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https://issues.apache.org/jira/browse/SPARK-6677?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14485521#comment-14485521
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Davies Liu commented on SPARK-6677:
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What is the expected output?
I got this:
{code}
key: a
res1 data as row: [Row(foo=1, key=u'a')]
res2 data as row: [Row(bar=3, key=u'a', other=u'foobar')]
res1 and res2 fields: (u'foo', u'key') (u'bar', u'key', u'other')
res1 data as tuple: 1 a
res2 data as tuple: 3 a foobar
key: c
res1 data as row: []
res2 data as row: [Row(bar=4, key=u'c', other=u'barfoo')]
key: b
res1 data as row: [Row(foo=2, key=u'b')]
res2 data as row: []
{code}
> pyspark.sql nondeterministic issue with row fields
> --------------------------------------------------
>
> Key: SPARK-6677
> URL: https://issues.apache.org/jira/browse/SPARK-6677
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 1.3.0
> Environment: spark version: spark-1.3.0-bin-hadoop2.4
> python version: Python 2.7.6
> operating system: MacOS, x86_64 x86_64 x86_64 GNU/Linux
> Reporter: Stefano Parmesan
> Labels: pyspark, row, sql
>
> The following issue happens only when running pyspark in the python
> interpreter, it works correctly with spark-submit.
> Reading two json files containing objects with a different structure leads
> sometimes to the definition of wrong Rows, where the fields of a file are
> used for the other one.
> I was able to write a sample code that reproduce this issue one out of three
> times; the code snippet is available at the following link, together with
> some (very simple) data samples:
> https://gist.github.com/armisael/e08bb4567d0a11efe2db
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