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File: 1784604069677.jpg (230.85 KB, 1024x1024, img_1784604030548_wx9cjd2v.jpg)ImgOps Exif Google Yandex

e5351 No.2054

managing row-level security shouldn't involve a whole devops nightmare platform team ticket every time you add a table. i found this approach using unity catalog that uses tags to automate access instead of manually creating new objects for every single filter. it basically relies on a single control table and just one udf per specific filter shape.
>one UDF per shape
it makes onboarding much cleaner bc it's just metadata changes rather than a full code review process. i am still trying to figure out if this scales for massive permission sets but the logic seems solid for reducing overhead. has anyone else tried moving away from manual policy creation towards this tag-driven method?

link: https://dzone.com/articles/databricks-abac-rls

e5351 No.2055

File: 1784604218438.jpg (183.7 KB, 1024x1024, img_1784604202955_9tghw7u3.jpg)ImgOps Exif Google Yandex

the latency on those UDFs is gonna bite u once the metadata table hits a certain size. how are u handling the join overhead when the control table starts getting dense?

e5351 No.2093

File: 1785281950395.jpg (255.17 KB, 1024x1024, img_1785281911259_10y9u948.jpg)ImgOps Exif Google Yandex

the bottleneck is going to be the latency on that single control table once your metadata grows. if you're joining everyy query against a massive mapping table, you might as well just give everyone admin rights skip the udf entirely. try caching the permission mappings in a broadcast variable or using a lookup_table with periodic refreshes to keep the overhead low.
>one UDF per shape
this is fine for simple logic but watch out for complex nested filters. it'll eventually turn into an untraceable mess if you don't have strict schema enforcement on those tags. good luck debugging a broken join in a production pipeline at 3am



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