diff --git a/jvm-packages/README.md b/jvm-packages/README.md
index 8f0e89916..28dbba685 100644
--- a/jvm-packages/README.md
+++ b/jvm-packages/README.md
@@ -34,7 +34,7 @@ object XGBoostScalaExample {
     // number of iterations
     val round = 2
     // train the model
-    val model = XGBoost.train(paramMap, trainData, round)
+    val model = XGBoost.train(trainData, paramMap, round)
     // run prediction
     val predTrain = model.predict(trainData)
     // save model to the file.
@@ -43,34 +43,6 @@ object XGBoostScalaExample {
 }
 ```
 
-### XGBoost Flink
-```scala
-import ml.dmlc.xgboost4j.scala.flink.XGBoost
-import org.apache.flink.api.scala._
-import org.apache.flink.api.scala.ExecutionEnvironment
-import org.apache.flink.ml.MLUtils
-
-object DistTrainWithFlink {
-  def main(args: Array[String]) {
-    val env: ExecutionEnvironment = ExecutionEnvironment.getExecutionEnvironment
-    // read trainining data
-    val trainData =
-      MLUtils.readLibSVM(env, "/path/to/data/agaricus.txt.train")
-    // define parameters
-    val paramMap = List(
-      "eta" -> 0.1,
-      "max_depth" -> 2,
-      "objective" -> "binary:logistic").toMap
-    // number of iterations
-    val round = 2
-    // train the model
-    val model = XGBoost.train(paramMap, trainData, round)
-    val predTrain = model.predict(trainData.map{x => x.vector})
-    model.saveModelToHadoop("file:///path/to/xgboost.model")
-  }
-}
-```
-
 ### XGBoost Spark
 ```scala
 import org.apache.spark.SparkContext
@@ -101,3 +73,33 @@ object DistTrainWithSpark {
   }
 }
 ```
+
+### XGBoost Flink
+```scala
+import ml.dmlc.xgboost4j.scala.flink.XGBoost
+import org.apache.flink.api.scala._
+import org.apache.flink.api.scala.ExecutionEnvironment
+import org.apache.flink.ml.MLUtils
+
+object DistTrainWithFlink {
+  def main(args: Array[String]) {
+    val env: ExecutionEnvironment = ExecutionEnvironment.getExecutionEnvironment
+    // read trainining data
+    val trainData =
+      MLUtils.readLibSVM(env, "/path/to/data/agaricus.txt.train")
+    // define parameters
+    val paramMap = List(
+      "eta" -> 0.1,
+      "max_depth" -> 2,
+      "objective" -> "binary:logistic").toMap
+    // number of iterations
+    val round = 2
+    // train the model
+    val model = XGBoost.train(trainData, paramMap, round)
+    val predTrain = model.predict(trainData.map{x => x.vector})
+    model.saveModelToHadoop("file:///path/to/xgboost.model")
+  }
+}
+```
+
+