[jira] [Created] (SPARK-21441) Incorrect Codegen in SortMergeJoinExec results failures in some cases

["Feng Zhu (JIRA)" <ji...@apache.org>]

Feng Zhu created SPARK-21441:
--------------------------------

             Summary: Incorrect Codegen in SortMergeJoinExec results failures in some cases
                 Key: SPARK-21441
                 URL: https://issues.apache.org/jira/browse/SPARK-21441
             Project: Spark
          Issue Type: Bug
          Components: SQL
    Affects Versions: 2.2.0, 2.1.1, 2.1.0
            Reporter: Feng Zhu
            Priority: Critical


We noticed that the codegen mechanism in SortMergeJoinExec caused job fails in some cases. The below simple example demonstrates this issue. 

The query joins two relations with conditions containing a HiveUDF (i.e., base64) in OR predicates. 

{code:sql}
SELECT ca_zip
FROM customer, customer_address
WHERE customer.c_current_addr_sk = customer_address.ca_address_sk
AND (base64(ca_zip) = '85669' OR customer.c_birth_month > 2)
{code}


Physical plan before execution

*Project [ca_zip#27]
+- *SortMergeJoin [c_current_addr_sk#4], [ca_address_sk#18], Inner, ((HiveSimpleUDF#Base64(ca_zip#27) = 85669) || (c_birth_month#12 > 2))
   :- *Sort [c_current_addr_sk#4 ASC NULLS FIRST], false, 0
   :  +- Exchange hashpartitioning(c_current_addr_sk#4, 200)
   :     +- *Filter isnotnull(c_current_addr_sk#4)
   :        +- HiveTableScan [c_current_addr_sk#4, c_birth_month#12], MetastoreRelation test, customer
   +- *Sort [ca_address_sk#18 ASC NULLS FIRST], false, 0
      +- Exchange hashpartitioning(ca_address_sk#18, 200)
         +- *Filter isnotnull(ca_address_sk#18)
            +- HiveTableScan [ca_address_sk#18, ca_zip#27], MetastoreRelation test, customer_address


By default, the query will fail and throws the following exception:

{code:java}

org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 2.0 failed 4 times
...............................................................................................
Caused by: java.lang.NegativeArraySizeException
	at org.apache.spark.unsafe.types.UTF8String.getBytes(UTF8String.java:229)
	at org.apache.spark.unsafe.types.UTF8String.toString(UTF8String.java:821)
	at java.lang.String.valueOf(String.java:2994)
	at scala.collection.mutable.StringBuilder.append(StringBuilder.scala:200)
	at scala.collection.TraversableOnce$$anonfun$addString$1.apply(TraversableOnce.scala:359)
	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
	at scala.collection.TraversableOnce$class.addString(TraversableOnce.scala:357)
	at scala.collection.AbstractTraversable.addString(Traversable.scala:104)
	at scala.collection.TraversableOnce$class.mkString(TraversableOnce.scala:323)
	at scala.collection.AbstractTraversable.mkString(Traversable.scala:104)
	at scala.collection.TraversableLike$class.toString(TraversableLike.scala:600)
	at scala.collection.SeqLike$class.toString(SeqLike.scala:682)
	at scala.collection.AbstractSeq.toString(Seq.scala:41)
	at java.lang.String.valueOf(String.java:2994)
	at scala.collection.mutable.StringBuilder.append(StringBuilder.scala:200)
	at org.apache.spark.sql.hive.HiveSimpleUDF$$anonfun$eval$1.apply(hiveUDFs.scala:179)
	at org.apache.spark.sql.hive.HiveSimpleUDF$$anonfun$eval$1.apply(hiveUDFs.scala:179)
	at org.apache.spark.internal.Logging$class.logInfo(Logging.scala:54)
	at org.apache.spark.sql.hive.HiveSimpleUDF.logInfo(hiveUDFs.scala:130)
	at org.apache.spark.sql.hive.HiveSimpleUDF.eval(hiveUDFs.scala:179)
	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
	at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
	at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$2.hasNext(WholeStageCodegenExec.scala:396)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
	...................................................................
{code}


However, when we close the codegen (i.e., spark.sql.codegen.wholeStage=false, spark.sql.codegen=false), it works well.





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