alamb commented on PR #8991: URL: https://github.com/apache/arrow-datafusion/pull/8991#issuecomment-1915509262
> As I mentioned in the description, adding an additional Projection under Join (e.g., SortMergeJoin) doesn't make a lot sense for Spark due to its distribution nature. 🤔 > (note that by adding additional projection before join in Spark it means more data to be shuffled/sorted which can be bad for performance) I don't understand this If the join is on `lcol_1 + lcol_2` = `rcol_1 + rcol_2` the plan will have to materialize four columns `lcol_1`, `lcol_2`, `rcol_1` and `rcol_2`, and would have to feed all 4 of those columns to the join wouldn't it actually make more sense to compute the expressions prior to the networked shuffle so only 2 columns of data (`lcol_1 + lcol_2` and `rcol_1 + rcol_2`) need to be sent, rather than the 4 original columns 🤔 -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
