[
https://issues.apache.org/jira/browse/SPARK-17993?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15645322#comment-15645322
]
Keith Kraus commented on SPARK-17993:
-------------------------------------
Following the pull request discussion on GitHub, this is currently blocking us
from using Spark 2.0 as we have months of data in parquet generated from Spark
1.6 and trying to do any real job in Spark 2.0 causes 100GB+ of log files until
our partition is full. A fix prior to the Parquet-1.9 upgrade would be GREATLY
appreciated!
> Spark spews a slew of harmless but annoying 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.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
