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Posted to issues@kylin.apache.org by "Hokyung Song (JIRA)" <ji...@apache.org> on 2018/04/25 08:32:00 UTC

[jira] [Created] (KYLIN-3349) Cube Build NumberFormatException when using Spark

Hokyung Song created KYLIN-3349:
-----------------------------------

             Summary: Cube Build NumberFormatException when using Spark
                 Key: KYLIN-3349
                 URL: https://issues.apache.org/jira/browse/KYLIN-3349
             Project: Kylin
          Issue Type: Bug
          Components: Job Engine
    Affects Versions: v2.3.1, v2.3.0, v2.2.0
            Reporter: Hokyung Song


When I use spark engine to build cube, I have this error in spark when building cube.

In my opinion, data has 0.00 as string, it cannot cast to long or double.

stack trace as follows
{code:java}
2018-04-24 12:54:11,685 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : 18/04/24 12:54:11 WARN TaskSetManager: Lost task 193.0 in stage 0.0 (TID 1, hadoop, executor 1): java.lang.NumberFormatException: For input string: "0.0000"
2018-04-24 12:54:11,686 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at java.lang.NumberFormatException.forInputString(NumberFormatException.java:65)
2018-04-24 12:54:11,686 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at java.lang.Long.parseLong(Long.java:589)
2018-04-24 12:54:11,686 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at java.lang.Long.valueOf(Long.java:803)
2018-04-24 12:54:11,686 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at org.apache.kylin.measure.basic.LongIngester.valueOf(LongIngester.java:38)
2018-04-24 12:54:11,686 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at org.apache.kylin.measure.basic.LongIngester.valueOf(LongIngester.java:28)
2018-04-24 12:54:11,686 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at org.apache.kylin.engine.mr.common.BaseCuboidBuilder.buildValueOf(BaseCuboidBuilder.java:163)
2018-04-24 12:54:11,686 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at org.apache.kylin.engine.mr.common.BaseCuboidBuilder.buildValueObjects(BaseCuboidBuilder.java:128)
2018-04-24 12:54:11,687 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at org.apache.kylin.engine.spark.SparkCubingByLayer$EncodeBaseCuboid.call(SparkCubingByLayer.java:309)
2018-04-24 12:54:11,687 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at org.apache.kylin.engine.spark.SparkCubingByLayer$EncodeBaseCuboid.call(SparkCubingByLayer.java:271)
2018-04-24 12:54:11,687 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at org.apache.spark.api.java.JavaPairRDD$$anonfun$pairFunToScalaFun$1.apply(JavaPairRDD.scala:1043)
2018-04-24 12:54:11,687 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at org.apache.spark.api.java.JavaPairRDD$$anonfun$pairFunToScalaFun$1.apply(JavaPairRDD.scala:1043)
2018-04-24 12:54:11,687 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
2018-04-24 12:54:11,687 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:193)
2018-04-24 12:54:11,687 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:63)
2018-04-24 12:54:11,687 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
2018-04-24 12:54:11,687 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
2018-04-24 12:54:11,688 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at org.apache.spark.scheduler.Task.run(Task.scala:99)
2018-04-24 12:54:11,688 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:325)
2018-04-24 12:54:11,688 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
2018-04-24 12:54:11,688 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
2018-04-24 12:54:11,688 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 : at java.lang.Thread.run(Thread.java:745)
2018-04-24 12:54:11,688 INFO [Scheduler 1401715751 Job c1e5e47c-89fc-4ad6-8ae0-629879919aa5-264] spark.SparkExecutable:38 :{code}



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