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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 04:00:32 UTC
[jira] [Updated] (SPARK-19575) Reading from or writing to a hive
serde table with a non pre-existing location should succeed
[ https://issues.apache.org/jira/browse/SPARK-19575?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon updated SPARK-19575:
---------------------------------
Labels: bulk-closed (was: )
> Reading from or writing to a hive serde table with a non pre-existing location should succeed
> ---------------------------------------------------------------------------------------------
>
> Key: SPARK-19575
> URL: https://issues.apache.org/jira/browse/SPARK-19575
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.2.0
> Reporter: Song Jun
> Priority: Major
> Labels: bulk-closed
>
> currently when we select from a hive serde table which has a non pre-existing location will throw an exception:
> ```
> Input path does not exist: file:/tmp/spark-37caa4e6-5a6a-4361-a905-06cc56afb274
> org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: file:/tmp/spark-37caa4e6-5a6a-4361-a905-06cc56afb274
> 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:313)
> at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:194)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
> at scala.Option.getOrElse(Option.scala:121)
> at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
> at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
> at scala.Option.getOrElse(Option.scala:121)
> at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
> at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
> at scala.Option.getOrElse(Option.scala:121)
> at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
> at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
> at scala.Option.getOrElse(Option.scala:121)
> at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
> at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
> at scala.Option.getOrElse(Option.scala:121)
> at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
> at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
> at scala.Option.getOrElse(Option.scala:121)
> at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2080)
> at org.apache.spark.rdd.RDD.count(RDD.scala:1157)
> at org.apache.spark.sql.QueryTest$.checkAnswer(QueryTest.scala:258)
> ```
> this is a folllowup work from SPARK-19329 which has unify the action when we reading from or writing to a datasource table with a non pre-existing locaiton, so here we should also unify the hive serde tables
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