give me some examples of when I would want a resource to

configure_at_launch()

, and explain the pros and cons of this mechanism of instantiating a resource.

Certainly! The

configure_at_launch()

method is used when you want to defer the configuration of a resource until the time a job or asset is launched. This can be particularly useful in scenarios where the configuration is dynamic and determined at runtime, rather than being static or known upfront. ### Examples of when to use `configure_at_launch()`: 1. Database Connections with Dynamic Parameters: If you have a job that interacts with different databases or tables based on some runtime condition, you might use

configure_at_launch()

to specify the target database or table at launch time. 2. Environment-Specific Resources: In cases where you have multiple deployment environments (development, staging, production), you might want to configure resources differently in each environment. Using

configure_at_launch()

, you can pass environment-specific configurations when the job is triggered. 3. User-Driven Workflows: If the configuration of a resource depends on user input or some external event, you can use

configure_at_launch()

to provide the necessary parameters when the job is initiated in response to that input or event. 4. Testing and Mocking: When writing tests, you might want to configure a resource with different settings or mock implementations.

configure_at_launch()

allows you to provide these configurations at the time the test job is run. ### Pros and Cons of `configure_at_launch()`: Pros: - Flexibility: It allows for dynamic configuration of resources based on runtime conditions, user inputs, or other external factors. - Environment Agnosticism: It enables the same job or asset code to be used across different environments with different configurations. - Simplicity: It can simplify the configuration management by allowing you to specify resource configurations in one place at launch time, rather than having to manage multiple configuration files or environment variables. - Security: It can enhance security by allowing sensitive information to be passed at runtime, reducing the exposure of secrets in code or configuration files. Cons: - Complexity: It can add complexity to the launch process, as you need to ensure that the correct configurations are provided every time a job is launched. - Observability: It might make it harder to understand the configuration that was used for a particular run, as it is not statically defined in the codebase. - Error-Prone: There is a risk of misconfiguration or forgetting to provide necessary configuration parameters at launch time, which can lead to runtime errors. The documentation provides an example of how to use

configure_at_launch()

with a sensor, where the sensor dynamically provides the table name to a database resource for ea

@Scout I don't see

table

defined in this example. Show me a complete example of how a configurable property of a resource is set at launch time in an asset.

Apologies for the confusion. Let's clarify how a configurable property of a resource is set at launch time in an asset with a more complete example. The following example demonstrates how you can define a

DatabaseResource

with a configurable property

table

and how you can set this property at launch time using a sensor. First, define the

DatabaseResource

class with a configurable property `table`:

python from dagster import ConfigurableResource, asset  class DatabaseResource(ConfigurableResource):     table: str      def read(self):         # Here you would have logic to read from the specified table         # For example, using an SQL query like f"SELECT * FROM {self.table}"         ...

Next, define an asset that uses this resource:

python @asset(required_resource_keys={"db_conn"}) def data_from_database(db_conn: DatabaseResource):     return db_conn.read()

Now, define a sensor that triggers runs of a job that includes this asset. The sensor will dynamically provide the

table

name to the

DatabaseResource

at launch time:

python from dagster import sensor, RunRequest, RunConfig, define_asset_job  update_data_job = define_asset_job(     name="update_data_job",     selection=[data_from_database] )  @sensor(job=update_data_job) def table_update_sensor():     tables = ["table1", "table2"]  # This could be dynamically generated     for table_name in tables:         yield RunRequest(             run_config=RunConfig(                 resources={                     "db_conn": {"config": {"table": table_name}},                 },             ),         )

In this example, the

table_update_sensor

sensor yields

RunRequest

objects that include a

RunConfig

. The

RunConfig

specifies the configuration for the

db_conn

resource, setting the

table

property to the name of the table that you want to read from in each run. When the sensor triggers a run, it will pass the

table

configuration to the

DatabaseResource

, and the

data_from_database

asset will read from the specified table. This example shows how you can set a configurable property of a resource at launch time in an asset using Dagster's sensor mechanism. The actual implementation details of the

DatabaseResource.read

method would depend on the specifics of your database interaction, such as using an ORM or executing raw SQL queries.

@Scout can I configure an asset at launch time without a sensor, and as part of a normal job run?

In Dagster, assets are typically configured as part of their job definition, and this configuration is static for all runs of the job. However, if you want to provide dynamic configuration at launch time without using a sensor, you would typically use the Dagster UI or the GraphQL API to provide run-time configuration when manually launching a run. Here's how you can do it: 1. Define your asset and resource: As in the previous examples, you would define your asset and the resource it requires. 2. Define a job that includes your asset: Create a job that includes the asset you want to configure. 3. Launch a run with custom configuration: When you launch a run of the job through the Dagster UI or the GraphQL API, you can provide a custom run configuration that includes the dynamic values you want to use for that specific run. Here's an example of how you might define the job:

python from dagster import job  @job(resource_defs={"db_conn": db_resource}) def my_data_processing_job():     data_from_database()

When you go to launch a run of

my_data_processing_job

through the Dagster UI, you would be presented with a configuration editor where you can input the dynamic configuration for the

db_conn

resource. Alternatively, you could use the GraphQL API to launch the run with a provided configuration. It's important to note that while you can provide dynamic configuration at launch time, the asset itself is not directly receiving configuration; rather, it's the resources or ops within the job that are being configured. The asset will use these configured resources or ops as part of its computation. If you need to configure assets dynamically and frequently, using sensors or schedules to automate this process might be more efficient. However, for one-off runs or testing purposes, manually providing configuration at launch time is a suitable approach.

@Scout can I partially configure a resource in Definitions and then fill in the blanks at launch?

As of the last update from the documentation, Dagster does not support partial configuration of resources directly in the

Definitions

and then filling in the remaining configuration at launch time. Resources must be either fully configured at definition time or configured at launch time. However, there has been discussion in the community about the desire for such a feature, where users can provide some configuration when defining the resource and then supply the rest of the configuration at runtime. This feature would allow for more flexible resource configuration patterns, especially in cases where some resource parameters are known upfront, and others are only known at runtime. As of now, if you want to configure a resource at launch time, you would use the

configure_at_launch()

method when defining the resource in

Definitions

. This means that all configuration for the resource must be provided at launch time, and none of it is specified in the

Definitions

. Here's an example of how you would define a resource that is fully configured at launch time:

python from dagster import ConfigurableResource, Definitions, asset  class MyResource(ConfigurableResource):     # Configuration schema for the resource     foo: str     bar: str      # ...  @asset(required_resource_keys={"my_resource"}) def my_asset(context):     # Use the resource within the asset     resource = context.resources.my_resource     # ...  # Define the resource to be configured at launch time defs = Definitions(     assets=[my_asset],     resources={"my_resource": MyResource.configure_at_launch()}, )

In this example, when you launch a run that includes

my_asset

, you would need to provide the configuration for

my_resource

, including values for both

foo

and

bar

. If the feature for partial configuration becomes available in a future release, the documentation will be updated to reflect how to use it. For now, you would need to manage partial configurations outside of Dagster's resource system, such as by using environment variables or other mechanisms to supply the additional configuration at runtime.

@Scout How should foo and bar be configured in a run containing the

my_asset

asset? Show me a full example.

To configure the

foo

and

bar

properties of the

MyResource

resource in a run containing the

my_asset

asset, you would provide a run configuration that specifies the values for these properties. This configuration is typically provided as a YAML file or a dictionary when launching the run through the Dagster UI, the Dagster CLI, or programmatically. Here's an example of how you might provide this configuration as a YAML file:

yaml resources:   my_resource:     config:       foo: "value_for_foo"       bar: "value_for_bar"

If you're using the Dagster UI, you would paste this configuration into the appropriate field in the launchpad when triggering a run. Alternatively, if you're launching the run programmatically, you would provide the configuration as a dictionary to the

run_config

parameter. Here's an example of how you might do this using the

materialize

function:

python from dagster import materialize  result = materialize(     [my_asset],     run_config={         "resources": {             "my_resource": {                 "config": {                     "foo": "value_for_foo",                     "bar": "value_for_bar",                 }             }         }     } )

In this example, the

materialize

function is used to execute a run that materializes the

my_asset

asset with the provided resource configuration. The

run_config

dictionary specifies the configuration for the

my_resource

resource, including the

foo

and

bar

properties. Please note that the actual implementation of the

MyResource

class and how it uses the

foo

and

bar

properties would depend on your specific use case. The above examples show how to pass configuration values for these properties when launching a run.