@Scout How do I create an IOManager instance and add it to definition, when I actually want the values to be picked up when the job is run, the values are picked up from environment variables

To create an

IOManager

instance that picks up values from environment variables when the job is run, you can define a custom

IOManager

class that reads from environment variables within its methods. Here's a step-by-step guide based on the documentation: 1. Define a custom

IOManager

class by subclassing

IOManager

and implementing the

handle_output

and

load_input

methods. Within these methods, you can use Python's

os

module to read environment variables. 2. Use the

@io_manager

decorator to create an

IOManagerDefinition

from your custom class. This decorator allows you to define an

IOManager

that can be attached to a job or asset. 3. Attach the

IOManagerDefinition

to your job's

resource_defs

argument to make it available to the job when it runs. Here's an example of how you might implement this, based on the documentation:

python import os from dagster import IOManager, io_manager, job, op  class EnvVarIOManager(IOManager):     def handle_output(self, context, obj):         # Use environment variables to determine where to write the output         output_path = os.getenv('MY_OUTPUT_PATH')         # Implement logic to write 'obj' to the output path      def load_input(self, context):         # Use environment variables to determine where to read the input from         input_path = os.getenv('MY_INPUT_PATH')         # Implement logic to read data from the input path and return it  @io_manager def env_var_io_manager():     return EnvVarIOManager()  @op def my_op():     # Your op logic here     pass  @job(resource_defs={"io_manager": env_var_io_manager}) def my_job():     my_op()  # When you run the job, the custom IOManager will use the environment variables # to determine input/output paths my_job.execute_in_process()

In this example,

MY_OUTPUT_PATH

and

MY_INPUT_PATH

are the names of the environment variables that your

IOManager

will read at runtime to determine where to store and load data. You would set these environment variables in your execution environment before running the job. Remember to replace the placeholder logic for writing and reading data with your actual implementation, and ensure that the environment variables are set in your job's execution environment.

@Scout is there any significance of EnvVar here ?

Yes,

EnvVar

has significance in the context of Dagster when you want to access environment variables as part of a Dagster configuration.

EnvVar

is a special syntax provided by Dagster that allows you to specify that a configuration parameter should be sourced from an environment variable at runtime. This is particularly useful for handling secrets or environment-specific configurations. When you use

EnvVar

, the value of the environment variable is retrieved at the time the job or pipeline is executed, and it won't be visible in the Dagster UI. This helps to keep sensitive information like passwords or API keys hidden. Here's an example of how you might use

EnvVar

in a resource definition:

python from dagster import resource, Field, String, EnvVar  @resource({     "api_key": Field(String, is_required=False, default_value=EnvVar("MY_API_KEY_ENV")) }) def my_resource(context):     api_key = context.resource_config["api_key"]     # Use the api_key in your resource logic     ...

In this example,

MY_API_KEY_ENV

is the name of the environment variable that contains the API key. When the resource is initialized, Dagster will automatically fetch the value of

MY_API_KEY_ENV

from the environment and pass it to the resource. Using

EnvVar

in this way allows you to define configurations that are sensitive or environment-specific without hardcoding them into your codebase, which is a best practice for maintaining security and flexibility in your applications.