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Posted to issues@spark.apache.org by "Reynold Xin (JIRA)" <ji...@apache.org> on 2015/05/06 06:32:02 UTC
[jira] [Commented] (SPARK-7035) Drop __getattr__ on
pyspark.sql.DataFrame
[ https://issues.apache.org/jira/browse/SPARK-7035?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14529890#comment-14529890 ]
Reynold Xin commented on SPARK-7035:
------------------------------------
I talked to more people offline about this also, and I think it is best not to change anything now. One thing we should do is to update the documentation to indicate the danger of using df.attribute, and have df["attribute"] be the standard in our documentations.
We need to update https://github.com/apache/spark/blob/master/docs/sql-programming-guide.md
[~kalle] do you want to submit a patch to update the documentation to warn users?
> Drop __getattr__ on pyspark.sql.DataFrame
> -----------------------------------------
>
> Key: SPARK-7035
> URL: https://issues.apache.org/jira/browse/SPARK-7035
> Project: Spark
> Issue Type: Sub-task
> Components: PySpark
> Affects Versions: 1.4.0
> Reporter: Kalle Jepsen
>
> I think the {{\_\_getattr\_\_}} method on the DataFrame should be removed.
> There is no point in having the possibility to address the DataFrames columns as {{df.column}}, other than the questionable goal to please R developers. And it seems R people can use Spark from their native API in the future.
> I see the following problems with {{\_\_getattr\_\_}} for column selection:
> * It's un-pythonic: There should only be one obvious way to solve a problem, and we can already address columns on a DataFrame via the {{\_\_getitem\_\_}} method, which in my opinion is by far superior and a lot more intuitive.
> * It leads to confusing Exceptions. When we mistype a method-name the {{AttributeError}} will say 'No such column ... '.
> * And most importantly: we cannot load DataFrames that have columns with the same name as any attribute on the DataFrame-object. Imagine having a DataFrame with a column named {{cache}} or {{filter}}. Calling {{df.cache()}} will be ambiguous and lead to broken code.
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