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https://issues.apache.org/jira/browse/SPARK-15044?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15266042#comment-15266042
]
Niranjan Molkeri` commented on SPARK-15044:
-------------------------------------------
I tried to reproduce the error in Spark 1.5.0, There is no problem with the
path. May be we should also try to reproduce in 2.0. Can someone suggest me how
to proceed with the bug. I will try to solve this bug.
> spark-sql will throw "input path does not exist" exception if it handles a
> partition which exists in hive table, but the path is removed manually
> -------------------------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-15044
> URL: https://issues.apache.org/jira/browse/SPARK-15044
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.6.1
> Reporter: huangyu
>
> spark-sql will throw "input path not exist" exception if it handles a
> partition which exists in hive table, but the path is removed manually.The
> situation is as follows:
> 1) Create a table "test". "create table test (n string) partitioned by (p
> string)"
> 2) Load some data into partition(p='1')
> 3)Remove the path related to partition(p='1') of table test manually. "hadoop
> fs -rmr ..../warehouse/..../test/p=1"
> 4)Run spark sql, spark-sql -e "select n from test where p='1';"
> Then it throws exception:
> {code}
> org.apache.hadoop.mapred.InvalidInputException: Input path does not exist:
> ...../test/p=1
> at
> org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:285)
> at
> org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:228)
> at
> org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:304)
> at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:199)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
> at scala.Option.getOrElse(Option.scala:120)
> at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
> at
> org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
> at scala.Option.getOrElse(Option.scala:120)
> at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
> at
> org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
> at scala.Option.getOrElse(Option.scala:120)
> at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
> at org.apache.spark.rdd.UnionRDD$$anonfun$1.apply(UnionRDD.scala:66)
> at org.apache.spark.rdd.UnionRDD$$anonfun$1.apply(UnionRDD.scala:66)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> at scala.collection.immutable.List.foreach(List.scala:318)
> at
> scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
> at scala.collection.AbstractTraversable.map(Traversable.scala:105)
> at org.apache.spark.rdd.UnionRDD.getPartitions(UnionRDD.scala:66)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
> at scala.Option.getOrElse(Option.scala:120)
> at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
> at
> org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
> at scala.Option.getOrElse(Option.scala:120)
> {code}
> The bug is in spark 1.6.1, if I use spark 1.4.0, It is OK
> I think spark-sql should ignore the path, just like hive or it dose in early
> versions, rather than throw an exception.
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