[SPARK-19430] Cannot read external tables with VARCHAR columns if they're backed by ORC files written by Hive 1.2.1

• Type: Bug
• Status: Resolved
• Priority: Major
• Resolution: Duplicate
• Affects Version/s: 1.6.3, 2.0.2, 2.1.0
• Component/s: SQL
• Assignee: Unassigned
• Reporter: Sameer Agarwal
• Created: 01/Feb/17 20:40
• Updated: 09/Oct/17 20:14
• Resolved: 09/Oct/17 20:14

Description

Spark throws an exception when trying to read external tables with VARCHAR columns if they're backed by ORC files that were written by Hive 1.2.1 (and possibly other versions of hive).

Steps to reproduce (credits to lian cheng):

1. Write an ORC table using Hive 1.2.1 with

   CREATE TABLE orc_varchar_test STORED AS ORC
   AS SELECT CASTE('a' AS VARCHAR(10)) AS c0

2. Get the raw path of the written ORC file
3. Create an external table pointing to this file and read the table using Spark

   val path = "/tmp/orc_varchar_test"
   sql(s"create external table if not exists test (c0 varchar(10)) stored as orc location '$path'")
   spark.table("test").show()

The problem here is that the metadata in the ORC file written by Hive is different from those written by Spark. We can inspect the ORC file written above:

$ hive --orcfiledump file:///Users/lian/local/var/lib/hive/warehouse_1.2.1/orc_varchar_test/000000_0
Structure for file:///Users/lian/local/var/lib/hive/warehouse_1.2.1/orc_varchar_test/000000_0
File Version: 0.12 with HIVE_8732
Rows: 1
Compression: ZLIB
Compression size: 262144
Type: struct<_col0:varchar(10)>       <----
...

On the other hand, if you create an ORC table using the same DDL and inspect the written ORC file, you'll see:

...
Type: struct<c0:string>
...

Note that all tests are done with spark.sql.hive.convertMetastoreOrc set to false, which is the default case.

I've verified that Spark 1.6.x, 2.0.x and 2.1.x all fail with instances of the following error:

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$$anonfun$unwrapperFor$23.apply(HiveInspectors.scala:529)
    at org.apache.spark.sql.hive.HadoopTableReader$$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:328)
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.agg_doAggregateWithKeys$(Unknown Source)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
    at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
    at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:377)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:126)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
    at org.apache.spark.scheduler.Task.run(Task.scala:99)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)
    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)

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