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https://issues.apache.org/jira/browse/SPARK-20294?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15965590#comment-15965590
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João Pedro Jericó commented on SPARK-20294:
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Yes, I think that this would be a nice solution, i.e., given that we *know*
it's not empty, we should always try to do something. Right now spark skips
directly to sampling and raises an error if it fails.
> _inferSchema for RDDs fails if sample returns empty RDD
> -------------------------------------------------------
>
> Key: SPARK-20294
> URL: https://issues.apache.org/jira/browse/SPARK-20294
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.1.0
> Reporter: João Pedro Jericó
> Priority: Minor
>
> Currently the _inferSchema function on
> [session.py](https://github.com/apache/spark/blob/master/python/pyspark/sql/session.py#L354)
> line 354 fails if applied to an RDD for which the sample call returns an
> empty RDD. This is possible for example if one has a small RDD but that needs
> the schema to be inferred by more than one Row. For example:
> {code}
> small_rdd = sc.parallelize([(1, 2), (2, 'foo')])
> small_rdd.toDF(samplingRatio=0.01).show()
> {code}
> This will fail with high probability because when sampling the small_rdd with
> the .sample method it will return an empty RDD most of the time. However,
> this is not the desired result because we are able to sample at least 1% of
> the RDD.
> This is probably a problem with the other Spark APIs however I don't have the
> knowledge to look at the source code for other languages.
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