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Title
# ask-ai
p

Pink Deer

03/05/2024, 3:08 PM
give me an example of how to set automaterialize rule require_update_for_all_parent_partitions to False in CustomDagsterDbtTranslator
s

Scout

03/05/2024, 3:08 PM
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`:
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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.
To enable asset checks in a custom Dagster DBT translator, you can set the
enable_asset_checks
flag to
True
in the
DagsterDbtTranslatorSettings
when initializing your custom translator. Here's how you can modify your
CustomDagsterDbtTranslator
class to include this setting:
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python from dagster_dbt import DagsterDbtTranslator, DagsterDbtTranslatorSettings from typing import Mapping, Any, Optional from dagster import AutoMaterializePolicy, AutoMaterializeRule  class CustomDagsterDbtTranslator(DagsterDbtTranslator):     settings = DagsterDbtTranslatorSettings(enable_asset_checks=True)      def get_auto_materialize_policy(         self, dbt_resource_props: Mapping[str, Any]     ) -> Optional[AutoMaterializePolicy]:         auto_materialize_policy = super().get_auto_materialize_policy(dbt_resource_props)                  model_names_without_rules = ["..."]         if auto_materialize_policy and dbt_resource_props["name"] in model_names_without_rules:             return auto_materialize_policy.without_rules(AutoMaterializeRule.skip_on_parent_missing())                  return auto_materialize_policy
In this example, the
settings
class variable is set to an instance of
DagsterDbtTranslatorSettings
with
enable_asset_checks
set to
True
. This will enable asset checks for the assets that are created by this translator. Please ensure that you have the necessary version of
dagster-dbt
installed that supports the
DagsterDbtTranslatorSettings
and
enable_asset_checks
flag, as this is a feature that may have been introduced in a specific version of the library. If you encounter any issues, make sure to check the documentation or release notes for the version of
dagster-dbt
you are using.
p

Pink Deer

03/05/2024, 3:37 PM
@Scout give me a dummy asset example that is partitioned hourly
s

Scout

03/05/2024, 3:37 PM
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.
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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.
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