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Title
# ask-ai
f

Fernando Cagua

02/27/2024, 6:48 PM
Can you give me an example of a resource that let me copy files from a public aws s3 bucket?
s

Scout

02/27/2024, 6:48 PM
Certainly! To copy files from a public AWS S3 bucket, you can use the
s3_resource
provided by the
dagster-aws
library. This resource allows you to interact with S3 and perform operations such as copying files. Here's an example of how you might define and use an
s3_resource
to copy files from a public S3 bucket:
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python from dagster_aws.s3.resources import s3_resource from dagster import job, op  @op(required_resource_keys={"s3"}) def copy_files_from_public_s3_bucket(context):     s3 = context.resources.s3     source_bucket = "source-bucket-name"     source_key = "source-file-key"     destination_bucket = "destination-bucket-name"     destination_key = "destination-file-key"          # Copy file from the source bucket to the destination bucket     s3.copy_object(         Bucket=destination_bucket,         CopySource={'Bucket': source_bucket, 'Key': source_key},         Key=destination_key     )  @job(resource_defs={"s3": s3_resource}) def my_copy_job():     copy_files_from_public_s3_bucket()
In this example, the
copy_files_from_public_s3_bucket
op requires the
s3
resource, which is provided to the job via the
resource_defs
argument. The
s3_resource
is configured to interact with AWS S3, and you can use its
copy_object
method to copy a file from one bucket to another. Please note that this example assumes that the S3 bucket is public and that the AWS credentials available to your environment have the necessary permissions to read from the source bucket and write to the destination bucket. If the bucket is not public, you would need to configure the
s3_resource
with the appropriate credentials. Remember to replace
"source-bucket-name"
,
"source-file-key"
,
"destination-bucket-name"
, and
"destination-file-key"
with the actual names of your buckets and keys. For more detailed information on how to configure and use the
s3_resource
, please refer to the documentation for the
dagster-aws
library.
f

Fernando Cagua

02/27/2024, 7:09 PM
@Scout How can I inherit from
dagster_aws.s3.S3Resource
. To create a resource that has the bucket and other utility functions preconfigured?
s

Scout

02/27/2024, 7:09 PM
To create a custom resource that inherits from
dagster_aws.s3.S3Resource
and has the bucket and other utility functions preconfigured, you can subclass
S3Resource
and override its
__init__
method to set default values or add additional utility functions. Here's an example of how you might do this:
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python from dagster_aws.s3.resources import S3Resource  class MyCustomS3Resource(S3Resource):     def __init__(self, s3_bucket, *args, **kwargs):         self.s3_bucket = s3_bucket         super().__init__(*args, **kwargs)      def upload_file(self, file_path, key):         """         Upload a file to the preconfigured S3 bucket.         """         self.s3.meta.client.upload_file(file_path, self.s3_bucket, key)      def download_file(self, key, file_path):         """         Download a file from the preconfigured S3 bucket.         """         self.s3.meta.client.download_file(self.s3_bucket, key, file_path)      # Add more utility functions as needed
In this subclass,
MyCustomS3Resource
, you can add methods like
upload_file
and
download_file
that use the preconfigured
s3_bucket
. When you initialize this resource, you would provide the bucket name and any other necessary configuration. To use this custom resource in a Dagster job, you would define it in your job's
resource_defs
like so:
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python from dagster import job, op  @op(required_resource_keys={"custom_s3"}) def my_op(context):     # Use the custom S3 resource to upload a file     context.resources.custom_s3.upload_file("/path/to/my/file", "my-key")  @job(resource_defs={"custom_s3": MyCustomS3Resource(s3_bucket="my-preconfigured-bucket")}) def my_job():     my_op()
In this job,
my_op
uses the
custom_s3
resource to upload a file to the preconfigured S3 bucket. The
MyCustomS3Resource
is instantiated with the bucket name "my-preconfigured-bucket" and passed to the job's
resource_defs
. Please ensure that you have the necessary permissions and that your AWS credentials are configured correctly to access the specified S3 bucket.
f

Fernando Cagua

02/27/2024, 7:12 PM
@Scout, any clue why I may be getting this error:
AttributeError: module 'dagster_aws' has no attribute 's3'
s

Scout

02/27/2024, 7:13 PM
The error
AttributeError: module 'dagster_aws' has no attribute 's3'
suggests that the Python interpreter is unable to find an
s3
attribute within the
dagster_aws
module. This could be due to several reasons: 1. Incorrect Installation: The
dagster_aws
package might not be installed correctly in your environment. Ensure that you have installed the
dagster-aws
library using pip:
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shell
   pip install dagster-aws
2. Incorrect Import: You might be trying to import the
s3
module incorrectly. Make sure you are using the correct import statement. For example, to import the
S3Resource
, you should use:
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python
   from dagster_aws.s3 import S3Resource
3. Outdated Version: If you have an outdated version of
dagster-aws
, it might not include the
s3
module. Ensure that you have the latest version of the
dagster-aws
package that includes the
s3
module. 4. Module Shadowing: There might be a file or a folder named
dagster_aws
in your project directory or Python path that is shadowing the actual
dagster-aws
library. Check your directory and remove or rename any such files or folders. 5. Virtual Environment Issues: If you are using a virtual environment, make sure that you have activated the correct environment where
dagster-aws
is installed and that you are running your code within that environment. To debug this issue, you can perform the following steps: - Verify that
dagster-aws
is installed in your current Python environment by running
pip list
and checking for
dagster-aws
in the list of installed packages. - Check the version of
dagster-aws
to ensure it's up to date. - Try importing
dagster_aws
and inspecting its attributes using
dir(dagster_aws)
to see if
s3
is listed. - Ensure there are no naming conflicts with files or directories in your project. If you've checked all the above and the issue persists, you might want to look for any open issues or discussions in the Dagster GitHub repository or community forums that could provide more context or report a bug if it seems to be an issue with the library itself. Dagster code is failing to load with: "A... button AttributeError: 'FieldInfo' object has n... button AttributeError: 'InputMappingNode' objec... button S3PickleIOManager fails on dagster 1.5.5... button Thank you for your feedback!
f

Fernando Cagua

02/27/2024, 7:18 PM
@Scout When implementing the code above I get
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DagsterInvalidInvocationError: 'GFSResource' is a Pythonic resource and does not support item assignment, as it inherits from 'pydantic.BaseModel' with frozen=True. If trying to maintain state on this resource, consider building a separate, stateful client class, and provide a method on the resource to construct and return the stateful client.
How can I fix it?
s

Scout

02/27/2024, 7:18 PM
The
DagsterInvalidInvocationError
you're encountering indicates that you're trying to assign a value to an instance of a resource that is immutable due to its inheritance from
pydantic.BaseModel
with
frozen=True
. In Pydantic, a model with
frozen=True
means that the model is immutable and does not allow item assignment after initialization. To fix this issue, you should avoid trying to assign values to the resource after it has been constructed. Instead, you can build a separate, stateful client class that handles the state changes, and provide a method on the resource to construct and return this stateful client. Here's an example of how you might refactor your resource to use a stateful client class:
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python from dagster import resource, Field from dagster_aws.s3 import S3Resource from my_custom_client import MyCustomS3Client  # This is a hypothetical client class you create  class MyCustomS3Resource(S3Resource):     def __init__(self, s3_bucket, *args, **kwargs):         self.s3_bucket = s3_bucket         super().__init__(*args, **kwargs)         self._client = None  # Initialize the client as None      def get_client(self):         # Lazily create the client if it doesn't exist yet         if self._client is None:             self._client = MyCustomS3Client(self.s3, self.s3_bucket)         return self._client  # Define the resource configuration @resource({     "s3_bucket": Field(str, is_required=True, description="The S3 bucket name"), }) def my_custom_s3_resource(init_context):     return MyCustomS3Resource(s3_bucket=init_context.resource_config["s3_bucket"])  # Usage in a job @op(required_resource_keys={"custom_s3"}) def my_op(context):     client = context.resources.custom_s3.get_client()     # Use the client to perform operations     client.upload_file("/path/to/my/file", "my-key")  @job(resource_defs={"custom_s3": my_custom_s3_resource}) def my_job():     my_op()
In this example,
MyCustomS3Resource
has a method
get_client
that lazily initializes and returns an instance of
MyCustomS3Client
, a hypothetical client class that you would create to handle the stateful interactions with S3. This way, you avoid mutating the resource directly and instead work with a mutable client that can handle state changes. Make sure to replace
my_custom_client
and
MyCustomS3Client
with the actual module and class names that you have implemented for your S3 client.
f

Fernando Cagua

02/27/2024, 7:32 PM
@Scout, what would be example code of the MyCustomS3Client class?
s

Scout

02/27/2024, 7:32 PM
The
MyCustomS3Client
class would be a separate, stateful client class that you create to handle interactions with S3. This class would encapsulate the logic for uploading, downloading, and other S3 operations, using the
boto3
client provided by the
S3Resource
. Below is an example of what the
MyCustomS3Client
class might look like:
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python import boto3  class MyCustomS3Client:     def __init__(self, s3_resource, bucket_name):         # Assume s3_resource is an instance of boto3 S3 client         self.s3_client = s3_resource.s3         self.bucket_name = bucket_name      def upload_file(self, file_path, key):         """         Upload a file to the S3 bucket.         """         self.s3_client.upload_file(file_path, self.bucket_name, key)      def download_file(self, key, file_path):         """         Download a file from the S3 bucket.         """         self.s3_client.download_file(self.bucket_name, key, file_path)      def list_files(self, prefix=''):         """         List files in the S3 bucket with the given prefix.         """         response = self.s3_client.list_objects_v2(Bucket=self.bucket_name, Prefix=prefix)         return [obj['Key'] for obj in response.get('Contents', [])]      # Add more methods as needed for your use case
In this example,
MyCustomS3Client
is initialized with an S3 client from the
S3Resource
and a bucket name. It provides methods for uploading and downloading files, as well as listing files in the bucket. You can extend this class with additional methods to cover other S3 operations you need. Please note that this is a simplified example, and in a production environment, you would want to handle exceptions and edge cases, such as handling large file uploads with multipart upload, paginating results for listing files, and so on. You would use this client class in conjunction with your custom resource class, as shown in the previous example. The resource class would be responsible for creating instances of this client class when needed.
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