Github user felixcheung commented on the issue:
https://github.com/apache/spark/pull/17105
@actuaryzhang there's a bit of a history about this... but long story
short, Spark does support DataFrame with multiple columns having the same name,
for example
```
# in pyspark
>>> from pyspark.sql import Row
>>> from pyspark.sql.types import *
>>> data = [(1, 2, 'Foo')]
>>> df = spark.createDataFrame(data, ("key", "key", "value"))
>>> df
DataFrame[key: bigint, key: bigint, value: string]
```
And each column will get a unique id, so underneath the cover they are not
actually "duplicating".
You could in fact get columns with same name when doing a self-join, for
instance.
Now the reason why you are getting an error with `df$a = df$a * 2.0` is
because "a" is not a full unique id. You get the same in python
```
>>> df.select(col("key"))
...
raise AnalysisException(s.split(': ', 1)[1], stackTrace)
pyspark.sql.utils.AnalysisException: u"Reference 'key' is ambiguous, could
be: key#0L, key#1L.;"
```
And in R, `df$a` is essentially a shortcut to that and so it will also fail
similarly.
As for why it is disallowed in `mutate` - it is just a factor of the
implementation. I think we could potentially implement it to support duplicated
names.
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