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Exploration of complete separation of metadata and compute: observable assets #16424
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Current dependencies on/for this PR: This comment was auto-generated by Graphite. |
Nice, I'm still optimistic about this. Most of the questions I have about the ergonomics kinda boil down to the question of whether the Asset Graph is strictly derived from the python definitions or if its derived from some DB state + the python definitions.
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Seems really cool. A few thoughts:
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def report_asset_materialization(asset_key: str, metadata: dict):
instance = DagsterInstance.get()
dagster_event = DagsterEvent.from_external(
event_type=DagsterEventType.ASSET_MATERIALIZATION,
event_specific_data=StepMaterializationData(
AssetMaterialization(asset_key=asset_key, metadata=metadata)
)
)
instance.report_dagster_event(dagster_event, run_id="dummy") This is almost exactly what some larger Dagster Cloud customers have done on their own. One such iuser created a job that looped through a bunch of asset partitions emitting materializations in order to represent an external backfill in Dagster. So a few questions:
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For sure
I mean it does a db insert and a log per event. Is the problem for sure in the Python log handler? Either way it would be straightforward to provide a bulk insertion capability for backfills. |
I'll write up a summary of this exploration later today. I didn't expect so many eyes on it. |
Agreed. Great idea.
That should not be a problem. I'll add an example.
Yes this should be straightforward. The only capability that this does not replicate is ad hoc observation from the UI. However I've wanted to consolidate the "observe sources" and "materialize" buttons for some time, so this would be a good excuse to address sure.
For sure. |
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I moved this to an internal discussion (6739)
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update storage tests to ensure runless asset events work across storages related to #16424 ## How I Tested These Changes
Summary & Motivation
How I Tested These Changes