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https://issues.apache.org/jira/browse/SPARK-11844?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17342447#comment-17342447
 ] 

Nick Hryhoriev commented on SPARK-11844:
----------------------------------------

I turn off `fast.data.upload` in spark Hadoop config for `hadoop-aws` module.
And this help, I stop get such kind of exception.
Spark does not damage written files anymore.

Few interesting points.
1. In spark 2.4.7 I get this issue once in 3-4 months.
2. In spark 3.1.1/3.0.2 I get this issue every 2 weeks.
Hadoop fs.s3a.xxx config was same for both cases.
Hadoop 2.9.2 for both cases.

> can not read class org.apache.parquet.format.PageHeader: don't know what 
> type: 13
> ---------------------------------------------------------------------------------
>
>                 Key: SPARK-11844
>                 URL: https://issues.apache.org/jira/browse/SPARK-11844
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>            Reporter: Yin Huai
>            Priority: Minor
>              Labels: bulk-closed
>
> I got the following error once when I was running a query
> {code}
> java.io.IOException: can not read class org.apache.parquet.format.PageHeader: 
> don't know what type: 13
>       at org.apache.parquet.format.Util.read(Util.java:216)
>       at org.apache.parquet.format.Util.readPageHeader(Util.java:65)
>       at 
> org.apache.parquet.hadoop.ParquetFileReader$Chunk.readPageHeader(ParquetFileReader.java:534)
>       at 
> org.apache.parquet.hadoop.ParquetFileReader$Chunk.readAllPages(ParquetFileReader.java:546)
>       at 
> org.apache.parquet.hadoop.ParquetFileReader.readNextRowGroup(ParquetFileReader.java:496)
>       at 
> org.apache.parquet.hadoop.InternalParquetRecordReader.checkRead(InternalParquetRecordReader.java:127)
>       at 
> org.apache.parquet.hadoop.InternalParquetRecordReader.nextKeyValue(InternalParquetRecordReader.java:208)
>       at 
> org.apache.parquet.hadoop.ParquetRecordReader.nextKeyValue(ParquetRecordReader.java:201)
>       at 
> org.apache.spark.rdd.SqlNewHadoopRDD$$anon$1.hasNext(SqlNewHadoopRDD.scala:168)
>       at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
>       at 
> org.apache.spark.sql.execution.joins.HashJoin$$anon$1.hasNext(HashJoin.scala:77)
>       at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
>       at 
> org.apache.spark.sql.execution.joins.HashJoin$$anon$1.hasNext(HashJoin.scala:77)
>       at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
>       at 
> org.apache.spark.sql.execution.joins.HashJoin$$anon$1.hasNext(HashJoin.scala:77)
>       at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
>       at 
> org.apache.spark.sql.execution.joins.HashJoin$$anon$1.hasNext(HashJoin.scala:77)
>       at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
>       at 
> org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:88)
>       at 
> org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:86)
>       at 
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:704)
>       at 
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:704)
>       at 
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>       at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
>       at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>       at 
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>       at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
>       at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>       at 
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
>       at 
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
>       at org.apache.spark.scheduler.Task.run(Task.scala:88)
>       at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
>       at 
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>       at 
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>       at java.lang.Thread.run(Thread.java:745)
> Caused by: parquet.org.apache.thrift.protocol.TProtocolException: don't know 
> what type: 13
>       at 
> parquet.org.apache.thrift.protocol.TCompactProtocol.getTType(TCompactProtocol.java:806)
>       at 
> parquet.org.apache.thrift.protocol.TCompactProtocol.readFieldBegin(TCompactProtocol.java:500)
>       at 
> org.apache.parquet.format.InterningProtocol.readFieldBegin(InterningProtocol.java:158)
>       at 
> parquet.org.apache.thrift.protocol.TProtocolUtil.skip(TProtocolUtil.java:108)
> {code}
> The next retry was good. Right now, seems not critical. But, let's still 
> track it in case we see it in future.



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