Forward port of Mario's PySpark PRs#19
Merged
Merged
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
This PR is a forward port of (parts of) duckdb/duckdb#15462 and duckdb/duckdb#15036 by @mariotaddeucci with a couple of omissions after a careful review:
explode()is mapped to unnest but that has significantly different semantics:explode()on a map creates two new columnskeyandvalue<->unnest()creates new columns for each key.explode()on a list creates a new columncol<->unnest()creates a column named "unnest()". This is probably fixable thoughcount_if()is problematic (andcount()is as well). The examples in the docstring don't work and right now can't work, afaict, becauseselectcreates a projection and for some reason we can't aggregate on that (e.g. this throws an error:df.select(sf.count('b'), sf.count_if('b')).show()). Before we put this in we'd need to figure out a way to fix that, otherwise the semantics aren't even close to those of Spark.every()suffers from the same problem. E.g. this example from the docstring throws the same exception:spark.createDataFrame([[False], [False], [False]], ["flag"]).select(sf.every("flag")).