Hi Shiti, here is the issue [1].
Cheers, Till [1] https://issues.apache.org/jira/browse/FLINK-2203 On Thu, Jun 11, 2015 at 8:42 AM Shiti Saxena <ssaxena....@gmail.com> wrote: > Hi Aljoscha, > > Could you please point me to the JIRA tickets? If you could provide some > guidance on how to resolve these, I will work on them and raise a > pull-request. > > Thanks, > Shiti > > On Thu, Jun 11, 2015 at 11:31 AM, Aljoscha Krettek <aljos...@apache.org> > wrote: > >> Hi, >> yes, I think the problem is that the RowSerializer does not support >> null-values. I think we can add support for this, I will open a Jira issue. >> >> Another problem I then see is that the aggregations can not properly deal >> with null-values. This would need separate support. >> >> Regards, >> Aljoscha >> >> On Thu, 11 Jun 2015 at 06:41 Shiti Saxena <ssaxena....@gmail.com> wrote: >> >>> Hi, >>> >>> In our project, we are using the Flink Table API and are facing the >>> following issues, >>> >>> We load data from a CSV file and create a DataSet[Row]. The CSV file can >>> also have invalid entries in some of the fields which we replace with null >>> when building the DataSet[Row]. >>> >>> This DataSet[Row] is later on transformed to Table whenever required and >>> specific operation such as select or aggregate, etc are performed. >>> >>> When a null value is encountered, we get a null pointer exception and >>> the whole job fails. (We can see this by calling collect on the resulting >>> DataSet). >>> >>> The error message is similar to, >>> >>> Job execution failed. >>> org.apache.flink.runtime.client.JobExecutionException: Job execution >>> failed. >>> at >>> org.apache.flink.runtime.jobmanager.JobManager$$anonfun$receiveWithLogMessages$1.applyOrElse(JobManager.scala:315) >>> at >>> scala.runtime.AbstractPartialFunction$mcVL$sp.apply$mcVL$sp(AbstractPartialFunction.scala:33) >>> at >>> scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:33) >>> at >>> scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:25) >>> at >>> org.apache.flink.runtime.ActorLogMessages$$anon$1.apply(ActorLogMessages.scala:43) >>> at >>> org.apache.flink.runtime.ActorLogMessages$$anon$1.apply(ActorLogMessages.scala:29) >>> at scala.PartialFunction$class.applyOrElse(PartialFunction.scala:118) >>> at >>> org.apache.flink.runtime.ActorLogMessages$$anon$1.applyOrElse(ActorLogMessages.scala:29) >>> at akka.actor.Actor$class.aroundReceive(Actor.scala:465) >>> at >>> org.apache.flink.runtime.jobmanager.JobManager.aroundReceive(JobManager.scala:94) >>> at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516) >>> at akka.actor.ActorCell.invoke(ActorCell.scala:487) >>> at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:254) >>> at akka.dispatch.Mailbox.run(Mailbox.scala:221) >>> at akka.dispatch.Mailbox.exec(Mailbox.scala:231) >>> at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260) >>> at >>> scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339) >>> at >>> scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979) >>> at >>> scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107) >>> Caused by: java.lang.NullPointerException >>> at >>> org.apache.flink.api.common.typeutils.base.IntSerializer.serialize(IntSerializer.java:63) >>> at >>> org.apache.flink.api.common.typeutils.base.IntSerializer.serialize(IntSerializer.java:27) >>> at >>> org.apache.flink.api.table.typeinfo.RowSerializer.serialize(RowSerializer.scala:80) >>> at >>> org.apache.flink.api.table.typeinfo.RowSerializer.serialize(RowSerializer.scala:28) >>> at >>> org.apache.flink.runtime.plugable.SerializationDelegate.write(SerializationDelegate.java:51) >>> at >>> org.apache.flink.runtime.io.network.api.serialization.SpanningRecordSerializer.addRecord(SpanningRecordSerializer.java:76) >>> at >>> org.apache.flink.runtime.io.network.api.writer.RecordWriter.emit(RecordWriter.java:83) >>> at >>> org.apache.flink.runtime.operators.shipping.OutputCollector.collect(OutputCollector.java:65) >>> at >>> org.apache.flink.runtime.operators.chaining.ChainedMapDriver.collect(ChainedMapDriver.java:78) >>> at >>> org.apache.flink.runtime.operators.chaining.ChainedMapDriver.collect(ChainedMapDriver.java:78) >>> at >>> org.apache.flink.runtime.operators.DataSourceTask.invoke(DataSourceTask.java:177) >>> at org.apache.flink.runtime.taskmanager.Task.run(Task.java:559) >>> at java.lang.Thread.run(Thread.java:724) >>> >>> Could this be because the RowSerializer does not support null values? >>> (Similar to Flink-629 <https://issues.apache.org/jira/browse/FLINK-629> >>> ) >>> >>> Currently, to overcome this issue, we are ignoring all the rows which >>> may have null values. For example, we have a method cleanData defined as, >>> >>> def cleanData(table:Table, relevantColumns:Seq[String]):Table = { >>> val whereClause: String = relevantColumns.map{ >>> cName=> >>> s"$cName.isNotNull" >>> }.mkString(" && ") >>> >>> val result :Table = >>> table.select(relevantColumns.mkString(",")).where(whereClause) >>> result >>> } >>> >>> Before operating on any Table, we use this method and then continue with >>> task. >>> >>> Is this the right way to handle this? If not please let me know how to >>> go about it. >>> >>> >>> Thanks, >>> Shiti >>> >>> >>> >>> >
