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From "Michael Allman (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-17993) Spark prints an avalanche of warning messages from Parquet when reading parquet files written by older versions of Parquet-mr
Date Wed, 11 Jan 2017 01:32:58 GMT

    [ https://issues.apache.org/jira/browse/SPARK-17993?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15816771#comment-15816771
] 

Michael Allman commented on SPARK-17993:
----------------------------------------

Cool. I'll work on a simple PR to silence those warnings in the default log configuration.

> Spark prints an avalanche of warning messages from Parquet when reading parquet files
written by older versions of Parquet-mr
> -----------------------------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-17993
>                 URL: https://issues.apache.org/jira/browse/SPARK-17993
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>            Reporter: Michael Allman
>            Assignee: Michael Allman
>             Fix For: 2.1.0
>
>
> It looks like https://github.com/apache/spark/pull/14690 broke parquet log output redirection.
After that patch, when querying parquet files written by Parquet-mr 1.6.0 Spark prints a torrent
of (harmless) warning messages from the Parquet reader:
> {code}
> Oct 18, 2016 7:42:18 PM WARNING: org.apache.parquet.CorruptStatistics: Ignoring statistics
because created_by could not be parsed (see PARQUET-251): parquet-mr version 1.6.0
> org.apache.parquet.VersionParser$VersionParseException: Could not parse created_by: parquet-mr
version 1.6.0 using format: (.+) version ((.*) )?\(build ?(.*)\)
> 	at org.apache.parquet.VersionParser.parse(VersionParser.java:112)
> 	at org.apache.parquet.CorruptStatistics.shouldIgnoreStatistics(CorruptStatistics.java:60)
> 	at org.apache.parquet.format.converter.ParquetMetadataConverter.fromParquetStatistics(ParquetMetadataConverter.java:263)
> 	at org.apache.parquet.hadoop.ParquetFileReader$Chunk.readAllPages(ParquetFileReader.java:583)
> 	at org.apache.parquet.hadoop.ParquetFileReader.readNextRowGroup(ParquetFileReader.java:513)
> 	at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.checkEndOfRowGroup(VectorizedParquetRecordReader.java:270)
> 	at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextBatch(VectorizedParquetRecordReader.java:225)
> 	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$$anon$1.hasNext(FileScanRDD.scala:102)
> 	at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:162)
> 	at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:102)
> 	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.scan_nextBatch$(Unknown
Source)
> 	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
Source)
> 	at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> 	at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:372)
> 	at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:231)
> 	at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:225)
> 	at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:803)
> 	at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:803)
> 	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> 	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
> 	at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
> 	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
> 	at org.apache.spark.scheduler.Task.run(Task.scala:99)
> 	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
> 	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)
> {code}
> This only happens during execution, not planning, and it doesn't matter what log level
the {{SparkContext}} is set to.
> This is a regression I noted as something we needed to fix as a follow up to PR 14690.
I feel responsible, so I'm going to expedite a fix for it. I suspect that PR broke Spark's
Parquet log output redirection. That's the premise I'm going by.



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