diff --git a/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostModel.scala b/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostModel.scala
index 14fa3c0f6..75a91e64c 100644
--- a/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostModel.scala
+++ b/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostModel.scala
@@ -32,8 +32,12 @@ class XGBoostModel(_booster: Booster)(implicit val sc: SparkContext) extends Ser
     import DataUtils._
     val broadcastBooster = testSet.sparkContext.broadcast(_booster)
     testSet.mapPartitions { testSamples =>
-      val dMatrix = new DMatrix(new JDMatrix(testSamples, null))
-      Iterator(broadcastBooster.value.predict(dMatrix))
+      if (testSamples.hasNext) {
+        val dMatrix = new DMatrix(new JDMatrix(testSamples, null))
+        Iterator(broadcastBooster.value.predict(dMatrix))
+      } else {
+        Iterator()
+      }
     }
   }
 
diff --git a/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostSuite.scala b/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostSuite.scala
index 6f4e98aa3..711ea35f0 100644
--- a/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostSuite.scala
+++ b/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostSuite.scala
@@ -23,7 +23,7 @@ import scala.collection.mutable.ListBuffer
 import scala.io.Source
 
 import org.apache.commons.logging.LogFactory
-import org.apache.spark.mllib.linalg.DenseVector
+import org.apache.spark.mllib.linalg.{Vector => SparkVector, Vectors, DenseVector}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.rdd.RDD
 import org.apache.spark.{SparkConf, SparkContext}
@@ -190,4 +190,24 @@ class XGBoostSuite extends FunSuite with BeforeAndAfter {
     assert(eval.eval(xgBoostModel.predict(testSetDMatrix), testSetDMatrix) < 0.1)
     customSparkContext.stop()
   }
+
+  test("test with empty partition") {
+
+    def buildEmptyRDD(sparkContext: Option[SparkContext] = None): RDD[SparkVector] = {
+      val sampleList = new ListBuffer[SparkVector]
+      sparkContext.getOrElse(sc).parallelize(sampleList, numWorkers)
+    }
+
+    val eval = new EvalError()
+    val trainingRDD = buildTrainingRDD()
+    val testRDD = buildEmptyRDD()
+    import DataUtils._
+    val tempDir = Files.createTempDirectory("xgboosttest-")
+    val tempFile = Files.createTempFile(tempDir, "", "")
+    val paramMap = List("eta" -> "1", "max_depth" -> "2", "silent" -> "0",
+      "objective" -> "binary:logistic").toMap
+    val xgBoostModel = XGBoost.train(trainingRDD, paramMap, 5, numWorkers)
+
+    println(xgBoostModel.predict(testRDD))
+  }
 }