[jira] [Commented] (SPARK-11303) sample (without replacement) + filter returns wrong results in DataFrame
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https://issues.apache.org/jira/browse/SPARK-11303?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=14990795#comment-14990795
]
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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[jira] [Commented] (SPARK-11303) sample (without replacement) + filter returns wrong results in DataFrame
[
https://issues.apache.org/jira/browse/SPARK-11303?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=14978406#comment-14978406
]
Michael Armbrust commented on SPARK-11303:
--
I picked it into branch-1.5, but I'm not sure if it made the cut off. [~rxin]?
> 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
> Fix For: 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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[jira] [Commented] (SPARK-11303) sample (without replacement) + filter returns wrong results in DataFrame
[
https://issues.apache.org/jira/browse/SPARK-11303?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=14975918#comment-14975918
]
Apache Spark commented on SPARK-11303:
--
User 'yanboliang' has created a pull request for this issue:
https://github.com/apache/spark/pull/9294
> 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
>
> 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.
> 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()
> output:
> 14
> 7
> 8
> 14
> 7
> 7
> Thanks!
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[jira] [Commented] (SPARK-11303) sample (without replacement) + filter returns wrong results in DataFrame
[
https://issues.apache.org/jira/browse/SPARK-11303?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=14976317#comment-14976317
]
Yuval Tanny commented on SPARK-11303:
-
Is the fix is going to be merged to 1.5 (and 1.5.2)?
Thanks
> 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
> Fix For: 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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[jira] [Commented] (SPARK-11303) sample (without replacement) + filter returns wrong results in DataFrame
[
https://issues.apache.org/jira/browse/SPARK-11303?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=14973993#comment-14973993
]
Yanbo Liang commented on SPARK-11303:
-
It looks like this bug caused by mutable row copy related problem similar with
SPARK-4963. But after adding *copy* to *sample*, it still can not resolve this
issue. I found *map(_copy())* was removed by
https://github.com/apache/spark/pull/8040/files, [~rxin] Could you tell us the
motivation of removing *map(_copy())* in that PR?
> 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
>
> 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.
> 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()
> output:
> 14
> 7
> 8
> 14
> 7
> 7
> Thanks!
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[jira] [Commented] (SPARK-11303) sample (without replacement) + filter returns wrong results in DataFrame
[
https://issues.apache.org/jira/browse/SPARK-11303?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=14973896#comment-14973896
]
Yanbo Liang commented on SPARK-11303:
-
I think the reason of this bug is the same as SPARK-4963, I will send a PR to
resolve it.
> 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
>
> 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.
> 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()
> output:
> 14
> 7
> 8
> 14
> 7
> 7
> Thanks!
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