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What is the best way to dynamically generate asset...
# ask-ai
t
What is the best way to dynamically generate assets within an asset factory when new config files are added to a folder?
s
The best way to dynamically generate assets within an asset factory when new config files are added to a folder is to use the 
@multi_asset
decorator provided by Dagster. This decorator allows you to define a factory function that can create multiple assets dynamically based on the input configuration. Here's a step-by-step approach based on the documentation: 1. Define the Asset Factory Function: Create a function that reads the new config files from the folder and generates a list of asset definitions. Each asset definition should correspond to a config file. 2. Use the 
@multi_asset
Decorator: Apply the 
@multi_asset
decorator to the factory function. This decorator allows you to define multiple assets that can be created dynamically. You can specify the 
name
, 
group_name
, and 
outs
for each asset, where 
outs
is a dictionary of 
AssetOut
objects with a 
key_prefix
that can be used to differentiate the assets. 3. Implement Asset Generation Logic: Inside the factory function, implement the logic to read each config file and create an asset for it. You can use the 
context
and any resources you need to interact with the config files and generate the data for the assets. 4. Return the Assets: Return a list of the dynamically generated assets from the factory function. Each element in the list should be a callable that represents an asset. Here is a simplified example based on the documentation:
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python
from dagster import multi_asset, AssetOut, OpExecutionContext

# Define the asset factory function
def dlt_asset_factory(config_folder_path):
    multi_assets = []
    
    # Logic to read new config files from the folder
    config_files = read_config_files(config_folder_path)
    
    for config_file in config_files:
        # Define a dynamic asset for each config file
        @multi_asset(
            name=config_file.name,
            group_name="dynamic_assets",
            outs={
                "result": AssetOut(key_prefix=[f'generated_{config_file.name}'])
            }
        )
        def generated_asset(context: OpExecutionContext):
            # Logic to process the config file and generate data
            data = process_config_file(config_file)
            return data
        
        multi_assets.append(generated_asset)
    
    return multi_assets

# Call the asset factory function with the path to the config folder
dlt_assets = dlt_asset_factory("/path/to/config/folder")
In this example, 
read_config_files
is a hypothetical function that reads new config files from the specified folder, and 
process_config_file
is a hypothetical function that processes each config file to generate the data for the asset. Please note that the actual implementation details, such as how to read and process co
nfig files, will depend on your specific use case and environment. The provided example is based on the structure and usage of 
@multi_asset
as described in the documentation.
t
@Scout will the code location need to be re-loaded if additional config files are added to the directory? Or, will dagster automatically pick up new configs and generate the additional assets?
s
In Dagster, code locations need to be reloaded to pick up changes to the code, including the addition of new config files that define assets. Dagster does not automatically detect changes to the filesystem and reload code locations or regenerate assets based on those changes. If you are running Dagster in a development environment using 
dagster dev
, you can manually reload code locations using the "Reload" button in the Dagster UI, which is available on the Code Locations tab. This will cause Dagster to restart the code server and reload your code, including any new config files that have been added. For production deployments, where you might be running code servers in separate containers or processes using the 
dagster api grpc
command, you would typically need to restart the process or container to pick up changes. However, as of Dagster version 1.3.6, an experimental 
dagster code-server start
command was introduced, which can be used instead of 
dagster api grpc
. When using this command, pressing "Reload" from the Code Locations tab in the Dagster UI will restart the subprocess, allowing you to reload your definitions without needing to restart the whole server. In summary, to dynamically generate assets when new config files are added to a folder, you would need to: 1. Ensure that your asset factory function or asset generation logic is designed to detect and process new config files. 2. Manually trigger a reload of the code location in the Dagster UI or restart the relevant process/container to pick up the new config files and generate the corresponding assets. It's important to note that if you are relying on filesystem changes to trigger asset generation, you may need to implement additional automation outside of Dagster to monitor the directory for changes and initiate the reload or restart process. This could involve custom scripts, cron jobs, or other automation tools that fit into your deployment workflow.
t
@Scout I am getting the following error using the provided asset factory code with Dagster 1.6.3. Any ideas? 
dagster._core.errors.DagsterInvariantViolationError: Expected Tuple annotation for multiple outputs, but received non-tuple annotation.
s
The error message you're encountering, 
dagster._core.errors.DagsterInvariantViolationError: Expected Tuple annotation for multiple outputs, but received non-tuple annotation
, suggests that Dagster is expecting a tuple type annotation for an operation (op) that is supposed to yield multiple outputs, but it's receiving a different type annotation instead. This error typically occurs when you define an op with multiple outputs but the return type annotation does not match the expected format. When using the 
@op
decorator with multiple outputs, you should return a tuple where each element corresponds to an output defined in the 
out
dictionary of the op. Here's an example of how you might define an op with multiple outputs:
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python from dagster import op, Out, Output  @op(out={"output1": Out(), "output2": Out()}) def my_op():     # Some logic here     return Output(1, "output1"), Output(2, "output2")
In the case of a 
@multi_asset
decorator, you would need to ensure that the return type of the decorated function matches the expected output structure. If you are using a 
@multi_asset
decorator and dynamically generating assets, make sure that the return type of your asset generation function is consistent with the outputs you are defining. If you are still encountering issues, please provide the exact code snippet that is causing the error, and I can help you debug it further. It's important to match the expected output structure with the type annotations and the actual outputs being returned or yielded by your asset factory function.
t
@Scout what could the following error mean upon? 
ImportError: cannot import name 'AssetConditionEvaluation' from 'dagster._core.definitions.asset_condition' (/opt/homebrew/anaconda3/envs/watttime/lib/python3.11/site-packages/dagster/_core/definitions/asset_condition/__init__.py)
s
The error message you're seeing indicates that the Python interpreter is unable to import the name 
AssetConditionEvaluation
from the module 
dagster._core.definitions.asset_condition
. This could be due to several reasons: 1. Version Mismatch: The 
AssetConditionEvaluation
class or function may not exist in the version of Dagster you are using. It's possible that this is a feature that was added in a later version than the one you have installed, or it could have been removed or renamed in the version you are using. 2. Incorrect Import Path: The import path specified in the error message may be incorrect, or the structure of the Dagster package may have changed in the version you are using. This could lead to an 
ImportError
if the interpreter is looking for the name in the wrong place. 3. Installation Issue: There could be an issue with your Dagster installation. The package may not have been installed correctly, or there could be a corrupted file that is preventing the import from working as expected. To troubleshoot this issue, you can take the following steps: - Check the Dagster Version: Ensure that you are using a version of Dagster that includes the 
AssetConditionEvaluation
class or function. You can check the release notes or documentation for the version you are using to see if this feature is included. - Verify the Import Path: Check the documentation to verify that the import path you are using is correct. If the structure of the package has changed, you may need to update your import statements to match the new structure. - Reinstall Dagster: If you suspect there may be an issue with your installation, try reinstalling Dagster. You can do this by running 
pip install --upgrade dagster
to ensure you have the latest version, or 
pip install dagster==<version>
to install a specific version. - Check for Deprecations or Renaming: It's possible that 
AssetConditionEvaluation
has been deprecated or renamed in the version of Dagster you are using. Check the documentation or release notes for any deprecation warnings or renaming announcements. If you continue to experience issues after taking these steps, you may want to reach out to the Dagster community or support channels for further assistance. They may be able to provide more specific guidance based on the version of Dagster you are using and the context of how you are trying to use the 
AssetConditionEvaluation
class or function.
t
@Scout how would I trigger the reload of a code location using a cron job outside of dagster? I am using 
dagster code-server start
s
To trigger the reload of a code location using a cron job outside of Dagster, you would need to interact with the Dagster instance to initiate the reload. Since you are using 
dagster code-server start
, you can take advantage of the fact that this command allows you to reload your code by pressing "Reload" from the Code Locations tab in the Dagster UI. However, to automate this process with a cron job, you would need to use an approach that does not rely on manual interaction with the UI. One way to do this is to send a request to the Dagster GraphQL API to trigger a reload. Here's a general approach you could take: 1. Set up a GraphQL client: Write a small script that uses a GraphQL client to send a mutation to the Dagster GraphQL endpoint to reload the code location. The mutation you would use is 
reloadRepositoryLocation
. 2. Create a cron job: Schedule this script to run at the desired intervals using cron. Here's an example of what the script might look like in Python using the 
requests
library:
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python import requests  # Replace these variables with your actual values dagster_graphql_endpoint = "<http://localhost:3000/graphql>" repository_location_name = "my_repository_location"  # GraphQL mutation for reloading a repository location mutation = """ mutation($repositoryLocationName: String!) {   reloadRepositoryLocation(repositoryLocationName: $repositoryLocationName) {     __typename     ... on RepositoryLocation {       id       name     }     ... on ReloadNotSupported {       message     }     ... on RepositoryLocationLoadFailure {       error {         message       }     }   } } """  # Send the request to the Dagster GraphQL endpoint response = <http://requests.post|requests.post>(     dagster_graphql_endpoint,     json={         "query": mutation,         "variables": {"repositoryLocationName": repository_location_name},     }, )  # Check the response if response.ok:     print("Reload request sent successfully.") else:     print(f"Failed to send reload request: {response.text}")
To schedule this script with cron, you would add an entry to your crontab file. For example, to reload the code location every hour, you might add the following line to your crontab:
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0 * * * * /usr/bin/python /path/to/your/script.py
Make sure to replace 
/usr/bin/python
with the path to the Python interpreter you want to use, and 
/path/to/your/script.py
with the path to the script you created. Please note that this approach assumes that you have the necessary authentication in place to communicate with the Dagster GraphQL endpoint, and that your Dagster instance is configured to allow reloading via the GraphQL API. If your Dagster instance requires authentication, you will need to modify the script to include the appropriate authentication headers.
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