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https://issues.apache.org/jira/browse/SPARK-20294?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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João Pedro Jericó updated SPARK-20294:
--------------------------------------
Description:
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.
was:
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.
> _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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