Giacomo Colombo
03/11/2024, 5:13 PMScout
03/11/2024, 5:13 PMdefine_asset_job
using the execute_job
method, you would typically follow these steps: 1. Define your assets using the @asset
decorator. 2. Create a job using define_asset_job
, specifying the assets that the job should include. 3. Use the execute_job
method to run the job, providing any necessary configuration and specifying the instance of DagsterInstance
to use. Here's a step-by-step example based on the documentation: python from dagster import asset, define_asset_job, execute_job, DagsterInstance # Define your assets @asset def my_asset(): # Your asset computation logic return "result" # Create a job with the defined assets my_job = define_asset_job( name="my_job", selection=["my_asset"] # Specify the assets included in the job ) # Execute the job result = execute_job( my_job, instance=DagsterInstance.get() # Use the default Dagster instance or provide your own ) # You can inspect the result object for details about the execution
Please note that the execute_job
method is used to execute the job in-process, which means that the job will run within the same Python process as the script that calls execute_job
. This can be useful for testing or running jobs in a simple environment. If you need to run the job with a specific configuration, you can pass a run_config
dictionary to the execute_job
method. If your job requires resources, you can also provide them in the execute_job
call. Keep in mind that the exact usage of execute_job
may vary depending on the version of Dagster you are using and the specific requirements of your job. Always refer to the latest documentation for the most accurate and up-to-date guidance.Giacomo Colombo
03/12/2024, 10:33 AMScout
03/12/2024, 10:33 AMdefine_asset_job
and you want to execute it programmatically, you should ensure that the job is reconstructable if it needs to cross process boundaries or if you're executing it in a different system. To execute a job defined with define_asset_job
, you would typically make it reconstructable and then use the execute_job
function. Here's how you can do it: 1. Define your assets using the @asset
decorator. 2. Create a job using define_asset_job
. 3. Make the job reconstructable by wrapping it in a function and using the reconstructable
decorator. 4. Use the execute_job
function from the dagster
API to run the job. Here's an example based on the documentation: python from dagster import asset, define_asset_job, reconstructable, execute_pipeline from dagster.core.execution import execute_pipeline_iterator from dagster.core.instance import DagsterInstance # Define your assets @asset def my_asset(): # Your asset computation logic return "result" # Define a function that returns your job def define_my_job(): return define_asset_job( name="my_job", selection=["my_asset"] # Specify the assets included in the job ) # Make the job reconstructable reconstructable_job = reconstructable(define_my_job) # Execute the job instance = DagsterInstance.get() # Use the default Dagster instance or provide your own result = execute_pipeline( reconstructable_job, instance=instance ) # You can inspect the result object for details about the execution
Please note that execute_pipeline
is used here instead of execute_job
, as execute_job
expects a JobDefinition
object, which is typically obtained from a job decorated with @job
. Since define_asset_job
does not return a JobDefinition
directly, you need to use execute_pipeline
with a reconstructable job. Remember to check the documentation for the version of Dagster you are using, as APIs and best practices may evolve over time.Giacomo Colombo
03/12/2024, 11:03 AMScout
03/12/2024, 11:03 AMexecute_pipeline
and execute_pipeline_iterator
functions are not the correct functions to use for executing jobs in Dagster. To execute a job defined with define_asset_job
, you should use the execute_job
function from the dagster
API. However, as you mentioned, execute_job
expects a JobDefinition
object, which is typically obtained from a job decorated with @job
. Since define_asset_job
returns a JobDefinition
, you should be able to execute it directly with execute_job
. Here's how you can do it: python from dagster import asset, define_asset_job, execute_job from dagster.core.instance import DagsterInstance # Define your assets @asset def my_asset(): # Your asset computation logic return "result" # Create a job with the defined assets my_job = define_asset_job( name="my_job", selection=["my_asset"] # Specify the assets included in the job ) # Execute the job instance = DagsterInstance.get() # Use the default Dagster instance or provide your own result = execute_job( job=my_job, instance=instance ) # You can inspect the result object for details about the execution
Please ensure that you have the correct imports and that you are using the appropriate version of Dagster that supports these functions. If you are still encountering issues, it may be helpful to consult the latest Dagster documentation or seek support from the Dagster community for the version-specific guidance.Giacomo Colombo
03/12/2024, 11:12 AMScout
03/12/2024, 11:12 AMUnresolvedAssetJobDefinition
, which needs to be resolved to a JobDefinition
before it can be executed. This typically happens because the asset job is not yet bound to the assets it targets within a repository context. To resolve an UnresolvedAssetJobDefinition
, you need to have it within a repository. The repository provides the necessary context to resolve the job definition. Here's how you can define a repository and retrieve a resolved job definition from it: python from dagster import asset, define_asset_job, repository, execute_job from dagster.core.instance import DagsterInstance # Define your assets @asset def my_asset(): # Your asset computation logic return "result" # Create an unresolved asset job unresolved_asset_job = define_asset_job( name="my_job", selection=["my_asset"] # Specify the assets included in the job ) # Define a repository that includes the job @repository def my_repo(): return [unresolved_asset_job, my_asset] # Retrieve the resolved job definition from the repository resolved_job = my_repo.get_job("my_job") # Execute the job instance = DagsterInstance.get() # Use the default Dagster instance or provide your own result = execute_job( job=resolved_job, instance=instance ) # You can inspect the result object for details about the execution
In this example, we define a repository that includes both the unresolved asset job and the asset itself. We then use the get_job
method on the repository to retrieve the resolved job definition, which can be executed with execute_job
. Please ensure that you are using the correct version of Dagster and that the get_job
method is available in your version. If you continue to encounter issues, it may be helpful to consult the latest Dagster documentation or seek support from the Dagster community.Giacomo Colombo
03/12/2024, 11:31 AM# Define a function that returns your job
unresolved_asset_job = define_asset_job(
name="job_sample_2",
selection=[
"my_asset_1",
"my_asset_2"
]
)
# Define a repository that includes the job
@repository
def my_repo():
return [unresolved_asset_job, my_asset_1, my_asset_2]
# Retrieve the resolved job definition from the repository
resolved_job = my_repo.get_job("job_sample_2")
reconstructable_job = reconstructable(resolved_job)
# Execute the job
instance = DagsterInstance.get() # Use the default Dagster instance or provide your own
result = execute_job(
job=reconstructable_job,
instance=instance,
)
Namely, rhe error is :
esource with key 'custom_resource_1' required by op 'my_asset_1' was not provided. Please provide a ResourceDefinition to key 'custom_dataset_destination_resource', or change the required key to one of the following keys which points to an ResourceDefinition: ['io_manager']Scout
03/12/2024, 11:31 AMresource_defs
argument. However, since you're using define_asset_job
, which doesn't directly accept resource_defs
, you'll need to use a different approach. One way to do this is to define a new job that wraps your assets and includes the necessary resources. Here's how you can do it: python from dagster import asset, define_asset_job, repository, job, execute_job from dagster.core.instance import DagsterInstance # Define your assets @asset(required_resource_keys={"custom_resource_1"}) def my_asset_1(context): # Your asset computation logic that uses context.resources.custom_resource_1 return "result1" @asset def my_asset_2(): # Your asset computation logic return "result2" # Define your custom resource @resource def custom_resource_1(_): # Your resource creation logic return "my_custom_resource" # Create an unresolved asset job unresolved_asset_job = define_asset_job( name="job_sample_2", selection=["my_asset_1", "my_asset_2"] ) # Define a new job that wraps the assets and includes the necessary resources @job(resource_defs={"custom_resource_1": custom_resource_1}) def my_wrapped_job(): my_asset_1() my_asset_2() # Define a repository that includes the job @repository def my_repo(): return [my_wrapped_job, my_asset_1, my_asset_2] # Retrieve the resolved job definition from the repository resolved_job = my_repo.get_job("my_wrapped_job") # Execute the job instance = DagsterInstance.get() # Use the default Dagster instance or provide your own result = execute_job( job=resolved_job, instance=instance, ) # You can inspect the result object for details about the execution
In this example, we define a custom_resource_1
resource and then create a new job my_wrapped_job
that wraps the assets and specifies the resource_defs
. We then include this wrapped job in the repository and retrieve it for execution. Please note that the reconstructable
function is not necessary in this context, as we are directly executing the job within the same Python process. The reconstructable
function is used when you need to reconstruct the job across process boundaries, such as when using the Dagster CLI or when executing in different systems. Make sure to replace the placeholder resource creation logic with your actual resource definition. This approach allows you to provide the necessary resources to your assets when executing the job.Giacomo Colombo
03/12/2024, 11:42 AMScout
03/12/2024, 11:42 AMpython from dagster import asset, ResourceDefinition, repository, define_asset_job, build_assets_job, execute_job from dagster.core.instance import DagsterInstance # Define your custom resource @resource def custom_resource_1(_): # Your resource creation logic return "my_custom_resource" # Define your assets @asset(required_resource_keys={"custom_resource_1"}) def my_asset_1(context): # Your asset computation logic that uses context.resources.custom_resource_1 return "result1" @asset def my_asset_2(): # Your asset computation logic return "result2" # Define a job that includes your assets and the required resources my_job = build_assets_job( "my_job_with_resources", assets=[my_asset_1, my_asset_2], resource_defs={"custom_resource_1": custom_resource_1} ) # Define a repository that includes the job @repository def my_repo(): return [my_job] # Execute the job instance = DagsterInstance.get() # Use the default Dagster instance or provide your own result = execute_job( job=my_job, instance=instance, ) # You can inspect the result object for details about the execution
In this example, we define a custom_resource_1
resource and then use build_assets_job
to create a job that includes both the assets and the resource definitions. The build_assets_job
function is a way to construct a job from a list of assets while also specifying resource definitions. When you execute the job, the custom_resource_1
resource will be available to the my_asset_1
asset, which requires it. This should resolve the error you're encountering. Please ensure that the build_assets_job
function is available in your version of Dagster, as APIs can change over time. If you continue to encounter issues, it may be helpful to consult the latest Dagster documentation or seek support from the Dagster community.Giacomo Colombo
03/12/2024, 11:51 AMScout
03/12/2024, 11:51 AMbuild_assets_job
is not available in your version of Dagster, let's try a different approach using the @job
decorator to define a job that includes your assets and the necessary resources. Here's how you can define a job with resources: python from dagster import asset, job, op, ResourceDefinition, repository, execute_job from dagster.core.instance import DagsterInstance # Define your custom resource @resource def custom_resource_1(_): # Your resource creation logic return "my_custom_resource" # Define your assets @asset(required_resource_keys={"custom_resource_1"}) def my_asset_1(context): # Your asset computation logic that uses context.resources.custom_resource_1 return "result1" @asset def my_asset_2(): # Your asset computation logic return "result2" # Define a job that includes your assets and the required resources @job(resource_defs={"custom_resource_1": custom_resource_1}) def my_job_with_resources(): my_asset_1() my_asset_2() # Define a repository that includes the job @repository def my_repo(): return [my_job_with_resources] # Execute the job instance = DagsterInstance.get() # Use the default Dagster instance or provide your own result = execute_job( job=my_job_with_resources, instance=instance, ) # You can inspect the result object for details about the execution
In this example, we use the @job
decorator to define a job that includes the assets and specifies the resource_defs
. This job is then included in the repository and executed with execute_job
. Please ensure that you are using the correct version of Dagster and that the @job
decorator is available in your version. If you continue to encounter issues, it may be helpful to consult the latest Dagster documentation or seek support from the Dagster community.Giacomo Colombo
03/12/2024, 11:55 AMScout
03/12/2024, 11:55 AMresource_defs
dictionary when defining your job with the @job
decorator. Each resource is specified by a key that corresponds to the required_resource_keys
of your assets. Here's an example of how to define a job with multiple resources: python from dagster import asset, job, op, ResourceDefinition, repository, execute_job from dagster.core.instance import DagsterInstance # Define your custom resources @resource def custom_resource_1(_): # Your resource creation logic for custom_resource_1 return "my_custom_resource_1" @resource def custom_resource_2(_): # Your resource creation logic for custom_resource_2 return "my_custom_resource_2" # Define your assets @asset(required_resource_keys={"custom_resource_1"}) def my_asset_1(context): # Your asset computation logic that uses context.resources.custom_resource_1 return "result1" @asset(required_resource_keys={"custom_resource_2"}) def my_asset_2(context): # Your asset computation logic that uses context.resources.custom_resource_2 return "result2" # Define a job that includes your assets and the required resources @job(resource_defs={ "custom_resource_1": custom_resource_1, "custom_resource_2": custom_resource_2 }) def my_job_with_resources(): my_asset_1() my_asset_2() # Define a repository that includes the job @repository def my_repo(): return [my_job_with_resources] # Execute the job instance = DagsterInstance.get() # Use the default Dagster instance or provide your own result = execute_job( job=my_job_with_resources, instance=instance, ) # You can inspect the result object for details about the execution
In this example, my_asset_1
requires custom_resource_1
, and my_asset_2
requires custom_resource_2
. Both resources are included in the resource_defs
when defining my_job_with_resources
. This way, when the job is executed, both assets have access to their required resources. Remember to replace the placeholder resource creation logic with your actual resource definitions. This approach allows you to provide the necessary resources to your assets when executing the job. If you have additional resources that are not directly required by the assets but are needed for the job, you can also include them in the resource_defs
dictionary.Giacomo Colombo
03/12/2024, 12:08 PMScout
03/12/2024, 12:08 PM@asset
decorator. 2. Job Configuration: When you execute a job, Dagster expects a certain structure in the run configuration. If you're manually providing a run configuration, ensure it's correctly structured. However, if your assets are defined correctly, you typically don't need to provide this part of the configuration manually. 3. Dagster Version: There might be a version-specific issue or a bug. Ensure you're using a version of Dagster where the behavior you expect is supported. Here's a simplified example of how you might define dependent assets: python from dagster import asset, repository, job, execute_job from dagster.core.instance import DagsterInstance # Define your assets @asset def my_asset_1(): # Your asset computation logic return "result1" @asset def my_asset_2(my_asset_1): # Your asset computation logic that depends on my_asset_1 return f"result2 with input {my_asset_1}" # Define a job that includes your assets @job def my_job(): my_asset_2(my_asset_1()) # Define a repository that includes the job @repository def my_repo(): return [my_job] # Execute the job instance = DagsterInstance.get() # Use the default Dagster instance or provide your own result = execute_job( job=my_job, instance=instance, ) # You can inspect the result object for details about the execution
In this example, my_asset_2
depends on my_asset_1
, and this dependency is expressed by passing my_asset_1
as an input to my_asset_2
within the job definition. Dagster should automatically handle the passing of the output from my_asset_1
to my_asset_2
. If you continue to encounter issues, please ensure that your assets are defined correctly with the necessary dependencies, and that you're using a compatible version of Dagster. If the problem persists, it may be helpful to consult the latest Dagster documentation or seek support from the Dagster community.Giacomo Colombo
03/12/2024, 1:00 PMScout
03/12/2024, 1:00 PMbash dagster dev
This will start the Dagit webserver, and you should be able to access the Dagit UI by going to <http://localhost:3000>
in your web browser. 2. Execute the Job: To execute a job from the command line, you can use the dagster job execute
command. You'll need to specify the file that contains your job definition and the repository. For example, if your job is defined in a file named my_repo.py
, you would run: bash dagster job execute -f my_repo.py -j my_job
Here, -f
specifies the file containing the repository, and -j
specifies the name of the job you want to execute. Make sure that the DAGSTER_HOME
environment variable is set to a directory where Dagster can write instance data. This is where run logs, event logs, and other metadata are stored. If DAGSTER_HOME
is not set, Dagster uses the default location, which is typically ~/.dagster
. By executing the job via the CLI, the run will be recorded, and you should be able to see it in the Dagit UI under the "Runs" tab. If you encounter any issues with the CLI commands, please ensure that you have the latest version of Dagster installed and that your repository and job definitions are correctly set up. If the problem persists, you may want to consult the Dagster documentation or seek support from the Dagster community for further assistance.Giacomo Colombo
03/12/2024, 1:11 PM@job(resource_defs={
"custom_resource_1": custom_resource_1,
"custom_resource_2": custom_resource_2,
"custom_resource_3": custom_resource_3}
)
def unresolved_asset_job():
my_asset_2(my_asset_1())
@repository
def my_repo():
return [unresolved_asset_job, my_asset_1, my_asset_2]
resolved_job = my_repo.get_job("unresolved_asset_job")
reconstructable_job = reconstructable(resolved_job)
instance = DagsterInstance.get()
result = execute_job(
job=reconstructable_job,
instance=instance,
)
The error is :
UserWarning: Error loading repository location italydagster. core.errors.DagsterInvalidDefinitionError resource
with key 'custom_resource_1' required by op 'my_asset_1' was not provided. Please provide a ResourceDefinition to key 'custom_dataset_destination_resource', or change the required key to one of the following keys which points to an ResourceDefinition: ['io_manager']Scout
03/12/2024, 1:11 PM'custom_resource_1'
required by the asset 'my_asset_1'
was not provided when attempting to execute the job. This suggests that there might be a mismatch between the resource keys expected by your assets and the resource definitions provided in the resource_defs
of your job. Here are a few things to check and correct in your code: 1. Resource Definitions: Ensure that the resource definitions for custom_resource_1
, custom_resource_2
, and custom_resource_3
are correctly defined and imported into the file where you're defining the job. 2. Required Resource Keys: Verify that the required_resource_keys
for each asset match the keys used in the resource_defs
of the job. For example, if my_asset_1
requires custom_resource_1
, the required_resource_keys
should be set accordingly: python @asset(required_resource_keys={"custom_resource_1"}) def my_asset_1(context): # Your asset computation logic that uses context.resources.custom_resource_1 return "result1"
3. Job Definition: When defining the job, make sure that all required resources are included in the `resource_defs`: python @job(resource_defs={ "custom_resource_1": custom_resource_1, "custom_resource_2": custom_resource_2, "custom_resource_3": custom_resource_3 }) def my_job(): my_asset_2(my_asset_1())
4. Repository Definition: In your repository definition, you only need to include the job, not the individual assets, since the job already encapsulates the assets: python @repository def my_repo(): return [my_job]
5. Execution: When executing the job, you don't need to make it reconstructable if you're executing it in the same process. You can directly execute the resolved job: python instance = DagsterInstance.get() result = execute_job( job=resolved_job, instance=instance, )
Make sure that the execute_job
function is being used correctly according to your version of Dagster. If you're using an older version of Dagster, the API might be different, and you may need to use resolved_job.execute_in_process()
instead. If you've checked all the above and the error persists, please ensure that your Dagster environment is correctly set up, including the DAGSTER_HOME
environment variable. If the problem still occurs, you may want to consult the Dagster documentation or seek support from the Dagster community for further assistance.Giacomo Colombo
03/12/2024, 1:30 PMScout
03/12/2024, 1:30 PMunresolved_asset_job
is missing the required configuration for the resources at the root level. This means that when you're executing the job, Dagster expects configuration for the resources custom_resource_1
, custom_resource_2
, and custom_resource_3
, but it's not being provided. When you define resources that require configuration, you need to supply that configuration when you execute the job. This can be done in a few different ways, depending on how you want to execute the job. If you want to execute the job from the command line and view the results in the Dagit UI, you can do the following: 1. Start Dagit: Run the Dagit webserver by navigating to the directory containing your Dagster repository and running the command dagit
. This will allow you to access the Dagit UI at <http://localhost:3000>
. 2. Execute the Job with Configuration: Use the Dagster CLI to execute the job with the necessary configuration. You can provide the configuration as a YAML file or as JSON. For example, if you have a YAML file named config.yaml
with the following content: yaml resources: custom_resource_1: config: one_dataset_destination: "value_for_dataset_destination" custom_resource_2: config: one_dataset_type: "value_for_dataset_type" custom_resource_3: config: curr_year: 2023
You can execute the job using the following command: bash dagster job execute -f my_repo.py -j unresolved_asset_job -c config.yaml
This command specifies the file containing the repository (-f my_repo.py
), the name of the job (-j unresolved_asset_job
), and the configuration file (-c config.yaml
). Make sure that the configuration keys and values in config.yaml
match the expected configuration schema for your resources. The placeholders value_for_dataset_destination
, value_for_dataset_type
, and 2023
should be replaced with the actual configuration values required by your resources. By executing the job this way, the run will be recorded, and you should be able to see it in the Dagit UI under the "Runs" tab. If you continue to encounter issues, please ensure that your resources are correctly defined with the necessary configuration schema, and that you're using a compatible version of Dagster. If the problem persists, you may want to consult the Dagster documentation or seek support from the Dagster community for further assistance.Giacomo Colombo
03/12/2024, 1:42 PMwhole_defs = Definitions(
assets=all_assets,
jobs=[....],
resources = {
"custom_resource_1": {"curr_year": 2023},
"custom_resource_2": {"one_dataset_type": 'businesses'},
"custom_resource_3": {"one_dataset_destination": 'managed'}
}
how to replicate / copy this behaviour to make the resource be used correclty?
Remember that the error is :
dagster._core.errors.DagsterInvalidConfigError: Error in config for job unresolved_asset_job
Error 1: Missing required config entry "resources" at the root. Sample config for missing entry: {'resources': {'custom_resource_1': {'config': {'one_dataset_destination': '...'}}, 'custom_resource_2': {'config': {'one_dataset_type': '...'}}, 'custom_resource_3': {'config': {'curr_year': 0}}}}Scout
03/12/2024, 1:42 PMDefinitions
object where you specify resources, you can use the execute_in_process
method on the job definition. This method allows you to pass the configuration directly as a Python dictionary. Here's how you can do it: python from dagster import execute_in_process, DagsterInstance # Assuming you have a job definition like this: @job(resource_defs={ "custom_resource_1": custom_resource_1, "custom_resource_2": custom_resource_2, "custom_resource_3": custom_resource_3 }) def unresolved_asset_job(): my_asset_2(my_asset_1()) # Define the configuration for your resources as a Python dictionary run_config = { "resources": { "custom_resource_1": { "config": { "curr_year": 2023 } }, "custom_resource_2": { "config": { "one_dataset_type": 'businesses' } }, "custom_resource_3": { "config": { "one_dataset_destination": 'managed' } } } } # Execute the job in process with the provided configuration result = unresolved_asset_job.execute_in_process( run_config=run_config, instance=DagsterInstance.get(), ) # You can inspect the result object for details about the execution
In this example, execute_in_process
is called on the unresolved_asset_job
job definition, and the run_config
dictionary is passed to provide the necessary configuration for the resources. The DagsterInstance.get()
method is used to get the default Dagster instance, but you can also provide a specific instance if needed. This approach allows you to execute the job within the same Python process and pass the configuration directly without the need for a YAML file. The execution result is captured in the result
object, which you can inspect to see the details of the execution. Please ensure that the custom_resource_1
, custom_resource_2
, and custom_resource_3
resource definitions are correctly defined and imported in your script. Also, make sure that the configuration keys and values match the expected configuration schema for your resources.Giacomo Colombo
03/12/2024, 1:51 PMScout
03/12/2024, 1:51 PMrun_config
dictionary in a Python file versus a YAML configuration file, it really depends on your specific use case and preferences. Both methods are valid, and each has its own advantages. Python run_config
dictionary: - Version Control: Easier to track changes in version control systems like Git. - Dynamic Configuration: Allows for dynamic generation of configuration, which can be useful if your configuration needs to change based on certain conditions at runtime. - Code Reuse: Enables the reuse of Python variables and functions within your configuration, which can reduce duplication. - Error Checking: Benefits from Python's syntax checking and can be checked by linters or IDEs for potential errors before runtime. YAML configuration file: - Separation of Concerns: Keeps configuration separate from code, which can make the codebase cleaner and more modular. - Non-technical Users: Can be more accessible for non-developers or less technical team members who may need to modify the configuration. - Standardization: YAML is a widely used standard for configuration files and can be easier to understand for those familiar with it. - Tooling: Some tools and systems expect configuration to be provided in YAML or another declarative format. It's generally discouraged to use a YAML file when: - The configuration needs to be generated dynamically based on complex logic that is cumbersome to express in YAML. - The configuration is closely tied to the code and changes frequently with the code. - You want to leverage Python's error checking and linting capabilities for your configuration. - You prefer to keep everything in Python for consistency and ease of use. Ultimately, the choice between a Python run_config
dictionary and a YAML file should be based on what works best for your team, your project's requirements, and your deployment practices. It's also possible to use a combination of both, where some base configuration is defined in YAML and overridden or extended by a Python run_config
dictionary when needed.Giacomo Colombo
03/12/2024, 4:51 PMScout
03/12/2024, 4:51 PMmain
function in another module. This is a common practice to separate the definition of your data pipelines from the execution logic. Here's an example of how you might structure your code: assets.py (or whatever you name your module with asset definitions) python from dagster import asset, ResourceDefinition # Define your custom resources @resource def custom_resource_1(_): # Your resource creation logic for custom_resource_1 return "my_custom_resource_1" # Define your assets @asset(required_resource_keys={"custom_resource_1"}) def my_asset_1(context): # Your asset computation logic that uses context.resources.custom_resource_1 return "result1" # ... other assets and resources ...
jobs.py (or whatever you name your module with job definitions) python from dagster import job from .assets import my_asset_1, custom_resource_1 # Define a job that includes your assets and the required resources @job(resource_defs={"custom_resource_1": custom_resource_1}) def my_job_with_resources(): my_asset_1()
main.py (the entry point for executing your job) python from dagster import execute_in_process, DagsterInstance from .jobs import my_job_with_resources def main(): # Define the configuration for your resources as a Python dictionary run_config = { "resources": { "custom_resource_1": { "config": { "curr_year": 2023 } }, # ... other resources ... } } # Execute the job in process with the provided configuration result = my_job_with_resources.execute_in_process( run_config=run_config, instance=DagsterInstance.get(), ) # You can inspect the result object for details about the execution print(result) if __name__ == "__main__": main()
In this setup, you have separated the definitions of your assets and jobs from the execution logic. The main.py
file serves as the entry point to run your job, and it can be executed directly with a Python interpreter. Make sure to adjust the imports based on your actual module and file names. If you're using a package structure, you might need to adjust the import statements to reflect the correct paths to your modules. This structure helps keep your code organized and makes it easier to maintain and understand. It also allows you to potentially reuse the assets.py
and jobs.py
modules in other contexts or entry points if needed.Giacomo Colombo
03/12/2024, 4:56 PMScout
03/12/2024, 4:56 PMmain.py
script just like any normal Python file. Here's how you can do it: 1. Open a terminal or command prompt. 2. Navigate to the directory containing your main.py
file. 3. Run the script using the Python interpreter: bash python main.py
This command will execute the main
function in your main.py
file, which in turn will execute the job with the provided configuration. Remember to ensure that your Python environment has all the necessary dependencies installed, including Dagster and any other libraries your assets and resources require. By running the Python script directly, you're invoking the Dagster job execution within the same process as your script, and you won't have the Dagit UI available for this execution. However, this approach is useful for testing, automation, and when running jobs in production environments where you may not need the Dagit UI.