It would be nice if OP noted what caused the change in their opinion?
did datafusion gain some feature that they noted was missing in the previous article, or did something in their understanding click so they could overcome the previous issues?
We shared with the author how databricks multi-node and single-node graphframes were wildly inefficient for this kind of thing: we were measuring doing billion-edge graph traversals & scans in single node in-memory in seconds with regular dataframe (cudf) libraries, so the core of pagerank, which is magnitudes more efficient than their original spark approach
So then the question became pandas/polars/datafusion/duckdb/etc, must of which are rust/native. I'm curious myself why datafusion vs others, it's an interesting project :)
We shared with the author how databricks multi-node and single-node graphframes were wildly inefficient for this kind of thing: we were measuring doing billion-edge graph traversals & scans in single node in-memory in seconds with regular dataframe (cudf) libraries, so the core of pagerank, which is magnitudes more efficient than their original spark approach
So then the question became pandas/polars/datafusion/duckdb/etc, must of which are rust/native. I'm curious myself why datafusion vs others, it's an interesting project :)