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<@U0667DNC02Y> I ran my dbt models as assets and I...
# ask-ai
i
@Scout I ran my dbt models as assets and I got this error Invariant failed. Description: Asset checks require a database schema migration. Run 
dagster instance migrate
.
s
Based on the documentation, it seems that you are encountering a schema migration issue related to asset checks within Dagster. The error message you received suggests that a database schema migration is required for the asset checks to function properly. To resolve this issue, you should run the 
dagster instance migrate
command. This command will migrate your instance storage to the latest schema, which includes the necessary changes for asset checks to work. Here's a step you can take to address the error: 1. Open your terminal or command prompt. 2. Navigate to the directory where your Dagster instance is configured. 3. Run the command 
dagster instance migrate
. This should perform the necessary migrations on your Dagster instance's database schema, allowing the asset checks to proceed without the invariant failure error. If you continue to experience issues after running the migration command, please ensure that your Dagster and dagster-dbt versions are compatible and up to date, as schema migrations and features can be dependent on specific versions of the software.
i
@Scout are you there?
s
Yes, I'm here to assist you! If you have any questions or need further assistance with Dagster, feel free to ask. Thank you for your feedback!
i
HELL YEAH
@Scout I'd like to know how can I create a dependency between a asset and a dbt model The situation is that I have a model that loads the table and I want my dbts models to start running after that The asset generates loads to 4 tables, not necessary at once, and I wanted to create this dependency so the dbts models could materialize after 2 of them are loaded
s
To create a dependency between a Dagster asset and a dbt model, you can leverage the fact that dbt models can be treated as assets within Dagster. This means you can define dependencies between your custom assets and dbt models in a similar way to how you define dependencies between assets in Dagster. Here's a general approach to achieve this: 1. Define your custom asset: This asset will perform the initial loading of data into your tables. You can define this asset using the 
@asset
decorator. 2. Load dbt models as assets: Use Dagster's dbt integration to load your dbt models as assets. This can be done using the 
load_assets_from_dbt_project
function, which will automatically convert your dbt models into Dagster assets. 3. Specify dependencies: When defining your dbt models as assets, you can specify the dependencies on the custom asset by using the 
deps
parameter within the 
@asset
decorator or by defining the dependencies within the dbt model's SQL using 
ref
or 
source
calls. Here's an example of how you might define these dependencies in code:
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python from dagster import asset, load_assets_from_dbt_project  # Define your custom asset that loads data into tables @asset def my_custom_asset():     # Your logic to load data into tables     pass  # Load your dbt models as assets dbt_assets = load_assets_from_dbt_project(project_dir="path/to/dbt/project")  # Define dependencies for dbt models that should run after `my_custom_asset` # This is a simplified example; you would replace `my_dbt_model` with the actual # asset key of your dbt model, which is typically the name of the dbt model. @asset(required_resource_keys={"dbt"}, deps={"my_custom_asset": my_custom_asset}) def my_dbt_model(context):     # This function will be called after `my_custom_asset` has completed.     # You can invoke dbt CLI commands here if needed, or rely on the dbt assets loaded earlier.     context.resources.dbt.run_op(context, select=["my_dbt_model"])
In this example, 
my_dbt_model
is a placeholder for the actual dbt model you want to depend on 
my_custom_asset
. The 
required_resource_keys
parameter is used to ensure that the dbt CLI is available to the asset. Please note that the actual implementation may vary based on your specific setup and the version of Dagster and dagster-dbt you are using. Make sure to refer to the [Dagster documentation](https://docs.dagster.io/integrations/dbt) for the most up-to-date instructions and examples on integrating dbt with Dagster.
i
@Scout Okay, ive found how to create the dependency, now I need to specify the asset_key + partition as the dependency to run the dbt_model, is there a way to do it?
s
Yes, you can specify an asset key and partition as a dependency for a dbt model in Dagster. To do this, you'll need to use the 
AssetKey
and 
PartitionMapping
concepts to define the relationship between the assets. Here's a general approach to define a dependency with a specific asset key and partition: 1. Define your dbt assets: Use the 
@dbt_assets
decorator to define your dbt models as assets. If your dbt models are partitioned, you can specify the partitioning scheme within the decorator. 2. Create a custom asset with dependencies: When defining your custom asset that depends on the dbt model, you can use the 
AssetKey
to specify the exact asset you depend on. If you need to depend on a specific partition of that asset, you can use a 
PartitionMapping
to define how the partitions relate to each other. Here's an example of how you might define an asset that depends on a specific partition of a dbt model asset:
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python from dagster import asset, AssetKey, AssetIn from dagster_dbt import dbt_assets  # Define your dbt models as assets @dbt_assets def my_dbt_assets():     # dbt CLI commands to run your dbt project     pass  # Define your custom asset that depends on a specific partition of a dbt model @asset(ins={"my_dbt_model_input": AssetIn(key=AssetKey("my_dbt_model"), partitions_def=my_partition_scheme)}) def my_custom_asset(my_dbt_model_input):     # Your logic that depends on the dbt model     pass
In this example, 
my_partition_scheme
would be a definition of how your partitions are structured, which you would have defined elsewhere in your code. The 
AssetIn
object allows you to specify the 
AssetKey
of the dbt model you depend on, and you can also specify the partitioning scheme if applicable. Please note that the actual implementation details may vary based on your specific use case, the structure of your dbt project, and the version of Dagster and dagster-dbt you are using. Always refer to the [Dagster documentation](https://docs.dagster.io/integrations/dbt) for the most accurate and detailed guidance on working with dbt assets and dependencies in Dagster.
i
@Scout acctually is the opposite, the dependency is dbt_model depends on assetkey + partition
s
To specify that a dbt model asset depends on a non-dbt asset with a specific asset key and partition in Dagster, you can use the 
AssetIn
and 
PartitionMapping
concepts to define the dependency relationship. This allows you to specify that the dbt model should only run after the specified partition of the upstream asset has been materialized. Here's a step-by-step approach to define this dependency: 1. Define your upstream asset: This is the non-dbt asset that your dbt model will depend on. If it's partitioned, make sure to define the partitioning scheme. 2. Define your dbt model as an asset: Use the 
@asset
decorator to define your dbt model as an asset in Dagster. Within this definition, you'll specify the dependency on the upstream asset using the 
AssetIn
object. 3. Specify the partition dependency: If you need the dbt model to depend on a specific partition of the upstream asset, you'll use a 
PartitionMapping
to define the relationship between the partitions of the dbt model and the upstream asset. Here's an example of how you might define a dbt model asset that depends on a specific partition of an upstream asset:
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python from dagster import asset, AssetKey, AssetIn, DailyPartitionsDefinition, PartitionMapping from dagster_dbt import dbt_cli_resource  # Define your upstream asset with a partitioning scheme @asset(partitions_def=DailyPartitionsDefinition(start_date="2021-01-01")) def upstream_asset():     # Logic to materialize the upstream asset     pass  # Define your dbt model as an asset that depends on a specific partition of the upstream asset @asset(     required_resource_keys={"dbt"},     ins={"upstream_asset_input": AssetIn(         key=AssetKey("upstream_asset"),         partition_mapping=PartitionMapping()     )} ) def my_dbt_model(context):     # Logic to run the dbt model, potentially using the dbt CLI resource     context.resources.dbt.run_op(context, select=["my_dbt_model"])
In this example, 
PartitionMapping()
is a placeholder for the actual partition mapping you would use to define the relationship between the partitions of the dbt model and the upstream asset. You would need to replace this with the appropriate 
PartitionMapping
that reflects your partitioning logic. Please note that the actual implementation details may vary based on your specific use case, the structure of your dbt project, and the version of Dagster and dagster-dbt you are using. Always refer to the [Dagster documentation](https://docs.dagster.io/integrations/dbt) for the most accurate and detailed guidance on working with dbt assets and dependencies in Dagster.
i
@Scout So in this scenario I wouldn't use @dbt_assets decorator?
s
In the scenario where you want to create dependencies between a dbt model and a non-dbt asset with a specific asset key and partition, you would not use the 
@dbt_assets
decorator directly. Instead, you would define your dbt model as a regular Dagster asset using the 
@asset
decorator and specify the dependencies using the 
AssetIn
object. The 
@dbt_assets
decorator is used to load dbt models as assets directly from a dbt project, and it automatically handles the conversion of dbt models into Dagster assets. However, when you need to create more complex dependencies, such as depending on a specific partition of an upstream asset, you have to define the dbt model as a Dagster asset manually and set up the dependencies explicitly. Here's a simplified example of how you might define a dbt model asset with a dependency on a specific partition of an upstream asset:
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python from dagster import asset, AssetKey, AssetIn, PartitionMapping  # Define your upstream asset with a partitioning scheme @asset(partitions_def=DailyPartitionsDefinition(start_date="2021-01-01")) def upstream_asset():     # Logic to materialize the upstream asset     pass  # Define your dbt model as a Dagster asset with a dependency on the upstream asset @asset(     required_resource_keys={"dbt"},     ins={"upstream_asset_input": AssetIn(         key=AssetKey("upstream_asset"),         partition_mapping=PartitionMapping()     )} ) def my_dbt_model(context):     # Logic to run the dbt model, potentially using the dbt CLI resource     context.resources.dbt.run_op(context, select=["my_dbt_model"])
In this code, 
PartitionMapping()
is a placeholder for the actual partition mapping logic you would implement to define the relationship between the partitions of the dbt model and the upstream asset. Please refer to the [Dagster documentation](https://docs.dagster.io/integrations/dbt) for the most up-to-date instructions and examples on integrating dbt with Dagster, as the implementation details may vary based on your specific setup and the versions of Dagster and dagster-dbt you are using.
i
@Scout how to make an asset that looks to a table and knows when to start my dbts assets loaded by @dbt_assets? It can be either an asset or a sensor
s
To trigger dbt assets loaded by the 
@dbt_assets
decorator based on changes to a table, you can use an asset sensor. An asset sensor in Dagster can monitor for new 
AssetMaterialization
events for a particular asset key and trigger a job or a set of asset computations when changes are detected. Here's a general approach to create an asset sensor that triggers dbt assets: 1. Define your dbt assets: Use the 
@dbt_assets
decorator to load your dbt models as assets from your dbt project. 2. Create an asset sensor: Define an asset sensor that monitors the table you're interested in. When the sensor detects a new materialization (indicating that the table has been updated), it can trigger a run that includes your dbt assets. Here's an example of how you might set up an asset sensor to trigger dbt assets:
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python from dagster import asset_sensor, RunRequest, AssetKey, repository from dagster_dbt import dbt_cli_resource  # Define your dbt assets using the @dbt_assets decorator @dbt_assets def my_dbt_assets():     # dbt CLI commands to run your dbt project     pass  # Define an asset sensor that triggers when the monitored table is updated @asset_sensor(asset_key=AssetKey("my_monitored_table"), job=my_dbt_assets) def my_table_updated_sensor(context, asset_event):     # Logic to determine whether to trigger the dbt assets     # For example, you can check the timestamp of the materialization or other metadata     if should_trigger_dbt_assets(asset_event):         return RunRequest(run_key="unique-run-key-for-this-materialization")  # Define a repository to hold your assets and sensors @repository def my_repo():     return [my_dbt_assets, my_table_updated_sensor]  def should_trigger_dbt_assets(asset_event):     # Implement your logic here to decide whether to trigger the dbt assets     # This could be based on timestamps, metadata, or other custom logic     return True
In this example, 
my_monitored_table
is the asset key for the table you want to monitor. The 
my_table_updated_sensor
function is an asset sensor that checks for new materializations of this table. When it detects a change, it creates a 
RunRequest
to trigger a run of the 
my_dbt_assets
job. Please note that the 
should_trigger_dbt_assets
function is a placeholder for your custom logic to determine when to trigger the dbt assets. You would need to implement this function based on your specific requirements. Remember to refer to the [Dagster documentation](https://docs.dagster.io/concepts/partitions-schedules-sensors/asset-sensors) for the most accurate and detailed guidance on working with asset sensors in Dagster.
i
@Scout okay, here's the thing, I have two assetkeys which I'll use to trigger my dbt assets loaded by @dbt_assets. How can I achieve that?
s
To trigger dbt assets loaded by the 
@dbt_assets
decorator based on updates to two specific asset keys, you can create an asset sensor that monitors both asset keys. The sensor will listen for materialization events for these assets, and when it detects that both have been updated, it can trigger a run of the dbt assets. Here's a step-by-step approach to achieve this: 1. Define your dbt assets: Use the 
@dbt_assets
decorator to load your dbt models as assets from your dbt project. 2. Create an asset sensor: Define an asset sensor that monitors the two asset keys you're interested in. The sensor will need to keep track of the latest materialization for each asset key and trigger the dbt assets only when both have been updated. Here's an example of how you might set up an asset sensor to trigger dbt assets based on two asset keys:
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python
from dagster import asset_sensor, RunRequest, AssetKey, repository, SensorExecutionContext
from dagster_dbt import dbt_cli_resource

# Define your dbt assets using the @dbt_assets decorator
@dbt_assets
def my_dbt_assets():
    # dbt CLI commands to run your dbt project
    pass

# Define an asset sensor that triggers when both monitored assets are updated
@asset_sensor(asset_keys=[AssetKey("first_asset"), AssetKey("second_asset")], job=my_dbt_assets)
def my_assets_updated_sensor(context: SensorExecutionContext):
    # Retrieve the latest materialization events for both assets
    first_asset_event = context.instance.get_latest_materialization_events(
        asset_key=AssetKey("first_asset")
    )
    second_asset_event = context.instance.get_latest_materialization_events(
        asset_key=AssetKey("second_asset")
    )

    # Check if both assets have been materialized
    if first_asset_event and second_asset_event:
        # Optionally, add additional logic to compare timestamps or other metadata
        # to ensure that both assets have been updated recently or meet other criteria

        # Trigger the dbt assets
        return RunRequest(run_key="unique-run-key-for-both-materializations")

# Define a repository to hold your assets and sensors
@repository
def my_repo():
    return [my_dbt_assets, my_assets_updated_sensor]
In this example, 
first_asset
and 
second_asset
are the asset keys for the two assets you want to monitor. The 
my_assets_updated_sensor
function is an asset sensor that checks for new materializations of both assets. When it detects that both assets have been updated, it creates a 
RunRequest
to trigger a run of the 
my_dbt_assets
job. Please adjust the logic within the 
my_assets_updated_sensor
function to suit your specific requirements, such as checking timestamps or other metadata to determine when to trigger the dbt assets. Refer to the [Dagster documentation
](https://docs.dagster.io/concepts/partitions-schedules-sensors/asset-sensors) for more details on asset sensors and how to configure them in your Dagster repository.
i
@Scout the @dbt_assets decorator does not make the method a job, it makes it a asset, so how can I change your solution do achieve what I want?
s
There was an error processing your request. Please try again.
i
@Scout the @dbt_assets decorator does not make the method a job, it makes it a asset, so how can I change your solution do achieve what I want?
s
Apologies for the confusion. You are correct that the 
@dbt_assets
decorator is used to define dbt models as Dagster assets, not jobs. To trigger these dbt assets based on updates to two specific asset keys, you can still use an asset sensor, but you'll need to adjust the approach to trigger the appropriate set of dbt assets. Here's how you can modify the solution: 1. Define your dbt assets: Use the 
@dbt_assets
decorator to load your dbt models as assets from your dbt project. 2. Create an asset sensor: Define an asset sensor that monitors the two asset keys you're interested in. The sensor will need to keep track of the latest materialization for each asset key and trigger the appropriate dbt assets when both have been updated. Here's an example of how you might set up an asset sensor to trigger dbt assets based on two asset keys:
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python
from dagster import asset_sensor, RunRequest, AssetKey, repository, SensorExecutionContext
from dagster_dbt import dbt_cli_resource

# Define your dbt assets using the @dbt_assets decorator
dbt_assets = my_dbt_assets()

# Define an asset sensor that triggers when both monitored assets are updated
@asset_sensor(asset_keys=[AssetKey("first_asset"), AssetKey("second_asset")])
def my_assets_updated_sensor(context: SensorExecutionContext):
    # Retrieve the latest materialization events for both assets
    first_asset_event = context.instance.get_latest_materialization_events(
        asset_key=AssetKey("first_asset")
    )
    second_asset_event = context.instance.get_latest_materialization_events(
        asset_key=AssetKey("second_asset")
    )

    # Check if both assets have been materialized
    if first_asset_event and second_asset_event:
        # Optionally, add additional logic to compare timestamps or other metadata
        # to ensure that both assets have been updated recently or meet other criteria

        # Trigger the dbt assets by creating a run request with the appropriate op selection
        return RunRequest(
            run_key="unique-run-key-for-both-materializations",
            run_config={
                "ops": {
                    "my_dbt_assets_op": {
                        "config": {
                            # Specify the dbt models to run or other dbt run configs here
                        }
                    }
                }
            }
        )

# Define a repository to hold your assets and sensors
@repository
def my_repo():
    return [dbt_assets, my_assets_updated_sensor]
In this example, 
my_dbt_assets_op
would be the name of the op that corresponds to the dbt assets you want to run. You would need to replace this with the actual op name that is generated for your dbt assets. The 
run_config
should be adjusted to include the necessary confi
guration for the dbt run. Please note that the actual implementation details may vary based on your specific use case and the version of Dagster and dagster-dbt you are using. Always refer to the [Dagster documentation](https://docs.dagster.io/concepts/partitions-schedules-sensors/asset-sensors) for the most accurate and detailed guidance on working with asset sensors in Dagster.
i
@Scout can I achieve this by setting meta.dagster.asset_key propertie on a dbt source.yml and then on the model setting a
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depends_on: {{ source('dagster','import_filter_job__import_wf') }}
?
s
Yes, you can achieve this by setting the 
meta.dagster.asset_key
property in your dbt 
source.yml
file and then specifying a dependency in your dbt model using the 
depends_on
Jinja directive. Here's how you can set it up: 1. Define the dbt source with a Dagster asset key: In your dbt 
source.yml
file, define a source and include the 
meta.dagster.asset_key
property to specify the corresponding Dagster asset key.
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yaml # source.yml  sources:   - name: dagster     tables:       - name: import_filter_job__import_wf         meta:           dagster:             asset_key: ["import_filter_job__import_wf"]
2. Specify the dependency in your dbt model: In your dbt model file, use the 
depends_on
directive to declare a dependency on the source you defined in the 
source.yml
file.
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sql -- my_dbt_model.sql  -- depends_on: {{ source('dagster', 'import_filter_job__import_wf') }}  SELECT ... FROM {{ source('dagster', 'import_filter_job__import_wf') }}
By setting up your dbt project in this way, you are explicitly defining a dependency relationship between your dbt model and the Dagster asset corresponding to the dbt source. When Dagster reads the contents of the dbt project, it will infer the correspondence based on the metadata provided in the 
source.yml
file. When you materialize your dbt assets in Dagster, it will recognize the dependency and ensure that the asset corresponding to the dbt source (
import_filter_job__import_wf
) is materialized before running the downstream dbt model that depends on it. Please ensure that your dbt project is set up correctly and that you have the latest versions of Dagster and dagster-dbt that support these features. For the most accurate and detailed guidance, refer to the [Dagster documentation](https://docs.dagster.io/integrations/dbt).
i
@Scout the asset import_filter_job__import_wf does not return any data, it only ingests it on redshift, doing a select from this source wont cause any errors?
s
In dbt, a source typically represents a table or view in your database that dbt models can select from. If the 
import_filter_job__import_wf
asset represents a process that ingests data into a Redshift table without returning any data to dbt, you should not encounter errors when selecting from this source in your dbt model, as long as the ingestion process successfully creates or updates the table in Redshift. When you define a source in dbt using the 
source.yml
file and then reference it in a dbt model with the 
source()
function, dbt generates the appropriate SQL to select data from the corresponding table in the database. It does not require the source to return data to dbt; it only needs the table to exist in the database with the expected schema. Here's an example of how you might reference the source in a dbt model:
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sql -- my_dbt_model.sql  SELECT ... FROM {{ source('dagster', 'import_filter_job__import_wf') }}
In this SQL file, the 
source()
function is used to reference the 
import_filter_job__import_wf
table that your asset is expected to have ingested data into. As long as the table exists and contains the data that your dbt model expects, the 
SELECT
statement should work without errors. If the 
import_filter_job__import_wf
asset is set up in Dagster to perform an ingestion job that populates a Redshift table, and if the dbt source is configured correctly to point to that table, then your dbt model should be able to declare a dependency on that source and select from it as needed. Always ensure that your ingestion process completes successfully and that the table is in the expected state before dbt attempts to run the model that depends on it. If the table does not exist or is not in the expected state, then the dbt model will encounter errors when trying to select from it.
i
@Scout so if I know which table will hold the data, when my asset materializes I can give my sources.yml name the name of the asset to create the dependency and then somehow specify with other properties from dbt the original table name, schema and database, right? Can you give me a example of that?
s
Yes, you can create a dependency between a dbt model and a Dagster asset by specifying the asset key in the 
sources.yml
file of your dbt project. You can then reference this source in your dbt model to create the dependency. The source in 
sources.yml
should be configured with the original table name, schema, and database where the data resides. Here's an example of how you might set up your 
sources.yml
and dbt model to create this dependency: 1. Define the source in 
sources.yml
: Specify the original table name, schema, and database, and use the 
meta.dagster.asset_key
property to link it to the corresponding Dagster asset.
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yaml # sources.yml  version: 2  sources:   - name: dagster     database: your_database_name     schema: your_schema_name     tables:       - name: import_filter_job__import_wf         identifier: original_table_name  # This is the actual table name in the database         meta:           dagster:             asset_key: ["import_filter_job__import_wf"]  # This matches the Dagster asset key
2. Reference the source in your dbt model: Use the 
source()
function to reference the source you defined in the 
sources.yml
file.
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sql -- my_dbt_model.sql  SELECT ... FROM {{ source('dagster', 'import_filter_job__import_wf') }}
In this SQL file, the 
source()
function is used to reference the 
import_filter_job__import_wf
source defined in 
sources.yml
. The 
identifier
property in the source definition is used to specify the actual table name in the database, while the 
meta.dagster.asset_key
property links the source to the corresponding Dagster asset. By setting up your dbt project in this way, you are creating a dependency between your dbt model and the Dagster asset. When Dagster reads the contents of the dbt project, it will infer the correspondence based on the metadata provided in the 
sources.yml
file. This allows Dagster to manage the execution order, ensuring that the dbt model runs after the Dagster asset has materialized the data in the specified table. Please ensure that your dbt and Dagster configurations are set up correctly and that the table specified by the 
identifier
property exists in your database with the expected schema. For the most accurate and detailed guidance, refer to the [Dagster documentation](https://docs.dagster.io/integrations/dbt).
i
@Scout but if I need to use meta.dagster.asset_key I wouldn't need to define the table name equals to the generated asset name, is that right?
s
That's correct. When you use the 
meta.dagster.asset_key
property in your dbt 
sources.yml
file, you are explicitly specifying the asset key that Dagster should use for the corresponding dbt source. This means that the asset key you define does not need to match the table name in your database. The 
meta.dagster.asset_key
property allows you to map a dbt source to a Dagster asset with a custom asset key, which can be different from the actual table name. This is useful when you want to create dependencies between dbt models and Dagster assets that have specific asset keys. Here's an example of how you might define a dbt source with a custom asset key that does not match the table name:
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yaml # sources.yml  version: 2  sources:   - name: dagster     database: your_database_name     schema: your_schema_name     tables:       - name: import_filter_job__import_wf         identifier: actual_table_name_in_database  # The real table name in the database         meta:           dagster:             asset_key: ["custom_asset_key"]  # The custom asset key for the Dagster asset
In this example, 
actual_table_name_in_database
is the real name of the table in your database, while 
custom_asset_key
is the asset key you want to use in Dagster. When you reference this source in your dbt model, Dagster will use the asset key specified in the 
meta.dagster.asset_key
property to establish the dependency. By using the 
meta.dagster.asset_key
property, you have the flexibility to define asset keys in Dagster that are meaningful and appropriate for your data pipeline, independent of the underlying database table names. This can help you manage and organize your assets more effectively within Dagster.
i
@Scout how to make a graph_asset generate multiples assets names at the end of its execution? Example, I have a graph_asset and I want it to generate 1 asset with a specific name, this name beign a variable
s
As of my knowledge cutoff date in early 2023, Dagster provides the 
@graph_asset
decorator to define a software-defined asset that's computed using a graph of ops. However, the 
@graph_asset
decorator is designed to create a single asset from a graph, not multiple assets with dynamic names. If you want to create multiple assets from a single graph, you can use the 
@multi_asset
decorator, which allows you to define multiple assets that are computed by a single function. The names of the assets can be specified statically in the decorator. Here's an example of how you might use the 
@multi_asset
decorator to create multiple assets:
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python from dagster import multi_asset, AssetOut  @multi_asset(     outs={         "asset_name_1": AssetOut(),         "asset_name_2": AssetOut(),         # Add more assets as needed     } ) def my_multi_asset_function():     # Your computation logic here     result_for_asset_1 = ...     result_for_asset_2 = ...     # Return a dictionary with keys matching the asset names     return {         "asset_name_1": result_for_asset_1,         "asset_name_2": result_for_asset_2,     }
However, if you need to generate asset names dynamically based on a variable, you would typically handle this by creating a separate asset for each possible name and using conditional logic within your computation to determine which assets to materialize. As of my knowledge cutoff date, Dagster does not support dynamically generating asset names at runtime directly within the 
@graph_asset
or 
@multi_asset
decorators. If you need to generate asset names dynamically based on variables, you may need to consider a different approach, such as programmatically generating your asset definitions based on your requirements. For the most up-to-date information and potential new features that may support dynamic asset naming, please refer to the [Dagster documentation](https://docs.dagster.io/). If there have been updates or new features added after my knowledge cutoff date, they may provide additional capabilities that could help you achieve your goal.
i
@Scout I dont want a multi_asset, imagine that for every execution, I want to generate a assetkey different from the last, example my_asset_name_20240207
s
In Dagster, if you want to create assets with dynamic names based on runtime information such as a date, you would typically use partitioned assets. Partitions allow you to create a series of assets that are logically the same but are separated by a partition key, which can be a date or any other meaningful identifier. Here's an example of how you might define a partitioned asset that uses a date as the partition key:
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python from dagster import asset, DailyPartitionsDefinition, build_assets_job from datetime import datetime  # Define a partitions definition with a daily schedule starting from a specific date partitions_def = DailyPartitionsDefinition(start_date="2023-01-01")  # Define your asset with the partitions_def @asset(partitions_def=partitions_def) def my_partitioned_asset(context):     # Your logic here     date_str = context.partition_key  # e.g., "20240207"     # Use date_str to influence the behavior of your asset computation     ...  # Build a job for your asset my_assets_job = build_assets_job("my_assets_job", assets=[my_partitioned_asset])  # Now you can run the job for a specific partition result = my_assets_job.execute_in_process(partition_key="20240207")
In this example, 
my_partitioned_asset
is a partitioned asset that uses 
DailyPartitionsDefinition
to define its partitions. Each partition corresponds to a different date, and the 
context.partition_key
provides the partition key (which is a date string) that you can use within your asset computation. When you run the job for a specific partition, Dagster will materialize an asset for that partition, effectively creating an asset with a name that includes the date, such as 
my_asset_name_20240207
. Please note that while the asset itself does not have a dynamic name, the partition key acts as a dynamic element that differentiates each materialization of the asset. This is the recommended approach in Dagster for handling cases where you want to create assets that vary based on runtime information like dates. For more details on working with partitioned assets, please refer to the [Dagster documentation on partitions](https://docs.dagster.io/concepts/partitions-schedules-sensors/partitions).
i
@Scout how would I access a specific partition from a asset on a asset_sensor to trigger a dbt_assets asset?
s
To access a specific partition from an asset within an asset sensor and trigger a 
dbt_assets
asset, you can use the 
latest_materialization_records_by_partition
method provided by the 
MultiAssetSensorEvaluationContext
. This method allows you to fetch the most recent materialization event record for each partition of the monitored assets. Here's a step-by-step guide on how to set up an asset sensor to trigger a 
dbt_assets
asset based on a specific partition materialization: 1. Define your dbt assets: Use the 
@dbt_assets
decorator to load your dbt models as assets from your dbt project. 2. Create an asset sensor: Define an asset sensor that monitors the asset key of the partitioned asset. Within the sensor, use the 
latest_materialization_records_by_partition
method to access the latest materialization for the specific partition you're interested in. 3. Trigger the dbt assets: If the sensor detects a new materialization for the specified partition, create a 
RunRequest
to trigger the dbt assets. You can specify the partition key in the 
RunRequest
to ensure that the dbt assets run for the correct partition. Here's an example of how you might define an asset sensor to trigger dbt assets based on a specific partition:
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python
from dagster import asset_sensor, RunRequest, AssetKey, repository, SensorExecutionContext
from dagster_dbt import dbt_cli_resource

# Define your dbt assets using the @dbt_assets decorator
dbt_assets = my_dbt_assets()

# Define an asset sensor that triggers based on a specific partition materialization
@asset_sensor(asset_key=AssetKey("my_partitioned_asset"))
def my_asset_partition_sensor(context: SensorExecutionContext):
    # Fetch the latest materialization records for the monitored asset's partitions
    materialization_records = context.latest_materialization_records_by_partition(
        AssetKey("my_partitioned_asset")
    )

    # Check if there is a new materialization for the specific partition
    specific_partition_key = "20240207"  # Example partition key
    if specific_partition_key in materialization_records:
        # Trigger the dbt assets for the specific partition
        return RunRequest(
            run_key=f"run-for-partition-{specific_partition_key}",
            run_config={
                # Include any necessary run configuration for the dbt assets
            },
            tags={
                "dagster/partition": specific_partition_key,
            },
        )

# Define a repository to hold your assets and sensors
@repository
def my_repo():
    return [dbt_assets, my_asset_partition_sensor]
In this example, 
my_partitioned_asset
is the asset key for the partitioned asset you want to monitor, and 
specific_partition_key
is the partition key for the specific par
tition you're interested in. When the sensor detects a new materialization for this partition, it creates a 
RunRequest
to trigger a run of the dbt assets with the appropriate partition key. Please adjust the example to fit your specific use case, including the partition key you want to monitor and any necessary run configuration for the dbt assets. For more details on working with asset sensors and partitioned assets, please refer to the [Dagster documentation on asset sensors](https://docs.dagster.io/concepts/partitions-schedules-sensors/asset-sensors).
i
@Scout this only works for jobs, I have a dbt asset, c'mon
s
I apologize for the confusion. To trigger a 
dbt_assets
asset based on a specific partition of another asset using an asset sensor, you would typically follow these steps: 1. Define your dbt assets: Use the 
@dbt_assets
decorator to load your dbt models as assets from your dbt project. 2. Create an asset sensor: Define an asset sensor that monitors the asset key of the partitioned asset. Within the sensor, use the 
latest_materialization_records_by_partition
method to access the latest materialization for the specific partition you're interested in. 3. Trigger the dbt assets: If the sensor detects a new materialization for the specified partition, create a 
RunRequest
that includes the necessary configuration to run the dbt assets. This configuration may include specifying the dbt models to run or other dbt run configs. Here's an example of how you might define an asset sensor to trigger dbt assets based on a specific partition:
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python
from dagster import asset_sensor, RunRequest, AssetKey, repository, SensorExecutionContext
from dagster_dbt import dbt_cli_resource

# Assume `my_dbt_assets` is defined elsewhere using the @dbt_assets decorator
# and represents the dbt models loaded as Dagster assets.

# Define an asset sensor that triggers based on a specific partition materialization
@asset_sensor(asset_key=AssetKey("my_partitioned_asset"))
def my_asset_partition_sensor(context: SensorExecutionContext):
    # Fetch the latest materialization records for the monitored asset's partitions
    materialization_records = context.latest_materialization_records_by_partition(
        AssetKey("my_partitioned_asset")
    )

    # Check if there is a new materialization for the specific partition
    specific_partition_key = "20240207"  # Example partition key
    if specific_partition_key in materialization_records:
        # Trigger the dbt assets for the specific partition
        return RunRequest(
            run_key=f"run-for-partition-{specific_partition_key}",
            run_config={
                # Include any necessary run configuration for the dbt assets
            },
            tags={
                "dagster/partition": specific_partition_key,
            },
        )

# Define a repository to hold your assets and sensors
@repository
def my_repo():
    return [my_dbt_assets, my_asset_partition_sensor]
In this example, 
my_partitioned_asset
is the asset key for the partitioned asset you want to monitor, and 
specific_partition_key
is the partition key for the specific partition you're interested in. When the sensor detects a new materialization for this partition, it creates a 
RunRequest
to trigger a run of the dbt assets with the appropriate partition key. Please note that the 
run_config
in the 
RunRequest
should be adjusted to include the necessary configuration for the dbt assets. This might involve specifying which dbt models to run, any profiles or target configurations, or other dbt-specific settings. For more details on working with asset sensors and partitioned assets, please refer to the [Dagster documentation on asset sensors](https://docs.dagster.io/concepts/partitions-schedules-sensors/asset-sensors). If there have been updates or new features added after my knowledge cutoff date, they may provide additional capabilities that could help you achieve your goal.
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