[
https://issues.apache.org/jira/browse/SPARK-35700?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Dongjoon Hyun reassigned SPARK-35700:
-------------------------------------
Assignee: Kent Yao
> spark.sql.orc.filterPushdown not working with Spark 3.1.1 for tables with
> varchar data type
> -------------------------------------------------------------------------------------------
>
> Key: SPARK-35700
> URL: https://issues.apache.org/jira/browse/SPARK-35700
> Project: Spark
> Issue Type: Bug
> Components: Kubernetes, PySpark, Spark Core
> Affects Versions: 3.1.1
> Environment: Spark 3.1.1 on K8S
> Reporter: Arghya Saha
> Assignee: Kent Yao
> Priority: Major
>
> We are not able to upgrade to Spark 3.1.1 from Spark 2.4.x as the join on
> varchar column is failing which is unexpected and works on Spark 3.0.0. We
> are trying to run it on Spark 3.1.1 (MR 3.2) on K8s
> Below is my use case:
> Tables are external hive table and files are stored as ORC. We do have
> varchar column and when we are trying to perform join on varchar column we
> are getting the exception.
> As I understand Spark 3.1.1 have introduced varchar data type but seems its
> not well tested with ORC and does not have backward compatibility. I have
> even tried with below config without luck
> *spark.sql.legacy.charVarcharAsString: "true"*
> We are not getting the error when *spark.sql.orc.filterPushdown=false*
> Below is the code: Here col1 is of type varchar(32) in hive
> {code:java}
> df = spark.sql("select col1, col2 from table1 a inner join table2 on b
> (a.col1=b.col1 and a.col2 > b.col2 )")
> df.write.format("orc").option("compression",
> "zlib").mode("Append").save("<s3_path>")
> {code}
> Below is the error:
>
> {code:java}
> Job aborted due to stage failure: Task 43 in stage 5.0 failed 4 times, most
> recent failure: Lost task 43.3 in stage 5.0 (TID 524) (10.219.36.64 executor
> 5): java.lang.UnsupportedOperationException: DataType: varchar(32)
> at
> org.apache.spark.sql.execution.datasources.orc.OrcFilters$.getPredicateLeafType(OrcFilters.scala:150)
> at
> org.apache.spark.sql.execution.datasources.orc.OrcFilters$.getType$1(OrcFilters.scala:222)
> at
> org.apache.spark.sql.execution.datasources.orc.OrcFilters$.buildLeafSearchArgument(OrcFilters.scala:266)
> at
> org.apache.spark.sql.execution.datasources.orc.OrcFilters$.convertibleFiltersHelper$1(OrcFilters.scala:132)
> at
> org.apache.spark.sql.execution.datasources.orc.OrcFilters$.$anonfun$convertibleFilters$4(OrcFilters.scala:135)
> at
> scala.collection.TraversableLike.$anonfun$flatMap$1(TraversableLike.scala:245)
> at scala.collection.immutable.List.foreach(List.scala:392)
> at scala.collection.TraversableLike.flatMap(TraversableLike.scala:245)
> at scala.collection.TraversableLike.flatMap$(TraversableLike.scala:242)
> at scala.collection.immutable.List.flatMap(List.scala:355)
> at
> org.apache.spark.sql.execution.datasources.orc.OrcFilters$.convertibleFilters(OrcFilters.scala:134)
> at
> org.apache.spark.sql.execution.datasources.orc.OrcFilters$.createFilter(OrcFilters.scala:73)
> at
> org.apache.spark.sql.execution.datasources.orc.OrcFileFormat.$anonfun$buildReaderWithPartitionValues$4(OrcFileFormat.scala:189)
> at
> org.apache.spark.sql.execution.datasources.orc.OrcFileFormat.$anonfun$buildReaderWithPartitionValues$4$adapted(OrcFileFormat.scala:188)
> at scala.Option.foreach(Option.scala:407)
> at
> org.apache.spark.sql.execution.datasources.orc.OrcFileFormat.$anonfun$buildReaderWithPartitionValues$1(OrcFileFormat.scala:188)
> at
> org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.org$apache$spark$sql$execution$datasources$FileScanRDD$$anon$$readCurrentFile(FileScanRDD.scala:116)
> at
> org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:169)
> at
> org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:93)
> at
> org.apache.spark.sql.execution.FileSourceScanExec$$anon$1.hasNext(DataSourceScanExec.scala:503)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3.columnartorow_nextBatch_0$(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:755)
> at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
> at
> org.apache.spark.shuffle.sort.UnsafeShuffleWriter.write(UnsafeShuffleWriter.java:177)
> at
> org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52)
> at org.apache.spark.scheduler.Task.run(Task.scala:131)
> at
> org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)
> at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown
> Source)
> at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown
> Source)
> at java.base/java.lang.Thread.run(Unknown Source)
> Driver stacktrace:3
> {code}
>
> I can see there is no mapping of varchar in OrcFilters.scala:150
> [https://github.com/apache/spark/blob/v3.1.1/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcFilters.scala#L142]
>
>
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