@Scout how do I defined a depedency of a daily partitioned asset and an a hourly one?

To define a dependency between a daily partitioned asset and an hourly partitioned asset, you can use the

TimeWindowPartitionMapping

to specify how the partitions of the daily asset depend on the partitions of the hourly asset. Here's an example from the documentation that demonstrates how to set up such a dependency:

python from datetime import datetime from dagster import (     AssetExecutionContext,     DailyPartitionsDefinition,     HourlyPartitionsDefinition,     asset,     TimeWindowPartitionMapping,     AssetIn, )  start = datetime(2022, 1, 1)  hourly_partitions = HourlyPartitionsDefinition(start_date=f"{start:%Y-%m-%d-%H:%M}") daily_partitions = DailyPartitionsDefinition(start_date=f"{start:%Y-%m-%d}")  @asset(partitions_def=hourly_partitions) def upstream_asset(context: AssetExecutionContext) -> pd.DataFrame:     # Your logic to produce the hourly partitioned data     ...  @asset(     partitions_def=daily_partitions,     ins={"upstream_asset": AssetIn(partition_mapping=TimeWindowPartitionMapping())}, ) def downstream_asset(upstream_asset: pd.DataFrame) -> pd.DataFrame:     # Your logic to consume the hourly partitioned data and produce daily partitioned data     ...

In this example, the

downstream_asset

is a daily partitioned asset that depends on the

upstream_asset

, which is an hourly partitioned asset. The

TimeWindowPartitionMapping

is used to define the relationship between the partitions of these two assets. By default,

TimeWindowPartitionMapping

will map each daily partition to the corresponding 24 hourly partitions of the previous day. If you need to customize this behavior, you can provide

start_offset

and

end_offset

arguments to

TimeWindowPartitionMapping

. Please note that the example above assumes you have the necessary logic to produce and consume the data for the hourly and daily partitioned assets, respectively. The actual implementation of the assets will depend on your specific use case and data processing requirements.

@Scout I have defined a depency between an two asserts. One that has a partitioned def of hourly and a downstream one of daily that depends on this one. I have all the 24 hour files that one day compromises but the downstream assett is not materializing. Why is that?

If your downstream daily asset is not materializing despite having all the required 24 hourly partitions for the upstream asset, there could be several reasons for this issue. Here are some steps you can take to troubleshoot the problem: 1. Check Partition Mappings: Ensure that the

TimeWindowPartitionMapping

is correctly defined and that it maps the daily partitions to the correct set of hourly partitions. If there's a mismatch in the mapping, the downstream asset might not recognize that its dependencies are fulfilled. 2. Check Asset Definitions: Verify that both assets are correctly defined with their respective

partitions_def

and that the

ins

parameter in the downstream asset correctly references the upstream asset. 3. Check Materialization Logic: Ensure that the logic within your downstream asset's function correctly handles the data from the upstream asset and that it's capable of producing the expected output. 4. Check Execution Environment: Make sure that the environment in which the assets are being materialized has access to all necessary resources, such as data sources, and that there are no environmental issues preventing the materialization. 5. Check Asset Materialization Logs: Look at the logs for the materialization of the downstream asset to see if there are any errors or warnings that could indicate what's going wrong. 6. Check Asset Observability: Use Dagster's asset observability features to inspect the status of your assets. This can help you identify discrepancies, such as whether the asset's dependencies are considered fulfilled. 7. Check Scheduling: If you're using a scheduler to trigger materializations, ensure that it's correctly configured and that there are no issues with the schedule that would prevent the downstream asset from materializing. 8. Check for Known Issues: Look at the Dagster GitHub issues and discussions to see if there's a known bug or limitation that matches the behavior you're experiencing. If after checking these areas you still cannot identify the issue, you may need to provide more specific details about your asset definitions, the code you're using to materialize the assets, and any error messages or logs you're seeing. This additional context can help pinpoint the cause of the materialization failure.