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Title
# ask-ai
k

Kaushik Tadikonda

02/22/2024, 6:49 PM
I'm new to dagster and would like some guidance! I have some pyspark based assets that need to communicate with a databricks cluster. Before these assets get materialized, I need to ensure my databricks cluster is started or else they will fail to materialize. I like the declarative framework of using assets to define dependencies. Would making
start_cluster
operation an asset and defining dependencies using
deps
in downstream pyspark based assets be the correct way to achieve this?
s

Scout

02/22/2024, 6:49 PM
In Dagster, assets represent a unit of computation that produces a piece of data, and they can have dependencies on other assets, which ensures that computations are executed in the correct order. If you have a
start_cluster
operation that needs to be performed before your PySpark-based assets can be materialized, you can indeed model this operation as an asset and define dependencies using the
deps
parameter in your downstream assets. However, it's important to note that while assets can represent operations like starting a cluster, the actual implementation of starting a Databricks cluster might not fit neatly into the asset model if it involves external side effects or state changes that are not captured in the asset's outputs. Assets are typically used to represent data transformations and computations that produce data artifacts. If you still want to model the
start_cluster
operation as an asset, you would define it as an asset and then specify it as a dependency for your PySpark-based assets. Here's a simplified example based on the documentation:
Copy code
python from dagster import asset, AssetsDefinition, AssetOut, AssetIn  @asset def start_cluster():     # Logic to start the Databricks cluster     # This could involve using a resource that interacts with the Databricks API     # to start the cluster and ensure it's ready for computations     pass  @asset(deps={'start_cluster': AssetIn()}) def pyspark_asset(start_cluster):     # Your PySpark computation that depends on the Databricks cluster being started     pass  # Define your assets in a repository assets_defs = AssetsDefinition(     assets=[start_cluster, pyspark_asset], )
In this example,
pyspark_asset
declares a dependency on
start_cluster
using the
deps
parameter. When you materialize
pyspark_asset
, Dagster will ensure that
start_cluster
is executed first. Keep in mind that managing external resources like a Databricks cluster might require a more dynamic approach, such as using sensors or hooks to check the cluster's status and start it if necessary before running your assets. This could be more aligned with operational practices, as it allows for more control over the cluster lifecycle outside of the asset materialization process. For more complex scenarios or if you need to manage the lifecycle of external resources like a Databricks cluster, you might want to look into Dagster's resources, sensors, and hooks, which can provide more flexibility and control. The documentation provides more details on these concepts and how to use them effectively.
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