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| author | Wenchen Fan <wenchen@databricks.com> | 2015-11-05 14:53:16 -0800 |
|---|---|---|
| committer | Yin Huai <yhuai@databricks.com> | 2015-11-05 14:53:16 -0800 |
| commit | d9e30c59cede7f57786bb19e64ba422eda43bdcb (patch) | |
| tree | 6914b0d2abfb5c0c1fd3513a1ad673b60541165b /.gitattributes | |
| parent | b9455d1f1810e1e3f472014f665ad3ad3122bcc0 (diff) | |
| download | spark-d9e30c59cede7f57786bb19e64ba422eda43bdcb.tar.gz spark-d9e30c59cede7f57786bb19e64ba422eda43bdcb.tar.bz2 spark-d9e30c59cede7f57786bb19e64ba422eda43bdcb.zip | |
[SPARK-10656][SQL] completely support special chars in DataFrame
the main problem is: we interpret column name with special handling of `.` for DataFrame. This enables us to write something like `df("a.b")` to get the field `b` of `a`. However, we don't need this feature in `DataFrame.apply("*")` or `DataFrame.withColumnRenamed`. In these 2 cases, the column name is the final name already, we don't need extra process to interpret it.
The solution is simple, use `queryExecution.analyzed.output` to get resolved column directly, instead of using `DataFrame.resolve`.
close https://github.com/apache/spark/pull/8811
Author: Wenchen Fan <wenchen@databricks.com>
Closes #9462 from cloud-fan/special-chars.
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