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} -- This message was sent by Atlassian JIRA (v7.6.3#76005)