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https://issues.apache.org/jira/browse/SPARK-13748?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-13748:
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Assignee: (was: Apache Spark)
> createDataFrame and rows with omitted fields
> --------------------------------------------
>
> Key: SPARK-13748
> URL: https://issues.apache.org/jira/browse/SPARK-13748
> Project: Spark
> Issue Type: Improvement
> Components: Documentation, PySpark, SQL
> Affects Versions: 1.6.0
> Reporter: Ethan Aubin
> Priority: Minor
>
> I found it confusing that a Row with an omitted field is different from a row
> with field present but value missing. This was originally problematic for
> json files will varying fields, but it's comes down to something like:
> def test(rows):
> ds = sc.parallelize(rows)
> df = sqlContext.createDataFrame(ds,None,1)
> print df[['y']].collect()
> test([Row(x=1,y=None),Row(x=2, y='asdf')]) # Works
> test([Row(x=1),Row(x=2, y='asdf')]) # Fails with an
> ArrayIndexOutOfBoundsException.
> maybe more could be said in the documentation for createDataFrame or Row
> about what's expected. Validation or correction would be helpful, as would a
> function creating a well formed row from a structtype and dictionary.
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