Cool, good to hear. The PojoSerializer already handles null fields. The RowSerializer can be modified in pretty much the same way. So you should start by looking at the copy()/serialize()/deserialize() methods of PojoSerializer and then modify RowSerializer in a similar way.
You can also send me a private mail if you want more in-depth explanations. On Thu, 11 Jun 2015 at 09:33 Till Rohrmann <trohrm...@apache.org> wrote: > 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 >>>> >>>> >>>> >>>> >>
