@Scout How do I pass a resource to a function that constructs a sensordefinition

To pass a resource to a function that constructs a

SensorDefinition

, you should annotate the resource as a parameter to the sensor's function. Here's how you can do it according to the documentation: 1. Annotate the resource type as a parameter to the sensor's function. 2. Attach the resource to the

Definitions

objects to provide resource values to the sensor. Here is an example of how you might define a sensor that uses a resource:

python from dagster import sensor, SensorEvaluationContext, ResourceDefinition  # Define your resource here class MyResource:     # Resource implementation...  # Define your sensor function with the resource as a parameter @sensor def my_sensor(context: SensorEvaluationContext, my_resource: MyResource):     # You can now use my_resource within your sensor logic     ...  # Then, when constructing your Definitions, you provide the resource defs = Definitions(     sensors=[my_sensor],     resources={"my_resource": ResourceDefinition.hardcoded_resource(MyResource())}, )

In this example,

my_resource

is a parameter to the

my_sensor

function, and it is provided when the

Definitions

object is constructed. The

resources

argument in

Definitions

is a dictionary where the keys are the names of the resources, and the values are instances of

ResourceDefinition

. Please note that the actual implementation details of your resource and how you use it within the sensor function will depend on your specific use case. The above code is a general pattern based on the documentation's guidance on using resources in sensors.

@Scout Without using the @sensor decorator

If you are constructing a

SensorDefinition

without using the

@sensor

decorator, you can pass resources by including them in the

required_resource_keys

set when you instantiate the

SensorDefinition

. Here's an example of how you might do this:

python from dagster import SensorDefinition, SensorEvaluationContext, RunRequest, ResourceDefinition  # Define your resource here class MyResource:     # Resource implementation...  def my_sensor_fn(context: SensorEvaluationContext):     # You can access the resource via context.resources     my_resource = context.resources.my_resource     # Sensor logic using the resource...  # Create a SensorDefinition and specify required_resource_keys my_sensor = SensorDefinition(     name="my_sensor",     evaluation_fn=my_sensor_fn,     required_resource_keys={"my_resource"}, )  # Then, when constructing your Definitions, you provide the resource defs = Definitions(     sensors=[my_sensor],     resources={"my_resource": ResourceDefinition.hardcoded_resource(MyResource())}, )

In this example,

my_sensor_fn

is the evaluation function for the sensor, and it accesses the resource through the

context.resources

property. The

required_resource_keys

parameter of the

SensorDefinition

constructor is a set of strings that specify the keys of the required resources. When you construct the

Definitions

object, you provide the actual resource instances in the

resources

dictionary, similar to how you would with the

@sensor

decorator. This approach allows you to define sensors programmatically without using decorators, while still being able to specify and use resources within your sensor's evaluation function.

@Scout I have a function that uses

slack-dagster

lib to create a sensor. I want to pass a resources to it. How would I do that?

def make_slack_on_failure_sensor(
    deployment: str, slack_resource: SlackResource
) -> SensorDefinition:
    """Make a Slack sensor for run failures."""
    channel_name = slack_sensor_config[deployment]["channel"]
    base_url = slack_sensor_config[deployment]["base_url"]

    return make_slack_on_run_failure_sensor(
        channel=channel_name,
        slack_token=slack_resource.token,
        text_fn=my_message_fn,
        webserver_base_url=base_url,
    )

The

make_slack_on_run_failure_sensor

function from the

dagster-slack

library is a factory function that creates a

SensorDefinition

for you. However, this function does not directly accept a

SlackResource

object as an argument. Instead, it expects individual configuration values such as the Slack token. If you want to use a

SlackResource

with this factory function, you would typically configure the

SlackResource

in your repository definition and then pass the token from that resource to the

make_slack_on_run_failure_sensor

function. Here's how you might do it: 1. Define your

SlackResource

in your repository configuration. 2. Access the

SlackResource

within your sensor construction function and pass the token to the

make_slack_on_run_failure_sensor

function.

python from dagster import repository, SensorDefinition from dagster_slack import make_slack_on_run_failure_sensor, SlackResource  # Assuming you have a dictionary `slack_sensor_config` defined somewhere slack_sensor_config = {     # Your deployment configurations }  def my_message_fn(context) -> str:     # Define how you want to format your Slack message     return f"Job {context.dagster_run.job_name} failed!"  def make_slack_on_failure_sensor(deployment: str) -> SensorDefinition:     """Make a Slack sensor for run failures."""     channel_name = slack_sensor_config[deployment]["channel"]     base_url = slack_sensor_config[deployment]["base_url"]      # Access the Slack token from the SlackResource     slack_resource = context.resources.slack      return make_slack_on_run_failure_sensor(         channel=channel_name,         slack_token=slack_resource.token,         text_fn=my_message_fn,         webserver_base_url=base_url,     )  @repository def my_repo():     return [         my_job,  # Replace with your actual job definition         make_slack_on_failure_sensor("my_deployment")     ]

In this example, the

make_slack_on_failure_sensor

function is called within the repository definition, and it's assumed that the

SlackResource

is configured elsewhere in your repository setup with the key

"slack"

. The

context

variable used to access

context.resources.slack

is not explicitly shown here, but it would be part of the sensor's execution context when the sensor runs. Please note that the

make_slack_on_run_failure_sensor

function expects a Slack token as a string, so you need to ensure that the

SlackResource

object has a

token

attribute that provides this token. If you need to pass the

SlackResource

directly to the sensor function, you would have to manually create a

SensorDefinition

without using the

make_slack_on_run_failure_sensor

factory function, which would involve more custom code to replicate the behavior of the factory function.

@Scout Explain Factory Functions

Factory functions are a design pattern in programming that involve creating a function that returns a new instance of an object or a specific configuration of an object. This pattern is commonly used to abstract the creation process of an object, allowing for more flexible and reusable code. Factory functions can be particularly useful when the creation process is complex or when it needs to vary depending on the context. Here are some key points about factory functions: 1. Abstraction: Factory functions abstract the instantiation process of an object. The consumer of the factory function does not need to know the details of how the object is created or configured. 2. Customization: Factory functions can accept parameters that customize the returned object. This allows for the creation of objects with different configurations from the same factory function. 3. Encapsulation: By using a factory function, the details of the object's construction are encapsulated within the function. This helps to keep the code that uses the object clean and focused on its own logic. 4. Reusability: Factory functions promote reusability. The same function can be used in different parts of the application or even across different applications to create objects as needed. 5. Substitution: Factory functions can return instances of different classes that share a common interface. This makes it easy to swap out implementations without changing the code that uses the objects. In the context of Dagster, factory functions can be used to create reusable components such as sensors, resources, or types. For example, a factory function could be used to create a sensor that monitors different data sources by accepting a parameter that specifies which source to monitor. This allows for the creation of multiple sensors from the same function, each configured for a different data source. Here's a simple example of a factory function in Python:

python class Dog:     def __init__(self, name, breed):         self.name = name         self.breed = breed  def dog_factory(name, breed):     return Dog(name, breed)  # Create different dog instances using the factory function buddy = dog_factory("Buddy", "Golden Retriever") max = dog_factory("Max", "Bulldog")

In this example,

dog_factory

is a factory function that creates new instances of the

Dog

class with the given

name

and

breed

. The function encapsulates the creation logic, allowing for easy instantiation of

Dog

objects.

@Scout Dagster context resource how to access?

In Dagster, resources are a way to provide reusable functionality to your jobs, ops, and assets. Resources can encapsulate anything from database connections to client libraries for external services. To access a resource within the context of a job, op, or asset, you use the

context

object that is passed to the function. Here's how you can access a resource from within an op or asset:

python from dagster import op, asset, ResourceDefinition, build_op_context  # Define your resource class MyResource:     def __init__(self, value):         self.value = value      def do_something(self):         return f"Resource value is: {self.value}"  # Define an op that uses the resource @op(required_resource_keys={"my_resource"}) def my_op(context):     # Access the resource via context.resources     result = context.resources.my_resource.do_something()     <http://context.log.info|context.log.info>(result)     return result  # Define an asset that uses the resource @asset(required_resource_keys={"my_resource"}) def my_asset(context):     # Access the resource via context.resources     result = context.resources.my_resource.do_something()     <http://context.log.info|context.log.info>(result)     return result  # Define your resource definition my_resource_def = ResourceDefinition.hardcoded_resource(MyResource(value="Hello, World!"))  # Build a context for testing or running outside of a job context = build_op_context(resources={"my_resource": my_resource_def})  # Now you can call the op or asset with the context my_op(context) my_asset(context)

In this example,

MyResource

is a simple class that has a method

do_something

. The

my_op

op and

my_asset

asset both require a resource with the key

my_resource

. When defining the op and asset, you specify the required resource keys using the

required_resource_keys

parameter. Inside the body of the op or asset, you access the resource through

context.resources.my_resource

. When you run the op or asset within a job, Dagster automatically provides the context, including the resources defined in the job's configuration. If you need to build a context for testing or running an op or asset outside of a job, you can use the

build_op_context

function, passing in a dictionary of resources. Remember that the resource keys used in

required_resource_keys

must match the keys you use when defining the resources in your job or repository configuration.

@Scout How to use dagster resources in a regular function

In Dagster, resources are typically used within the context of ops, assets, or other Dagster-defined computations. However, if you want to use Dagster resources in a regular Python function, you would need to pass the resource objects explicitly to the function as arguments. This is because regular Python functions do not have the context object that Dagster provides to ops and assets. Here's an example of how you might use a Dagster resource in a regular Python function:

python # Define your resource class class MyResource:     def __init__(self, value):         self.value = value      def do_something(self):         return f"Resource value is: {self.value}"  # Define a regular Python function that expects a resource as an argument def my_regular_function(my_resource: MyResource):     result = my_resource.do_something()     print(result)     return result  # Instantiate your resource my_resource_instance = MyResource(value="Hello, World!")  # Now you can call the regular function with the resource instance my_regular_function(my_resource_instance)

In this example,

MyResource

is a simple class with a method

do_something

. The

my_regular_function

function is a regular Python function that takes an instance of

MyResource

as an argument. You create an instance of

MyResource

and pass it to

my_regular_function

when you call it. If you are using resources within a Dagster pipeline and want to use them in a regular function, you would access the resources from the context and pass them to your function:

python from dagster import op, job, ResourceDefinition  @op(required_resource_keys={"my_resource"}) def my_op(context):     # Access the resource from the context     my_resource_instance = context.resources.my_resource     # Pass the resource to the regular function     my_regular_function(my_resource_instance)  @job(resource_defs={"my_resource": ResourceDefinition.hardcoded_resource(MyResource(value="Hello, World!"))}) def my_job():     my_op()  # Execute the job my_job.execute_in_process()

In this case,

my_op

is an op that requires the

my_resource

resource. It accesses the resource from the context and passes it to the

my_regular_function

. The

my_job

job defines the resource and includes

my_op

. When you execute the job, Dagster will provide the resource to

my_op

, which in turn passes it to the regular function.

@Scout Using a resource with a normal function, where the resource uses EnvVar

When using a resource that relies on

EnvVar

to pull in environment variables, you typically define this within the Dagster framework, which handles the retrieval of environment variables at runtime. However, if you want to use such a resource in a regular Python function, you would need to ensure that the environment variables are available in your environment and then manually instantiate the resource with those environment variables. Here's an example of how you might define a resource that uses

EnvVar

and how you could use it in a regular Python function:

python import os from dagster import resource, StringSource  # Define your resource using EnvVar @resource(config_schema={"my_env_var": StringSource}) def my_resource(init_context):     env_var_value = init_context.resource_config["my_env_var"]     return MyResource(env_var_value)  # Define the MyResource class class MyResource:     def __init__(self, value):         self.value = value      def do_something(self):         return f"Resource value is: {self.value}"  # Define a regular Python function that expects a resource as an argument def my_regular_function(my_resource: MyResource):     result = my_resource.do_something()     print(result)     return result  # Manually retrieve the environment variable my_env_var_value = os.getenv("MY_ENV_VAR")  # Check if the environment variable is set if my_env_var_value is None:     raise ValueError("Environment variable 'MY_ENV_VAR' is not set")  # Instantiate your resource with the environment variable my_resource_instance = MyResource(my_env_var_value)  # Now you can call the regular function with the resource instance my_regular_function(my_resource_instance)

In this example, the

my_resource

function is a Dagster resource definition that expects an environment variable

MY_ENV_VAR

. The

MyResource

class is a simple class that takes a value and has a method

do_something

. The

my_regular_function

is a regular Python function that takes an instance of

MyResource

as an argument. Outside of the Dagster context, you manually retrieve the value of the environment variable using

os.getenv

and then instantiate

MyResource

with this value. You then pass this instance to

my_regular_function

. Please note that when running this code outside of Dagster's execution context,

EnvVar

is not used because it is a Dagster-specific class for handling environment variables within the Dagster framework. Instead, you use the standard

os.getenv

method to retrieve the environment variable.