[GitHub] spark issue #17105: [SPARK-19773][SparkR] SparkDataFrame should not allow du...

[felixcheung <gi...@git.apache.org>]

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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