[SPARK-20515] Issue with reading Hive ORC tables having char/varchar columns in Spark SQL

• Type: Bug
• Status: Resolved
• Priority: Major
• Resolution: Duplicate
• Affects Version/s: 2.0.2
• Component/s: SQL

Environment

AWS EMR Cluster

• Assignee: Unassigned
• Reporter: Udit Mehrotra
• Created: 27/Apr/17 21:58
• Updated: 07/Jun/17 08:21
• Resolved: 07/Jun/17 08:21

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.

Issue Links

• blocks: SPARK-20901 Feature parity for ORC with Parquet
• duplicates: SPARK-19459 ORC tables cannot be read when they contain char/varchar columns
• links to: [Github] Pull Request #17791 (umehrot2)