I have a multi partitoined job and I just want to convert this to a single date dimension in the downstream asset and to make they execute automatically without errors

To convert a multi-partitioned job to a single date dimension in a downstream asset and ensure automatic execution without errors, you can use the

MultiToSingleDimensionPartitionMapping

provided by Dagster. This partition mapping allows you to define a dependency relationship between a multi-partitioned asset and a single-dimension partitioned asset, where the single dimension is a dimension of the multi-partitioned asset. Here's a step-by-step guide based on the documentation: 1. Define your multi-partitioned asset with the appropriate

MultiPartitionsDefinition

that includes the date dimension you want to use in your downstream asset. 2. Define your downstream asset with a single dimension

PartitionsDefinition

that corresponds to the date dimension of the multi-partitioned asset. 3. Use the

MultiToSingleDimensionPartitionMapping

to map the multi-partitioned asset to the single dimension partitioned asset. This mapping will automatically handle the dependency relationship between the two assets. 4. Apply an

AutoMaterializePolicy

to your downstream asset to ensure that it executes automatically when its upstream dependencies are updated. Here's an example code snippet from the documentation that illustrates how you might set this up:

python from dagster import (     DynamicPartitionsDefinition,     WeeklyPartitionsDefinition,     asset,     MultiPartitionsDefinition, )  projects_partitions_def = DynamicPartitionsDefinition(name="project") # a, b, c  @asset(     partitions_def=MultiPartitionsDefinition(         partitions_defs={             "week": WeeklyPartitionsDefinition("2023-01-01"),             "project": projects_partitions_def,         }     ) ) def upstream():     ...  @asset(     partitions_def=projects_partitions_def,     ins={         "upstream": AssetIn(             key=upstream.key,             partition_mapping=MultiToSingleDimensionPartitionMapping(                 partition_dimension_name="week"             )         ),     } ) def downstream(context, upstream):     ...

In this example, the

downstream

asset is defined with a single dimension partitioned on "week", and it depends on the

upstream

asset, which is multi-partitioned on both "week" and "project". The

MultiToSingleDimensionPartitionMapping

is used to map the "week" dimension from the

upstream

asset to the

downstream

asset. By following these steps and using the provided example as a guide, you should be able to convert your multi-partitioned job to a single date dimension in the downstream asset and set up automatic execution without errors.

@Scout I made your example, but the auto-materialization says its still waiting for the upstream data to be materialized, is there something else we need to do? The monitor is waiting for the asset "upstream" to be materialized. It's like the upstream materialized with the partition key, but the downstream is searching without the partition key

If the auto-materialization is indicating that it's waiting for the upstream data to be materialized, there might be a few things to check: 1. Upstream Materialization: Ensure that the upstream asset is indeed being materialized as expected. If the upstream asset is not materializing, the downstream asset will not be triggered. 2. Partition Mapping: Verify that the partition mapping is correctly set up. The

MultiToSingleDimensionPartitionMapping

should map the partition key from the upstream asset to the downstream asset correctly. If there is a mismatch in the partition keys, the downstream asset may not recognize that the upstream asset has been materialized. 3. Asset Dependencies: Check that the dependencies between assets are correctly defined. The downstream asset should specify the upstream asset as a dependency with the correct

AssetIn

definition. 4. Sensor Configuration: If you are using a sensor to trigger materialization, ensure that the sensor is configured correctly to detect changes in the upstream asset and trigger the downstream asset. 5. Freshness Policy: If you have a

FreshnessPolicy

attached to your asset definitions, make sure that the policy's expectations are being met. The

FreshnessPolicy

may prevent materialization if the upstream data is not considered fresh. 6. Asset Materialization Events: Confirm that the asset materialization events are being emitted correctly. If you are using an

@op

or a job to materialize the upstream asset, ensure that it yields an

AssetMaterialization

event. 7. Dagit UI: Use the Dagit UI to inspect the asset graph and materialization history. This can help you identify if there are any discrepancies between the expected and actual materializations. 8. Logs and Error Messages: Check the logs for any error messages or warnings that might indicate why the downstream asset is not being triggered. The logs can provide valuable information about the execution flow and any issues that may have occurred. If after checking these items the issue persists, it may be helpful to look at the specific configuration of your assets and the execution environment. There might be additional context or configuration that is affecting the behavior of the auto-materialization. If you continue to experience difficulties, please provide more details about your asset definitions, partition mappings, and any relevant configuration or error messages. This information can help diagnose the issue more effectively.

@Scout I got the problem. To consider that a date materialization occurred, Dagster need to evaluate every single one of the partitions for that day, so if 1 partition like 2024-02-02|WOLF don't materialize, the auto-materialization won't occur

It sounds like you've identified the issue related to the auto-materialization process. In Dagster, when dealing with partitioned assets, each partition is treated as a distinct slice of data. If your auto-materialization policy is set up to trigger when all partitions for a given date are materialized, and one of those partitions (e.g.,

2024-02-02|WOLF

) does not materialize, then the auto-materialization for the downstream asset will not occur. This behavior ensures that the downstream asset only materializes when it has a complete set of data from the upstream asset. If a partition is missing, the downstream asset might not have all the necessary data to perform its computations correctly, which is why the auto-materialization is not triggered. To resolve this issue, you would need to ensure that all partitions for the required date are successfully materialized. This might involve: - Investigating why the

2024-02-02|WOLF

partition did not materialize and addressing the root cause. - If the missing partition is expected (e.g., due to missing data for that partition), you might need to adjust your partitioning strategy or auto-materialization policy to handle such cases. If you need to allow the downstream asset to materialize even when some partitions are missing, you could consider implementing a custom partition mapping or auto-materialization rule that accommodates this scenario. However, be cautious with this approach, as it could lead to incomplete or incorrect data being processed by the downstream asset. Remember to test any changes thoroughly to ensure that the auto-materialization behaves as expected and that the integrity of your data processing is maintained.

@Scout Is there any way of configuring the auto_materialize_policy to get this specific case? Because my downstream asset only need 2 partitions to materialize to go on

The documentation indicates that you can customize the

AutoMaterializePolicy

by adding or removing rules. If you want your downstream asset to materialize even if some upstream partitions are missing, you can modify the policy to remove the rule that skips materialization when parent partitions are missing. Here's an example of how you might customize the

AutoMaterializePolicy

to allow materialization even if some upstream partitions are missing:

python from dagster import AutoMaterializePolicy, AutoMaterializeRule, asset  # Define a custom auto-materialize policy that does not skip materialization # when parent partitions are missing. my_policy = AutoMaterializePolicy.eager().without_rules(     AutoMaterializeRule.skip_on_parent_missing(), )  @asset(auto_materialize_policy=my_policy) def downstream_asset():     ...

In this example, the

my_policy

is an eager auto-materialization policy that has been customized to remove the

skip_on_parent_missing

rule. This means that the

downstream_asset

will attempt to materialize even if some of its upstream partitions have not been materialized. Please note that this customization should be used with caution, as it may lead to the downstream asset materializing with incomplete data from upstream. Make sure that this behavior aligns with your business logic and data integrity requirements. If you have specific logic that determines when the downstream asset should materialize (e.g., it only needs 2 specific partitions to proceed), you may need to implement more complex logic in your asset definitions or use sensors to control the materialization process based on your custom conditions.

@Scout I understand, but now my doubt is: This rule will make my assets materialize for all the assets that not materialized? Including the assets from the partition I want and every other one at a single time?

The

AutoMaterializePolicy

with the

skip_on_parent_missing

rule removed will not necessarily trigger materialization for all unmaterialized partitions at once. Instead, it will allow the downstream asset to materialize even if some of its upstream partitions have not been materialized. This means that the downstream asset will not be blocked by missing upstream partitions. However, the behavior of the auto-materialization process also depends on other factors, such as the specific rules you have in place and the configuration of your assets. For instance, if you have an eager auto-materialization policy, it will attempt to materialize the asset whenever one of its parents updates. If you remove the

skip_on_parent_missing

rule, it will no longer check if all parent partitions have been materialized before attempting to materialize the downstream asset. If you want to ensure that the downstream asset only materializes when specific upstream partitions are present, you may need to implement custom logic within your asset definition to check for the presence of those partitions. This could involve querying the materialization history of the upstream assets and making a decision based on whether the required partitions have been materialized. The documentation does not provide a built-in rule that allows you to specify a minimum number of materialized partitions before triggering downstream materialization. Therefore, you would need to handle this logic programmatically within your asset definitions or by using sensors that can check the state of upstream partitions and trigger downstream materialization accordingly.