give me an example of how to set automaterialize rule require_update_for_all_parent_partitions to False in CustomDagsterDbtTranslator

The

require_update_for_all_parent_partitions

parameter is used to control the behavior of asset materialization based on the state of its parent assets. However, the

CustomDagsterDbtTranslator

does not directly use this parameter, as it is not part of the dbt-dagster integration or the

DbtCliOutput

class that the translator typically works with. The

require_update_for_all_parent_partitions

parameter is relevant to the

AutoMaterializeRule

class and is used when defining auto-materialization policies for assets. The

CustomDagsterDbtTranslator

is a different concept that is used to translate dbt CLI output into Dagster events. If you are looking to define an auto-materialization policy for an asset and want to set the

require_update_for_all_parent_partitions

to

False

, you would do so when defining the asset, not within the

CustomDagsterDbtTranslator

. Here is an example of how you might define an asset with an auto-materialization policy that sets

require_update_for_all_parent_partitions

to `False`:

python from dagster import asset, AutoMaterializePolicy, AutoMaterializeRule  @asset(auto_materialize_policy=AutoMaterializePolicy.eager().with_rules(     AutoMaterializeRule.skip_on_not_all_parents_updated(require_update_for_all_parent_partitions=False) )) def my_asset():     # Asset computation logic here     ...

In this example, the

skip_on_not_all_parents_updated

rule is added to the eager policy with

require_update_for_all_parent_partitions

set to

False

. This means that the asset will be materialized if at least one upstream partition in each upstream asset has been materialized since the last materialization of the downstream asset. Please note that this is a general example based on the

AutoMaterializeRule

class and the

AutoMaterializePolicy

as described in the documentation. If you have a specific use case or further questions about the

CustomDagsterDbtTranslator

, please provide more details, and I will do my best to assist you.

@Scout give me a dummy asset example that is partitioned hourly

Certainly! Below is an example of a dummy asset that is partitioned hourly using Dagster's asset API. This asset will represent a simple computation that could, for example, process data on an hourly basis.

python from dagster import asset, HourlyPartitionsDefinition from datetime import datetime  # Define the partitions to be hourly hourly_partitions_def = HourlyPartitionsDefinition(     start_date=datetime(2023, 1, 1)  # Specify the start date for the partitions )  @asset(partitions_def=hourly_partitions_def) def my_hourly_partitioned_asset(context):     # Here you would add the logic for your asset computation.     # The context provides access to the partition key if needed.     partition_key = context.partition_key     # Dummy computation or data processing logic     result = f"Processed data for partition: {partition_key}"     <http://context.log.info|context.log.info>(result)     return result  # Note: In a real-world scenario, you would replace the dummy computation # with actual logic to process your data for each hourly partition.

In this example,

my_hourly_partitioned_asset

is an asset that is partitioned hourly, starting from January 1, 2023. The

HourlyPartitionsDefinition

is used to define the partitioning scheme for the asset. The asset function itself contains a placeholder for the computation or data processing logic that you would implement. When this asset is materialized, Dagster will handle each hourly partition independently, allowing you to process data in hourly chunks. The

context.partition_key

provides the specific hour for which the asset is being materialized, which can be used within your computation logic to process the correct slice of data.