diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
index 4a8450901e3a7..d8d9e97282bf8 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
@@ -303,6 +303,13 @@ private[hive] case class HiveGenericUDTF(
  *  - `wrap()`/`wrapperFor()`: from 3 to 1
  *  - `unwrap()`/`unwrapperFor()`: from 1 to 3
  *  - `GenericUDAFEvaluator.terminatePartial()`: from 2 to 3
+ *
+ *  Note that, Hive UDAF is initialized with aggregate mode, and some specific Hive UDAFs can't
+ *  mix UPDATE and MERGE actions during its life cycle. However, Spark may do UPDATE on a UDAF and
+ *  then do MERGE, in case of hash aggregate falling back to sort aggregate. To work around this
+ *  issue, we track the ability to do MERGE in the Hive UDAF aggregate buffer. If Spark does
+ *  UPDATE then MERGE, we can detect it and re-create the aggregate buffer with a different
+ *  aggregate mode.
  */
 private[hive] case class HiveUDAFFunction(
     name: String,
@@ -311,7 +318,7 @@ private[hive] case class HiveUDAFFunction(
     isUDAFBridgeRequired: Boolean = false,
     mutableAggBufferOffset: Int = 0,
     inputAggBufferOffset: Int = 0)
-  extends TypedImperativeAggregate[GenericUDAFEvaluator.AggregationBuffer]
+  extends TypedImperativeAggregate[HiveUDAFBuffer]
   with HiveInspectors
   with UserDefinedExpression {
 
@@ -352,12 +359,14 @@ private[hive] case class HiveUDAFFunction(
     HiveEvaluator(evaluator, evaluator.init(GenericUDAFEvaluator.Mode.PARTIAL1, inputInspectors))
   }
 
-  // The UDAF evaluator used to merge partial aggregation results.
+  // The UDAF evaluator used to consume partial aggregation results and produce final results.
+  // Hive `ObjectInspector` used to inspect final results.
   @transient
-  private lazy val partial2ModeEvaluator = {
+  private lazy val finalHiveEvaluator = {
     val evaluator = newEvaluator()
-    evaluator.init(GenericUDAFEvaluator.Mode.PARTIAL2, Array(partial1HiveEvaluator.objectInspector))
-    evaluator
+    HiveEvaluator(
+      evaluator,
+      evaluator.init(GenericUDAFEvaluator.Mode.FINAL, Array(partial1HiveEvaluator.objectInspector)))
   }
 
   // Spark SQL data type of partial aggregation results
@@ -365,16 +374,6 @@ private[hive] case class HiveUDAFFunction(
   private lazy val partialResultDataType =
     inspectorToDataType(partial1HiveEvaluator.objectInspector)
 
-  // The UDAF evaluator used to compute the final result from a partial aggregation result objects.
-  // Hive `ObjectInspector` used to inspect the final aggregation result object.
-  @transient
-  private lazy val finalHiveEvaluator = {
-    val evaluator = newEvaluator()
-    HiveEvaluator(
-      evaluator,
-      evaluator.init(GenericUDAFEvaluator.Mode.FINAL, Array(partial1HiveEvaluator.objectInspector)))
-  }
-
   // Wrapper functions used to wrap Spark SQL input arguments into Hive specific format.
   @transient
   private lazy val inputWrappers = children.map(x => wrapperFor(toInspector(x), x.dataType)).toArray
@@ -401,41 +400,74 @@ private[hive] case class HiveUDAFFunction(
     s"$name($distinct${children.map(_.sql).mkString(", ")})"
   }
 
-  override def createAggregationBuffer(): AggregationBuffer =
-    partial1HiveEvaluator.evaluator.getNewAggregationBuffer
+  // The hive UDAF may create different buffers to handle different inputs: original data or
+  // aggregate buffer. However, the Spark UDAF framework does not expose this information when
+  // creating the buffer. Here we return null, and create the buffer in `update` and `merge`
+  // on demand, so that we can know what input we are dealing with.
+  override def createAggregationBuffer(): HiveUDAFBuffer = null
 
   @transient
   private lazy val inputProjection = UnsafeProjection.create(children)
 
-  override def update(buffer: AggregationBuffer, input: InternalRow): AggregationBuffer = {
+  override def update(buffer: HiveUDAFBuffer, input: InternalRow): HiveUDAFBuffer = {
+    // The input is original data, we create buffer with the partial1 evaluator.
+    val nonNullBuffer = if (buffer == null) {
+      HiveUDAFBuffer(partial1HiveEvaluator.evaluator.getNewAggregationBuffer, false)
+    } else {
+      buffer
+    }
+
+    assert(!nonNullBuffer.canDoMerge, "can not call `merge` then `update` on a Hive UDAF.")
+
     partial1HiveEvaluator.evaluator.iterate(
-      buffer, wrap(inputProjection(input), inputWrappers, cached, inputDataTypes))
-    buffer
+      nonNullBuffer.buf, wrap(inputProjection(input), inputWrappers, cached, inputDataTypes))
+    nonNullBuffer
   }
 
-  override def merge(buffer: AggregationBuffer, input: AggregationBuffer): AggregationBuffer = {
+  override def merge(buffer: HiveUDAFBuffer, input: HiveUDAFBuffer): HiveUDAFBuffer = {
+    // The input is aggregate buffer, we create buffer with the final evaluator.
+    val nonNullBuffer = if (buffer == null) {
+      HiveUDAFBuffer(finalHiveEvaluator.evaluator.getNewAggregationBuffer, true)
+    } else {
+      buffer
+    }
+
+    // It's possible that we've called `update` of this Hive UDAF, and some specific Hive UDAF
+    // implementation can't mix the `update` and `merge` calls during its life cycle. To work
+    // around it, here we create a fresh buffer with final evaluator, and merge the existing buffer
+    // to it, and replace the existing buffer with it.
+    val mergeableBuf = if (!nonNullBuffer.canDoMerge) {
+      val newBuf = finalHiveEvaluator.evaluator.getNewAggregationBuffer
+      finalHiveEvaluator.evaluator.merge(
+        newBuf, partial1HiveEvaluator.evaluator.terminatePartial(nonNullBuffer.buf))
+      HiveUDAFBuffer(newBuf, true)
+    } else {
+      nonNullBuffer
+    }
+
     // The 2nd argument of the Hive `GenericUDAFEvaluator.merge()` method is an input aggregation
     // buffer in the 3rd format mentioned in the ScalaDoc of this class. Originally, Hive converts
     // this `AggregationBuffer`s into this format before shuffling partial aggregation results, and
     // calls `GenericUDAFEvaluator.terminatePartial()` to do the conversion.
-    partial2ModeEvaluator.merge(buffer, partial1HiveEvaluator.evaluator.terminatePartial(input))
-    buffer
+    finalHiveEvaluator.evaluator.merge(
+      mergeableBuf.buf, partial1HiveEvaluator.evaluator.terminatePartial(input.buf))
+    mergeableBuf
   }
 
-  override def eval(buffer: AggregationBuffer): Any = {
-    resultUnwrapper(finalHiveEvaluator.evaluator.terminate(buffer))
+  override def eval(buffer: HiveUDAFBuffer): Any = {
+    resultUnwrapper(finalHiveEvaluator.evaluator.terminate(buffer.buf))
   }
 
-  override def serialize(buffer: AggregationBuffer): Array[Byte] = {
+  override def serialize(buffer: HiveUDAFBuffer): Array[Byte] = {
     // Serializes an `AggregationBuffer` that holds partial aggregation results so that we can
     // shuffle it for global aggregation later.
-    aggBufferSerDe.serialize(buffer)
+    aggBufferSerDe.serialize(buffer.buf)
   }
 
-  override def deserialize(bytes: Array[Byte]): AggregationBuffer = {
+  override def deserialize(bytes: Array[Byte]): HiveUDAFBuffer = {
     // Deserializes an `AggregationBuffer` from the shuffled partial aggregation phase to prepare
     // for global aggregation by merging multiple partial aggregation results within a single group.
-    aggBufferSerDe.deserialize(bytes)
+    HiveUDAFBuffer(aggBufferSerDe.deserialize(bytes), false)
   }
 
   // Helper class used to de/serialize Hive UDAF `AggregationBuffer` objects
@@ -450,11 +482,19 @@ private[hive] case class HiveUDAFFunction(
     private val mutableRow = new GenericInternalRow(1)
 
     def serialize(buffer: AggregationBuffer): Array[Byte] = {
+      // The buffer may be null if there is no input. It's unclear if the hive UDAF accepts null
+      // buffer, for safety we create an empty buffer here.
+      val nonNullBuffer = if (buffer == null) {
+        partial1HiveEvaluator.evaluator.getNewAggregationBuffer
+      } else {
+        buffer
+      }
+
       // `GenericUDAFEvaluator.terminatePartial()` converts an `AggregationBuffer` into an object
       // that can be inspected by the `ObjectInspector` returned by `GenericUDAFEvaluator.init()`.
       // Then we can unwrap it to a Spark SQL value.
       mutableRow.update(0, partialResultUnwrapper(
-        partial1HiveEvaluator.evaluator.terminatePartial(buffer)))
+        partial1HiveEvaluator.evaluator.terminatePartial(nonNullBuffer)))
       val unsafeRow = projection(mutableRow)
       val bytes = ByteBuffer.allocate(unsafeRow.getSizeInBytes)
       unsafeRow.writeTo(bytes)
@@ -466,12 +506,14 @@ private[hive] case class HiveUDAFFunction(
       // returned by `GenericUDAFEvaluator.terminatePartial()` back to an `AggregationBuffer`. The
       // workaround here is creating an initial `AggregationBuffer` first and then merge the
       // deserialized object into the buffer.
-      val buffer = partial2ModeEvaluator.getNewAggregationBuffer
+      val buffer = finalHiveEvaluator.evaluator.getNewAggregationBuffer
       val unsafeRow = new UnsafeRow(1)
       unsafeRow.pointTo(bytes, bytes.length)
       val partialResult = unsafeRow.get(0, partialResultDataType)
-      partial2ModeEvaluator.merge(buffer, partialResultWrapper(partialResult))
+      finalHiveEvaluator.evaluator.merge(buffer, partialResultWrapper(partialResult))
       buffer
     }
   }
 }
+
+case class HiveUDAFBuffer(buf: AggregationBuffer, canDoMerge: Boolean)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDAFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDAFSuite.scala
index 7402c9626873c..27aff263d35bc 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDAFSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDAFSuite.scala
@@ -31,6 +31,7 @@ import test.org.apache.spark.sql.MyDoubleAvg
 import org.apache.spark.sql.{AnalysisException, QueryTest, Row}
 import org.apache.spark.sql.execution.aggregate.ObjectHashAggregateExec
 import org.apache.spark.sql.hive.test.TestHiveSingleton
+import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.test.SQLTestUtils
 
 class HiveUDAFSuite extends QueryTest with TestHiveSingleton with SQLTestUtils {
@@ -39,6 +40,7 @@ class HiveUDAFSuite extends QueryTest with TestHiveSingleton with SQLTestUtils {
   protected override def beforeAll(): Unit = {
     sql(s"CREATE TEMPORARY FUNCTION mock AS '${classOf[MockUDAF].getName}'")
     sql(s"CREATE TEMPORARY FUNCTION hive_max AS '${classOf[GenericUDAFMax].getName}'")
+    sql(s"CREATE TEMPORARY FUNCTION mock2 AS '${classOf[MockUDAF2].getName}'")
 
     Seq(
       (0: Integer) -> "val_0",
@@ -91,6 +93,35 @@ class HiveUDAFSuite extends QueryTest with TestHiveSingleton with SQLTestUtils {
     ))
   }
 
+  test("SPARK-24935: customized Hive UDAF with two aggregation buffers") {
+    withTempView("v") {
+      spark.range(100).createTempView("v")
+      val df = sql("SELECT id % 2, mock2(id) FROM v GROUP BY id % 2")
+
+      val aggs = df.queryExecution.executedPlan.collect {
+        case agg: ObjectHashAggregateExec => agg
+      }
+
+      // There should be two aggregate operators, one for partial aggregation, and the other for
+      // global aggregation.
+      assert(aggs.length == 2)
+
+      withSQLConf(SQLConf.OBJECT_AGG_SORT_BASED_FALLBACK_THRESHOLD.key -> "1") {
+        checkAnswer(df, Seq(
+          Row(0, Row(50, 0)),
+          Row(1, Row(50, 0))
+        ))
+      }
+
+      withSQLConf(SQLConf.OBJECT_AGG_SORT_BASED_FALLBACK_THRESHOLD.key -> "100") {
+        checkAnswer(df, Seq(
+          Row(0, Row(50, 0)),
+          Row(1, Row(50, 0))
+        ))
+      }
+    }
+  }
+
   test("call JAVA UDAF") {
     withTempView("temp") {
       withUserDefinedFunction("myDoubleAvg" -> false) {
@@ -126,12 +157,22 @@ class MockUDAF extends AbstractGenericUDAFResolver {
   override def getEvaluator(info: Array[TypeInfo]): GenericUDAFEvaluator = new MockUDAFEvaluator
 }
 
+class MockUDAF2 extends AbstractGenericUDAFResolver {
+  override def getEvaluator(info: Array[TypeInfo]): GenericUDAFEvaluator = new MockUDAFEvaluator2
+}
+
 class MockUDAFBuffer(var nonNullCount: Long, var nullCount: Long)
   extends GenericUDAFEvaluator.AbstractAggregationBuffer {
 
   override def estimate(): Int = JavaDataModel.PRIMITIVES2 * 2
 }
 
+class MockUDAFBuffer2(var nonNullCount: Long, var nullCount: Long)
+  extends GenericUDAFEvaluator.AbstractAggregationBuffer {
+
+  override def estimate(): Int = JavaDataModel.PRIMITIVES2 * 2
+}
+
 class MockUDAFEvaluator extends GenericUDAFEvaluator {
   private val nonNullCountOI = PrimitiveObjectInspectorFactory.javaLongObjectInspector
 
@@ -183,3 +224,80 @@ class MockUDAFEvaluator extends GenericUDAFEvaluator {
 
   override def terminate(agg: AggregationBuffer): AnyRef = terminatePartial(agg)
 }
+
+// Same as MockUDAFEvaluator but using two aggregation buffers, one for PARTIAL1 and the other
+// for PARTIAL2.
+class MockUDAFEvaluator2 extends GenericUDAFEvaluator {
+  private val nonNullCountOI = PrimitiveObjectInspectorFactory.javaLongObjectInspector
+
+  private val nullCountOI = PrimitiveObjectInspectorFactory.javaLongObjectInspector
+  private var aggMode: Mode = null
+
+  private val bufferOI = {
+    val fieldNames = Seq("nonNullCount", "nullCount").asJava
+    val fieldOIs = Seq(nonNullCountOI: ObjectInspector, nullCountOI: ObjectInspector).asJava
+    ObjectInspectorFactory.getStandardStructObjectInspector(fieldNames, fieldOIs)
+  }
+
+  private val nonNullCountField = bufferOI.getStructFieldRef("nonNullCount")
+
+  private val nullCountField = bufferOI.getStructFieldRef("nullCount")
+
+  override def getNewAggregationBuffer: AggregationBuffer = {
+    // These 2 modes consume original data.
+    if (aggMode == Mode.PARTIAL1 || aggMode == Mode.COMPLETE) {
+      new MockUDAFBuffer(0L, 0L)
+    } else {
+      new MockUDAFBuffer2(0L, 0L)
+    }
+  }
+
+  override def reset(agg: AggregationBuffer): Unit = {
+    val buffer = agg.asInstanceOf[MockUDAFBuffer]
+    buffer.nonNullCount = 0L
+    buffer.nullCount = 0L
+  }
+
+  override def init(mode: Mode, parameters: Array[ObjectInspector]): ObjectInspector = {
+    aggMode = mode
+    bufferOI
+  }
+
+  override def iterate(agg: AggregationBuffer, parameters: Array[AnyRef]): Unit = {
+    val buffer = agg.asInstanceOf[MockUDAFBuffer]
+    if (parameters.head eq null) {
+      buffer.nullCount += 1L
+    } else {
+      buffer.nonNullCount += 1L
+    }
+  }
+
+  override def merge(agg: AggregationBuffer, partial: Object): Unit = {
+    if (partial ne null) {
+      val nonNullCount = nonNullCountOI.get(bufferOI.getStructFieldData(partial, nonNullCountField))
+      val nullCount = nullCountOI.get(bufferOI.getStructFieldData(partial, nullCountField))
+      val buffer = agg.asInstanceOf[MockUDAFBuffer2]
+      buffer.nonNullCount += nonNullCount
+      buffer.nullCount += nullCount
+    }
+  }
+
+  // As this method is called for both states, Partial1 and Partial2, the hack in the method
+  // to check for class of aggregation buffer was necessary.
+  override def terminatePartial(agg: AggregationBuffer): AnyRef = {
+    var result: AnyRef = null
+    if (agg.getClass.toString.contains("MockUDAFBuffer2")) {
+      val buffer = agg.asInstanceOf[MockUDAFBuffer2]
+      result = Array[Object](buffer.nonNullCount: java.lang.Long, buffer.nullCount: java.lang.Long)
+    } else {
+      val buffer = agg.asInstanceOf[MockUDAFBuffer]
+      result = Array[Object](buffer.nonNullCount: java.lang.Long, buffer.nullCount: java.lang.Long)
+    }
+    result
+  }
+
+  override def terminate(agg: AggregationBuffer): AnyRef = {
+    val buffer = agg.asInstanceOf[MockUDAFBuffer2]
+    Array[Object](buffer.nonNullCount: java.lang.Long, buffer.nullCount: java.lang.Long)
+  }
+}