Github user chenghao-intel commented on a diff in the pull request:
https://github.com/apache/spark/pull/2802#discussion_r19850776
--- Diff:
sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala
---
@@ -87,8 +87,23 @@ class HiveUdfSuite extends QueryTest {
test("SPARK-2693 udaf aggregates test") {
checkAnswer(sql("SELECT percentile(key,1) FROM src LIMIT 1"),
sql("SELECT max(key) FROM src").collect().toSeq)
+
+ checkAnswer(sql("SELECT percentile(key,array(1,1)) FROM src LIMIT 1"),
+ sql("SELECT array(max(key),max(key)) FROM src").collect().toSeq)
+
+ TestHive.reset()
--- End diff --
This test case only `ReadOnly` for metastore/data, removing the
`TestHive.reset()` seems reasonable. But It's quite strange the next test case
will fail with call stack below:
```
[info] - spark sql udf test that returns a struct (26 seconds, 855
milliseconds)
[info] - hive struct udf (443 milliseconds)
[info] - SPARK-2693 udaf aggregates test (2 seconds, 355 milliseconds)
[info] - Generic UDAF aggregates *** FAILED *** (586 milliseconds)
[info] org.apache.hadoop.hive.ql.exec.UDFArgumentTypeException: The
second argument must be a constant, but array<double> was passed instead.
[info] at
org.apache.hadoop.hive.ql.udf.generic.GenericUDAFPercentileApprox.getEvaluator(GenericUDAFPercentileApprox.java:146)
[info] at
org.apache.spark.sql.hive.HiveGenericUdaf.objectInspector$lzycompute(hiveUdfs.scala:204)
[info] at
org.apache.spark.sql.hive.HiveGenericUdaf.objectInspector(hiveUdfs.scala:202)
[info] at
org.apache.spark.sql.hive.HiveGenericUdaf.dataType(hiveUdfs.scala:211)
[info] at
org.apache.spark.sql.catalyst.expressions.Alias.toAttribute(namedExpressions.scala:104)
[info] at
org.apache.spark.sql.catalyst.plans.logical.Aggregate$$anonfun$output$6.apply(basicOperators.scala:143)
[info] at
org.apache.spark.sql.catalyst.plans.logical.Aggregate$$anonfun$output$6.apply(basicOperators.scala:143)
[info] at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
[info] at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
```
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