[GitHub] [spark] 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

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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_r397307022
 
 

 ##########
 File path: sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveSessionCatalog.scala
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 @@ -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:
   Found a potential problem: here we call `HiveSimpleUDF.dateType` (which is a lazy val), to force to load the class with the corrected class loader.
   
   However, if the expression gets transformed later, which copies `HiveSimpleUDF`, then calling  `HiveSimpleUDF.dataType` will re-trigger the class loading, and at that time there is no guarantee that the corrected classloader is used.
   
   I think we should materialize the loaded class in `HiveSimpleUDF`.
   
   @HeartSaVioR can you take a look?

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