diff --git a/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostRankerSuite.scala b/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostRankerSuite.scala
index 7d42995e4ff6..81a770bfe327 100644
--- a/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostRankerSuite.scala
+++ b/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostRankerSuite.scala
@@ -22,7 +22,7 @@ import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.ml.linalg.{DenseVector, Vectors}
 import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.sql.{DataFrame, Dataset}
+import org.apache.spark.sql.{DataFrame, Dataset, Row}
 import org.scalatest.funsuite.AnyFunSuite
 
 import ml.dmlc.xgboost4j.scala.{DMatrix, XGBoost => ScalaXGBoost}
@@ -131,6 +131,26 @@ class XGBoostRankerSuite extends AnyFunSuite with PerTest with TmpFolderPerSuite
     )
   }
 
+  test("The group col should be sorted in each partition") {
+    val trainingDF = buildDataFrameWithGroup(Ranking.train)
+
+    val ranker = new XGBoostRanker()
+      .setNumRound(1)
+      .setNumWorkers(numWorkers)
+      .setGroupCol("group")
+
+    val (df, _) = ranker.preprocess(trainingDF)
+    df.rdd.foreachPartition { iter => {
+      var prevGroup = Int.MinValue
+      while (iter.hasNext) {
+        val curr = iter.next()
+        val group = curr.asInstanceOf[Row].getAs[Int](2)
+        assert(prevGroup <= group)
+        prevGroup = group
+      }
+    }}
+  }
+
   private def runLengthEncode(input: Seq[Int]): Seq[Int] = {
     if (input.isEmpty) return Seq(0)