Hi @DavidKxx,

Thanks for flagging this. You're right, the Sample Data previewer in Catalog Explorer is choking because your column is a Spark ML vector type (pyspark.ml.linalg.VectorUDT, what Vectors.sparse(...) returns). The previewer is trying to JSON-parse the stringified vector ((8000,[0,2,...],[...])), which obviously isn't JSON, and that's why the whole tab fails rather than just the one column. UC's Overview tab and DESCRIBE surface the same column differently (as a struct and as vector), which is consistent with this being a rendering issue rather than a problem with the data itself.

While we get this fixed on the product side, a couple of workarounds for previewing... Run a quick SELECT in SQL Editor or a notebook with the vector cast to a string or broken out via vector_to_array(col) into a regular array column, and those will render fine. Creating a view that exposes the vector as an array column is a nice way to keep the "quick preview" experience without changing your base table.

I've gone ahead and logged this internally so the Catalog Explorer team can pick it up and prioritise a fix. One quick thing that would help when they triage... Could you confirm roughly how large the vectors are in your table (the 8000 in the error suggests that dimension), and whether you're seeing this only on sparse vectors or also on dense ones? No need for the actual data, just a rough shape.

If this answer resolves your question, could you mark it as “Accept as Solution”? That helps other users quickly find the correct fix.