cloud-fan 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_r399923532
##########
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:
OK let me put my findings: If you look at
`HiveFunctionWrapper.createFunction`, it says we don't cache the instance for
Simple UDF
```
def createFunction[UDFType <: AnyRef](): UDFType = {
if (instance != null) {
instance.asInstanceOf[UDFType]
} else {
val func = Utils.getContextOrSparkClassLoader
.loadClass(functionClassName).newInstance.asInstanceOf[UDFType]
if (!func.isInstanceOf[UDF]) {
// We cache the function if it's no the Simple UDF,
// as we always have to create new instance for Simple UDF
instance = func
}
func
}
}
```
I don't know the history but I assume "we always have to create new instance
for Simple UDF" is correct. I think what we can do is to cache the loaded
`Class` as well as the instance.
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