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From Reynold Xin <r...@databricks.com>
Subject Re: If you use Spark 1.5 and disabled Tungsten mode ...
Date Sun, 01 Nov 2015 07:22:39 GMT
Thanks for reporting it, Sjoerd. You might have a different version of
Janino brought in from somewhere else.

This should fix your problem: https://github.com/apache/spark/pull/9372

Can you give it a try?



On Tue, Oct 27, 2015 at 9:12 PM, Sjoerd Mulder <sjoerdmulder@gmail.com>
wrote:

> No the job actually doesn't fail, but since our tests is generating all
> these stacktraces i have disabled the tungsten mode just to be sure (and
> don't have gazilion stacktraces in production).
>
> 2015-10-27 20:59 GMT+01:00 Josh Rosen <joshrosen@databricks.com>:
>
>> Hi Sjoerd,
>>
>> Did your job actually *fail* or did it just generate many spurious
>> exceptions? While the stacktrace that you posted does indicate a bug, I
>> don't think that it should have stopped query execution because Spark
>> should have fallen back to an interpreted code path (note the "Failed to
>> generate ordering, fallback to interpreted" in the error message).
>>
>> On Tue, Oct 27, 2015 at 12:56 PM Sjoerd Mulder <sjoerdmulder@gmail.com>
>> wrote:
>>
>>> I have disabled it because of it started generating ERROR's when
>>> upgrading from Spark 1.4 to 1.5.1
>>>
>>> 2015-10-27T20:50:11.574+0100 ERROR TungstenSort.newOrdering() - Failed
>>> to generate ordering, fallback to interpreted
>>> java.util.concurrent.ExecutionException: java.lang.Exception: failed to
>>> compile: org.codehaus.commons.compiler.CompileException: Line 15, Column 9:
>>> Invalid character input "@" (character code 64)
>>>
>>> public SpecificOrdering
>>> generate(org.apache.spark.sql.catalyst.expressions.Expression[] expr) {
>>>   return new SpecificOrdering(expr);
>>> }
>>>
>>> class SpecificOrdering extends
>>> org.apache.spark.sql.catalyst.expressions.codegen.BaseOrdering {
>>>
>>>   private org.apache.spark.sql.catalyst.expressions.Expression[]
>>> expressions;
>>>
>>>
>>>
>>>   public
>>> SpecificOrdering(org.apache.spark.sql.catalyst.expressions.Expression[]
>>> expr) {
>>>     expressions = expr;
>>>
>>>   }
>>>
>>>   @Override
>>>   public int compare(InternalRow a, InternalRow b) {
>>>     InternalRow i = null;  // Holds current row being evaluated.
>>>
>>>     i = a;
>>>     boolean isNullA2;
>>>     long primitiveA3;
>>>     {
>>>       /* input[2, LongType] */
>>>
>>>       boolean isNull0 = i.isNullAt(2);
>>>       long primitive1 = isNull0 ? -1L : (i.getLong(2));
>>>
>>>       isNullA2 = isNull0;
>>>       primitiveA3 = primitive1;
>>>     }
>>>     i = b;
>>>     boolean isNullB4;
>>>     long primitiveB5;
>>>     {
>>>       /* input[2, LongType] */
>>>
>>>       boolean isNull0 = i.isNullAt(2);
>>>       long primitive1 = isNull0 ? -1L : (i.getLong(2));
>>>
>>>       isNullB4 = isNull0;
>>>       primitiveB5 = primitive1;
>>>     }
>>>     if (isNullA2 && isNullB4) {
>>>       // Nothing
>>>     } else if (isNullA2) {
>>>       return 1;
>>>     } else if (isNullB4) {
>>>       return -1;
>>>     } else {
>>>       int comp = (primitiveA3 > primitiveB5 ? 1 : primitiveA3 <
>>> primitiveB5 ? -1 : 0);
>>>       if (comp != 0) {
>>>         return -comp;
>>>       }
>>>     }
>>>
>>>     return 0;
>>>   }
>>> }
>>>
>>> at
>>> org.spark-project.guava.util.concurrent.AbstractFuture$Sync.getValue(AbstractFuture.java:306)
>>> at
>>> org.spark-project.guava.util.concurrent.AbstractFuture$Sync.get(AbstractFuture.java:293)
>>> at
>>> org.spark-project.guava.util.concurrent.AbstractFuture.get(AbstractFuture.java:116)
>>> at
>>> org.spark-project.guava.util.concurrent.Uninterruptibles.getUninterruptibly(Uninterruptibles.java:135)
>>> at
>>> org.spark-project.guava.cache.LocalCache$Segment.getAndRecordStats(LocalCache.java:2410)
>>> at
>>> org.spark-project.guava.cache.LocalCache$Segment.loadSync(LocalCache.java:2380)
>>> at
>>> org.spark-project.guava.cache.LocalCache$Segment.lockedGetOrLoad(LocalCache.java:2342)
>>> at
>>> org.spark-project.guava.cache.LocalCache$Segment.get(LocalCache.java:2257)
>>> at org.spark-project.guava.cache.LocalCache.get(LocalCache.java:4000)
>>> at
>>> org.spark-project.guava.cache.LocalCache.getOrLoad(LocalCache.java:4004)
>>> at
>>> org.spark-project.guava.cache.LocalCache$LocalLoadingCache.get(LocalCache.java:4874)
>>> at
>>> org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator.compile(CodeGenerator.scala:362)
>>> at
>>> org.apache.spark.sql.catalyst.expressions.codegen.GenerateOrdering$.create(GenerateOrdering.scala:139)
>>> at
>>> org.apache.spark.sql.catalyst.expressions.codegen.GenerateOrdering$.create(GenerateOrdering.scala:37)
>>> at
>>> org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator.generate(CodeGenerator.scala:425)
>>> at
>>> org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator.generate(CodeGenerator.scala:422)
>>> at
>>> org.apache.spark.sql.execution.SparkPlan.newOrdering(SparkPlan.scala:294)
>>> at org.apache.spark.sql.execution.TungstenSort.org
>>> $apache$spark$sql$execution$TungstenSort$$preparePartition$1(sort.scala:131)
>>> at
>>> org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169)
>>> at
>>> org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169)
>>> at
>>> org.apache.spark.rdd.MapPartitionsWithPreparationRDD.compute(MapPartitionsWithPreparationRDD.scala:59)
>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>>> at
>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>>> at
>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>>> at org.apache.spark.scheduler.Task.run(Task.scala:88)
>>> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
>>> at
>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>>> at
>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>>> at java.lang.Thread.run(Thread.java:745)
>>>
>>>
>>> 2015-10-14 21:00 GMT+02:00 Reynold Xin <rxin@databricks.com>:
>>>
>>>> Can you reply to this email and provide us with reasons why you disable
>>>> it?
>>>>
>>>> Thanks.
>>>>
>>>>
>>>
>

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