what is the best way to execute a system bash command within an sensor and log the output?

To execute a system bash command within a sensor and log the output, you can use the

subprocess

module in Python to run the command and capture its output. Then, you can use the sensor's context to log the output. However, it's important to note that sensors in Dagster are designed for monitoring and are not typically used to execute commands or perform heavy computations. Here's a general approach based on the documentation: 1. Import the necessary modules (

subprocess

for executing the command and

sensor

from Dagster for defining the sensor). 2. Define a sensor function that uses

subprocess.run

to execute the bash command. 3. Capture the output and error streams from the command. 4. Use the sensor context's logging methods to log the output. Here's an example of how you might define such a sensor:

python from dagster import sensor, SensorExecutionContext import subprocess  @sensor def my_sensor(context: SensorExecutionContext):     try:         # Replace 'your_bash_command_here' with your actual bash command         result = subprocess.run(             ['your_bash_command_here'],             capture_output=True,             text=True,             check=True         )         # Log the standard output         <http://context.log.info|context.log.info>(result.stdout)         # Log the standard error, if any         if result.stderr:             context.log.error(result.stderr)     except subprocess.CalledProcessError as e:         # Log the error if the command fails         context.log.error(f"Command failed with error: {e}")

Please note that this is a general example and not taken directly from the documentation. The actual implementation may vary based on your specific requirements and the environment in which the sensor is running. If you need to execute a command that interacts with Dagster's job execution, you should consider using an op or a solid instead, as they are designed for executing tasks. Remember that running system commands from sensors can be risky, especially if the commands are constructed dynamically or include user input, as this can lead to security vulnerabilities such as command injection. Always validate and sanitize any input that will be used in system commands.

@Scout would there be any benefit to using the dagster-shell library instead of subprocess?

The

dagster-shell

library provides a set of pre-built Dagster assets and ops that wrap shell commands, making it easier to integrate shell command execution into your Dagster pipelines without having to manually use the

subprocess

module. This library can simplify the process of running shell commands and can provide a more declarative way to define these operations within your Dagster jobs. Some benefits of using

dagster-shell

over

subprocess

directly include: 1. Convenience:

dagster-shell

provides a higher-level API that abstracts away some of the boilerplate code associated with setting up

subprocess

calls. 2. Integration: The library is designed to work seamlessly with Dagster's event and logging system, making it easier to track the execution of shell commands within the Dagster UI. 3. Error Handling:

dagster-shell

may have built-in error handling that can simplify the process of managing the success or failure of shell commands. 4. Consistency: Using

dagster-shell

can provide a consistent way to execute shell commands across different parts of your Dagster project. 5. Configurability:

dagster-shell

assets and ops can be configured through Dagster's configuration system, allowing for dynamic parameterization of the shell commands. However, it's important to note that

dagster-shell

is typically used within the context of Dagster jobs, not sensors. Sensors are meant for monitoring and triggering runs based on external changes, and they are not the ideal place to perform heavy computations or run potentially long-running shell commands. If you need to execute a shell command as part of a sensor's logic (for example, to check the status of a system before triggering a run), you should ensure that the command is lightweight and quick to avoid blocking the sensor's execution. In such cases, using

subprocess

directly within the sensor might be more appropriate. For actual task execution within a Dagster job,

dagster-shell

could be a good fit. If you decide to use

subprocess

within a sensor, you should handle it with care, as mentioned in the previous response. Always consider the implications of running system commands from your sensors and ensure that it aligns with best practices for monitoring and triggering in Dagster. Dagster Pipes tutorial | Dagster Docs button Dagster Pipes subprocess reference | Dag... button Alternatives to subprocess execution button Using Dagster for fast not python code e... button Thank you for your feedback!

@Scout can a sensor context cursor only be a str, or could it be other types like a pandas DataFrame?

In Dagster, the sensor context cursor is designed to be a simple, serializable piece of information that represents the state of a sensor between evaluations. The cursor is typically used to store a string that can be easily persisted and retrieved across sensor evaluations. The documentation indicates that the

update_cursor

method on the

SensorEvaluationContext

expects a string as its parameter. This string is then provided to the sensor in subsequent evaluations through the

cursor

attribute on the context. The design is intended to keep track of progress and avoid duplicate work across sensor evaluations. Here's an example of how you might use the cursor in a sensor:

python @sensor def my_sensor(context):     last_processed_id = context.cursor  # Retrieve the last processed ID from the cursor     new_last_processed_id = ...  # Logic to determine the new last processed ID      # ... logic to check for new events or data to process ...      context.update_cursor(str(new_last_processed_id))  # Update the cursor with the new last processed ID

Using complex objects like a pandas DataFrame as a cursor would not be appropriate because: 1. Serialization: Cursors need to be serialized to be stored between sensor evaluations. DataFrames are not inherently serializable to a simple string format without conversion. 2. Performance: Storing large or complex objects as cursors could lead to performance issues, as the sensor framework is optimized for quick checks of simple state indicators. 3. Simplicity: The cursor is intended to be a simple checkpoint or indicator, not a complex data structure. If you need to maintain more complex state information between sensor evaluations, you should consider persisting that state to an external system (like a database or a file) and using the cursor to store a simple reference to that state (like a database row ID or a file path). Then, your sensor can retrieve and update the complex state from the external system as needed.

@Scout can i have optional parameters in an asset's Config?

Yes, you can have optional parameters in an asset's

Config

in Dagster. Optional parameters are defined using Python's typing system, where you can specify a parameter as

Optional

to indicate that it is not required to be provided in the configuration. Here's an example from the documentation that shows how to define an optional parameter in an asset's `Config`:

python from typing import Optional from dagster import asset, Config  class MyAssetConfig(Config):     # Optional parameter with a default value of None     person_name: Optional[str] = None  @asset def greeting(config: MyAssetConfig) -> str:     if config.person_name:         return f"Hello, {config.person_name}"     else:         return "Hello"  # Usage with the optional parameter provided materialize([greeting], run_config={"ops": {"greeting": {"config": {"person_name": "Alice"}}}})  # Usage without the optional parameter materialize([greeting])

In this example,

person_name

is an optional parameter in the

MyAssetConfig

class. If it is not provided in the run configuration, it defaults to

None

. The asset function

greeting

checks if

person_name

is provided and adjusts its behavior accordingly. When defining optional parameters, you can also use the

Optional

type hint along with a default value, which allows you to specify a default behavior when the parameter is not provided in the configuration. If you want to require that a field be specified in the config, you can use an ellipsis (

...

) to indicate that the field is required. Remember that when you define an asset's

Config

, Dagster will validate the run configuration against the schema and proceed only if validation is successful. If a parameter is optional and not provided, Dagster will use the default value specified in the

Config

class.

@Scout if my dagster cloud launchpad says that I'm missing some config scaffolding, does that mean my job is missing the run_config?

If the Dagster Cloud Launchpad indicates that you are missing some config scaffolding, it typically means that the run configuration (

run_config

) for your job is incomplete or not provided for certain components that require it. The Launchpad is informing you that there are expected configuration entries that have not been specified. In Dagster, each job may have ops or assets that require specific configuration to run. The

run_config

is a dictionary where you specify the configuration for these components. If a component has a

config_schema

defined and you haven't provided the necessary configuration, Dagster will raise an error indicating that the required config is missing. The Launchpad's config scaffolding feature can help you by generating a template for the required configuration based on the

config_schema

of the ops or assets within your job. You can then fill in the necessary values in this scaffolded configuration. To resolve this issue, you should: 1. Review the

config_schema

of your ops or assets to understand what configuration is required. 2. Use the Launchpad's config scaffolding feature to generate the necessary configuration structure. 3. Provide the missing configuration values as required by your job's components. If you continue to encounter issues with missing configuration, you may need to double-check that all required fields are specified and that the values provided match the expected types and formats defined in the

config_schema

.

@Scout can i have multiple sensors hooked up to the same job?

Yes, you can have multiple sensors hooked up to the same job in Dagster. Each sensor can monitor different conditions or events and trigger the same job based on those conditions. This is a common pattern when you have a job that needs to run in response to several different types of events. For example, you might have one sensor that triggers a job based on changes in a file system, another sensor that triggers the same job based on a message in a message queue, and yet another sensor that triggers the job based on a schedule or a time-based event. Each sensor would be defined separately, with its own logic for detecting changes and creating

RunRequest

objects. When the conditions for a sensor are met, it can issue a

RunRequest

for the job it's associated with. Here's a simplified example of how multiple sensors could be set up for the same job:

python from dagster import sensor, RunRequest, repository from <http://my_project.jobs|my_project.jobs> import my_job  @sensor(job=my_job) def sensor_one(_):     # Logic to check for condition one     if condition_one_met():         yield RunRequest(run_key="unique_run_key_for_condition_one")  @sensor(job=my_job) def sensor_two(_):     # Logic to check for condition two     if condition_two_met():         yield RunRequest(run_key="unique_run_key_for_condition_two")  @repository def my_repository():     return [my_job, sensor_one, sensor_two]

In this example,

sensor_one

and

sensor_two

are both monitoring different conditions and can trigger

my_job

independently when their respective conditions are met. It's important to ensure that each

RunRequest

has a unique

run_key

if you want to avoid duplicate job runs for the same condition. Remember that each sensor will run independently, and the job will be triggered as many times as the sensors issue a

RunRequest

. Therefore, you should design your sensors and job to handle this behavior appropriately.

@Scout is

Config

basically

TypedDict

with better typing?

The

Config

class in Dagster is not directly equivalent to

TypedDict

. While both are used to define types with specific expected fields, they serve different purposes and have different features within their respective ecosystems.

TypedDict

, which is part of Python's

typing

module, allows you to define a dictionary type with expected keys and values of specific types. It is a way to give type hints for dictionaries where you know the structure in advance. This can be useful for type checking and editor support, but

TypedDict

does not provide any runtime validation or parsing capabilities. Here's an example of `TypedDict`:

python from typing import TypedDict  class MyTypedDict(TypedDict):     name: str     age: int  # This dictionary is expected to have a 'name' key with a string value # and an 'age' key with an integer value. my_dict: MyTypedDict = {'name': 'Alice', 'age': 30}

On the other hand,

Config

in Dagster is a class that you can subclass to define configuration schemas for Dagster assets, ops, and resources. It is built on top of Pydantic and provides runtime validation, parsing, and error reporting for configuration data. When you define a

Config

subclass, you are creating a schema that Dagster will use to validate the configuration provided for a job run. Here's an example of

Config

in Dagster:

python from dagster import Config  class MyConfig(Config):     name: str     age: int  # Dagster will use MyConfig to validate the configuration for an asset, op, or resource. # If the configuration does not match the schema, Dagster will raise an error.

In summary, while

TypedDict

and

Config

both allow you to specify types for dictionaries,

Config

is specifically designed for use within the Dagster framework and provides additional functionality such as runtime validation and integration with Dagster's configuration system.

TypedDict

is a more general-purpose tool for type hinting dictionaries in Python code.

@Scout can i have asset code reference the output from the prior run of the asset?

In Dagster, assets are typically defined as functions that compute their values based on inputs, which can include parameters, upstream assets, and resources. However, assets themselves do not natively have the capability to reference their output from a prior run directly within the asset computation function. If you need to access the output of an asset from a prior run, you would typically use an IOManager to manage the storage and retrieval of asset values. The IOManager is responsible for loading and materializing (saving) asset values. You can implement custom logic in your IOManager to load the value of an asset from a previous run. Here's a conceptual example of how you might use an IOManager to access the value of an asset from a prior run:

python from dagster import asset, IOManager, io_manager  class MyIOManager(IOManager):     def handle_output(self, context, obj):         # Logic to save the asset's value, e.g., to a file or database      def load_input(self, context):         # Logic to load the asset's value from the previous run  @io_manager def my_io_manager():     return MyIOManager()  @asset(io_manager_key="my_io_manager") def my_asset(context):     # You can access the previous value using the context provided by the IOManager     previous_value = context.get_io_manager().load_input(context.upstream_output())     # Compute the new value based on the previous value     new_value = compute_new_value(previous_value)     return new_value

In this example,

MyIOManager

is a custom IOManager that implements the

handle_output

method to save the asset's value and the

load_input

method to load the asset's value from the previous run. The

my_asset

function then uses this IOManager to access the previous value and compute a new value. Please note that the actual implementation of the IOManager's

handle_output

and

load_input

methods will depend on how and where you want to store your asset values (e.g., in a file system, database, cloud storage, etc.). If you need to access the previous value of an asset within the same run (e.g., for incremental computation), you would typically use an upstream asset as an input to your asset function and manage the state through the IOManager or another form of state management. Keep in mind that the exact approach may vary based on your specific use case and the capabilities of the storage system you are using. If you have further questions about implementing this pattern or need more detailed guidance, please refer to the Dagster documentation or seek support from the Dagster community.

@Scout how might I debug this error? dagster._check.CheckError: Invariant failed. Description: Cannot provide a default value when using a Config class

The error message you're encountering,

dagster._check.CheckError: Invariant failed. Description: Cannot provide a default value when using a Config class

, suggests that there is an issue with the way you've defined default values in your Dagster

Config

class. This error typically occurs when you try to set a default value for a field within a

Config

class that is not supposed to have one, possibly due to the presence of nested

Config

classes or when the field is marked as required. Here are some steps to debug this error: 1. Review Config Class Definitions: Check your

Config

class definitions to ensure that you're not providing default values for fields that are marked as required or for fields that are themselves of type

Config

. According to the Dagster documentation, you should not provide default values for required fields. 2. Check Nested Config Classes: If you have nested

Config

classes, make sure that you're not providing a default value for the nested

Config

class itself. Instead, you should provide default values for the individual fields within the nested

Config

class if needed. 3. Examine Field Definitions: Look at the fields within your

Config

class and ensure that each field is defined correctly with the appropriate type and default value (if applicable). Remember that default values are not allowed for fields marked as required. 4. Use Field Class: If you're using Pydantic models, make sure you're using the

Field

class from Pydantic to define default values and not directly assigning them in the class body. 5. Check for Typos: Ensure that there are no typos or copy-paste errors in your

Config

class that might have led to the incorrect assignment of default values. 6. Consult Documentation: Review the Dagster documentation for

Config

classes and default value usage to ensure that your implementation aligns with the documented best practices and constraints. 7. Simplify Config: If you're still having trouble, try simplifying your

Config

class to the most basic form and gradually add fields back in, testing at each step to identify where the error is introduced. 8. Check for Updates: Ensure that you're using a version of Dagster that supports the features you're trying to use. If necessary, update Dagster to the latest version. 9. Seek Help: If you've gone through these steps and are still encountering issues, consider reaching out to the Dagster community for support. You can ask for help on the Dagster Slack channel or create a GitHub issue with a detailed description of your problem and the steps you've taken to debug it. Remember to provide as much context as possible when seeking help, including code snippets, Dagster version, and the full error message and stac