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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2017/11/29 09:53:00 UTC
[jira] [Commented] (SPARK-22635) FileNotFoundException again while
reading ORC files containing special characters
[ https://issues.apache.org/jira/browse/SPARK-22635?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16270505#comment-16270505 ]
Apache Spark commented on SPARK-22635:
--------------------------------------
User 'mgaido91' has created a pull request for this issue:
https://github.com/apache/spark/pull/19844
> FileNotFoundException again while reading ORC files containing special characters
> ---------------------------------------------------------------------------------
>
> Key: SPARK-22635
> URL: https://issues.apache.org/jira/browse/SPARK-22635
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.2.0, 2.2.1, 2.3.0
> Reporter: Marco Gaido
>
> SPARK-22146 fix the issue only for the {{inferSchema}}, ie. only for the schema inference, but it doesn't fix the problem when actually reading the data. Thus nearly the same exception happens when someone tries to use the data.
> {code}
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 2.0 failed 4 times, most recent failure: Lost task 0.3 in stage 2.0 (TID 98, host-172-22-127-77.example.com, executor 3): java.io.FileNotFoundException: File does not exist: hdfs://XXX/tmp/aaa/start=2017-11-27%2009%253A30%253A00/part-00000-c1477c9f-9d48-4341-89de-81056b6b618e.c000.snappy.orc
> It is possible the underlying files have been updated. You can explicitly invalidate the cache in Spark by running 'REFRESH TABLE tableName' command in SQL or by recreating the Dataset/DataFrame involved.
> at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.org$apache$spark$sql$execution$datasources$FileScanRDD$$anon$$readCurrentFile(FileScanRDD.scala:127)
> at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:174)
> at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:105)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
> at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at org.apache.spark.sql.execution.WholeStageCodegenExec$$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)
> at java.lang.Thread.run(Thread.java:748)
> {code}
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