@Scout based on whether a variable == 's3' or 'fs', write an example that sets the IOManager for a Definitions.py to be a dagster maintained IOManagerDefinition of either FileSystem/PickledFileSystem or PickledS3IOManager

Based on the variable value, you can conditionally configure the

Definitions

object to use either the

FilesystemIOManager

or the

S3PickleIOManager

. Here's an example of how you might set up your `Definitions.py`:

python from dagster import Definitions, asset from dagster_aws.s3 import S3PickleIOManager, S3Resource from dagster import fs_io_manager  # Assume 'storage_system' is the variable that can be 's3' or 'fs' storage_system = 's3'  # or 'fs'  # Define your assets @asset def my_asset():     # Your asset logic here     return "data"  # Configure resources based on the storage_system variable if storage_system == 's3':     # Configure S3 resources and IO manager     resources = {         "io_manager": S3PickleIOManager(             s3_resource=S3Resource(), s3_bucket="my-bucket"         ),     } elif storage_system == 'fs':     # Use the filesystem IO manager     resources = {         "io_manager": fs_io_manager,     } else:     raise ValueError("Invalid storage system specified")  # Create the Definitions object with the configured resources defs = Definitions(     assets=[my_asset],     resources=resources, )

In this example, we first define an asset called

my_asset

. Then, based on the value of the

storage_system

variable, we conditionally configure the resources dictionary to use either the

S3PickleIOManager

or the

fs_io_manager

. Finally, we create a

Definitions

object with the assets and the configured resources. Please note that you need to replace

"my-bucket"

with the actual name of your S3 bucket if you're using S3. Additionally, ensure that you have the necessary permissions and configurations set up for accessing the S3 bucket or the filesystem as required.

@Scout Is

fs_io_manager

not using the deprecated syntax, and should I not use an IOManager class instead?

The

fs_io_manager

is a built-in IO manager provided by Dagster for managing file system-based inputs and outputs. It is not deprecated and is the correct way to attach a file system IO manager to your pipeline or asset as a resource. The example provided earlier uses the

fs_io_manager

correctly for the file system case. However, if you are referring to the transition from the older

storage

and

intermediate_storage

configurations to the newer IO manager-based approach, then yes, you should use the IO manager API. The

fs_io_manager

is part of this newer approach and is an instance of

IOManager

that is configured to work with the local filesystem. Here's an updated example that uses the

fs_io_manager

correctly:

python from dagster import asset, fs_io_manager, ResourceDefinition from dagster_aws.s3 import s3_pickle_io_manager  # Assume 'storage_system' is the variable that can be 's3' or 'fs' storage_system = 's3'  # or 'fs'  # Define your assets @asset def my_asset():     # Your asset logic here     return "data"  # Configure resources based on the storage_system variable if storage_system == 's3':     # Configure S3 IO manager     resources = {         "io_manager": s3_pickle_io_manager.configured({'s3_bucket': 'my-bucket'}),     } elif storage_system == 'fs':     # Use the filesystem IO manager     resources = {         "io_manager": fs_io_manager,     } else:     raise ValueError("Invalid storage system specified")  # Create a resource definition with the configured resources resource_defs = {key: ResourceDefinition.hardcoded_resource(value) for key, value in resources.items()}

In this example, we use the

configured

method to configure the

s3_pickle_io_manager

with the S3 bucket name. The

fs_io_manager

is used as is for the file system case. The

resource_defs

dictionary is then created with the configured resources, which can be attached to your pipeline or asset definitions. Please ensure that you have the necessary dependencies installed for using S3, such as

dagster-aws

, and that you have correctly configured your AWS credentials.

@Scout Can you confirm that I should not use

FileSystemIOManager

or

PickledObjectFilesystemIOManager

?

Yes, I can confirm that you should not use

FileSystemIOManager

or

PickledObjectFilesystemIOManager

. Instead, you should use the

fs_io_manager

, which is the built-in IO manager provided by Dagster for managing file system-based inputs and outputs. The

fs_io_manager

is the recommended way to handle file system IO in Dagster, and it is designed to work with the modern asset-based APIs. The

fs_io_manager

is an instance of

IOManager

that is pre-configured to read from and write to the local filesystem, and it handles the serialization and deserialization of objects using Python's pickle module. It is part of Dagster's core library and is maintained to be compatible with the latest versions of Dagster. For S3, you should use the

s3_pickle_io_manager

, which is specifically designed for working with AWS S3 and handles the pickling and unpickling of objects stored in S3 buckets. Here's a brief example of how you would use these IO managers:

python from dagster import asset, fs_io_manager from dagster_aws.s3 import s3_pickle_io_manager  # Assume 'storage_system' is the variable that can be 's3' or 'fs' storage_system = 's3'  # or 'fs'  # Define your assets @asset def my_asset():     # Your asset logic here     return "data"  # Configure resources based on the storage_system variable if storage_system == 's3':     # Configure S3 IO manager     resources = {         "io_manager": s3_pickle_io_manager.configured({'s3_bucket': 'my-bucket'}),     } elif storage_system == 'fs':     # Use the filesystem IO manager     resources = {         "io_manager": fs_io_manager,     } else:     raise ValueError("Invalid storage system specified")  # The resources dictionary can be used to configure your asset or pipeline definitions

In this example, the

resources

dictionary is configured based on the

storage_system

variable, and it can be used to attach the appropriate IO manager to your assets or pipelines. Remember to replace

'my-bucket'

with your actual S3 bucket name and ensure that your AWS credentials are set up correctly if you're using S3.

@Scout How about for s3, can I use

S3PickleIOManager

?

Yes, for S3, you can use the

S3PickleIOManager

. This IO manager is designed for use with AWS S3 and handles the serialization and deserialization of objects using Python's pickle module, storing them in an S3 bucket. Here's an example of how you would configure the

S3PickleIOManager

for use with your assets:

python from dagster import asset, ResourceDefinition from dagster_aws.s3 import S3PickleIOManager, s3_resource  # Define your assets @asset def my_asset():     # Your asset logic here     return "data"  # Configure S3 IO manager s3_pickle_io_manager = S3PickleIOManager(     s3_bucket="my-bucket",     s3_prefix="my-prefix",  # Optional: specify a prefix within the bucket     s3_resource=s3_resource,  # Pass the s3_resource configured with your AWS credentials )  # Create a resource definition with the S3PickleIOManager resource_defs = {     "io_manager": ResourceDefinition.hardcoded_resource(s3_pickle_io_manager), }  # The resource_defs dictionary can be used to configure your asset or pipeline definitions

In this example, the

s3_pickle_io_manager

is configured with a bucket name and an optional prefix. The

s3_resource

should be configured with your AWS credentials and any other necessary settings for accessing S3. Please ensure that you have the necessary dependencies installed, such as

dagster-aws

, and that your AWS credentials are correctly configured for accessing the S3 bucket.

@Scout Can you write me an example that uses the

FilesystemIOManager

, where I don't have to provide the

base_dir

Certainly! The

FilesystemIOManager

is a built-in IO manager in Dagster that stores and retrieves values from the local filesystem. If you don't provide a

base_dir

, it will use the default local filesystem path to store the outputs. Here's an example of how you can use the

FilesystemIOManager

without specifying a `base_dir`:

python from dagster import asset, IOManager, io_manager from dagster.core.storage.files_manager import FilesystemFileHandle, LocalFileHandle  class FilesystemIOManager(IOManager):     def handle_output(self, context, obj):         # Here you would implement the logic to write your object to the filesystem         # For example, if `obj` is a pandas DataFrame:         # obj.to_csv(context.get_output_path())          # If you're just writing a string to a file, it could be as simple as:         with open(context.get_output_path(), "w") as file:             file.write(obj)      def load_input(self, context):         # Here you would implement the logic to read your object from the filesystem         # For example, if you're reading a CSV into a pandas DataFrame:         # return pandas.read_csv(context.upstream_output.get_path())          # If you're just reading a string from a file, it could be as simple as:         with open(context.upstream_output.get_path(), "r") as file:             return file.read()  @io_manager def my_filesystem_io_manager(_):     return FilesystemIOManager()  # Define your assets @asset(io_manager_key="my_filesystem_io_manager") def my_asset():     # Your asset logic here     return "data"  # The my_filesystem_io_manager can be used to configure your asset or pipeline definitions

In this example, we define a custom

FilesystemIOManager

that uses the

context.get_output_path()

method to determine where to write the output. This method will automatically generate a file path based on the asset's name and the run ID. Since we don't specify a

base_dir

, it will default to a directory within Dagster's managed storage. The

my_filesystem_io_manager

is a factory function decorated with

@io_manager

that returns an instance of our custom

FilesystemIOManager

. This function is used to create an IO manager resource that can be associated with our asset

my_asset

using the

io_manager_key

parameter. Please note that the

handle_output

and

load_input

methods should be implemented according to the specific data types you are working with. The examples provided here are for illustrative purposes and show simple read/write operations for string data. If you are working with more complex data types, such as pandas DataFrames, you would need to use the appropriate read/write methods for those types.

@Scout No, I want to use the built in dagster maintained class

FilesystemIOManager

which is found in

dagster/_core/stoage/fs_io_manager.py

I apologize for the confusion. To use the built-in

FilesystemIOManager

provided by Dagster, you don't need to define a custom class or provide a

base_dir

. The

FilesystemIOManager

will use the default local filesystem path to store outputs if no

base_dir

is specified. Here's an example of how to use the built-in

FilesystemIOManager

with your assets:

python from dagster import asset, fs_io_manager  # Define your assets @asset(io_manager_key="fs_io_manager") def my_asset():     # Your asset logic here     return "data"  # The fs_io_manager can be used to configure your asset or pipeline definitions

In this example, we use the

fs_io_manager

directly with the

@asset

decorator by specifying the

io_manager_key

. The

fs_io_manager

is a pre-configured instance of

FilesystemIOManager

that is included with Dagster, and it will manage the storage of asset outputs on the local filesystem. When you run this asset within a Dagster job, the output will be stored in the local filesystem at a path determined by Dagster's execution context. You don't need to provide any additional configuration for the

FilesystemIOManager

unless you want to customize the storage behavior, such as specifying a different base directory.

@Scout I want to Explicitly set "io_manager" resource to the default "fs_io_manager" one in my Definitions.py

To explicitly set the "io_manager" resource to the default

fs_io_manager

in your

Definitions.py

, you can define a

Definitions

object and specify the

fs_io_manager

as the resource for the "io_manager" key. Here's how you can do it:

python from dagster import Definitions, fs_io_manager, asset  # Define your assets @asset def my_asset():     # Your asset logic here     return "data"  # Define the resources, explicitly setting the "io_manager" to the default fs_io_manager resources = {     "io_manager": fs_io_manager }  # Create the Definitions object with the configured resources defs = Definitions(     assets=[my_asset],     resources=resources, )  # Now, defs contains your asset definitions and the default filesystem IO manager

In this example, we create a

resources

dictionary that maps the "io_manager" key to the

fs_io_manager

. Then, we pass this dictionary to the

Definitions

object along with the list of assets. This setup ensures that the default filesystem IO manager is used for all assets unless otherwise specified.

@Scout Do I need to use

fs_io_manager.configured

or will dagster configure the IO manager based on

$DAGSTER_HOME

and what is specified in

dagster.yaml

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