# ask-ai

Alex Berndt

02/29/2024, 8:40 AM
How can I use value resources to ensure my pipelines write to different locations when developing locally vs when running in production?


02/29/2024, 8:40 AM
To ensure your pipelines write to different locations when developing locally versus when running in production, you can utilize resources that are configured differently based on the environment. Resources in Dagster are logical units that encapsulate external services or systems, such as databases, storage, or API clients. By configuring these resources with environment-specific settings, you can control where your pipelines write data without changing the code of your assets or jobs. Here's a step-by-step guide based on the documentation: 1. Define Reusable Resources: Create resources that encapsulate the logic for writing to different locations. For example, you might have a resource for a database connection or a file system storage. 2. Use the
: Use the
method to pre-define resource configurations for different environments. This allows you to set up different credentials, connection strings, or file paths for local development and production environments. 3. Dynamic Environment Configuration: In your repository definition, dynamically select the appropriate resource configuration based on the environment. You can use environment variables or other mechanisms to determine the current environment and select the corresponding resource configuration. 4. Bind Resources to Assets: When defining your assets, bind them to the appropriate resource keys. This ensures that the assets will use the resources you've configured for the environment they're running in. 5. Environment Variables in Deployment: If you're using Dagster Cloud or deploying with Kubernetes, you can take advantage of environment variables to determine the current environment and configure your resources accordingly. Here's a simplified example of how you might structure your code: ```python from dagster import asset, repository, ResourceDefinition, configured # Define a resource for writing data class DataWriter: def __init__(self, write_location): self.write_location = write_location def write(self, data): # Logic to write data to the specified location pass # Create a local development configuration of the resource local_data_writer = configured(DataWriter)({"write_location": "local/path"}) # Create a production configuration of the resource prod_data_writer = configured(DataWriter)({"write_location": "production/path"}) # Define an asset that uses the data writer resource @asset(required_resource_keys={"data_writer"}) def my_asset(context): data = "some data" context.resources.data_writer.write(data) # Define a repository that selects the appropriate resource configuration def get_current_env(): # Logic to determine the current environment pass @repository def my_repos
itory(): resources_by_env = { "local": {"data_writer": local_data_writer}, "prod": {"data_writer": prod_data_writer}, } return [my_asset.with_resources(resources_by_env[get_current_env()])] ``` In this example, the
resource is configured differently for local development and production. The
asset uses this resource to write data, and the
function dynamically selects the appropriate resource configuration based on the current environment. Remember to adjust the example to fit your specific use case and environment detection logic. This approach allows you to maintain a single codebase that behaves differently based on the environment it's running in, as recommended by the documentation.