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author | Yanbo Liang <ybliang8@gmail.com> | 2016-09-29 00:54:26 -0700 |
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committer | Yanbo Liang <ybliang8@gmail.com> | 2016-09-29 00:54:26 -0700 |
commit | a19a1bb59411177caaf99581e89098826b7d0c7b (patch) | |
tree | 649a504d904cce2f0783def6e0114ab68a9e1024 /docs/img/java-sm.png | |
parent | 37eb9184f1e9f1c07142c66936671f4711ef407d (diff) | |
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[SPARK-16356][FOLLOW-UP][ML] Enforce ML test of exception for local/distributed Dataset.
## What changes were proposed in this pull request?
#14035 added ```testImplicits``` to ML unit tests and promoted ```toDF()```, but left one minor issue at ```VectorIndexerSuite```. If we create the DataFrame by ```Seq(...).toDF()```, it will throw different error/exception compared with ```sc.parallelize(Seq(...)).toDF()``` for one of the test cases.
After in-depth study, I found it was caused by different behavior of local and distributed Dataset if the UDF failed at ```assert```. If the data is local Dataset, it throws ```AssertionError``` directly; If the data is distributed Dataset, it throws ```SparkException``` which is the wrapper of ```AssertionError```. I think we should enforce this test to cover both case.
## How was this patch tested?
Unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #15261 from yanboliang/spark-16356.
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