diff --git a/sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala b/sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala
index a3cbea9021f2..0da52d432d25 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala
@@ -520,6 +520,71 @@ class KeyValueGroupedDataset[K, V] private[sql](
       col4: TypedColumn[V, U4]): Dataset[(K, U1, U2, U3, U4)] =
     aggUntyped(col1, col2, col3, col4).asInstanceOf[Dataset[(K, U1, U2, U3, U4)]]
 
+  /**
+   * Computes the given aggregations, returning a [[Dataset]] of tuples for each unique key
+   * and the result of computing these aggregations over all elements in the group.
+   *
+   * @since 3.0.0
+   */
+  def agg[U1, U2, U3, U4, U5](
+      col1: TypedColumn[V, U1],
+      col2: TypedColumn[V, U2],
+      col3: TypedColumn[V, U3],
+      col4: TypedColumn[V, U4],
+      col5: TypedColumn[V, U5]): Dataset[(K, U1, U2, U3, U4, U5)] =
+    aggUntyped(col1, col2, col3, col4, col5).asInstanceOf[Dataset[(K, U1, U2, U3, U4, U5)]]
+
+  /**
+   * Computes the given aggregations, returning a [[Dataset]] of tuples for each unique key
+   * and the result of computing these aggregations over all elements in the group.
+   *
+   * @since 3.0.0
+   */
+  def agg[U1, U2, U3, U4, U5, U6](
+      col1: TypedColumn[V, U1],
+      col2: TypedColumn[V, U2],
+      col3: TypedColumn[V, U3],
+      col4: TypedColumn[V, U4],
+      col5: TypedColumn[V, U5],
+      col6: TypedColumn[V, U6]): Dataset[(K, U1, U2, U3, U4, U5, U6)] =
+    aggUntyped(col1, col2, col3, col4, col5, col6)
+      .asInstanceOf[Dataset[(K, U1, U2, U3, U4, U5, U6)]]
+
+  /**
+   * Computes the given aggregations, returning a [[Dataset]] of tuples for each unique key
+   * and the result of computing these aggregations over all elements in the group.
+   *
+   * @since 3.0.0
+   */
+  def agg[U1, U2, U3, U4, U5, U6, U7](
+      col1: TypedColumn[V, U1],
+      col2: TypedColumn[V, U2],
+      col3: TypedColumn[V, U3],
+      col4: TypedColumn[V, U4],
+      col5: TypedColumn[V, U5],
+      col6: TypedColumn[V, U6],
+      col7: TypedColumn[V, U7]): Dataset[(K, U1, U2, U3, U4, U5, U6, U7)] =
+    aggUntyped(col1, col2, col3, col4, col5, col6, col7)
+      .asInstanceOf[Dataset[(K, U1, U2, U3, U4, U5, U6, U7)]]
+
+  /**
+   * Computes the given aggregations, returning a [[Dataset]] of tuples for each unique key
+   * and the result of computing these aggregations over all elements in the group.
+   *
+   * @since 3.0.0
+   */
+  def agg[U1, U2, U3, U4, U5, U6, U7, U8](
+      col1: TypedColumn[V, U1],
+      col2: TypedColumn[V, U2],
+      col3: TypedColumn[V, U3],
+      col4: TypedColumn[V, U4],
+      col5: TypedColumn[V, U5],
+      col6: TypedColumn[V, U6],
+      col7: TypedColumn[V, U7],
+      col8: TypedColumn[V, U8]): Dataset[(K, U1, U2, U3, U4, U5, U6, U7, U8)] =
+    aggUntyped(col1, col2, col3, col4, col5, col6, col7, col8)
+      .asInstanceOf[Dataset[(K, U1, U2, U3, U4, U5, U6, U7, U8)]]
+
   /**
    * Returns a [[Dataset]] that contains a tuple with each key and the number of items present
    * for that key.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala
index 4b08a4b0d1a0..ff6143162ff2 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala
@@ -603,6 +603,70 @@ class DatasetSuite extends QueryTest with SharedSQLContext {
       ("a", 30L, 32L, 2L, 15.0), ("b", 3L, 5L, 2L, 1.5), ("c", 1L, 2L, 1L, 1.0))
   }
 
+  test("typed aggregation: expr, expr, expr, expr, expr") {
+    val ds = Seq(("a", 10), ("a", 20), ("b", 1), ("b", 2), ("c", 1)).toDS()
+
+    checkDatasetUnorderly(
+      ds.groupByKey(_._1).agg(
+        sum("_2").as[Long],
+        sum($"_2" + 1).as[Long],
+        count("*").as[Long],
+        avg("_2").as[Double],
+        countDistinct("*").as[Long]),
+      ("a", 30L, 32L, 2L, 15.0, 2L), ("b", 3L, 5L, 2L, 1.5, 2L), ("c", 1L, 2L, 1L, 1.0, 1L))
+  }
+
+  test("typed aggregation: expr, expr, expr, expr, expr, expr") {
+    val ds = Seq(("a", 10), ("a", 20), ("b", 1), ("b", 2), ("c", 1)).toDS()
+
+    checkDatasetUnorderly(
+      ds.groupByKey(_._1).agg(
+        sum("_2").as[Long],
+        sum($"_2" + 1).as[Long],
+        count("*").as[Long],
+        avg("_2").as[Double],
+        countDistinct("*").as[Long],
+        max("_2").as[Long]),
+      ("a", 30L, 32L, 2L, 15.0, 2L, 20L),
+      ("b", 3L, 5L, 2L, 1.5, 2L, 2L),
+      ("c", 1L, 2L, 1L, 1.0, 1L, 1L))
+  }
+
+  test("typed aggregation: expr, expr, expr, expr, expr, expr, expr") {
+    val ds = Seq(("a", 10), ("a", 20), ("b", 1), ("b", 2), ("c", 1)).toDS()
+
+    checkDatasetUnorderly(
+      ds.groupByKey(_._1).agg(
+        sum("_2").as[Long],
+        sum($"_2" + 1).as[Long],
+        count("*").as[Long],
+        avg("_2").as[Double],
+        countDistinct("*").as[Long],
+        max("_2").as[Long],
+        min("_2").as[Long]),
+      ("a", 30L, 32L, 2L, 15.0, 2L, 20L, 10L),
+      ("b", 3L, 5L, 2L, 1.5, 2L, 2L, 1L),
+      ("c", 1L, 2L, 1L, 1.0, 1L, 1L, 1L))
+  }
+
+  test("typed aggregation: expr, expr, expr, expr, expr, expr, expr, expr") {
+    val ds = Seq(("a", 10), ("a", 20), ("b", 1), ("b", 2), ("c", 1)).toDS()
+
+    checkDatasetUnorderly(
+      ds.groupByKey(_._1).agg(
+        sum("_2").as[Long],
+        sum($"_2" + 1).as[Long],
+        count("*").as[Long],
+        avg("_2").as[Double],
+        countDistinct("*").as[Long],
+        max("_2").as[Long],
+        min("_2").as[Long],
+        mean("_2").as[Double]),
+      ("a", 30L, 32L, 2L, 15.0, 2L, 20L, 10L, 15.0),
+      ("b", 3L, 5L, 2L, 1.5, 2L, 2L, 1L, 1.5),
+      ("c", 1L, 2L, 1L, 1.0, 1L, 1L, 1L, 1.0))
+  }
+
   test("cogroup") {
     val ds1 = Seq(1 -> "a", 3 -> "abc", 5 -> "hello", 3 -> "foo").toDS()
     val ds2 = Seq(2 -> "q", 3 -> "w", 5 -> "e", 5 -> "r").toDS()