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Title
# ask-ai
j

Jerry Bao

03/02/2024, 12:10 AM
@Scout how can I make a job dependent on the materialization of a partitioned asset?
s

Scout

03/02/2024, 12:10 AM
To make a job dependent on the materialization of a partitioned asset, you can define dependencies between assets using the
ins
parameter in the
@asset
decorator. When you define a job that includes these assets, Dagster will respect the dependencies and ensure that the upstream assets are materialized before downstream assets. Here's an example from the documentation that shows how to define a partitioned asset and another asset that depends on it:
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python from dagster import (     AssetIn,     DailyPartitionsDefinition,     TimeWindowPartitionMapping,     asset, )  partitions_def = DailyPartitionsDefinition(start_date="2023-01-21")  @asset(partitions_def=partitions_def) def events():     ...  @asset(     partitions_def=partitions_def,     ins={         "events": AssetIn(             partition_mapping=TimeWindowPartitionMapping(                 start_offset=-1, end_offset=-1             ),         )     }, ) def yesterday_event_stats(events):     ...
In this example, the
yesterday_event_stats
asset depends on the
events
asset, and the dependency is defined using the
ins
parameter. The
TimeWindowPartitionMapping
specifies that
yesterday_event_stats
should use the partition of
events
from the previous day. Once you have defined your assets with their dependencies, you can define a job that materializes a particular set of partitioned assets every time it runs. Here's an example of how to define such a job:
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python from dagster import (     AssetSelection,     Definitions,     HourlyPartitionsDefinition,     asset,     define_asset_job, )  hourly_partitions_def = HourlyPartitionsDefinition(start_date="2022-05-31-00:00")  @asset(partitions_def=hourly_partitions_def) def asset1():     ...  @asset(partitions_def=hourly_partitions_def) def asset2():     ...  partitioned_asset_job = define_asset_job(     name="asset_1_and_2_job",     selection=AssetSelection.assets(asset1, asset2),     partitions_def=hourly_partitions_def, )  defs = Definitions(     assets=[asset1, asset2],     jobs=[partitioned_asset_job], )
In this job definition,
partitioned_asset_job
will materialize both
asset1
and
asset2
every time it runs, respecting the partitioning defined for each asset. Remember that when you launch a job or materialize an asset, Dagster will automatically handle the dependencies based on the definitions you've provided. If you have a more complex scenario or need further assistance, please provide additional details, and I'll be happy to help!
j

Jerry Bao

03/02/2024, 12:12 AM
@Scout I have an existing
@job
that I want to make dependent on two assets partitions
s

Scout

03/02/2024, 12:12 AM
To make an existing
@job
dependent on the partitions of two assets, you will need to ensure that the job is aware of the partitioning scheme of the assets and that it respects the dependencies between them. However, as of my knowledge cutoff in early 2023,
@job
does not directly support partitioned assets. Instead, you should use
define_asset_job
to create a job that operates on partitioned assets. Here's a general approach to making a job dependent on two partitioned assets: 1. Define your partitioned assets with their respective
partitions_def
. 2. Use the
ins
parameter in the
@asset
decorator to specify dependencies between assets, if necessary. 3. Use
define_asset_job
to create a job that includes the partitioned assets. Here's an example based on the documentation:
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python from dagster import (     AssetIn,     DailyPartitionsDefinition,     asset,     define_asset_job, )  # Define your partition definitions daily_partitions_def = DailyPartitionsDefinition(start_date="2023-01-01")  # Define your partitioned assets @asset(partitions_def=daily_partitions_def) def asset1():     # Your asset1 computation logic     ...  @asset(partitions_def=daily_partitions_def) def asset2():     # Your asset2 computation logic     ...  # Define a job that includes both assets my_partitioned_job = define_asset_job(     name="my_partitioned_job",     selection=[         "asset1",         "asset2",     ],     partitions_def=daily_partitions_def, )  # If asset2 depends on asset1, you can specify that dependency using the `ins` parameter @asset(     partitions_def=daily_partitions_def,     ins={"asset1": AssetIn()}, ) def asset2(asset1):     # Your asset2 computation logic that depends on asset1     ...
In this example,
my_partitioned_job
is a job that will materialize both
asset1
and
asset2
according to the daily partitioning scheme. If
asset2
depends on
asset1
, the dependency is specified using the
ins
parameter. If you have a more complex scenario where your assets have different partitioning schemes or you need to define more complex dependencies, you may need to use
PartitionMappings
to map the dependencies between different partitions. Please note that the exact implementation details may vary depending on your specific use case and the version of Dagster you are using. If you need further assistance, please provide more details about your assets, their partitioning schemes, and the dependencies between them.