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https://issues.apache.org/jira/browse/SPARK-20294?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15965257#comment-15965257
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Hyukjin Kwon commented on SPARK-20294:
--------------------------------------
Just for other guys,
{code}
>>> small_rdd = sc.parallelize([(1, 2), (2, 'foo')])
>>> small_rdd.toDF(sampleRatio=0.01).show()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../spark/python/pyspark/sql/session.py", line 57, in toDF
return sparkSession.createDataFrame(self, schema, sampleRatio)
File ".../spark/python/pyspark/sql/session.py", line 524, in createDataFrame
rdd, schema = self._createFromRDD(data.map(prepare), schema, samplingRatio)
File ".../spark/python/pyspark/sql/session.py", line 364, in _createFromRDD
struct = self._inferSchema(rdd, samplingRatio)
File ".../spark/python/pyspark/sql/session.py", line 356, in _inferSchema
schema = rdd.map(_infer_schema).reduce(_merge_type)
File ".../spark/python/pyspark/rdd.py", line 838, in reduce
raise ValueError("Can not reduce() empty RDD")
ValueError: Can not reduce() empty RDD
{code}
> _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:
> ```python
> small_rdd = sc.parallelize([(1, 2), (2, 'foo')])
> small_rdd.toDF(samplingRatio=0.01).show()
> ```
> 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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