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Data structure for filters #2153
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flexibility
Support more workflow-execution use cases
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This was referenced Dec 19, 2024
I'd be in favor of 3. I do think there is likely a point for dataset attribute filters. |
tcompa
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To discuss: Data structure for filters
Data structure for filters
Jan 7, 2025
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As of fractal-analytics-platform/fractal-web#678 (comment), we are not anymore defining attribute filters for workflow tasks. But we still have type filters. On the other hand, we are introducing attribute filters for jobs, but not type filters. This would be a validation layer which is not defined in the database schema but rather in the application/API schemas, which is a scenario we always try to avoid (it's more complex and error prone, and it requires redundant definitions).
Our suggestion (with @mfranzon) is that we should also split the data structure in two parts, attributes and types, rather than having single homogeneous filters objects + validation.
We describe the different options below, and suggest that we pick the third (or even fourth) one.
Notes:
attributes_exclude
and keepattributes_include
#2154), but this is not a critical point.input_types
are not used for filtering, but rather to validate the image list after filtering took place. Thus they are not related to the current discussion, and they would not require any change.1. Current situation
2. Original plan for complex filters
3. Improved plan for complex filters
At the cost of one additional field, we are removing one level of nesting from all these JSON objects.
4. Improved plan for complex filters / without attribute filters for dataset
To discuss: are dataset attribute filters actually relevant? If not, we could go even further and have
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