I merged your PR for the RowSerializer. Teaching the aggregators to deal with null values should be a very simple fix in ExpressionAggregateFunction.scala. There it is simply always aggregating the values without checking whether they are null. If you want you can also fix that or I can quickly fix it.
On Thu, 11 Jun 2015 at 10:40 Aljoscha Krettek <aljos...@apache.org> wrote: > 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 >>>>> >>>>> >>>>> >>>>> >>>
