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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2017/06/07 08:22:18 UTC
[jira] [Resolved] (SPARK-20515) Issue with reading Hive ORC tables
having char/varchar columns in Spark SQL
[ https://issues.apache.org/jira/browse/SPARK-20515?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-20515.
-------------------------------
Resolution: Duplicate
> Issue with reading Hive ORC tables having char/varchar columns in Spark SQL
> ---------------------------------------------------------------------------
>
> Key: SPARK-20515
> URL: https://issues.apache.org/jira/browse/SPARK-20515
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.2
> Environment: AWS EMR Cluster
> Reporter: Udit Mehrotra
>
> Reading from a Hive ORC table containing char/varchar columns fails in Spark SQL. This is caused by the fact that Spark SQL internally replaces the char/varchar columns with String data type. So, while reading from the table created in Hive which has varchar/char columns, it ends up using the wrong reader and causes a ClassCastException.
>
> Here is the exception:
>
> java.lang.ClassCastException: org.apache.hadoop.hive.serde2.io.HiveVarcharWritable cannot be cast to org.apache.hadoop.io.Text
> at org.apache.hadoop.hive.serde2.objectinspector.primitive.WritableStringObjectInspector.getPrimitiveWritableObject(WritableStringObjectInspector.java:41)
> at org.apache.spark.sql.hive.HiveInspectors$class.unwrap(HiveInspectors.scala:324)
> at org.apache.spark.sql.hive.HadoopTableReader$.unwrap(TableReader.scala:333)
> at org.apache.spark.sql.hive.HadoopTableReader$$anonfun$14$$anonfun$apply$15.apply(TableReader.scala:419)
> at org.apache.spark.sql.hive.HadoopTableReader$$anonfun$14$$anonfun$apply$15.apply(TableReader.scala:419)
> at org.apache.spark.sql.hive.HadoopTableReader$$anonfun$fillObject$2.apply(TableReader.scala:435)
> at org.apache.spark.sql.hive.HadoopTableReader$$anonfun$fillObject$2.apply(TableReader.scala:426)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:247)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:240)
> 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$24.apply(RDD.scala:803)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
> at org.apache.spark.scheduler.Task.run(Task.scala:86)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
> 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)
>
> While the issue has been fixed in Spark 2.1.1 and 2.2.0 with SPARK-19459, it still needs to be fixed Spark 2.0.
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