[
https://issues.apache.org/jira/browse/SPARK-27994?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16860442#comment-16860442
]
Nicolas Pascal commented on SPARK-27994:
----------------------------------------
I'll push a PR with failing test cases to reproduce the issue
> Spark Avro Failed to read logical type decimal backed by bytes
> --------------------------------------------------------------
>
> Key: SPARK-27994
> URL: https://issues.apache.org/jira/browse/SPARK-27994
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.4.0
> Reporter: Nicolas Pascal
> Priority: Major
>
> Fields with this following schema provokes Spark to fail reading the Avro
> file.
> {noformat}
>
> {"name":"process_insert_id","type":["null",{"type":"bytes","logicalType":"decimal","precision":10,"scale":0}
> {noformat}
> The following record is failing:
> {code:java}
> Array[Byte] [32 30 30 30 31 31 30 39 37 34]
> actual: BigDecimal 237007240188420354029364
> expected: 2000110974
> {code}
> The following code in Spark Avro Library 2.4.0 in the
> org.apache.spark.sql.avro.AvroDeserializer line 149
> {noformat}
> val bigDecimal =
> decimalConversions.fromFixed(value.asInstanceOf[GenericFixed], avroType,
> LogicalTypes.decimal(d.precision, d.scale))
> {noformat}
> The avro file is readable and produces expected values when converted to json
> using the Apache Avro tool jar
> (https://search.maven.org/artifact/org.apache.avro/avro-tools/1.8.2/jar)
> Full stacktrace bellow:
> {noformat}
> 19/04/17 05:50:45 INFO Client:
> client token: N/A
> diagnostics: User class threw exception:
> org.apache.spark.SparkException: Job aborted.
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:196)
> at
> org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:159)
> at
> org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)
> at
> org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)
> at
> org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
> at
> org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)
> at
> org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)
> at
> org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:668)
> at
> org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:668)
> at
> org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
> at
> org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
> at
> org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:668)
> at
> org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:276)
> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:270)
> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:228)
> at
> org.apache.spark.sql.DataFrameWriter.parquet(DataFrameWriter.scala:557)
> at au.com.nbnco.io.Io$.writeParquet(Io.scala:38)
> at au.com.nbnco.fwk.Outputs$.write(Output.scala:27)
> at au.com.nbnco.fwk.Context.write(Context.scala:41)
> at
> au.com.nbnco.job.merge.MergeToActiveDatasetJob$.run(MergeToActiveDatasetJob.scala:10)
> at
> au.com.nbnco.fwk.SparkJobRunner$.au$com$nbnco$fwk$SparkJobRunner$$executeJobRunner(SparkJobRunner.scala:63)
> at
> au.com.nbnco.fwk.SparkJobRunner$$anonfun$2$$anonfun$apply$1.apply$mcV$sp(SparkJobRunner.scala:40)
> at
> au.com.nbnco.fwk.SparkJobRunner$$anonfun$2$$anonfun$apply$1.apply(SparkJobRunner.scala:37)
> at
> au.com.nbnco.fwk.SparkJobRunner$$anonfun$2$$anonfun$apply$1.apply(SparkJobRunner.scala:37)
> at
> scala.concurrent.impl.Future$PromiseCompletingRunnable.liftedTree1$1(Future.scala:24)
> at
> scala.concurrent.impl.Future$PromiseCompletingRunnable.run(Future.scala:24)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748)
> Caused by: org.apache.spark.SparkException: Job aborted due to stage failure:
> Task 5 in stage 10.0 failed 4 times, most recent failure: Lost task 5.3 in
> stage 10.0 (TID 77, ip-10-11-100-120.aws.nbndc.local, executor 5):
> java.lang.IllegalArgumentException: Unscaled value too large for precision
> at org.apache.spark.sql.types.Decimal.set(Decimal.scala:79)
> at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:456)
> at
> org.apache.spark.sql.avro.AvroDeserializer.org$apache$spark$sql$avro$AvroDeserializer$$createDecimal(AvroDeserializer.scala:285)
> at
> org.apache.spark.sql.avro.AvroDeserializer$$anonfun$org$apache$spark$sql$avro$AvroDeserializer$$newWriter$17.apply(AvroDeserializer.scala:157)
> at
> org.apache.spark.sql.avro.AvroDeserializer$$anonfun$org$apache$spark$sql$avro$AvroDeserializer$$newWriter$17.apply(AvroDeserializer.scala:154)
> at
> org.apache.spark.sql.avro.AvroDeserializer$$anonfun$8.apply(AvroDeserializer.scala:313)
> at
> org.apache.spark.sql.avro.AvroDeserializer$$anonfun$8.apply(AvroDeserializer.scala:309)
> at
> org.apache.spark.sql.avro.AvroDeserializer$$anonfun$getRecordWriter$1.apply(AvroDeserializer.scala:331)
> at
> org.apache.spark.sql.avro.AvroDeserializer$$anonfun$getRecordWriter$1.apply(AvroDeserializer.scala:328)
> at
> org.apache.spark.sql.avro.AvroDeserializer$$anonfun$3.apply(AvroDeserializer.scala:56)
> at
> org.apache.spark.sql.avro.AvroDeserializer$$anonfun$3.apply(AvroDeserializer.scala:54)
> at
> org.apache.spark.sql.avro.AvroDeserializer.deserialize(AvroDeserializer.scala:70)
> at
> org.apache.spark.sql.avro.AvroFileFormat$$anonfun$buildReader$1$$anon$1.next(AvroFileFormat.scala:216)
> at
> org.apache.spark.sql.avro.AvroFileFormat$$anonfun$buildReader$1$$anon$1.next(AvroFileFormat.scala:195)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:410)
> at
> org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.next(FileScanRDD.scala:104)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:410)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:410)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:410)
> at
> org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:149)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
> at org.apache.spark.scheduler.Task.run(Task.scala:121)
> at
> org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748)
> {noformat}
>
>
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
