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Posted to issues@spark.apache.org by "Michael Allman (JIRA)" <ji...@apache.org> on 2016/11/07 21:57:58 UTC
[jira] [Updated] (SPARK-17993) Spark prints an avalanche of warning
messages from Parquet when reading parquet files written by older versions
of Parquet-mr
[ https://issues.apache.org/jira/browse/SPARK-17993?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Michael Allman updated SPARK-17993:
-----------------------------------
Summary: Spark prints an avalanche of warning messages from Parquet when reading parquet files written by older versions of Parquet-mr (was: Spark spews a slew of harmless but annoying warning messages from Parquet when reading parquet files written by older versions of Parquet-mr)
> 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
>
> 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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