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Posted to issues@spark.apache.org by "nirav patel (JIRA)" <ji...@apache.org> on 2019/03/18 20:53:00 UTC
[jira] [Commented] (SPARK-26844) Parquet Reader exception -
ArrayIndexOutOfBound should give more information to user
[ https://issues.apache.org/jira/browse/SPARK-26844?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16795382#comment-16795382 ]
nirav patel commented on SPARK-26844:
-------------------------------------
[~hyukjin.kwon] Yes, Paruqet file is corrupt. It has newline character in field value somewhere. Is it possible for spark to provide more information when it fails to read such file?
> Parquet Reader exception - ArrayIndexOutOfBound should give more information to user
> ------------------------------------------------------------------------------------
>
> Key: SPARK-26844
> URL: https://issues.apache.org/jira/browse/SPARK-26844
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.2.1, 2.3.1
> Reporter: nirav patel
> Priority: Minor
>
> I get following error while reading parquet file which has primitive datatypes (INT32, binary)
>
>
> spark.read.format("parquet").load(path).show() // error happens here
>
> Caused by: java.lang.ArrayIndexOutOfBoundsException
> at java.lang.System.arraycopy(Native Method)
> at org.apache.spark.sql.execution.vectorized.OnHeapColumnVector.putBytes(OnHeapColumnVector.java:163)
> at org.apache.spark.sql.execution.vectorized.ColumnVector.appendBytes(ColumnVector.java:733)
> at org.apache.spark.sql.execution.vectorized.OnHeapColumnVector.putByteArray(OnHeapColumnVector.java:410)
> at org.apache.spark.sql.execution.datasources.parquet.VectorizedPlainValuesReader.readBinary(VectorizedPlainValuesReader.java:167)
> at org.apache.spark.sql.execution.datasources.parquet.VectorizedRleValuesReader.readBinarys(VectorizedRleValuesReader.java:402)
> at org.apache.spark.sql.execution.datasources.parquet.VectorizedColumnReader.readBinaryBatch(VectorizedColumnReader.java:419)
> at org.apache.spark.sql.execution.datasources.parquet.VectorizedColumnReader.readBatch(VectorizedColumnReader.java:203)
> at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextBatch(VectorizedParquetRecordReader.java:230)
> at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextKeyValue(VectorizedParquetRecordReader.java:137)
> at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
> at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:105)
> at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:177)
> at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:105)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.scan_nextBatch$(Unknown Source)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
> at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:234)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:228)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
> at org.apache.spark.scheduler.Task.run(Task.scala:108)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
>
>
>
> Point if ArrayIndexOutOfBoundsException raised on a column/field spark should say what particular column/field it is. it helps in troubleshoot.
>
> e.g. I get following error while reading same file using Drill reader.
> org.apache.drill.common.exceptions.UserRemoteException: DATA_READ ERROR: Error reading page data File: /.../../part-00016-00000-m-00016.parquet *Column: GROUP_NAME* Row Group Start: 5539 Fragment 0:0
> I also get more specific information in Drillbit.log
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