HeartSaVioR commented on a change in pull request #27025: [SPARK-26560][SQL]
Spark should be able to run Hive UDF using jar regardless of current thread
context classloader
URL: https://github.com/apache/spark/pull/27025#discussion_r399612923
##########
File path:
sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveSessionCatalog.scala
##########
@@ -66,49 +66,52 @@ private[sql] class HiveSessionCatalog(
name: String,
clazz: Class[_],
input: Seq[Expression]): Expression = {
-
- Try(super.makeFunctionExpression(name, clazz, input)).getOrElse {
- var udfExpr: Option[Expression] = None
- try {
- // When we instantiate hive UDF wrapper class, we may throw exception
if the input
- // expressions don't satisfy the hive UDF, such as type mismatch,
input number
- // mismatch, etc. Here we catch the exception and throw
AnalysisException instead.
- if (classOf[UDF].isAssignableFrom(clazz)) {
- udfExpr = Some(HiveSimpleUDF(name, new
HiveFunctionWrapper(clazz.getName), input))
- udfExpr.get.dataType // Force it to check input data types.
- } else if (classOf[GenericUDF].isAssignableFrom(clazz)) {
- udfExpr = Some(HiveGenericUDF(name, new
HiveFunctionWrapper(clazz.getName), input))
- udfExpr.get.dataType // Force it to check input data types.
- } else if
(classOf[AbstractGenericUDAFResolver].isAssignableFrom(clazz)) {
- udfExpr = Some(HiveUDAFFunction(name, new
HiveFunctionWrapper(clazz.getName), input))
- udfExpr.get.dataType // Force it to check input data types.
- } else if (classOf[UDAF].isAssignableFrom(clazz)) {
- udfExpr = Some(HiveUDAFFunction(
- name,
- new HiveFunctionWrapper(clazz.getName),
- input,
- isUDAFBridgeRequired = true))
- udfExpr.get.dataType // Force it to check input data types.
- } else if (classOf[GenericUDTF].isAssignableFrom(clazz)) {
- udfExpr = Some(HiveGenericUDTF(name, new
HiveFunctionWrapper(clazz.getName), input))
- udfExpr.get.asInstanceOf[HiveGenericUDTF].elementSchema // Force it
to check data types.
+ // Current thread context classloader may not be the one loaded the class.
Need to switch
+ // context classloader to initialize instance properly.
+ Utils.withContextClassLoader(clazz.getClassLoader) {
+ Try(super.makeFunctionExpression(name, clazz, input)).getOrElse {
+ var udfExpr: Option[Expression] = None
+ try {
+ // When we instantiate hive UDF wrapper class, we may throw
exception if the input
+ // expressions don't satisfy the hive UDF, such as type mismatch,
input number
+ // mismatch, etc. Here we catch the exception and throw
AnalysisException instead.
+ if (classOf[UDF].isAssignableFrom(clazz)) {
+ udfExpr = Some(HiveSimpleUDF(name, new
HiveFunctionWrapper(clazz.getName), input))
+ udfExpr.get.dataType // Force it to check input data types.
Review comment:
I figured out above code doesn't give error - HiveFunctionWrapper stores
`instance` which is copied in `makeCopy()` - so once the instance is created it
doesn't seems to require changing classloader.
That said, below code gives error:
```
// uses classloader which loads clazz
val udf = HiveGenericUDTF(name, new HiveFunctionWrapper(clazz.getName),
input)
// make sure HiveFunctionWrapper.createFunction is not called here
val newUdf =
udf.makeCopy(udf.productIterator.map(_.asInstanceOf[AnyRef]).toArray)
// change classloader which doesn't load clazz
newUdf.dataType
```
we force call `.dataType` after creating HiveXXXUDF, so if my understanding
is correct it won't be matter.
Could you please check whether my observation is correct, or please let me
know if I'm missing something?
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