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#ask-ai
Title
# ask-ai
m

Mikael Frosthage

02/06/2024, 4:55 PM
I'm using the S3PickleIOManager. Is it possible to control the name of the file?
s

Scout

02/06/2024, 4:55 PM
Yes, it is possible to control the name of the file when using the
S3PickleIOManager
. The
S3PickleIOManager
assigns each op output to a unique filepath containing the run ID, step key, and output name. For assets, it assigns each asset to a single filesystem path, at
<base_dir>/<asset_key>
. If the asset key has multiple components, the final component is used as the name of the file, and the preceding components as parent directories under the base_dir. Here's an example from the documentation on how to use the `S3PickleIOManager`:
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python from dagster import asset, Definitions from dagster_aws.s3 import S3PickleIOManager, S3Resource  @asset def asset1():     # create df ...     return df  @asset def asset2(asset1):     return asset1[:5]  defs = Definitions(     assets=[asset1, asset2],     resources={         "io_manager": S3PickleIOManager(             s3_resource=S3Resource(),             s3_bucket="my-cool-bucket",             s3_prefix="my-cool-prefix",         )     } )
In this example, the
S3PickleIOManager
is configured with a specific S3 bucket and prefix. The assets
asset1
and
asset2
will be stored in the specified S3 bucket with the prefix
my-cool-prefix
. If you need to customize the file path further, you might consider subclassing the
S3PickleIOManager
and overriding the methods responsible for generating the file paths. This approach is suggested in a discussion on GitHub where a user wanted to save files with specific metadata using the S3 IO Manager. The user was able to subclass and create a custom IO Manager that met their requirements. Keep in mind that the exact file path and name will depend on the implementation of the IO Manager and the asset key structure you define. If you need to customize the behavior beyond what is provided by default, creating a subclass and overriding the necessary methods would be the way to go.
m

Mikael Frosthage

02/07/2024, 1:09 PM
@Scout I meant to control it on a more granular level what the files is supposed to be called. I want generate a filename containing the current date. so basically a new filename for each day
s

Scout

02/07/2024, 1:09 PM
To generate a filename containing the current date for each output, you would typically need to customize the IOManager to include the logic for naming files based on the current date. However, the
S3PickleIOManager
does not provide a direct way to customize filenames with dynamic content such as the current date out of the box. One approach to achieve this is to create a custom IOManager that inherits from
S3PickleIOManager
or another suitable base class and override the
_get_path
method to incorporate the current date into the filename. Here's a conceptual example of how you might implement such a custom IOManager:
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python from dagster_aws.s3.io_manager import S3PickleIOManager from datetime import datetime  class DailyS3PickleIOManager(S3PickleIOManager):     def _get_path(self, context):         # Generate the current date string         date_str = datetime.now().strftime("%Y-%m-%d")         # Construct the path with the current date         return f"{super()._get_path(context)}/{date_str}.pkl"  # Then, in your Dagster setup, you would use your custom IOManager: from dagster import resource  @resource def daily_s3_pickle_io_manager(init_context):     return DailyS3PickleIOManager(         s3_bucket=init_context.resource_config["s3_bucket"],         s3_prefix=init_context.resource_config["s3_prefix"],         s3_client=init_context.resources.s3,     )  # And in your pipeline definition, you would specify your custom IOManager: from dagster import pipeline  @pipeline(     mode_defs=[         ModeDefinition(             resource_defs={                 "io_manager": daily_s3_pickle_io_manager,                 "s3": s3_resource,             }         )     ] ) def my_pipeline():     # define your pipeline here
Please note that this is a conceptual example and may require adjustments to fit your specific use case. You would need to ensure that the
_get_path
method constructs the S3 path according to your requirements, including the correct handling of the context to access the necessary metadata for path construction. Remember to test your custom IOManager thoroughly to ensure that it behaves as expected and that the generated file paths are correct.
m

Mikael Frosthage

02/07/2024, 1:28 PM
@Scout are you sure that is correct. I think pipeline is depcrecated, furthermore I don't think S3PickleIOManager have function called getpath
s

Scout

02/07/2024, 1:28 PM
You are correct that the
pipeline
decorator is deprecated in favor of the
@job
decorator in recent versions of Dagster, and I apologize for the oversight. Additionally, the
S3PickleIOManager
does not have a method called
_get_path
. Instead, you would need to override the methods responsible for generating the file paths. To customize the file path with a date, you would need to subclass the
IOManager
and implement the logic for path generation within the
handle_output
and
load_input
methods. Here's a revised conceptual example using the
@io_manager
decorator and the
@job
decorator:
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python from dagster import io_manager, OutputContext, IOManager from dagster_aws.s3 import s3_resource from datetime import datetime  class DailyS3IOManager(IOManager):     def __init__(self, s3_bucket, s3_prefix, s3_client):         self.s3_bucket = s3_bucket         self.s3_prefix = s3_prefix         self.s3_client = s3_client      def handle_output(self, context: OutputContext, obj):         # Generate the current date string         date_str = datetime.now().strftime("%Y-%m-%d")         # Construct the S3 key with the current date         s3_key = f"{self.s3_prefix}/{context.asset_key.path[-1]}/{date_str}.pkl"         # Use the S3 client to upload the object to S3         # ...      def load_input(self, context):         # Logic to retrieve and return the data from S3         # ...  @io_manager def daily_s3_io_manager(init_context):     return DailyS3IOManager(         s3_bucket=init_context.resource_config["s3_bucket"],         s3_prefix=init_context.resource_config["s3_prefix"],         s3_client=init_context.resources.s3,     )  # And in your job definition, you would specify your custom IOManager: from dagster import job  @job(     resource_defs={         "io_manager": daily_s3_io_manager,         "s3": s3_resource,     } ) def my_job():     # define your job here
In this example,
DailyS3IOManager
is a custom IOManager that generates an S3 key that includes the current date when handling outputs. The
handle_output
method is responsible for uploading the object to S3, and the
load_input
method would contain the logic to download the object from S3. Please ensure that you implement the S3 upload and download logic within the
handle_output
and
load_input
methods, respectively. This example is a starting point and will need to be fleshed out with the actual logic for interacting with S3. Again, I apologize for any confusion, and thank you for pointing out the inaccuracies in the initial response.