How do I access the resource config of an io manager within a asset

To access the resource configuration of an IO manager within an asset, you can define a custom IO manager class that inherits from

ConfigurableIOManager

. This class can have attributes that represent the configuration parameters you need. During the execution of the asset's input or output methods, you can access these configuration parameters through the

context

parameter provided to the

load_input

and

handle_output

methods of your IO manager. Here's an example from the documentation that illustrates how you might define a custom IO manager with a configurable

root_path

attribute:

python from dagster import AssetKey, OutputContext, InputContext, ConfigurableIOManager  class MyIOManager(ConfigurableIOManager):     root_path: str      def _get_path(self, asset_key: AssetKey) -> str:         return self.root_path + "/".join(asset_key.path)      def handle_output(self, context: OutputContext, obj):         write_csv(self._get_path(context.asset_key), obj)      def load_input(self, context: InputContext):         return read_csv(self._get_path(context.asset_key))

In this example,

root_path

is a configuration parameter for the

MyIOManager

class. When you define your assets, you can attach this IO manager to them, and it will use the

root_path

specified in the configuration to determine where to read from or write to. To provide the configuration for this IO manager, you would include it in the

resources

section of your run configuration when you launch a job that materializes the asset. The configuration would look something like this:

yaml resources:   io_manager:     config:       root_path: "/path/to/directory"

This configuration would then be accessible within the

MyIOManager

instance during the asset computation, allowing you to use the

root_path

to construct file paths for reading and writing data. Remember that the exact way you define and access the configuration may vary depending on your specific use case and the version of Dagster you are using. Always refer to the latest documentation for the most accurate and up-to-date guidance.

@Scout that's not what I meant. I want to access the configuration of any resource from within an operation/observable function. Is this possible?

Yes, it is possible to access the configuration of a resource from within an operation (op) or an observable function in Dagster. When you define an op, you can specify a resource as a parameter to the op function. This allows the op to access the resource and its configuration during execution. Here's an example from the documentation that shows how to access a resource's configuration within an op:

python from dagster import op, job, resource, Resources  # Define a resource with configuration @resource(config_schema={"config_param": str}) def my_resource(context):     return context.resource_config["config_param"]  # Define an op that uses the resource @op(required_resource_keys={"my_resource"}) def my_op(context):     # Access the resource configuration     resource_config = context.resources.my_resource     <http://context.log.info|context.log.info>(f"The resource config is: {resource_config}")  # Define a job that includes the resource @job(resource_defs={"my_resource": my_resource}) def my_job():     my_op()  # Example run configuration to provide to the job run_config = {     "resources": {         "my_resource": {             "config": {                 "config_param": "my_value"             }         }     } }

In this example, the

my_resource

resource has a configuration schema that expects a

config_param

. The

my_op

op specifies that it requires a resource with the key

my_resource

. Within the body of

my_op

, you can access the resource's configuration using

context.resources.my_resource

. When you execute the

my_job

job, you would provide the

run_config

that includes the configuration for

my_resource

. The op

my_op

will then be able to access the configuration value

"my_value"

for

config_param

during its execution. For observable functions, the process is similar. You would access the resource and its configuration through the context provided to the function. Please note that the exact syntax and methods may vary depending on the version of Dagster you are using, so it's important to consult the latest documentation for the most accurate information.

@Scout let me rephrase, how do I access the instantiates settings of an IO manager from within an observable function?

As of my knowledge cutoff in early 2023, accessing the instantiated settings of an IO manager directly from within an observable function (such as an op) is not a typical pattern encouraged by Dagster's design. IO managers in Dagster are meant to abstract away the details of how data is read and written, allowing ops to focus on computation rather than data management. However, if you need to access certain configurations or settings from within an op, you would typically pass these as resources or use context attributes that are available within the op's execution context. The IO manager's configuration is usually not directly accessible from an op because it's meant to be used within the IO manager's

handle_output

and

load_input

methods, which are called by the Dagster framework to handle data loading and storage. If you have a use case where you need to access the IO manager's settings from within an op, you might consider the following approaches: 1. Pass the necessary configuration as a resource: Define a separate resource that holds the configuration you need and include it in the

required_resource_keys

for your op. This way, you can access the configuration directly from the op's context. 2. Use custom logic in your IO manager: If the configuration affects how data is loaded or saved, you might encapsulate that logic within the IO manager itself, rather than trying to access the IO manager's settings from an op. 3. Refactor your design: If you find yourself needing to access IO manager settings from within an op, it might be a sign that you need to refactor your design. Consider whether the logic that requires these settings can be moved into the IO manager or into a separate resource. 4. Accessing context in IO manager methods: If you need to make decisions based on the IO manager's settings within its

handle_output

or

load_input

methods, you can access the

resource_config

from the

context

parameter that is passed to these methods. If none of these approaches meet your needs and you have a strong reason to access the IO manager's settings from within an op, you might need to consider a more unconventional approach, such as using global variables or a singleton pattern to store and access the configuration. However, these approaches are not recommended as they can lead to less maintainable and more error-prone code. It's also possible that newer versions of Dagster beyond my knowledge cutoff may introduce new features or patterns that could change this answer. Therefore, it's always a good idea to check the latest Dagster documentation or reach out to the Dagster community for the most current best practices.