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  1. Spark
  2. SPARK-20515

Issue with reading Hive ORC tables having char/varchar columns in Spark SQL

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    Details

    • Type: Bug
    • Status: Resolved
    • Priority: Major
    • Resolution: Duplicate
    • Affects Version/s: 2.0.2
    • Fix Version/s: None
    • Component/s: SQL
    • Labels:
      None
    • Environment:

      AWS EMR Cluster

      Description

      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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              • Assignee:
                Unassigned
                Reporter:
                uditme Udit Mehrotra
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                Dates

                • Created:
                  Updated:
                  Resolved: