Hi, Thanks for the information. I replied in the comment of this issue: https://issues.apache.org/jira/browse/FLINK-16693?focusedCommentId=17065486&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-17065486
Best Regards, Brian -----Original Message----- From: Timo Walther <twal...@apache.org> Sent: Tuesday, March 24, 2020 16:40 To: Zhou, Brian; imj...@gmail.com Cc: user@flink.apache.org Subject: Re: Need help on timestamp type conversion for Table API on Pravega Connector [EXTERNAL EMAIL] This issue is tracked under: https://issues.apache.org/jira/browse/FLINK-16693 Could you provide us a little reproducible example in the issue? I think that could help us in resolving this issue quickly in the next minor release. Thanks, Timo On 20.03.20 03:28, b.z...@dell.com wrote: > Hi, > > Thanks for the reference, Jark. In Pravega connector, user will define > Schema first and then create the table with the descriptor using the > schema, see [1] and error also came from this test case. We also tried > the recommended `bridgedTo(Timestamp.class)` method in the schema > construction, it came with the same error stack trace. > > We are also considering switching to Blink planner implementation, do > you think we can get this issue fixed with the change? > > Here is the full stacktrace: > > ``` > > org.apache.flink.table.codegen.CodeGenException: Unsupported cast from > 'LocalDateTime' to 'Long'. > > at > org.apache.flink.table.codegen.calls.ScalarOperators$.generateCast(Sca > larOperators.scala:815) > > at > org.apache.flink.table.codegen.CodeGenerator.visitCall(CodeGenerator.s > cala:941) > > at > org.apache.flink.table.codegen.CodeGenerator.visitCall(CodeGenerator.s > cala:66) > > at > org.apache.calcite.rex.RexCall.accept(RexCall.java:191) > > at > org.apache.flink.table.codegen.CodeGenerator.$anonfun$visitCall$1(Code > Generator.scala:752) > > at > scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala: > 233) > > at > scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:5 > 8) > > at > scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala: > 51) > > at > scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) > > at > scala.collection.TraversableLike.map(TraversableLike.scala:233) > > at > scala.collection.TraversableLike.map$(TraversableLike.scala:226) > > at > scala.collection.AbstractTraversable.map(Traversable.scala:104) > > at > org.apache.flink.table.codegen.CodeGenerator.visitCall(CodeGenerator.s > cala:742) > > at > org.apache.flink.table.codegen.CodeGenerator.visitCall(CodeGenerator.s > cala:66) > > at > org.apache.calcite.rex.RexCall.accept(RexCall.java:191) > > at > org.apache.flink.table.codegen.CodeGenerator.generateExpression(CodeGe > nerator.scala:247) > > at > org.apache.flink.table.codegen.CodeGenerator.$anonfun$generateConverte > rResultExpression$1(CodeGenerator.scala:273) > > at > org.apache.flink.table.codegen.CodeGenerator.$anonfun$generateConverte > rResultExpression$1$adapted(CodeGenerator.scala:269) > > at > scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala: > 233) > > at > scala.collection.IndexedSeqOptimized.foreach(IndexedSeqOptimized.scala > :32) > > at > scala.collection.IndexedSeqOptimized.foreach$(IndexedSeqOptimized.scal > a:29) > > at > scala.collection.mutable.ArrayOps$ofInt.foreach(ArrayOps.scala:242) > > at > scala.collection.TraversableLike.map(TraversableLike.scala:233) > > at > scala.collection.TraversableLike.map$(TraversableLike.scala:226) > > at > scala.collection.mutable.ArrayOps$ofInt.map(ArrayOps.scala:242) > > at > org.apache.flink.table.codegen.CodeGenerator.generateConverterResultEx > pression(CodeGenerator.scala:269) > > at > org.apache.flink.table.plan.nodes.dataset.BatchScan.generateConversion > Mapper(BatchScan.scala:95) > > at > org.apache.flink.table.plan.nodes.dataset.BatchScan.convertToInternalR > ow(BatchScan.scala:59) > > at > org.apache.flink.table.plan.nodes.dataset.BatchScan.convertToInternalR > ow$(BatchScan.scala:35) > > at > org.apache.flink.table.plan.nodes.dataset.BatchTableSourceScan.convert > ToInternalRow(BatchTableSourceScan.scala:45) > > at > org.apache.flink.table.plan.nodes.dataset.BatchTableSourceScan.transla > teToPlan(BatchTableSourceScan.scala:165) > > at > org.apache.flink.table.plan.nodes.dataset.DataSetWindowAggregate.trans > lateToPlan(DataSetWindowAggregate.scala:114) > > at > org.apache.flink.table.plan.nodes.dataset.DataSetCalc.translateToPlan( > DataSetCalc.scala:92) > > at > org.apache.flink.table.api.internal.BatchTableEnvImpl.translate(BatchT > ableEnvImpl.scala:306) > > at > org.apache.flink.table.api.internal.BatchTableEnvImpl.translate(BatchT > ableEnvImpl.scala:281) > > at > org.apache.flink.table.api.java.internal.BatchTableEnvironmentImpl.toD > ataSet(BatchTableEnvironmentImpl.scala:87) > > at > io.pravega.connectors.flink.FlinkPravegaTableITCase.testTableSourceBat > chDescriptor(FlinkPravegaTableITCase.java:349) > > at > io.pravega.connectors.flink.FlinkPravegaTableITCase.testTableSourceUsi > ngDescriptor(FlinkPravegaTableITCase.java:246) > > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native > Method) > > at > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.j > ava:62) > > at > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccess > orImpl.java:43) > > at java.lang.reflect.Method.invoke(Method.java:498) > > at > org.junit.runners.model.FrameworkMethod$1.runReflectiveCall(FrameworkM > ethod.java:50) > > at > org.junit.internal.runners.model.ReflectiveCallable.run(ReflectiveCall > able.java:12) > > at > org.junit.runners.model.FrameworkMethod.invokeExplosively(FrameworkMet > hod.java:47) > > at > org.junit.internal.runners.statements.InvokeMethod.evaluate(InvokeMeth > od.java:17) > > at > org.junit.internal.runners.statements.FailOnTimeout$CallableStatement. > call(FailOnTimeout.java:298) > > at > org.junit.internal.runners.statements.FailOnTimeout$CallableStatement. > call(FailOnTimeout.java:292) > > at > java.util.concurrent.FutureTask.run(FutureTask.java:266) > > at java.lang.Thread.run(Thread.java:748) > > Process finished with exit code -1 > > ``` > > [1] > https://github.com/pravega/flink-connectors/blob/master/src/test/java/ > io/pravega/connectors/flink/FlinkPravegaTableITCase.java#L310 > > Best Regards, > > Brian > > *From:* Jark Wu <imj...@gmail.com> > *Sent:* Thursday, March 19, 2020 20:25 > *To:* Till Rohrmann > *Cc:* Zhou, Brian; Timo Walther; Jingsong Li; user > *Subject:* Re: Need help on timestamp type conversion for Table API on > Pravega Connector > > [EXTERNAL EMAIL] > > This maybe a similar issue to [1], we continue the discussion there. > > Best, > > Jark > > [1]: > http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/SQ > L-Timetamp-types-incompatible-after-migration-to-1-10-td33784.html#a33 > 791 > > On Tue, 17 Mar 2020 at 18:05, Till Rohrmann <trohrm...@apache.org > <mailto:trohrm...@apache.org>> wrote: > > Thanks for reporting this issue Brian. I'm not a Table API expert > but I know that there is some work on the type system ongoing. I've > pulled Timo and Jingsong into the conversation who might be able to > tell you what exactly changed and whether the timestamp issue might > be caused by the changes. > > Cheers, > > Till > > On Mon, Mar 16, 2020 at 5:48 AM <b.z...@dell.com > <mailto:b.z...@dell.com>> wrote: > > Hi community, > > Pravega connector is a connector that provides both Batch and > Streaming Table API implementation. We uses descriptor API to > build Table source. When we plan to upgrade to Flink 1.10, we > found the unit tests are not passing with our existing Batch > Table API. There is a type conversion error in the Timestamp > with our descriptor Table API. The detail is in the issue here: > https://github.com/pravega/flink-connectors/issues/341 Hope > someone from Flink community can help us with some suggestions > on this issue. Thanks. > > Best Regards, > > Brian >
