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Yes, you can provide arguments to assets in Dagster, and you can execute a module containing assets from the terminal while including arguments for configuration. When you define assets in Dagster, you can specify inputs and configuration options that act as arguments to the asset computation. These configurations can be provided when you materialize the assets. To run a module containing assets from the terminal, you can use the Dagster CLI. For example, you can use the 
dagster asset materialize
command to materialize assets defined in a module. If you need to provide configuration arguments, you can pass a YAML file containing the configuration using the 
--config
flag. Here's an example of how you might execute a job from the command line with a configuration file:
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shell dagster job execute --config my_config.yaml
In this example, 
my_config.yaml
is a YAML file that contains the necessary configuration for the job or assets you want to materialize. The documentation also mentions that Dagster validates any provided run config against the corresponding Pydantic model and will abort execution with a 
DagsterInvalidConfigError
or Pydantic 
ValidationError
if validation fails. For more complex scenarios where you want to pass custom data back from an external process for use in the orchestration code, you can use custom messages to create an I/O managed output that is returned from the asset. This involves using 
report_custom_message
in the external code to send messages that are JSON serializable. Remember that the exact syntax and capabilities may vary depending on the version of Dagster you are using, so it's always a good idea to consult the latest documentation for the most accurate and up-to-date information.
To run a module and a YAML file from the same module using Dagster, you can use the Dagster CLI commands to specify the module and the YAML file containing your run configuration. First, ensure that your module contains the necessary Dagster definitions, such as pipelines, jobs, or assets. Then, you can use the following command to execute a job defined in your module with a YAML file for configuration:
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shell dagster job execute -m your_module_name -c path/to/config.yaml
In this command: - 
-m your_module_name
specifies the module containing your Dagster job. - 
-c path/to/config.yaml
specifies the path to the YAML file containing the run configuration for the job. If your YAML file is also located within the same module, you can use a relative path from the module to the YAML file. For example, if your module is named 
my_dagster_module
and your YAML file is located in a subdirectory named 
config
within the module, you could run:
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shell dagster job execute -m my_dagster_module -c my_dagster_module/config/my_run_config.yaml
Alternatively, if you have a workspace set up with a 
workspace.yaml
file that specifies the module and the YAML file, you can simply run the 
dagster
command from the directory containing the 
workspace.yaml
file, and Dagster will use the configuration specified there. Please note that the exact command may vary depending on the version of Dagster you are using and the structure of your project. Always refer to the latest Dagster documentation for the most accurate and up-to-date information on CLI usage and command options.
The error message you're seeing indicates that the 
-w
option is not recognized by the 
dagster job execute
command. The 
-w
flag is typically used to specify a workspace file when using other Dagster CLI commands, but it is not a valid option for the 
dagster job execute
command. To execute a job using the Dagster CLI, you should specify the module containing your job with the 
-m
or 
--module-name
option and the configuration file with the 
-c
or 
--config
option. The workspace file is not directly used with the 
dagster job execute
command. Here's the correct way to execute a job from a module with a configuration file:
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shell dagster job execute -m dagster_etl -c path/to/config.yaml
In this command: - 
-m dagster_etl
specifies the Python module named 
dagster_etl
that contains your job. - 
-c path/to/config.yaml
specifies the path to the YAML file containing the configuration for the job. If you have a 
workspace.yaml
file and want to use it to execute jobs, you would typically use it with the 
dagster
command to start the Dagster development environment, which allows you to execute jobs from the UI or trigger other workspace-related CLI commands. If you want to use the workspace file to execute a job from the CLI, you might use a command like 
dagster job launch
instead, which can take a workspace file as an argument:
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shell dagster job launch -w workspace.yaml
Please ensure that you are using the correct command for your use case and that you are following the latest documentation for the version of Dagster you are using. The documentation will provide the most accurate information on the available CLI options and how to use them.
To run a Dagster job from a module with a YAML configuration file, you can use the 
dagster job execute
command in the terminal. You need to specify the module where your job is defined using the 
-m
or 
--module-name
option and provide the path to your YAML configuration file using the 
-c
or 
--config
option. Here's an example of how you would run the command in the terminal:
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shell dagster job execute -m your_module_name -c path/to/your_config.yaml
In this command: - Replace 
your_module_name
with the name of the Python module that contains your Dagster job. - Replace 
path/to/your_config.yaml
with the path to your YAML file that contains the configuration for the job. This command will execute the job defined in the specified module with the configuration provided in the YAML file. If you have a 
workspace.yaml
file that specifies the location of your jobs and repositories, you do not need to use it directly with the 
dagster job execute
command. The 
workspace.yaml
file is used by the Dagster instance to understand the structure of your code locations and is typically used when running Dagit or other workspace-related commands. Make sure that your current working directory in the terminal is set to the root of your Dagster project or the location where your Python module and YAML configuration file are accessible. If your module and YAML file are in different directories, you may need to adjust the paths accordingly.
The error message you're seeing indicates that the 
-w
option is not recognized by the 
dagster job execute
command. This is because the 
-w
flag is not a valid option for the 
dagster job execute
command. Instead, the 
-w
flag is typically used with other Dagster CLI commands that require specifying a workspace file, such as 
dagster instance migrate
. For the 
dagster job execute
command, you should only use the 
-m
or 
--module-name
option to specify the module containing your job and the 
-c
or 
--config
option to specify the path to your YAML configuration file. Here's the correct usage for the 
dagster job execute
command:
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shell dagster job execute -m dagster_etl -c /home/voziq/dagster_etl/dagster_etl/config.yaml
In this command: - 
-m dagster_etl
specifies the Python module named 
dagster_etl
that contains your job. - 
-c /home/voziq/dagster_etl/dagster_etl/config.yaml
specifies the path to the YAML file containing the configuration for the job. Please ensure that the path you provide with the 
-c
option points to the YAML file that contains the run configuration for your job, not the 
workspace.yaml
file. The 
workspace.yaml
file is used to configure the Dagster workspace, which is a different concept from the run configuration for a specific job. If you continue to encounter issues, make sure that your YAML configuration file is correctly formatted and contains the necessary configuration for your job. Additionally, ensure that the module name and path to the configuration file are correct and accessible from your current working directory in the terminal.
The error message you're encountering indicates that there are unexpected and missing configuration entries in your YAML file for the job you're trying to execute. Specifically, the errors are related to the 
inputs
configuration for the 
build_sling_asset_csv
and 
build_sling_asset_sql
operations (or assets). Here's a breakdown of the errors: 1. Unexpected 
inputs
entry for 
build_sling_asset_csv
. 2. Missing 
config
entry for 
build_sling_asset_csv
. 3. Unexpected 
inputs
entry for 
build_sling_asset_sql
. 4. Missing 
config
entry for 
build_sling_asset_sql
. The expected configuration structure for each operation seems to require a 
config
block with specific keys such as 
mode
and 
sling_resource_key
, and it does not expect an 
inputs
block at the root level of each operation's configuration. To resolve these errors, you'll need to adjust your YAML configuration file to match the expected schema for the job. This typically involves providing the required 
config
entries for each operation and ensuring that any 
inputs
are specified correctly according to the job's definition. Here's an example of what the corrected configuration might look like, based on the error messages:
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yaml ops:   build_sling_asset_csv:     config:       mode: "the_mode_for_this_op"       sling_resource_key: "the_resource_key_for_this_op"       # Include other required config entries as needed   build_sling_asset_sql:     config:       mode: "the_mode_for_this_op"       sling_resource_key: "the_resource_key_for_this_op"       # Include other required config entries as needed
Please note that the actual values for 
mode
and 
sling_resource_key
should be replaced with the appropriate values for your specific job configuration. Additionally, if there are other required config entries, you'll need to include them as well. If your job definition expects inputs to be provided via configuration, you'll need to ensure that they are specified in the correct location and format as defined by your job's schema. This might involve nesting them under the 
config
block or providing them in a different way, depending on how your job and its operations or assets are defined. Make sure to consult the documentation for the specific assets or operations you're using to understand the required configuration schema. If you're using custom assets or operations, you may need to refer to the code where they are defined to understand the expected configuration structure.
The error message you're seeing is a traceback that typically occurs when an exception is raised during the execution of a command. However, the provided traceback does not include the actual exception message that would indicate the root cause of the error. The traceback only shows the call stack up to the point where the 
invoke
method of the Click library is called, which is part of the command-line interface handling in Dagster. To troubleshoot this issue, you would need to look further down the traceback to find the specific exception message. This message will usually provide more information about what went wrong and can help guide you towards a solution. Here are some general steps you can take to debug this issue: 1. Complete Traceback: Look for the complete traceback message in your terminal or log files. The actual error message and exception type will be at the end of the traceback. 2. Check Configuration: Ensure that your YAML configuration file is correctly formatted and matches the expected schema for the job you are trying to execute. 3. Validate Job Definitions: Verify that the job and asset definitions in your Python module (
dagster_etl
) are correct and do not contain any errors. 4. Dagster Version: Make sure that you are using a compatible version of Dagster for your project. If you recently upgraded Dagster, check the release notes for any breaking changes or migration steps. 5. Environment Issues: Ensure that your Python environment is set up correctly and that all necessary dependencies are installed. Sometimes, issues can arise if there are conflicting package versions or missing dependencies. 6. Permissions: Check if there are any file system permissions issues that might prevent Dagster from reading configuration files or writing logs. 7. Run with Debugging: You can run the Dagster command with additional verbosity or debugging flags to get more detailed output, which might help identify the issue. If you're still unable to resolve the issue after taking these steps, you might consider reaching out to the Dagster community or support channels with the complete traceback and details about your job and environment for further assistance.
The error message you're encountering indicates that the 
inputs
configuration entries for 
build_sling_asset_csv
and 
build_sling_asset_sql
are unexpected according to the schema expected by Dagster for the job you're trying to execute. The expected configuration schema does not include an 
inputs
block at the level where you've placed it. In Dagster, the configuration for assets and ops within a job is typically specified under a 
config
block. However, the way inputs are provided to assets or ops can vary depending on how they are defined in your Dagster code. Here are some steps to resolve the issue: 1. Review Asset Definitions: Check your asset definitions in the Python code to see how inputs should be provided. If your assets are expecting inputs, ensure that they are defined to accept them in the code. 2. Correct Configuration Structure: Adjust your YAML configuration file to match the expected schema. If your assets are not defined to accept inputs via the configuration file, you should not include an 
inputs
block in the YAML. 3. Use Resources: If the inputs represent resources or data that your assets need to access, you may need to define these as resources in your Dagster code and reference them in the configuration under the 
resources
block instead of 
inputs
. 4. Check Documentation: Refer to the documentation for the specific version of Dagster you are using to ensure that you are following the correct configuration structure for assets and ops. 5. Schema Validation: Use Dagster's schema validation tools to check your configuration against the expected schema. This can help identify any discrepancies. 6. Dagster Community: If you're still having trouble, consider reaching out to the Dagster community with details about your asset definitions and the full configuration you're trying to use. They may be able to provide more specific guidance based on your use case. Remember to remove the 
inputs
block from your YAML configuration if it's not expected by your asset definitions, and follow the correct schema as defined by your Dagster assets. If inputs are required, ensure they are provided in the manner expected by the asset definitions in your code.
Based on the asset definitions you've provided and the YAML configuration, it looks like you're trying to configure assets with specific inputs and settings. However, the error message you received indicates that the 
inputs
are not expected in the configuration at the level where you've placed them. In the asset definitions, you're using 
context.op_config["inputs"]
to access the inputs, which suggests that you expect 
inputs
to be part of the op's configuration. However, in Dagster, 
inputs
are typically not part of the 
config
schema for an asset or op. Instead, they are defined as arguments to the asset or op function and are provided by upstream assets or resources. To resolve this issue, you should modify your asset definitions to accept 
source_stream
and 
target_object
as arguments to the asset functions, rather than trying to pull them from the 
op_config
. Here's an example of how you might redefine your assets:
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python from dagster_embedded_elt.sling import build_sling_asset, SlingMode from dagster import AssetSpec, asset  @asset def build_sling_asset_csv(source_stream: str, target_object: str):     # Your logic to build the sling asset using the provided source_stream and target_object     ...  @asset def build_sling_asset_sql(source_stream: str, target_object: str):     # Your logic to build the sling asset using the provided source_stream and target_object     ...
Then, you would provide the 
source_stream
and 
target_object
values directly when you materialize the assets, rather than through the YAML configuration file. If you need to pass configuration to your assets via a YAML file, you should only include keys that are part of the 
config_schema
you defined in the 
@asset
decorator. Any additional inputs that are not part of the 
config_schema
should be provided as arguments to the asset functions or through other means, such as resources or hardcoded values within the asset functions. Please adjust your asset definitions and YAML configuration accordingly, and ensure that you are following the correct usage patterns for Dagster assets and configurations as described in the documentation. If you continue to encounter issues, you may want to reach out to the Dagster community for further assistance.
If your Dagster job has run successfully and you're looking to find the output, there are several places you can look, depending on how your job is configured and how you're running Dagster: 1. Dagit UI: If you're using Dagit, Dagster's web-based UI, you can navigate to the Runs tab to see a list of job runs. Clicking on a specific run will show you the details of that run, including logs and materializations. If your assets or ops produce materializations, you should be able to see them here. 2. Console Output: If you executed the job from the command line using the Dagster CLI, the output would be printed to the console. This includes logs and any other information that your job is configured to output. 3. Event Logs: Dagster records structured event logs for each run. If you're running Dagster programmatically, you can query these logs using Dagster's Python API to retrieve information about the run. 4. Filesystem or Object Store: If your job writes output to a filesystem or an object store (like S3), you should check the location where your job is configured to write its output. This could be specified in your job's IOManager or within the logic of your ops/assets. 5. Database: If your job writes to a database, you can query the database to see the results of your job. 6. Dagster Instance: If you have set up a Dagster instance, it will store run records and other metadata. You can interact with the instance programmatically to retrieve information about job runs. 7. Custom Output Handlers: If you have custom output handlers or hooks defined in your job, they may handle outputs in a specific way, such as sending notifications or writing to a custom backend. To view the results in the Dagit UI, make sure that the DAGSTER_HOME environment variable is set to the directory where your Dagster instance is configured, and that the Dagit server is running and pointing to the same instance. If you're not seeing the expected output in any of these places, you may want to double-check your job's configuration to ensure that it's set up to produce and store outputs in the way you intend. Additionally, reviewing the logs from the job run can provide clues as to where outputs might be located or if there were any issues with output generation.
To view the results of a successfully executed Dagster job and materialize the assets, you can follow these steps: 1. Check the Dagit UI: If you have Dagit running, navigate to the Dagit UI in your web browser. By default, Dagit runs on 
<http://localhost:3000>
. In the Dagit UI, you can go to the "Runs" tab to see a list of job executions. Click on a specific run to view the details, including logs and materializations. 2. Materialize Assets: If you want to materialize assets from the command line, you can use the 
dagster asset materialize
command. This command requires you to specify the assets you want to materialize. If you want to materialize all assets, you can use the 
-a
or 
--all
flag. Here's an example command to materialize all assets:
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shell dagster asset materialize -a
If you want to materialize specific assets, you can specify them by name:
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shell dagster asset materialize -a asset_name1 asset_name2
3. Check Output Locations: If your assets or ops are configured to write outputs to specific locations (such as files, databases, or object stores), check those locations to see the results of your job. 4. Review Event Logs: You can also review the event logs for the run to see detailed information about what happened during execution. These logs can be accessed through the Dagit UI or programmatically via the Dagster Python API. 5. Start Dagit (if not running): If Dagit is not already running and you want to use it to view the results, you can start it by running the following command in your terminal:
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shell dagit -w /path/to/workspace.yaml
Replace 
/path/to/workspace.yaml
with the actual path to your workspace file. Once Dagit is running, you can navigate to 
<http://localhost:3000>
in your web browser to access the UI. Please note that the 
dagster job execute
command you mentioned is used to execute a job, not to view its results. The 
-c
flag should point to a YAML file containing the run configuration for the job, not the 
workspace.yaml
file. The 
workspace.yaml
file is used to configure the Dagster workspace for the Dagit UI and other workspace-related commands. If you have successfully executed the job and want to materialize the assets, you should use the 
dagster asset materialize
command as described above. If you need to view the results and logs of the job execution, you should use the Dagit UI.
If you want to run Dagit on a specific port because the default port (3000) is already in use, you can specify a different port using the 
-p
or 
--port
flag when starting Dagit. Here's how you can do it:
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shell dagit -w /path/to/workspace.yaml -p 3001
In this example, Dagit will run on port 3001. You can replace 
3001
with any port number that is free on your system. If you want Dagit to always run on this port, you can create an alias in your shell configuration file (such as 
.bashrc
or 
.zshrc
) to start Dagit with this command, or you can create a small script that runs this command and use the script to start Dagit. Additionally, if you're running Dagit as a service or using a process manager, you can configure the service or process manager to always use the specified port. Once you've started Dagit on the specified port, you can access it by navigating to 
<http://localhost:3001>
(or whichever port you chose) in your web browser.
To run a Dagster job from a module with a YAML configuration file and ensure that Dagit uses a specific port, you can follow these steps: 1. Execute the Job: Use the 
dagster job execute
command to run your job with the YAML configuration file. Here's how you can do it:
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shell dagster job execute -m your_module_name -c path/to/your_config.yaml
Replace 
your_module_name
with the name of the Python module that contains your Dagster job, and 
path/to/your_config.yaml
with the path to your YAML file that contains the configuration for the job. 2. Start Dagit: To start Dagit on a specific port, use the 
-p
or 
--port
flag. For example, if you want to run Dagit on port 3001, you can start it with the following command:
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shell dagit -w path/to/workspace.yaml -p 3001
Replace 
path/to/workspace.yaml
with the path to your workspace file. The workspace file tells Dagit where to find your jobs and repositories. 3. Access Dagit: Once Dagit is running, you can access it by navigating to 
<http://localhost:3001>
in your web browser (or whichever port you specified). Please note that the 
dagster job execute
command is separate from the Dagit UI and does not require specifying a port. The port specification is only relevant when starting Dagit to view the UI and interact with your jobs and runs. If you want to ensure that Dagit always uses the same port when you start it, you can create a script that includes the 
dagit
command with the desired port and workspace file path, or you can set an alias in your shell configuration file.
The message you're seeing indicates that the 
dagit
command is not found in your system's PATH, which suggests that Dagster may not be installed correctly in your current environment, or the PATH is not set up to include the location of the 
dagit
executable. Here are the steps you can take to resolve this issue: 1. Check if Dagster is Installed: Ensure that Dagster and Dagit are installed in your current Python environment. You can check if 
dagit
is installed by running:
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shell pip show dagit
If it's not installed, you can install it using pip:
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shell pip install dagit
2. Activate Virtual Environment: If you are using a virtual environment, make sure it is activated. You can activate a virtual environment with the following command:
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shell source /path/to/your/virtualenv/bin/activate
Replace 
/path/to/your/virtualenv
with the actual path to your virtual environment. 3. Check PATH: If Dagster and Dagit are installed but the command is not recognized, it might be due to the PATH not being set correctly. You can add the path to the 
dagit
executable to your PATH environment variable. The exact location of the 
dagit
executable depends on where Python packages are installed in your environment. 4. Install Dagster and Dagit Globally: If you prefer to install Dagster and Dagit globally (not recommended for Python development best practices), you can do so using:
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shell sudo pip install dagster dagit
However, using 
sudo
with 
pip
is generally discouraged because it can lead to permission issues and conflicts with system packages. 5. Use Correct Python Environment: Ensure that you are using the correct Python environment where Dagster and Dagit are installed. If you have multiple Python versions or environments, you might be using one that doesn't have Dagster and Dagit installed. After ensuring that Dagster and Dagit are installed and available in your PATH, try running the 
dagit
command again with the specified workspace file and port:
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shell dagit -w /home/voziq/dagster_etl/dagster_etl/workspace.yaml -p 3001
If you continue to face issues, please verify your installation and environment setup, and ensure that you are following the installation instructions from the documentation.
The error message you're encountering indicates that there is an unexpected 
ops
entry at the root of your 
workspace.yaml
file. The 
workspace.yaml
file is used to configure the Dagster workspace, and it should not contain job or asset configurations like 
ops
. The 
workspace.yaml
file should specify how to load your repository or repositories, which can include information about Python files, Python modules, or Python packages that contain your Dagster definitions, or it can point to a running gRPC server that serves your repository. Here's an example of what a 
workspace.yaml
file might look like:
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yaml load_from:   - python_file:       relative_path: "path/to/your_file.py"       location_name: "my_location"
Or, if you're loading from a module:
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yaml load_from:   - python_module:       module_name: "your_module_name"       location_name: "my_location"
The configuration you've posted appears to be for a job or asset, not for a workspace. This configuration should be placed in a separate YAML file that is used when executing a job, not in the 
workspace.yaml
file. To fix the error, you should: 1. Remove the 
ops
configuration from your 
workspace.yaml
file. 2. Ensure that your 
workspace.yaml
file contains the correct structure to load your repository. 3. Place your job or asset configuration (
ops
) in a separate YAML file, which you will reference when executing a job using the 
dagster job execute
command with the 
-c
flag. For example, to execute a job with your configuration, you would run:
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shell dagster job execute -m your_module_name -c path/to/your_job_config.yaml
Make sure that 
path/to/your_job_config.yaml
contains the job or asset configuration you've posted, and 
your_module_name
is the name of the Python module where your job is defined. The 
workspace.yaml
file should only contain the workspace configuration and should be used when starting Dagit or other workspace-related commands.
To materialize assets and ensure that Dagit uses a specific port such as 3001, you can start Dagit with the desired port and then use the Dagit UI to materialize the assets. Here's how you can do it: 1. Start Dagit on a Specific Port: Use the following command to start Dagit, specifying the workspace file and the port number:
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shell dagit -w /path/to/workspace.yaml -p 3001
Replace 
/path/to/workspace.yaml
with the actual path to your workspace file. 2. Access Dagit UI: Once Dagit is running, open your web browser and navigate to 
<http://localhost:3001>
. This will open the Dagit UI where you can interact with your Dagster instance. 3. Materialize Assets: In the Dagit UI, navigate to the Assets tab. Here, you can see a list of your assets. You can materialize individual assets by clicking on them and then clicking the "Materialize" button, or you can materialize all assets by clicking the "Materialize all" button. If you prefer to materialize assets from the command line without using the Dagit UI, you can use the 
dagster asset materialize
command. However, this command does not require specifying a port because it does not use Dagit. Here's how you can materialize assets from the command line:
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shell dagster asset materialize -a
The 
-a
flag indicates that you want to materialize all assets. If you want to materialize specific assets, you can list them by name:
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shell dagster asset materialize asset_name1 asset_name2
Remember, the port number is only relevant when starting Dagit to use the UI. The 
dagster asset materialize
command runs independently of Dagit and does not require a port.
The error message you're encountering indicates that there is an unexpected 
ops
entry at the root of your 
workspace.yaml
file. The 
workspace.yaml
file is intended to specify the loading of repositories and not the configuration of individual ops or jobs. Here's what you should do to resolve this issue: 1. Correct the 
workspace.yaml
File: The 
workspace.yaml
file should only contain information about how to load your repositories. It should not contain job or op configurations. Here's an example of what a typical 
workspace.yaml
might look like:
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yaml load_from:   - python_file:       relative_path: "path/to/your_file.py"       location_name: "my_location"
2. Remove the 
ops
Entry: You should remove the 
ops
entry from the 
workspace.yaml
file. If you have job or op configurations, they should be placed in a separate YAML file that you pass to the 
dagster job execute
command when running a job. 3. Start Dagit: Once you have corrected the 
workspace.yaml
file, you can start Dagit with the following command:
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shell dagit -w /path/to/corrected/workspace.yaml -p 3001
Replace 
/path/to/corrected/workspace.yaml
with the actual path to your corrected workspace file. 4. Access Dagit UI: Open your web browser and navigate to 
<http://localhost:3001>
to access the Dagit UI. Remember, the 
workspace.yaml
file is used by Dagit to understand where to find your Dagster repositories and is not used for job execution configuration. Job execution configuration should be provided in a separate YAML file when running a job with the 
dagster job execute
command.
To run a Dagster job using a YAML configuration file and a Python module at the same time, you will use the 
dagster job execute
command. This command allows you to specify the module where your job is defined and the YAML file that contains the configuration for the job. Here's how you can execute the job from the command line:
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shell dagster job execute -m your_module_name -c path/to/your_config.yaml
In this command: - Replace 
your_module_name
with the name of the Python module that contains your Dagster job. - Replace 
path/to/your_config.yaml
with the path to your YAML file that contains the configuration for the job. This command will execute the job defined in the specified module (
your_module_name
) with the configuration provided in the YAML file (
path/to/your_config.yaml
). Please ensure that your YAML file is correctly formatted and contains the necessary configuration for your job. The module should be accessible in your Python environment, and the YAML file should be located at the specified path. If you encounter any issues, make sure that the module name and the path to the YAML file are correct, and that you are running the command from an environment where Dagster is installed and properly configured.
To run a Dagster job using a YAML configuration file and a Python module, you use the 
dagster job execute
command. However, specifying a port number directly in the 
dagster job execute
command is not applicable because this command does not serve a web interface or require a port to execute a job. If you want to run Dagit, Dagster's web interface, on a specific port to view the results of your job execution or interact with your Dagster instance, you can start Dagit separately using the 
-p
or 
--port
flag to specify the desired port. Here's how you can start Dagit on a specific port:
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shell dagit -w path/to/workspace.yaml -p 3001
Replace 
path/to/workspace.yaml
with the actual path to your workspace file, and 
3001
with the port number you want Dagit to use. Once Dagit is running on the specified port, you can access it through your web browser by navigating to 
<http://localhost:3001>
or the corresponding port number you chose. To summarize, you execute the job and view the results in two separate steps: 1. Execute the Job:
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shell dagster job execute -m your_module_name -c path/to/your_config.yaml
2. Start Dagit on a Specific Port (to view results and interact with the instance):
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shell dagit -w path/to/workspace.yaml -p 3001
Remember, the port number is only relevant for starting Dagit and does not affect the execution of the job itself.
The structure of a YAML file for a Dagster job configuration typically includes the settings for resources, solvers, and inputs required to execute the job. Below is an example of what a YAML configuration file might look like for a Dagster job:
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yaml resources:   my_resource:     config:       param1: "value1"       param2: "value2"  solids:   my_solid:     config:       solid_param: "solid_value"  inputs:   my_input_solid:     inputs:       input_name: "input_value"
In this example: - 
resources
is a section where you define configurations for resources that your job will use. Each resource can have its own 
config
block. - 
solids
(or 
ops
in newer versions of Dagster) is where you configure individual solids (or ops). Each solid can have its own 
config
block. - 
inputs
is where you provide inputs to solids that require them. Each input is specified by the solid's name and the input's name. Please note that the actual structure of your YAML file will depend on the specific configuration schema required by your job, which is defined by the solids/ops, resources, and other components within your Dagster codebase. For a 
workspace.yaml
file, which is used to configure the Dagster workspace, the structure is different. It specifies how to load your repositories and might look like this:
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yaml load_from:   - python_file:       relative_path: "path/to/your_file.py"       location_name: "my_location"
Or, if you're loading from a module:
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yaml load_from:   - python_module:       module_name: "your_module_name"       location_name: "my_location"
In this 
workspace.yaml
example: - 
load_from
is a list of code locations that tell Dagster where to find your job and asset definitions. - 
python_file
and 
python_module
are two ways to specify code locations, either by a file path or a module name, respectively. - 
relative_path
and 
module_name
are used to point to the specific Python file or module. - 
location_name
is an optional human-readable name for the code location. Remember to replace the placeholders with the actual paths, module names, and other relevant information specific to your Dagster project.
The YAML configuration snippet you've provided appears to be intended for configuring specific assets or ops within a Dagster job. However, the structure of the configuration does not match the standard Dagster job configuration format. In Dagster, the configuration for a job typically includes settings for resources and ops (formerly known as solids), and it may look something like this:
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yaml resources:   my_resource:     config:       resource_param: "value"  ops:   my_op:     config:       op_param: "value"
In the context of your provided configuration, it seems like you are trying to configure two assets, 
build_sling_asset_csv
and 
build_sling_asset_sql
, with specific parameters. However, the 
ops
key at the root level is not a standard key in a Dagster job configuration file. Instead, you would typically define the configuration for each op within the 
ops
(or 
solids
in older versions of Dagster) section of the job configuration. Each op would have its own 
config
block where you can specify the parameters required by that op. If 
build_sling_asset_csv
and 
build_sling_asset_sql
are assets defined in your Dagster project, you would not typically include their configuration in the 
workspace.yaml
file or directly in the job configuration file. Instead, you would pass the configuration when you execute the job or materialize the assets. For example, to execute a job with the provided configuration, you might use a command like this:
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shell dagster job execute -m your_module_name -c path/to/your_config.yaml
And the 
your_config.yaml
file would contain the necessary configuration for the job, following the correct schema for your assets. If you are trying to materialize assets and need to pass configuration parameters to them, you would use the 
dagster asset materialize
command and provide the configuration as needed. Please ensure that your YAML configuration file follows the correct schema as defined by your Dagster assets and job. If you are unsure about the correct format, you should refer to the Dagster documentation or the code where your assets are defined to understand the expected configuration structure.
To view the results of a Dagster job execution in Dagit on port 3001, you need to ensure that Dagit is running and that it is configured to use the same Dagster instance as the job you executed. Here are the steps to do this: 1. Set 
DAGSTER_HOME
: Make sure that the 
DAGSTER_HOME
environment variable is set to the directory where your Dagster instance is configured. This is where Dagster stores information about runs, including event logs and run history.
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shell    export DAGSTER_HOME=/path/to/your/dagster_home
Replace 
/path/to/your/dagster_home
with the actual path to your Dagster home directory. 2. Start Dagit: Run Dagit with the 
-p
flag to specify the port number (3001 in your case). Use the 
-w
flag to specify the workspace file if it's not located in the 
DAGSTER_HOME
directory.
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shell    dagit -w path/to/workspace.yaml -p 3001
Replace 
path/to/workspace.yaml
with the actual path to your workspace file. 3. Access Dagit UI: Open a web browser and navigate to 
<http://localhost:3001>
. You should see the Dagit UI, where you can view the results of your job execution. 4. Check Runs: In the Dagit UI, go to the "Runs" tab to see a list of job executions. Click on a specific run to view the details, including logs and materializations. If you followed these steps and still cannot see the results in Dagit, here are some troubleshooting tips: - Ensure that the 
DAGSTER_HOME
environment variable is set correctly in the environment where you are running Dagit. - Check that there are no errors in the terminal where you started Dagit. - Verify that the workspace file (
workspace.yaml
) correctly points to the location of your Dagster repository or repositories. - Make sure that the job execution was successful and that it was run in the same Dagster instance that Dagit is connected to. - If you have multiple Dagster instances or configurations, ensure that you are not mixing them up. By following these steps and tips, you should be able to view the results of your job execution in Dagit on the specified port.
The YAML configuration you've provided appears to be intended for specifying the configuration for assets or ops within a Dagster job. However, the 
dagster dev
command you mentioned is not a standard Dagster CLI command based on the documentation. To run a Dagster job with a YAML configuration file, you would typically use the 
dagster job execute
command, as follows:
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shell dagster job execute -m your_module_name -c path/to/your_config.yaml
In this command: - Replace 
your_module_name
with the name of the Python module that contains your Dagster job. - Replace 
path/to/your_config.yaml
with the path to your YAML file that contains the configuration for the job. The YAML file should be structured according to the requirements of your job's configuration schema. If 
build_sling_asset_csv
and 
build_sling_asset_sql
are assets or ops within your job, and they are defined to accept the configuration as you've specified, then providing this YAML file with the 
-c
flag should work. If you are trying to run a job and view the results in Dagit, you would start Dagit separately using the 
dagit
command with the appropriate workspace file:
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shell dagit -w path/to/workspace.yaml -p desired_port_number
Once Dagit is running, you can navigate to the specified port in your web browser to view the job runs and interact with your Dagster instance. If you are prompted for configuration in the Dagit UI when trying to launch a job, you can provide the YAML configuration there as well. The Dagit UI has a config editor where you can paste your YAML configuration before launching the job. Please ensure that your YAML configuration file is correctly formatted and matches the expected schema for your job. If you encounter any issues, refer to the documentation for the specific version of Dagster you are using to ensure that you are following the correct configuration structure for assets and ops.
If you're encountering an error when running the 
dagit
command after executing a job with the 
dagster job execute
command, the error could be due to several reasons. To help you troubleshoot the issue, I would need more specific information about the error message you're receiving. However, here are some common reasons why you might encounter an error when starting Dagit: 1. Incorrect Workspace File: The 
workspace.yaml
file might be incorrectly configured or might not exist at the specified path. Ensure that the path to 
workspace.yaml
is correct and that the file is properly formatted according to the documentation. 2. Port Already in Use: The port you specified with 
-p desired_port_number
might already be in use by another application. Try using a different port number or check if any other application is using the desired port. 3. Environment Variables: If the 
DAGSTER_HOME
environment variable is not set or is pointing to the wrong directory, Dagit might not be able to access the necessary instance configuration. Make sure 
DAGSTER_HOME
is set to the directory where your Dagster instance is configured. 4. Dagit Installation: There might be an issue with the Dagit installation. Ensure that Dagit is installed correctly in your Python environment. 5. Permissions: There could be file system permissions issues that prevent Dagit from accessing the 
workspace.yaml
file or other necessary files. 6. Dagster Version: Ensure that you are using a compatible version of Dagster and Dagit. If you recently upgraded, check for any breaking changes or migration steps. To provide more targeted assistance, please share the exact error message you're seeing when you attempt to start Dagit. The error message will typically provide clues as to what the problem might be. If the error message mentions a specific issue, such as a problem with the workspace file or a port conflict, addressing that issue should help resolve the error.
m
how did yaml file config ? @Scout
s
In Dagster, YAML configuration files are used to provide runtime configuration for jobs, resources, and assets. The structure of a YAML configuration file will depend on the specific requirements of the job you are running. Here's a general guide on how to configure a YAML file for a Dagster job: 1. Resources: Define any resources that your job requires. Resources are reusable components that provide functionality to ops, such as database connections or compute resources.
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yaml resources:   my_resource:     config:       param1: "value1"       param2: "value2"
2. Ops (or Solids): Configure individual ops (or solids in older versions of Dagster) with the parameters they require to run.
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yaml ops:   my_op:     config:       op_param: "value"
3. Inputs: If your ops require inputs, you can specify them in the configuration file.
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yaml inputs:   my_input_op:     input_name: "input_value"
4. Execution: Specify execution settings if your job requires a specific execution strategy.
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yaml execution:   multiprocess:     config:       max_concurrent: 4
5. Loggers: Configure loggers if you need to customize logging behavior.
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yaml loggers:   console:     config:       log_level: "INFO"
6. Selectors: If you have multiple jobs or repositories, you may need to specify which one you want to run.
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yaml selector:   my_job:     config:       ...
When you run a job with the 
dagster job execute
command, you can specify the path to your YAML configuration file using the 
-c
flag:
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shell dagster job execute -m your_module_name -c path/to/your_config.yaml
Make sure that your YAML file is correctly formatted and matches the expected schema for your job. The schema is defined by the configuration fields you have set up in your Dagster job, resources, and ops. If you're using Dagit, Dagster's web interface, you can also provide the YAML configuration when launching a job. Dagit has a config editor where you can paste your YAML configuration before executing the job. For more detailed information on how to configure your YAML files for Dagster, please refer to the [Dagster documentation](https://docs.dagster.io/) for the specific version you are using, as the configuration schema can vary between versions.
To run a Dagster job with a YAML configuration file, you need to execute the job from the command line using the 
dagster job execute
command, specifying the module where your job is defined and the path to the YAML file that contains the configuration for the job. Here's the command structure:
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shell dagster job execute -m your_module_name -c path/to/your_config.yaml
In this command: - 
-m your_module_name
is the flag and argument where you specify the name of the Python module that contains your Dagster job. - 
-c path/to/your_config.yaml
is the flag and argument where you specify the path to the YAML file that contains the configuration for the job. Make sure that: - The Python module (
your_module_name
) is accessible in your Python environment and contains the definitions for your job, resources, and ops. - The YAML configuration file (
path/to/your_config.yaml
) is correctly formatted according to the schema expected by your job and is located at the specified path. Once you run this command, Dagster will execute the job using the configuration provided in the YAML file. If you want to view the results of the job execution in Dagit, Dagster's web interface, you can start Dagit separately. However, this does not involve running the job itself; it's for monitoring and interacting with your Dagster instance. You can start Dagit using the following command:
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shell dagit -w path/to/workspace.yaml -p desired_port_number
Replace 
path/to/workspace.yaml
with the actual path to your workspace file, and 
desired_port_number
with the port number you want Dagit to use. Once Dagit is running, you can access it through your web browser to view the job runs and interact with your Dagster instance. Remember, the execution of the job and the viewing of its results in Dagit are separate steps. The 
dagster job execute
command runs the job, and the 
dagit
command starts the web interface.
To run a Dagster job using specific Python files and a YAML configuration file, you would typically use the 
dagster job execute
command with the 
-f
or 
--python-file
option to specify the Python file that contains your job definition, and the 
-c
or 
--config
option to specify the YAML configuration file. Here's how you can execute the job from the command line:
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shell dagster job execute -f path/to/your_file.py -c path/to/your_config.yaml
In this command: - 
-f path/to/your_file.py
specifies the path to the Python file that contains your Dagster job definition. - 
-c path/to/your_config.yaml
specifies the path to the YAML file that contains the configuration for the job. Make sure that: - The Python file (
path/to/your_file.py
) is accessible and contains the definitions for your job, resources, and ops. - The YAML configuration file (
path/to/your_config.yaml
) is correctly formatted according to the schema expected by your job. After running the job with the 
dagster job execute
command, if you want to view the results in Dagit, you can start Dagit separately using the 
dagit
command with the appropriate workspace file and port number:
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shell dagit -w path/to/workspace.yaml -p desired_port_number
Replace 
path/to/workspace.yaml
with the actual path to your workspace file, and 
desired_port_number
with the port number you want Dagit to use. Once Dagit is running, you can access it through your web browser to view the job runs and interact with your Dagster instance. Again, remember that running the job and viewing its results in Dagit are separate steps. The 
dagster job execute
command is for running the job, and the 
dagit
command is for starting the web interface to monitor and interact with your Dagster instance.
If your YAML file contains dynamic values that need to be provided at runtime and does not contain static configuration, you can still use the 
dagster job execute
command to run your job, passing the YAML file that contains these dynamic values. Here's the command structure you would use:
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shell dagster job execute -f path/to/your_file.py -c path/to/your_dynamic_values.yaml
In this command: - 
-f path/to/your_file.py
specifies the path to the Python file that contains your Dagster job definition. - 
-c path/to/your_dynamic_values.yaml
specifies the path to the YAML file that contains the dynamic values for the job. The YAML file should be structured according to the requirements of your job's configuration schema. Even if the values are dynamic, they should be placed in the correct location within the YAML file so that Dagster can parse them and provide them to the job at runtime. If the dynamic values are meant to be inputs to specific ops or resources within the job, they should be specified under the appropriate keys in the YAML file. For example:
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yaml resources:   my_resource:     config:       dynamic_param: "dynamic_value"  ops:   my_op:     config:       dynamic_op_param: "dynamic_value"
After running the job with the 
dagster job execute
command, you can view the results in Dagit by starting it separately using the 
dagit
command with the appropriate workspace file and port number:
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shell dagit -w path/to/workspace.yaml -p desired_port_number
Replace 
path/to/workspace.yaml
with the actual path to your workspace file, and 
desired_port_number
with the port number you want Dagit to use. Once Dagit is running, you can access it through your web browser to view the job runs and interact with your Dagster instance. Please ensure that your YAML file is correctly formatted and matches the expected schema for your job. If you encounter any issues, refer to the documentation for the specific version of Dagster you are using to ensure that you are following the correct configuration structure for assets and ops.
It looks like there's a misunderstanding in the command you're trying to run. The 
dagster job execute
command is used to execute a Dagster job, and it does not accept a 
-p
flag because it does not start a server or require a port. The 
-p
flag is used with the 
dagit
command to specify the port for the Dagit web interface. Additionally, the 
-c
flag in the 
dagster job execute
command should point to a YAML file containing the run configuration for your job, not the 
workspace.yaml
file. The 
workspace.yaml
file is used to configure the Dagster workspace for the Dagit UI and other workspace-related commands. Here's how you should structure your commands: 1. Execute the Job: Use the 
dagster job execute
command with the 
-m
flag to specify the module name and the 
-c
flag to specify the path to the YAML file containing the run configuration for your job.
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shell dagster job execute -m dagster_etl -c path/to/your_job_config.yaml
Replace 
path/to/your_job_config.yaml
with the actual path to the YAML file that contains the configuration for your job. 2. Start Dagit: After executing the job, if you want to view the results in Dagit, start Dagit with the 
-w
flag to specify the workspace file and the 
-p
flag to specify the port.
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shell dagit -w path/to/workspace.yaml -p 3001
Replace 
path/to/workspace.yaml
with the actual path to your workspace file. Once Dagit is running, you can access it through your web browser by navigating to 
<http://localhost:3001>
. Remember to keep the execution of the job (
dagster job execute
) and the viewing of its results in Dagit (
dagit
) as separate steps.
The error message you're receiving indicates that the 
ops
entry is unexpected at the root of your YAML configuration. This error typically occurs when the YAML file is being interpreted as a workspace configuration rather than a run configuration for a job. In Dagster, the 
workspace.yaml
file is used to configure the workspace, which includes information about where to load repositories and definitions from. It does not include job-specific configurations like 
ops
. The error message you're seeing is showing the expected structure for a 
workspace.yaml
file, not a job run configuration file. The YAML snippet you've provided looks like it's intended to be a run configuration for a job, specifying configurations for individual ops (or assets). Here's what you should do: 1. Separate Workspace and Run Configurations: Ensure that your 
workspace.yaml
file only contains the workspace configuration, and create a separate YAML file for your job run configuration. 2. Correct Run Configuration File: Place your 
ops
configuration in a separate YAML file that is intended to be used as the run configuration when executing a job. This file should not be named 
workspace.yaml
to avoid confusion. 3. Execute the Job with the Correct Configuration: Use the 
dagster job execute
command with the 
-m
flag to specify the module name and the 
-c
flag to specify the path to the YAML file containing the run configuration for your job.
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shell dagster job execute -m dagster_etl -c path/to/your_run_config.yaml
Replace 
path/to/your_run_config.yaml
with the actual path to the YAML file that contains the run configuration for your job. 4. Start Dagit: If you want to view the results in Dagit, start Dagit with the 
-w
flag to specify the workspace file and the 
-p
flag to specify the port.
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shell dagit -w path/to/workspace.yaml -p 3001
Replace 
path/to/workspace.yaml
with the actual path to your workspace file. Once Dagit is running, you can access it through your web browser by navigating to 
<http://localhost:3001>
. By following these steps, you should be able to execute your job with the correct run configuration and view the results in Dagit without encountering the error related to the 
ops
entry.
If you only have a 
workspace.yaml
file and want to run a job defined in a Python module, you can do so by using the 
dagster job execute
command and providing the job name directly, without needing a separate run configuration file. The 
workspace.yaml
file will be used to load the code locations, including the module where your job is defined. Here's how you can execute a job using the 
workspace.yaml
file: 1. Start Dagit: First, start Dagit with the 
workspace.yaml
file to load your workspace. This step is optional if you only want to execute the job from the command line, but it can be useful for monitoring the job run and accessing the Dagit UI.
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shell dagit -w path/to/workspace.yaml -p 3001
2. Execute the Job: Use the 
dagster job execute
command with the 
-w
flag to specify the workspace file and the 
--job
flag to specify the name of the job you want to run. If your job does not require any run-time configuration, you can omit the 
-c
flag.
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shell dagster job execute -w path/to/workspace.yaml --job your_job_name
Replace 
path/to/workspace.yaml
with the actual path to your workspace file, and 
your_job_name
with the name of the job you want to execute. If your job requires run-time configuration and you don't have a separate run configuration file, you can provide the necessary configuration directly in the Dagit UI when launching the job, or you can define default configuration within your job's Python code. Remember that the 
workspace.yaml
file is used to tell Dagster how to load your code locations, including repositories and jobs. It does not contain run-time configuration for specific job executions. If you need to provide run-time configuration, you would typically do so with a separate YAML file or directly in the Dagit UI when launching the job.
To run a Dagster module with a 
workspace.yaml
file, you would typically use the 
dagster dev
command. This command is used to start a development environment where you can work with your jobs and assets interactively. The 
workspace.yaml
file is used to load the code locations, including the module where your jobs and assets are defined. Here's how you can start the development environment using the 
workspace.yaml
file:
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shell dagster dev -w path/to/workspace.yaml
Replace 
path/to/workspace.yaml
with the actual path to your workspace file. This command will load all the code locations defined by the workspace file. Once you have started the development environment with 
dagster dev
, you can use the Dagit UI to execute jobs and materialize assets. You can access Dagit by navigating to the default URL 
<http://localhost:3000>
in your web browser, or you can specify a different port using the 
-p
flag if needed. If you want to execute a specific job or materialize assets from the command line without using Dagit, you would use the 
dagster job execute
command for jobs or the 
dagster asset materialize
command for assets, providing the necessary configuration as needed. Please note that the 
dagster dev
command is meant for development purposes and provides a convenient way to work with your Dagster code in a local environment. For production scenarios, you would use the appropriate commands to execute jobs or materialize assets as part of your deployment and orchestration workflows.
Yes, you can pass command-line arguments to a Python script and provide the argument details when you run the script from the terminal. Here's a simple example of how you might handle command-line arguments in a Python script using the 
sys
module:
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python # my_script.py import sys  def main():     # Check if at least one additional argument is provided     if len(sys.argv) > 1:         argument = sys.argv[1]  # sys.argv[0] is the script name         print(f"The provided argument is: {argument}")     else:         print("No argument provided.")  if __name__ == "__main__":     main()
To run this script from the terminal and pass an argument, you would use the following command:
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shell python my_script.py your_argument
Replace 
your_argument
with the actual argument you want to pass to the script. If you're working with Dagster and want to pass arguments to a job or op, you would typically use the configuration system provided by Dagster, which allows you to specify inputs and configuration options in a YAML file or directly in the Dagit UI when launching a job. However, if you need to pass command-line arguments to a script that is invoked by a Dagster op, you can use the 
subprocess
module to call the script and pass the arguments from within the op. Here's an example of how you might do that:
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python # dagster_op.py from dagster import op import subprocess  @op(config_schema={"script_argument": str}) def my_dagster_op(context):     script_argument = context.op_config["script_argument"]     subprocess.run(["python", "my_script.py", script_argument])
In this example, the 
my_dagster_op
op takes a configuration parameter called 
script_argument
and passes it to the 
my_script.py
script using the 
subprocess.run
method. When you execute the job that includes this op, you would provide the 
script_argument
in the job's run configuration file or directly in the Dagit UI.
If you want to run a Python module (a directory with an 
__init__.py
file) and pass command-line arguments to it, you can use the 
-m
flag with the 
python
command. This flag allows Python to run modules as scripts. Here's how you can do it: 1. Structure Your Module: Make sure your module is structured correctly. For example, if your module is named 
mymodule
, your directory structure might look like this:
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mymodule/     __init__.py     my_script.py
2. Add Argument Handling to Your Script: Inside 
my_script.py
, you can use the 
sys
module to handle command-line arguments as shown in the previous example. 3. Run the Module as a Script: Use the 
-m
flag to run the module as a script from the terminal. Here's the command:
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shell python -m mymodule.my_script your_argument
Replace 
your_argument
with the actual argument you want to pass to the script. The 
python -m
command will look for an 
__init__.py
file in the 
mymodule
directory to treat it as a module, and then it will run 
my_script.py
as the main script within that module. If 
my_script.py
is designed to be run directly (i.e., it has a 
if __name__ == "__main__":
block), this will work as expected, and 
your_argument
will be passed to the script. Remember that when you use the 
-m
flag, you do not include the 
.py
extension of the script. You simply provide the module path to the script. If your script is in a subdirectory, you would include the path to the script using dot notation. For example, if 
my_script.py
is in a subdirectory named 
subdir
, you would run:
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shell python -m mymodule.subdir.my_script your_argument
In Python, you can pass any number of command-line arguments to a script. The 
sys.argv
list in the 
sys
module captures all the arguments provided on the command line when you run a Python script, with 
sys.argv[0]
being the script name and the subsequent elements (
sys.argv[1]
, 
sys.argv[2]
, ..., 
sys.argv[n]
) being the additional arguments passed. Here's an example of how you might handle multiple command-line arguments in a Python script:
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python # example_script.py import sys  def main():     # Print all arguments except the script name     for i, arg in enumerate(sys.argv[1:], start=1):         print(f"Argument {i}: {arg}")  if __name__ == "__main__":     main()
To run this script with multiple arguments from the terminal, you would use the following command:
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shell python example_script.py arg1 arg2 arg3 ... argN
Replace 
arg1
, 
arg2
, 
arg3
, ..., 
argN
with the actual arguments you want to pass to the script. The practical limit to the number of arguments is typically determined by the operating system's command-line length limit, not by Python itself. Most modern operating systems allow for very long command lines, so you are unlikely to hit this limit under normal circumstances. If you need to handle a large number of arguments or if you need more sophisticated parsing capabilities, you might consider using the 
argparse
module, which provides a more robust framework for command-line argument parsing in Python. This allows you to define options, default values, help messages, and more.
To run a Python module that is not a single script file but a directory with an 
__init__.py
file, you can use the 
-m
flag with the 
python
command. This allows Python to run the module as if it were a script. Here's how you can run a Python module and pass command-line arguments to it: 1. Structure Your Module: Make sure your module is structured correctly. For example, if your module is named 
mymodule
, your directory structure might look like this:
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mymodule/     __init__.py     main.py  # or any other entry point script
2. Add Argument Handling to Your Entry Point Script: Inside 
main.py
(or your entry point script), you can use the 
sys
module to handle command-line arguments as shown in the previous example. 3. Run the Module as a Script: Use the 
-m
flag to run the module as a script from the terminal. Here's the command:
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shell python -m mymodule arg1 arg2 arg3 ... argN
Replace 
mymodule
with the name of your module and 
arg1
, 
arg2
, 
arg3
, ..., 
argN
with the actual arguments you want to pass to the module. The 
python -m
command will look for an 
__init__.py
file in the 
mymodule
directory to treat it as a module, and then it will execute the module's entry point script (often 
main.py
or similar). The arguments will be passed to the script as if it were run directly. Remember that when you use the 
-m
flag, you do not include the 
.py
extension of the script. You simply provide the module path, and Python will attempt to run the 
__main__.py
file within that module. If your entry point script is named differently, you should ensure that it is invoked correctly when the module is run.
Running a Python module with the 
python -m
command is separate from running the Dagster development environment with 
dagster dev
. These are two distinct operations: 1. 
python -m mymodule arg1 arg2 arg3 ... argN
is a standard Python command to execute a module as a script, passing command-line arguments to it. 2. 
dagster dev
is a Dagster-specific command used to start a local development environment, which includes the Dagit UI and other Dagster services. If you want to run a Dagster module and use the 
dagster dev
command, you would not use the 
python -m
command. Instead, you would run 
dagster dev
with the appropriate flags to specify your module. According to the documentation, you can use the 
-m
flag with 
dagster dev
to specify the module name:
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shell dagster dev -m your_module_name
This command will load the definitions from the specified module and start the Dagster development environment, including Dagit and other services. If you need to pass specific configuration values when running 
dagster dev
, you would typically do this through a 
workspace.yaml
file or other configuration files, not through command-line arguments. To summarize, you would use 
python -m
to run a standard Python module as a script, and 
dagster dev
to start the Dagster development environment for working with Dagster jobs and assets. These commands serve different purposes and are used in different contexts.
m
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shell
dagster dev -m your_module_name
@Scout i run it but its not taking inputs from the workspace yaml file how to mention argvs here?
s
The 
dagster dev
command is used to start the Dagster development environment, and it does not directly accept command-line arguments for job or asset configuration. Instead, configuration values are typically provided through a 
workspace.yaml
file or directly in the Dagit UI when launching runs. When you start the development environment with 
dagster dev -m your_module_name
, Dagster loads the definitions from the specified module. To provide configuration for jobs or assets within that module, you would use one of the following methods: 1. Dagit UI: After starting the development environment, you can open Dagit in your web browser (usually at `http://localhost:3000`) and navigate to the job or asset you want to run. You can then provide the configuration values directly in the UI when launching the run. 2. Workspace File: If your 
workspace.yaml
file specifies code locations, Dagster will use this file to determine where to load your repositories and definitions from. The 
workspace.yaml
file itself does not contain run configuration for jobs or assets. 3. Run Configuration File: If you have a separate YAML file with run configuration for a job, you can specify this file when launching the job from the Dagit UI or from the command line using the 
dagster job execute
command with the 
-c
flag:
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shell dagster job execute -m your_module_name -c path/to/your_run_config.yaml
Replace 
path/to/your_run_config.yaml
with the actual path to the YAML file that contains the run configuration for your job. 4. Default Configuration: You can also define default configuration values within your job's Python code. This allows you to run jobs without needing to provide a separate configuration file each time. If you need to pass dynamic values or command-line arguments to your Dagster jobs or assets, you would typically handle this within your job's code by setting up configuration schemas that allow you to inject these values at runtime. Remember that 
dagster dev
is primarily for starting the development environment, and it is not used for passing run-time configuration directly via command-line arguments. Configuration for runs is handled separately through the mechanisms mentioned above.
The error message 
/usr/bin/python3: No module named dagster_etl.__main__; 'dagster_etl' is a package and cannot be directly executed
indicates that Python is trying to find a 
__main__.py
file within the 
dagster_etl
package to execute, but it cannot find one. This is because when you use the 
-m
flag, Python expects the module to be executable, which typically means it should have a 
__main__.py
file. To resolve this issue, you have a couple of options: 1. Create a 
__main__.py
File: If you want to make the 
dagster_etl
package directly executable, you can create a 
__main__.py
file within the 
dagster_etl
package directory. This file should contain the code that should be run when the package is executed. For example:
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dagster_etl/     __init__.py     __main__.py     ... (other modules and packages)
Inside 
__main__.py
, you would handle the command-line arguments and invoke the appropriate functionality of your package. 2. Run a Specific Script: If you have a specific script within the 
dagster_etl
package that you want to run, you should call that script directly instead of trying to run the entire package. For example:
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shell python3 -m dagster_etl.your_script /home/voziq/dbt_project/dag.csv /home/voziq/dbt_project/newoutput.csv app.dag
Replace 
your_script
with the name of the script (without the 
.py
extension) that you want to execute within the 
dagster_etl
package. Regarding the version mismatch warning, it seems that there is a discrepancy between the versions of 
dagster
and 
dagster-embedded-elt
. You should ensure that the versions of Dagster and its related libraries are compatible. You may need to update the 
dagster-embedded-elt
package to match the expected library version:
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shell pip install --upgrade dagster-embedded-elt==0.22.6
Replace 
0.22.6
with the version that is compatible with your current version of 
dagster
. It's important to keep the versions aligned to avoid potential issues and incompatibilities.
To make your 
dagster_etl
package executable with 
__main__.py
, you need to include code that will serve as the entry point for your package when it's run as a script. This code will typically handle the command-line arguments and invoke the appropriate functionality within your package. Here's a basic structure for your 
__main__.py
file that handles three command-line arguments:
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python # dagster_etl/__main__.py import sys from .file1 import function_from_file1 from .file2 import function_from_file2 from .file3 import function_from_file3  def main(args):     # Assuming you want to pass one argument to each function     if len(args) != 4:         print("Usage: python -m dagster_etl arg1 arg2 arg3")         sys.exit(1)      # Call functions from your module files with the provided arguments     function_from_file1(args[1])     function_from_file2(args[2])     function_from_file3(args[3])  if __name__ == "__main__":     main(sys.argv)
In this example, 
file1
, 
file2
, and 
file3
are the Python files within your 
dagster_etl
package, and 
function_from_file1
, 
function_from_file2
, and 
function_from_file3
are the functions you want to run from those files. Replace these with the actual file and function names from your module. The 
main
function checks that the correct number of arguments has been provided and then calls the functions from your module files with the provided arguments. To run your package with three arguments, you would use the following command:
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shell python -m dagster_etl arg1 arg2 arg3
Replace 
arg1
, 
arg2
, and 
arg3
with the actual arguments you want to pass to your package. Please ensure that the functions you're calling are designed to accept the arguments as you intend to pass them, and that they are properly imported in the 
__main__.py
file. This structure allows you to organize your code execution based on the command-line arguments provided when running the module.
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