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Posted to issues@spark.apache.org by "Erik Krogen (Jira)" <ji...@apache.org> on 2021/07/12 17:11:00 UTC
[jira] [Comment Edited] (SPARK-35957) Cannot convert Avro schema to
catalyst type because schema at path is not compatible
[ https://issues.apache.org/jira/browse/SPARK-35957?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17379278#comment-17379278 ]
Erik Krogen edited comment on SPARK-35957 at 7/12/21, 5:10 PM:
---------------------------------------------------------------
Based on the discussion in [the linked Hudi issue|https://github.com/apache/hudi/issues/3113], it sounds like you encountered two different issues:
{code}
ERROR Client: Application diagnostics message: User class threw exception: org.apache.spark.sql.avro.IncompatibleSchemaException: Unsupported type NULL
{code}
This was based on Spark 2.4.8, and is expected, because support for Avro NULL types was not added until SPARK-26765 in 3.0.0.
{code}
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)
{code}
This error is being thrown by Hudi, not Spark, so I don't understand why you've opened a Spark JIRA for it. It also appears that it is a Hudi bug based on the [PR opened to fix it|https://github.com/apache/hudi/pull/3195]. Can you elaborate if there you actually suspect any issue on the Spark side, and if so, provide more information on how to reproduce this within Spark w/o the Hudi context?
was (Author: xkrogen):
Based on the discussion in [the linked Hudi issue|https://github.com/apache/hudi/issues/3113], it sounds like you've creating this JIRA based on two different issues:
{code}
ERROR Client: Application diagnostics message: User class threw exception: org.apache.spark.sql.avro.IncompatibleSchemaException: Unsupported type NULL
{code}
This was based on Spark 2.4.8, and is expected, because support for Avro NULL types was not added until SPARK-26765 in 3.0.0.
{code}
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)
{code}
This error is being thrown by Hudi, not Spark, so I don't understand why you've opened a Spark JIRA for it. It also appears that it is a Hudi bug based on the [PR opened to fix it|https://github.com/apache/hudi/pull/3195]. Can you elaborate if there you actually suspect any issue on the Spark side, and if so, provide more information on how to reproduce this within Spark w/o the Hudi context?
> Cannot convert Avro schema to catalyst type because schema at path is not compatible
> ------------------------------------------------------------------------------------
>
> Key: SPARK-35957
> URL: https://issues.apache.org/jira/browse/SPARK-35957
> Project: Spark
> Issue Type: Bug
> Components: Spark Core, SQL
> Affects Versions: 3.0.0
> Reporter: Jake Dalli
> Priority: Major
>
> * The Apache Avro specification has a *null* primitive type.
> * Using org.apache.spark:spark-avro_2.12:3.0.3 on Spark 3.0.0 with Scala 2.12
> * I try to load an avro schema with the a field defined as follows:
>
> {code:java}
> {
> "name": "messageKey",
> "type": "null"
> },
> {code}
> * I get the following error:
> {code:java}
> ERROR Client: Application diagnostics message: User class threw exception: org.apache.spark.sql.avro.IncompatibleSchemaException: Unsupported type NULL
> {code}
> This issue is experienced when using Apache Hudi 0.7.0.
> Full stack trace:
> {code:java}
> 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
> {code}
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