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From Aris <arisofala...@gmail.com>
Subject Re: Spark 2.0.0 JaninoRuntimeException
Date Tue, 16 Aug 2016 20:58:20 GMT
My error is specifically:

Caused by: org.codehaus.janino.JaninoRuntimeException: Code of method
> "(Lorg/apache/spark/sql/catalyst/InternalRow;Lorg/apache/spark/sql/catalyst/InternalRow;)I"
> of class "org.apache.spark.sql.catalyst.ex
> pressions.GeneratedClass$SpecificOrdering" grows beyond 64 KB


Here is an easy way to reproduce in spark-shell on a cluster!

import org.apache.spark.sql.types.{DoubleType, StructType}
import org.apache.spark.sql.{Row, SparkSession}

//val spark = SparkSession.builder.getOrCreate

val COLMAX: Double = 1000.0
val ROWSIZE: Int = 1000

val intToRow: Int => Row = (i: Int) =>
Row.fromSeq(Range.Double.inclusive(1.0, COLMAX, 1.0).toSeq)
val schema: StructType = (1 to COLMAX.toInt).foldLeft(new
StructType())((s, i) => s.add(i.toString, DoubleType, nullable =
true))
val rdds = spark.sparkContext.parallelize((1 to ROWSIZE).map(intToRow))
val df = spark.createDataFrame(rdds, schema)
val Array(left, right) = df.randomSplit(Array(.8,.2))

// This crashes
left.count




On Tue, Aug 16, 2016 at 8:56 AM, Ted Yu <yuzhihong@gmail.com> wrote:

> Can you take a look at commit fa244e5a90690d6a31be50f2aa203ae1a2e9a1cf ?
>
> There was a test:
> SPARK-15285 Generated SpecificSafeProjection.apply method grows beyond 64KB
>
> See if it matches your use case.
>
> On Tue, Aug 16, 2016 at 8:41 AM, Aris <arisofalaska@gmail.com> wrote:
>
>> I am still working on making a minimal test that I can share without my
>> work-specific code being in there. However, the problem occurs with a
>> dataframe with several hundred columns being asked to do a tension split.
>> Random split works with up to about 350 columns so far. It breaks in my
>> code with 600 columns, but it's a converted dataset of case classes to
>> dataframe. This is deterministically causing the error in Scala 2.11.
>>
>> Once I can get a deterministically breaking test without work code I will
>> try to file a Jira bug.
>>
>> On Tue, Aug 16, 2016, 04:17 Ted Yu <yuzhihong@gmail.com> wrote:
>>
>>> I think we should reopen it.
>>>
>>> On Aug 16, 2016, at 1:48 AM, Kazuaki Ishizaki <ISHIZAKI@jp.ibm.com>
>>> wrote:
>>>
>>> I just realized it since it broken a build with Scala 2.10.
>>> https://github.com/apache/spark/commit/fa244e5a90690d6a31be5
>>> 0f2aa203ae1a2e9a1cf
>>>
>>> I can reproduce the problem in SPARK-15285 with master branch.
>>> Should we reopen SPARK-15285?
>>>
>>> Best Regards,
>>> Kazuaki Ishizaki,
>>>
>>>
>>>
>>> From:        Ted Yu <yuzhihong@gmail.com>
>>> To:        dhruve ashar <dhruveashar@gmail.com>
>>> Cc:        Aris <arisofalaska@gmail.com>, "user@spark.apache.org" <
>>> user@spark.apache.org>
>>> Date:        2016/08/15 06:19
>>> Subject:        Re: Spark 2.0.0 JaninoRuntimeException
>>> ------------------------------
>>>
>>>
>>>
>>> Looks like the proposed fix was reverted:
>>>
>>>     Revert "[SPARK-15285][SQL] Generated SpecificSafeProjection.apply
>>> method grows beyond 64 KB"
>>>
>>>     This reverts commit fa244e5a90690d6a31be50f2aa203ae1a2e9a1cf.
>>>
>>> Maybe this was fixed in some other JIRA ?
>>>
>>> On Fri, Aug 12, 2016 at 2:30 PM, dhruve ashar <*dhruveashar@gmail.com*
>>> <dhruveashar@gmail.com>> wrote:
>>> I see a similar issue being resolved recently:
>>> *https://issues.apache.org/jira/browse/SPARK-15285*
>>> <https://issues.apache.org/jira/browse/SPARK-15285>
>>>
>>> On Fri, Aug 12, 2016 at 3:33 PM, Aris <*arisofalaska@gmail.com*
>>> <arisofalaska@gmail.com>> wrote:
>>> Hello folks,
>>>
>>> I'm on Spark 2.0.0 working with Datasets -- and despite the fact that
>>> smaller data unit tests work on my laptop, when I'm on a cluster, I get
>>> cryptic error messages:
>>>
>>> Caused by: org.codehaus.janino.JaninoRuntimeException: Code of method
>>> "(Lorg/apache/spark/sql/catalyst/InternalRow;Lorg/apache/
>>> spark/sql/catalyst/InternalRow;)I" of class
>>> "org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificOrdering"
>>> grows beyond 64 KB
>>>
>>> Unfortunately I'm not clear on how to even isolate the source of this
>>> problem. I didn't have this problem in Spark 1.6.1.
>>>
>>> Any clues?
>>>
>>>
>>>
>>> --
>>> -Dhruve Ashar
>>>
>>>
>>>
>>>
>

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