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https://issues.apache.org/jira/browse/SPARK-11303?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14990795#comment-14990795
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Reynold Xin commented on SPARK-11303:
-------------------------------------
This made it into 1.5.2.
> sample (without replacement) + filter returns wrong results in DataFrame
> ------------------------------------------------------------------------
>
> Key: SPARK-11303
> URL: https://issues.apache.org/jira/browse/SPARK-11303
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.5.1
> Environment: pyspark local mode, linux.
> Reporter: Yuval Tanny
> Assignee: Yanbo Liang
> Fix For: 1.5.2, 1.6.0
>
>
> When sampling and then filtering DataFrame from python, we get inconsistent
> result when not caching the sampled DataFrame. This bug doesn't appear in
> spark 1.4.1.
> {code}
> d = sqlContext.createDataFrame(sc.parallelize([[1]] * 50 + [[2]] * 50),['t'])
> d_sampled = d.sample(False, 0.1, 1)
> print d_sampled.count()
> print d_sampled.filter('t = 1').count()
> print d_sampled.filter('t != 1').count()
> d_sampled.cache()
> print d_sampled.count()
> print d_sampled.filter('t = 1').count()
> print d_sampled.filter('t != 1').count()
> {code}
> output:
> {code}
> 14
> 7
> 8
> 14
> 7
> 7
> {code}
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