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# ask-ai
s

Simon Ammer

03/11/2024, 12:41 PM
I am using an AllPartitionMapping to aggregate partitions. Is there a way to skip specific partitions in the upstream asset so they don't have to be loaded?
s

Scout

03/11/2024, 12:41 PM
Based on the documentation, there isn't a direct mention of a feature that allows you to selectively skip specific partitions when using an
AllPartitionMapping
. The
AllPartitionMapping
is designed to map each partition in the downstream asset to all partitions in the upstream asset, which means it doesn't provide a mechanism to exclude certain partitions. However, if you need to handle cases where some partitions may not be relevant or available, you could potentially manage this within the asset computation itself. For example, you could write logic in your asset to check for the existence or relevance of each partition and process only the ones that meet your criteria. Additionally, the documentation mentions the use of
AssetIn
metadata to allow missing partitions when using an IOManager that inherits from
UPathIOManager
. You can set the
allow_missing_partitions
metadata to
True
to avoid errors when loading a partitioned asset with some partitions missing:
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python @asset(ins={"partitioned": AssetIn(metadata={"allow_missing_partitions": True})}) def downstream(partitioned):     # Your logic here
This approach doesn't skip the loading of specific partitions but rather allows the asset computation to proceed without error if certain partitions are missing. If you have a more complex use case or need more granular control over partition selection, you might need to consider implementing custom logic within your asset computations or exploring other partition mapping strategies that better fit your requirements.
s

Simon Ammer

03/11/2024, 12:44 PM
@Scout What is the data type when using AllPartitionMapping?
s

Scout

03/11/2024, 12:44 PM
When using
AllPartitionMapping
, the data type for the input to your asset that depends on multiple upstream partitions is typically a dictionary that maps partition keys to the corresponding data for each partition. This is because
AllPartitionMapping
maps each partition in the downstream asset to all partitions in the upstream asset, and the data for each upstream partition is passed as an entry in the dictionary. In the documentation, it is mentioned that when working with IOManagers that support reading multiple upstream partitions, the downstream asset should use
Dict[str, ...]
(or leave it blank) type for the upstream
DagsterType
. Here is an example of how you might define an asset that uses `AllPartitionMapping`:
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python from dagster import asset, AssetIn, AllPartitionMapping  @asset(ins={"upstream_asset": AssetIn(partition_mapping=AllPartitionMapping())}) def my_asset(upstream_asset: Dict[str, Any]):     # Your logic here, where `upstream_asset` is a dictionary with partition keys as keys
In this example,
upstream_asset
is expected to be a dictionary where the keys are the partition keys of the upstream asset, and the values are the data associated with each partition. This allows you to access and process the data from all the partitions of the upstream asset within your downstream asset computation.
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