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https://issues.apache.org/jira/browse/SPARK-27570?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16887498#comment-16887498
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Josh Rosen commented on SPARK-27570:
------------------------------------
[~ste...@apache.org], I finally got a chance to test your {{fadvise}}
configuration recommendation and that resolved my issue. *However*, I think
that there's a typo in your recommendation: this only worked when I used
{{fs.s3a.experimental.*input*.fadvise}} (the {{.input}} was missing in your
comment).
> java.io.EOFException Reached the end of stream - Reading Parquet from Swift
> ---------------------------------------------------------------------------
>
> Key: SPARK-27570
> URL: https://issues.apache.org/jira/browse/SPARK-27570
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.4.0
> Reporter: Harry Hough
> Priority: Major
>
> I did see issue SPARK-25966 but it seems there are some differences as his
> problem was resolved after rebuilding the parquet files on write. This is
> 100% reproducible for me across many different days of data.
> I get exceptions such as "Reached the end of stream with 750477 bytes left to
> read" during some read operations of parquet files. I am reading these files
> from Openstack swift using openstack-hadoop 2.7.7 on Spark 2.4.
> The issues seem to happen with the where statement. I have also tried filter
> and combining the statements into one as well as the dataset method with
> column without any luck. Which column or what the actual filter is on the
> where also doesn't seem to make a difference to the error occurring or not.
>
> {code:java}
> val engagementDS = spark
> .read
> .parquet(createSwiftAddr("engagements", folder))
> .where("engtype != 0")
> .where("engtype != 1000")
> .groupBy($"accid", $"sessionkey")
> .agg(collect_list(struct($"time", $"pid", $"engtype", $"pageid",
> $"testid")).as("engagements"))
> // Exiting paste mode, now interpreting.
> [Stage 53:> (0 + 32) / 32]2019-04-25 19:02:12 ERROR Executor:91 - Exception
> in task 24.0 in stage 53.0 (TID 688)
> java.io.EOFException: Reached the end of stream with 1323959 bytes left to
> read
> at
> org.apache.parquet.io.DelegatingSeekableInputStream.readFully(DelegatingSeekableInputStream.java:104)
> at
> org.apache.parquet.io.DelegatingSeekableInputStream.readFullyHeapBuffer(DelegatingSeekableInputStream.java:127)
> at
> org.apache.parquet.io.DelegatingSeekableInputStream.readFully(DelegatingSeekableInputStream.java:91)
> at
> org.apache.parquet.hadoop.ParquetFileReader$ConsecutiveChunkList.readAll(ParquetFileReader.java:1174)
> at
> org.apache.parquet.hadoop.ParquetFileReader.readNextRowGroup(ParquetFileReader.java:805)
> at
> org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.checkEndOfRowGroup(VectorizedParquetRecordReader.java:301)
> at
> org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextBatch(VectorizedParquetRecordReader.java:256)
> at
> org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextKeyValue(VectorizedParquetRecordReader.java:159)
> at
> org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
> at
> org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:101)
> at
> org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:181)
> at
> org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:101)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.scan_nextBatch_0$(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
> at
> org.apache.spark.sql.execution.aggregate.ObjectHashAggregateExec$$anonfun$doExecute$1$$anonfun$2.apply(ObjectHashAggregateExec.scala:107)
> at
> org.apache.spark.sql.execution.aggregate.ObjectHashAggregateExec$$anonfun$doExecute$1$$anonfun$2.apply(ObjectHashAggregateExec.scala:105)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndexInternal$1$$anonfun$12.apply(RDD.scala:823)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndexInternal$1$$anonfun$12.apply(RDD.scala:823)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
> at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
> at org.apache.spark.scheduler.Task.run(Task.scala:121)
> at
> org.apache.spark.executor.Executor$TaskRunner$$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)
> {code}
> The above gives the error 100% of the time.
> {code:java}
> val engagementDS = spark
> .read
> .parquet(createSwiftAddr("engagements", folder))
> .count
> {code}
> This works correctly as well as doing a .show(false)
> {code:java}
> val engagementDS = spark
> .read
> .parquet(createSwiftAddr("engagements", folder))
> .groupBy($"accid", $"sessionkey")
> .agg(collect_list(struct($"time", $"pid", $"engtype", $"pageid",
> $"testid")).as("engagements"))
> .show(false)
> {code}
> Works correctly.
> {code:java}
> val engagementDS = spark
> .read
> .parquet(createSwiftAddr("engagements", folder))
> .where("engtype != 0")
> .count
> {code}
> The above code works but if I do .show(false) instead of count it breaks with
> the reached end of stream error.
> {code:java}
> engagementDS.select($"engtype").where("engtype != 0").where("engtype !=
> 1000").show(false)
> +-------+
> |engtype|
> +-------+
> |10 |
> |17 |
> |4 |
> |4 |
> |10 |
> |17 |
> |15 |
> |10 |
> |17 |
> |10 |
> |16 |
> |15 |
> |10 |
> |16 |
> |15 |
> |15 |
> |10 |
> |4 |
> |10 |
> |17 |
> +-------+
> only showing top 20 rows
> {code}
> The above also works correctly.
> I can fix this issue with the below so it seems that all the data is there:
>
> {code:java}
> val engagementDS = spark
> .read
> .parquet(createSwiftAddr("engagements", folder))
> //.filter("engtype != 0 AND engtype != 1000")
> .groupBy($"accid", $"sessionkey")
> .agg(collect_list(struct($"time", $"pid", $"engtype", $"pageid",
> $"testid")).as("engagements"))
> .selectExpr("accid", "sessionkey", "filter(engagements, x -> x.engtype !=
> 1000 AND x.engtype != 0) AS engagements")
> {code}
>
>
> Even if I'm doing something incorrectly this seems like a very strange error
> message :)
> Thanks for any help in advance.
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