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

["Dongjoon Hyun (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-19430?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Dongjoon Hyun resolved SPARK-19430.
-----------------------------------
    Resolution: Duplicate

This is resolved via SPARK-19459 in Spark 2.2.

> Cannot read external tables with VARCHAR columns if they're backed by ORC files written by Hive 1.2.1
> -----------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-19430
>                 URL: https://issues.apache.org/jira/browse/SPARK-19430
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 1.6.3, 2.0.2, 2.1.0
>            Reporter: Sameer Agarwal
>
> 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]):
> # Write an ORC table using Hive 1.2.1 with
>    {noformat}
> CREATE TABLE orc_varchar_test STORED AS ORC
> AS SELECT CASTE('a' AS VARCHAR(10)) AS c0{noformat}
> # Get the raw path of the written ORC file
> # Create an external table pointing to this file and read the table using Spark
>   {noformat}
> 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(){noformat}
> 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:
> {noformat}
> $ 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)>       <----
> ...
> {noformat}
> On the other hand, if you create an ORC table using the same DDL and inspect the written ORC file, you'll see:
> {noformat}
> ...
> Type: struct<c0:string>
> ...
> {noformat}
> 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:
> {code}
> 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)
> {code}



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