Github user kevincox commented on the issue:
https://github.com/apache/spark/pull/12337
@holdenk The point is that this is inline. It doesn't require evaluating
the whole dataframe and counting the nulls you find. Instead you use this on a
column and it asserts that every value passing through is not null (or it
raises an error). Then you continue using the dataframe like you would have and
this check has almost no cost.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
