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Title
# ask-ai
a

Alejandro A

02/15/2024, 5:38 PM
I have an asset that reads multiple files from s3, and when its completed writes the asset to s3 again, I am using S3IOManager, is there a way to not necessarily materialize an asset? I am fine with it reading the files everytime instead of materializing it when the asset runs
s

Scout

02/15/2024, 5:39 PM
In Dagster, the concept of materialization is closely tied to how assets are managed and stored. By default, when an asset is computed, it is materialized, meaning that the result of the computation is persisted in a way that can be retrieved later. This is where I/O managers come into play, as they are responsible for handling the storage and retrieval of asset data. The
S3IOManager
you mentioned is designed to pickle and store the output of an asset to an S3 bucket. However, if you do not want to materialize an asset, you could potentially customize the behavior of your I/O manager. The documentation provides examples of how to use I/O managers with assets, including how to use the
S3PickleIOManager
for storing assets in an S3 bucket. However, it does not explicitly cover the scenario where you want to avoid materializing an asset altogether. If you want to read files from S3 and perform computations without persisting the results back to S3, you could consider the following approaches: 1. Custom I/O Manager: You could create a custom I/O manager that overrides the behavior of the
handle_output
method to perform a no-op (i.e., do nothing) instead of writing to S3. This way, when the asset runs, it won't persist its output. 2. Asset Without I/O Manager: You could define an asset without specifying an I/O manager. This would mean that the asset's computation runs, but no output is managed or stored by Dagster. However, this approach might not be ideal if you have downstream assets that depend on the output. 3. In-Memory Computation: If the asset's output is only needed during the pipeline execution and not for future runs, you could perform the computation in memory and pass the data directly to downstream computations without involving an I/O manager. It's important to note that if you have downstream assets that depend on the output of the asset in question, not materializing the asset could lead to inefficiencies, as the computation would need to be repeated every time any downstream asset runs. If you need further guidance on how to implement a custom I/O manager or other specific solutions, please refer to the Dagster documentation or consider reaching out to the community for examples and best practices.
a

Alejandro A

02/15/2024, 5:40 PM
@Scout I just want it to not write the output of the asset to S3
s

Scout

02/15/2024, 5:40 PM
If you want your asset to perform computations and read from S3 but not write the output back to S3, you can use the
output_required
parameter in your asset definition to conditionally skip materialization. When
output_required
is set to
False
, and your asset function does not yield an
Output
object, no asset materialization event will be created, and the I/O manager will not be invoked to store the output. Here's an example from the documentation on how to define an asset that may not always materialize:
Copy code
python from dagster import Output, asset  @asset(output_required=False) def may_not_materialize():     # Perform computations here     # To simulate an asset that may not always materialize.     if some_condition:         yield Output(result)
In this example,
some_condition
is a placeholder for whatever logic you want to use to determine whether to materialize the asset. If you never want to materialize the asset, you can simply omit the
yield Output(result)
line, and the asset will perform its computations without triggering the I/O manager to store the output. Remember that if you have downstream assets that depend on this asset's output, they will not run when the output is not materialized. If you want downstream assets to run and access the data, you'll need to ensure that the data is passed along in a way that does not involve materialization to S3, such as through in-memory data passing or using a different mechanism to store and retrieve the data.
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