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From "Bryan Cutler (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-13691) Scala and Python generate inconsistent results
Date Wed, 16 Mar 2016 17:44:33 GMT

    [ https://issues.apache.org/jira/browse/SPARK-13691?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15197775#comment-15197775
] 

Bryan Cutler commented on SPARK-13691:
--------------------------------------

Since the problem comes from the structure of the code in the driver, it's not just specific
to local mode, I believe.  For instance, with streaming kmeans, it can lead to an inconsistent
model that is not updated as quickly as the Scala version would - which is what led to the
flaky StreamingKMeans failures in SPARK-10086.  Whether or not it really leads to a problem
in practice, I'm not too sure.

> Scala and Python generate inconsistent results
> ----------------------------------------------
>
>                 Key: SPARK-13691
>                 URL: https://issues.apache.org/jira/browse/SPARK-13691
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark
>    Affects Versions: 1.4.1, 1.5.2, 1.6.0
>            Reporter: Shixiong Zhu
>
> Here is an example that Scala and Python generate different results
> {code}
> Scala:
> scala> var i = 0
> i: Int = 0
> scala> val rdd = sc.parallelize(1 to 10).map(_ + i)
> scala> rdd.collect()
> res0: Array[Int] = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
> scala> i += 1
> scala> rdd.collect()
> res2: Array[Int] = Array(2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
> Python:
> >>> i = 0
> >>> rdd = sc.parallelize(range(1, 10)).map(lambda x: x + i)
> >>> rdd.collect()
> [1, 2, 3, 4, 5, 6, 7, 8, 9]
> >>> i += 1
> >>> rdd.collect()
> [1, 2, 3, 4, 5, 6, 7, 8, 9]
> {code}
> The difference is Scala will capture all variables' values when running a job every time,
but Python just captures variables' values once and always uses them for all jobs.



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