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From "Harry Hough (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-27570) java.io.EOFException Reached the end of stream - Reading Parquet from Swift
Date Thu, 25 Apr 2019 19:07:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-27570?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Harry Hough updated SPARK-27570:
--------------------------------
    Description: 
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.

 
{code:java}
    val engagementDS = spark
      .read
      .parquet(createSwiftAddr("engagements", folder))
      .where("engtype != 0")
      .where("engtype != 1000")
      .groupBy($"accid", $"sessionkey")
      .agg(collect_list(struct($"time", $"promoid", $"engtype", $"pageid", $"abtestid")).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", $"promoid", $"engtype", $"pageid", $"abtestid")).as("engagements"))
      .show(false)
{code}
Works correctly.
{code:java}
    val engagementDS = spark
      .read
      .parquet(createSwiftAddr("engagements", folder))
      .where("engtype != 0")
      .count
{code}
The above code works but if I do .show(false) instead of count it breaks with the reached
end of stream error.
{code:java}
engagementDS.select($"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.

Even if I'm doing something incorrectly this seems like a very strange error message :)

Thanks for any help in advance.

  was:
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.

 
{code:java}
    val engagementDS = spark
      .read
      .parquet(createSwiftAddr("engagements", folder))
      .where("engtype != 0")
      .where("engtype != 1000")
      .groupBy($"accid", $"sessionkey")
      .agg(collect_list(struct($"time", $"promoid", $"engtype", $"pageid", $"abtestid")).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", $"promoid", $"engtype", $"pageid", $"abtestid")).as("engagements"))
      .show(false)
{code}
Works correctly.
{code:java}
    val engagementDS = spark
      .read
      .parquet(createSwiftAddr("engagements", folder))
      .where("engtype != 0")
      .count
{code}
The above code works but if I do .show(false) instead of count it breaks with the reached
end of stream error.
{code:java}
engagementDS.select($"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.

Even if I'm doing something incorrectly this seems like a very strange error message :)


> 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.
>  
> {code:java}
>     val engagementDS = spark
>       .read
>       .parquet(createSwiftAddr("engagements", folder))
>       .where("engtype != 0")
>       .where("engtype != 1000")
>       .groupBy($"accid", $"sessionkey")
>       .agg(collect_list(struct($"time", $"promoid", $"engtype", $"pageid", $"abtestid")).as("engagements"))
> // Exiting paste mode, now interpreting.
> [Stage 53:> (0 + 32) / 32]2019-04-25 19:02:12 ERROR Executor:91 - Exception in task
24.0 in stage 53.0 (TID 688)
> java.io.EOFException: Reached the end of stream with 1323959 bytes left to read
> at org.apache.parquet.io.DelegatingSeekableInputStream.readFully(DelegatingSeekableInputStream.java:104)
> at org.apache.parquet.io.DelegatingSeekableInputStream.readFullyHeapBuffer(DelegatingSeekableInputStream.java:127)
> at org.apache.parquet.io.DelegatingSeekableInputStream.readFully(DelegatingSeekableInputStream.java:91)
> at org.apache.parquet.hadoop.ParquetFileReader$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", $"promoid", $"engtype", $"pageid", $"abtestid")).as("engagements"))
>       .show(false)
> {code}
> Works correctly.
> {code:java}
>     val engagementDS = spark
>       .read
>       .parquet(createSwiftAddr("engagements", folder))
>       .where("engtype != 0")
>       .count
> {code}
> The above code works but if I do .show(false) instead of count it breaks with the reached
end of stream error.
> {code:java}
> engagementDS.select($"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.
> 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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