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?
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?
There is a bit of healthy competition going on between graphframes and icebug. See:
https://github.com/Ladybug-Memory/icebug-graphframes-compari...
I thought of datafusion work and the rust implementation as a way to address the delta seen vs the Spark/JVM GraphFrames implementation (author is a major contributor).
Looking forward to more such innovations, which will benefit the ecosystem as a whole. Why would anyone want to use a pure python graph algorithm package unless they're dealing with toy graphs?
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 :)