Github user bravo-zhang commented on the issue:
https://github.com/apache/spark/pull/18820
Hi @HyukjinKwon @gatorsmile @viirya I addressed your comments, added more
test coverage and provided more info in PR description.
One thing that is not clear to user is that they can still use
`df.na.replace("*", Map(10 -> 20, "Alpha" -> "Bravo"))`. The behavior is that
only fields that have same type as the 1st key in the replacement map will
perform replacement(so `"Alpha" -> "Bravo"` doesn't have effect). This is due
to the implementation of `targetColumnType`. This also creates a discrepancy
that in Python we check all keys and values should be of same type while in
Scala we don't check. This behavior exists before this PR.
I added 1 line comment `// Only fields of targetColumnType will perform
replacement.` Is it enough for now? If we are to make it more elegant, is it a
valid task to accept any replacement map so long as each key-value pair has the
same type?
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