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Posted to issues@spark.apache.org by "Wenchen Fan (JIRA)" <ji...@apache.org> on 2017/03/10 07:22:04 UTC
[jira] [Commented] (SPARK-19885) The config ignoreCorruptFiles
doesn't work for CSV
[ https://issues.apache.org/jira/browse/SPARK-19885?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15904606#comment-15904606 ]
Wenchen Fan commented on SPARK-19885:
-------------------------------------
This is because we support different charset for CSV files, and our text file format only supports UTF8, so we have to use `HadoopRDD` when infer schema for CSV data source.
I've checked the history, this feature was there the first day we introduce CSV data source. However, all other text-based data source support only UTF8, also CSV with wholeFile enabled only supports UTF8.
shall we just remove the support for different charsets? or support this feature for all text-based data source?
cc [~hyukjin.kwon]
> The config ignoreCorruptFiles doesn't work for CSV
> --------------------------------------------------
>
> Key: SPARK-19885
> URL: https://issues.apache.org/jira/browse/SPARK-19885
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.1.0
> Reporter: Shixiong Zhu
>
> CSVFileFormat.inferSchema doesn't use FileScanRDD so the SQL "ignoreCorruptFiles" doesn't work.
> {code}
> java.io.EOFException: Unexpected end of input stream
> at org.apache.hadoop.io.compress.DecompressorStream.decompress(DecompressorStream.java:145)
> at org.apache.hadoop.io.compress.DecompressorStream.read(DecompressorStream.java:85)
> at java.io.InputStream.read(InputStream.java:101)
> at org.apache.hadoop.util.LineReader.fillBuffer(LineReader.java:180)
> at org.apache.hadoop.util.LineReader.readDefaultLine(LineReader.java:216)
> at org.apache.hadoop.util.LineReader.readLine(LineReader.java:174)
> at org.apache.hadoop.mapred.LineRecordReader.next(LineRecordReader.java:248)
> at org.apache.hadoop.mapred.LineRecordReader.next(LineRecordReader.java:48)
> at org.apache.spark.rdd.HadoopRDD$$anon$1.getNext(HadoopRDD.scala:266)
> at org.apache.spark.rdd.HadoopRDD$$anon$1.getNext(HadoopRDD.scala:211)
> at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
> at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:461)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> at scala.collection.Iterator$class.foreach(Iterator.scala:893)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
> at scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:157)
> at scala.collection.AbstractIterator.foldLeft(Iterator.scala:1336)
> at scala.collection.TraversableOnce$class.aggregate(TraversableOnce.scala:214)
> at scala.collection.AbstractIterator.aggregate(Iterator.scala:1336)
> at org.apache.spark.rdd.RDD$$anonfun$aggregate$1$$anonfun$22.apply(RDD.scala:1112)
> at org.apache.spark.rdd.RDD$$anonfun$aggregate$1$$anonfun$22.apply(RDD.scala:1112)
> at org.apache.spark.SparkContext$$anonfun$33.apply(SparkContext.scala:1980)
> at org.apache.spark.SparkContext$$anonfun$33.apply(SparkContext.scala:1980)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
> at org.apache.spark.scheduler.Task.run(Task.scala:99)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
> Driver stacktrace:
> at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)
> at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
> at scala.Option.foreach(Option.scala:257)
> at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1918)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1981)
> at org.apache.spark.rdd.RDD$$anonfun$aggregate$1.apply(RDD.scala:1114)
> at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
> at org.apache.spark.rdd.RDD.aggregate(RDD.scala:1107)
> at org.apache.spark.sql.execution.datasources.csv.CSVInferSchema$.infer(CSVInferSchema.scala:47)
> at org.apache.spark.sql.execution.datasources.csv.CSVFileFormat.inferSchema(CSVFileFormat.scala:67)
> at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$7.apply(DataSource.scala:174)
> at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$7.apply(DataSource.scala:174)
> at scala.Option.orElse(Option.scala:289)
> at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:173)
> at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:377)
> at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:152)
> at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:135)
> {code}
> Right now a workaround is also setting "spark.files.ignoreCorruptFiles" to true.
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