic to process data     return "processed_data"  @graph def data_processing_graph():     data = prepare_data()     processed_data = process_data(data)     return processed_data  @graph_asset def my_asset():     return data_processing_graph()

In this example,

prepare_data

and

process_data

are ops that are part of a graph

data_processing_graph

. The

my_asset

graph-backed asset uses this graph to compute its value. When you execute the job that includes

my_asset

, Dagster will run the ops in the defined order as part of the asset computation. Remember to consult the documentation for more detailed examples and explanations on how to combine ops and assets in your specific use case.

@Scout How do I make an asset group dependent on an op?

As of my knowledge cutoff date in early 2023, in Dagster, assets are typically dependent on other assets, and the dependencies between assets are defined at the asset level. However, if you want to create a dependency between an asset group and an op, you would need to structure your workflow in a way that the op's output is used as an input to the assets within the group, effectively making the asset group dependent on the op. Here's a general approach to achieve this: 1. Define an op that performs the necessary computation or action. 2. Create an asset that depends on the output of the op. 3. Group assets together using the

asset_group

decorator, ensuring that at least one asset in the group depends on the op's output. Here's a simplified example:

python from dagster import op, asset, asset_group  @op def my_op():     # Perform some computation or action     return some_data  @asset def my_asset(input_data):     # Process the input data     return processed_data  # Define an asset group and include the asset that depends on the op my_asset_group = asset_group([my_asset])  # In your job definition, you would use the op and the asset group together @job def my_job():     data = my_op()     my_asset(input_data=data)

In this example,

my_asset

is an asset that depends on the output of

my_op

. The

my_asset_group

is an asset group that includes

my_asset

. When defining the job

my_job

, you explicitly pass the output of

my_op

to

my_asset

, creating a dependency between the op and the asset group. Please note that the exact implementation details may vary depending on the specific requirements of your workflow and the version of Dagster you are using. Always refer to the latest Dagster documentation for the most up-to-date guidance and best practices. If there have been updates to Dagster after my knowledge cutoff date that introduce new ways to create dependencies between ops and asset groups, please consult the latest documentation or community discussions for additional information.

@Scout What is the best place to build and teardown a cluster used for several assets in a workflow?

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