[
https://issues.apache.org/jira/browse/HUDI-2109?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17372276#comment-17372276
]
ASF GitHub Bot commented on HUDI-2109:
--------------------------------------
hudi-bot edited a comment on pull request #3195:
URL: https://github.com/apache/hudi/pull/3195#issuecomment-871791000
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## CI report:
* 8c3aabca64b2f65061864bac2fb1816df2fb9d89 Azure:
[FAILURE](https://dev.azure.com/apache-hudi-ci-org/785b6ef4-2f42-4a89-8f0e-5f0d7039a0cc/_build/results?buildId=576)
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> AvroConversionHelper does not handle Nulls
> ------------------------------------------
>
> Key: HUDI-2109
> URL: https://issues.apache.org/jira/browse/HUDI-2109
> Project: Apache Hudi
> Issue Type: Bug
> Reporter: Jake Dalli
> Priority: Trivial
> Labels: pull-request-available
>
> Given an avro schema containing a null field:
> ```
> {
> "name": "messageKey",
> "type": "null"
> }
> ```
> When using `org.apache.hudi.utilities.transform.SqlQueryBasedTransformer`
> with deltastreamer and AvroKafkaSource, I get the following error:
> ```
> Caused by: org.apache.spark.SparkException: Job aborted due to stage failure:
> Task 0 in stage 1.0 failed 4 times, most recent failure: Lost task 0.3 in
> stage 1.0 (TID 4, ip-10-102-8-124.eu-central-1.compute.internal, executor 1):
> org.apache.spark.sql.avro.IncompatibleSchemaException: Cannot convert Avro
> schema to catalyst type because schema at path messageKey is not compatible
> (avroType = NullType, sqlType = NULL).
> Source Avro Schema: ...
> Target Catalyst type: ...
> at
> org.apache.hudi.AvroConversionHelper$.createConverter$1(AvroConversionHelper.scala:265)
> at
> org.apache.hudi.AvroConversionHelper$.createConverter$1(AvroConversionHelper.scala:146)
> at
> org.apache.hudi.AvroConversionHelper$.createConverterToRow(AvroConversionHelper.scala:273)
> at
> org.apache.hudi.AvroConversionUtils$.$anonfun$createDataFrame$1(AvroConversionUtils.scala:42)
> at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2(RDD.scala:837)
> at
> org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2$adapted(RDD.scala:837)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
> at
> org.apache.spark.sql.execution.SQLExecutionRDD.$anonfun$compute$1(SQLExecutionRDD.scala:52)
> at
> org.apache.spark.sql.internal.SQLConf$.withExistingConf(SQLConf.scala:100)
> at
> org.apache.spark.sql.execution.SQLExecutionRDD.compute(SQLExecutionRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
> at org.apache.spark.scheduler.Task.run(Task.scala:127)
> at
> org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:444)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
> at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:447)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748)
> Driver stacktrace:
> at
> org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2175)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2124)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2123)
> at
> scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
> at
> scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2123)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:990)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:990)
> at scala.Option.foreach(Option.scala:407)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:990)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2355)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2304)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2293)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
> at
> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:792)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2093)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2114)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2133)
> at org.apache.spark.rdd.RDD.$anonfun$take$1(RDD.scala:1423)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:388)
> at org.apache.spark.rdd.RDD.take(RDD.scala:1396)
> at org.apache.spark.rdd.RDD.$anonfun$isEmpty$1(RDD.scala:1531)
> at
> scala.runtime.java8.JFunction0$mcZ$sp.apply(JFunction0$mcZ$sp.java:23)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:388)
> at org.apache.spark.rdd.RDD.isEmpty(RDD.scala:1531)
> at
> org.apache.spark.api.java.JavaRDDLike.isEmpty(JavaRDDLike.scala:544)
> at
> org.apache.spark.api.java.JavaRDDLike.isEmpty$(JavaRDDLike.scala:544)
> at
> org.apache.spark.api.java.AbstractJavaRDDLike.isEmpty(JavaRDDLike.scala:45)
> at
> org.apache.hudi.utilities.deltastreamer.DeltaSync.readFromSource(DeltaSync.java:380)
> at
> org.apache.hudi.utilities.deltastreamer.DeltaSync.syncOnce(DeltaSync.java:255)
> at
> org.apache.hudi.utilities.deltastreamer.HoodieDeltaStreamer$DeltaSyncService.lambda$startService$0(HoodieDeltaStreamer.java:587)
> ... 4 more
> Caused by: org.apache.spark.sql.avro.IncompatibleSchemaException: Cannot
> convert Avro schema to catalyst type because schema at path routingKey is not
> compatible (avroType = NullType, sqlType = NULL).
> at
> org.apache.hudi.AvroConversionHelper$.createConverter$1(AvroConversionHelper.scala:265)
> at
> org.apache.hudi.AvroConversionHelper$.createConverter$1(AvroConversionHelper.scala:146)
> at
> org.apache.hudi.AvroConversionHelper$.createConverterToRow(AvroConversionHelper.scala:273)
> at
> org.apache.hudi.AvroConversionUtils$.$anonfun$createDataFrame$1(AvroConversionUtils.scala:42)
> at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2(RDD.scala:837)
> at
> org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2$adapted(RDD.scala:837)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
> at
> org.apache.spark.sql.execution.SQLExecutionRDD.$anonfun$compute$1(SQLExecutionRDD.scala:52)
> at
> org.apache.spark.sql.internal.SQLConf$.withExistingConf(SQLConf.scala:100)
> at
> org.apache.spark.sql.execution.SQLExecutionRDD.compute(SQLExecutionRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
> at org.apache.spark.scheduler.Task.run(Task.scala:127)
> at
> org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:444)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
> at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:447)
> ... 3 more
> ```
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