Can you give me an example of a resource that let me copy files from a public aws s3 bucket?

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:

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.

@Scout How can I inherit from

dagster_aws.s3.S3Resource

. To create a resource that has the bucket and other utility functions preconfigured?

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:

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:

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.

@Scout, any clue why I may be getting this error:

AttributeError: module 'dagster_aws' has no attribute 's3'

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:

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:

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!

@Scout When implementing the code above I get

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?

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:

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.

@Scout, what would be example code of the MyCustomS3Client class?

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:

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.