diff --git a/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/DataUtils.scala b/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/DataUtils.scala
index df787d8eb..15ffe4c06 100644
--- a/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/DataUtils.scala
+++ b/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/DataUtils.scala
@@ -103,7 +103,8 @@ object DataUtils extends Serializable {
       case sparseVector: SparseVector =>
         featureValueOfSparseVector(rowHashCode, sparseVector)
     }
-    math.abs((rowHashCode.toLong + featureValue).toString.hashCode % numPartitions)
+    val nonNaNFeatureValue = if (featureValue.isNaN) { 0.0f } else { featureValue }
+    math.abs((rowHashCode.toLong + nonNaNFeatureValue).toString.hashCode % numPartitions)
   }
 
   private def attachPartitionKey(
diff --git a/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/DeterministicPartitioningSuite.scala b/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/DeterministicPartitioningSuite.scala
index 986b0843b..ff0492f41 100644
--- a/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/DeterministicPartitioningSuite.scala
+++ b/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/DeterministicPartitioningSuite.scala
@@ -16,6 +16,7 @@
 
 package ml.dmlc.xgboost4j.scala.spark
 
+import org.apache.spark.ml.linalg.Vectors
 import org.scalatest.FunSuite
 
 import org.apache.spark.sql.functions._
@@ -79,4 +80,34 @@ class DeterministicPartitioningSuite extends FunSuite with TmpFolderPerSuite wit
       map2
     }
   }
+
+  test("deterministic partitioning has a uniform repartition on dataset with missing values") {
+    val N = 10000
+    val dataset = (0 until N).map{ n =>
+      (n, n % 2, Vectors.sparse(3, Array(0, 1, 2), Array(Double.NaN, n, Double.NaN)))
+    }
+
+    val df = ss.createDataFrame(sc.parallelize(dataset)).toDF("id", "label", "features")
+
+    val dfRepartitioned = DataUtils.convertDataFrameToXGBLabeledPointRDDs(
+      col("label"),
+      col("features"),
+      lit(1.0),
+      lit(Float.NaN),
+      None,
+      10,
+      deterministicPartition = true,
+      df
+    ).head
+
+    val partitionsSizes = dfRepartitioned
+      .mapPartitions(iter => Array(iter.size.toDouble).iterator, true)
+      .collect()
+    val partitionMean = partitionsSizes.sum / partitionsSizes.length
+    val squaredDiffSum = partitionsSizes
+      .map(partitionSize => Math.pow(partitionSize - partitionMean, 2))
+    val standardDeviation = math.sqrt(squaredDiffSum.sum / squaredDiffSum.length)
+
+    assert(standardDeviation < math.sqrt(N.toDouble))
+  }
 }