[jira] [Updated] (SPARK-13691) Scala and Python generate inconsistent results

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-13691?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon updated SPARK-13691:
---------------------------------
    Labels: bulk-closed  (was: )

> 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
>            Priority: Major
>              Labels: bulk-closed
>
> 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.
> In addition, SQL UDF has the similar issue. It's better to fix that too if anyone wants to fix the bug.



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