diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 2436d1c2a6938..920b57756b625 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -208,12 +208,12 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
 
     val thisClassName = "org.apache.spark.mllib.clustering.LocalLDAModel"
 
-    // Store the distribution of terms of each topic as a Row in data.
-    case class Data(topic: Vector)
+    // Store the distribution of terms of each topic and the column index in topicsMatrix
+    // as a Row in data.
+    case class Data(topic: Vector, index: Int)
 
     def save(sc: SparkContext, path: String, topicsMatrix: Matrix): Unit = {
-
-      val sqlContext = new SQLContext(sc)
+      val sqlContext = SQLContext.getOrCreate(sc)
       import sqlContext.implicits._
 
       val k = topicsMatrix.numCols
@@ -224,24 +224,23 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
 
       val topicsDenseMatrix = topicsMatrix.toBreeze.toDenseMatrix
       val topics = Range(0, k).map { topicInd =>
-        Data(Vectors.dense((topicsDenseMatrix(::, topicInd).toArray)))
+        Data(Vectors.dense((topicsDenseMatrix(::, topicInd).toArray)), topicInd)
       }.toSeq
       sc.parallelize(topics, 1).toDF().write.parquet(Loader.dataPath(path))
     }
 
     def load(sc: SparkContext, path: String): LocalLDAModel = {
-
       val dataPath = Loader.dataPath(path)
       val sqlContext = SQLContext.getOrCreate(sc)
       val dataFrame = sqlContext.read.parquet(dataPath)
 
       Loader.checkSchema[Data](dataFrame.schema)
       val topics = dataFrame.collect()
-      val vocabSize = topics(0)(0).asInstanceOf[Vector].size
+      val vocabSize = topics(0).getAs[Vector](0).size
       val k = topics.size
 
       val brzTopics = BDM.zeros[Double](vocabSize, k)
-      topics.zipWithIndex.foreach { case (Row(vec: Vector), ind: Int) =>
+      topics.foreach { case Row(vec: Vector, ind: Int) =>
         brzTopics(::, ind) := vec.toBreeze
       }
       new LocalLDAModel(Matrices.fromBreeze(brzTopics))
@@ -249,7 +248,6 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
   }
 
   override def load(sc: SparkContext, path: String): LocalLDAModel = {
-
     val (loadedClassName, loadedVersion, metadata) = Loader.loadMetadata(sc, path)
     implicit val formats = DefaultFormats
     val expectedK = (metadata \ "k").extract[Int]
@@ -267,10 +265,10 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
 
     val topicsMatrix = model.topicsMatrix
     require(expectedK == topicsMatrix.numCols,
-      s"LocalLDAModel requires $expectedK topics, got $topicsMatrix.numCols topics")
+      s"LocalLDAModel requires $expectedK topics, got ${topicsMatrix.numCols} topics")
     require(expectedVocabSize == topicsMatrix.numRows,
       s"LocalLDAModel requires $expectedVocabSize terms for each topic, " +
-      s"but got $topicsMatrix.numRows")
+      s"but got ${topicsMatrix.numRows}")
     model
   }
 }
@@ -456,16 +454,15 @@ class DistributedLDAModel private (
 
 
 @Experimental
-object DistributedLDAModel extends Loader[DistributedLDAModel]{
-
+object DistributedLDAModel extends Loader[DistributedLDAModel] {
 
-  object SaveLoadV1_0 {
+  private object SaveLoadV1_0 {
 
     val thisFormatVersion = "1.0"
 
     val classNameV1_0 = "org.apache.spark.mllib.clustering.DistributedLDAModel"
 
-    // Store the weight of each topic separately in a row.
+    // Store globalTopicTotals as a Vector.
     case class Data(globalTopicTotals: Vector)
 
     // Store each term and document vertex with an id and the topicWeights.
@@ -484,7 +481,6 @@ object DistributedLDAModel extends Loader[DistributedLDAModel]{
         docConcentration: Double,
         topicConcentration: Double,
         iterationTimes: Array[Double]): Unit = {
-
       val sqlContext = SQLContext.getOrCreate(sc)
       import sqlContext.implicits._
 
@@ -517,11 +513,10 @@ object DistributedLDAModel extends Loader[DistributedLDAModel]{
         docConcentration: Double,
         topicConcentration: Double,
         iterationTimes: Array[Double]): DistributedLDAModel = {
-
       val dataPath = new Path(Loader.dataPath(path), "globalTopicTotals").toUri.toString
       val vertexDataPath = new Path(Loader.dataPath(path), "topicCounts").toUri.toString
       val edgeDataPath = new Path(Loader.dataPath(path), "tokenCounts").toUri.toString
-      val sqlContext = new SQLContext(sc)
+      val sqlContext = SQLContext.getOrCreate(sc)
       val dataFrame = sqlContext.read.parquet(dataPath)
       val vertexDataFrame = sqlContext.read.parquet(vertexDataPath)
       val edgeDataFrame = sqlContext.read.parquet(edgeDataPath)
@@ -540,15 +535,13 @@ object DistributedLDAModel extends Loader[DistributedLDAModel]{
       }
       val graph: Graph[LDA.TopicCounts, LDA.TokenCount] = Graph(vertices, edges)
 
-      new DistributedLDAModel(
-        graph, globalTopicTotals, globalTopicTotals.length, vocabSize,
+      new DistributedLDAModel(graph, globalTopicTotals, globalTopicTotals.length, vocabSize,
         docConcentration, topicConcentration, iterationTimes)
     }
 
   }
 
   override def load(sc: SparkContext, path: String): DistributedLDAModel = {
-
     val (loadedClassName, loadedVersion, metadata) = Loader.loadMetadata(sc, path)
     implicit val formats = DefaultFormats
     val expectedK = (metadata \ "k").extract[Int]
@@ -569,15 +562,15 @@ object DistributedLDAModel extends Loader[DistributedLDAModel]{
     }
 
     require(model.vocabSize == vocabSize,
-      s"DistributedLDAModel requires $vocabSize vocabSize, got $model.vocabSize vocabSize")
+      s"DistributedLDAModel requires $vocabSize vocabSize, got ${model.vocabSize} vocabSize")
     require(model.docConcentration == docConcentration,
       s"DistributedLDAModel requires $docConcentration docConcentration, " +
-      s"got $model.docConcentration docConcentration")
+      s"got ${model.docConcentration} docConcentration")
     require(model.topicConcentration == topicConcentration,
       s"DistributedLDAModel requires $topicConcentration docConcentration, " +
-      s"got $model.topicConcentration docConcentration")
+      s"got ${model.topicConcentration} docConcentration")
     require(expectedK == model.k,
-      s"DistributedLDAModel requires $expectedK topics, got $model.k topics")
+      s"DistributedLDAModel requires $expectedK topics, got ${model.k} topics")
     model
   }
 
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index 86888f7dea50f..0b851e8cb03d3 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -222,12 +222,12 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
 
     // Test for DistributedLDAModel.
     val k = 3
-    val topicSmoothing = 1.2
-    val termSmoothing = 1.2
+    val docConcentration = 1.2
+    val topicConcentration = 1.5
     val lda = new LDA()
     lda.setK(k)
-      .setDocConcentration(topicSmoothing)
-      .setTopicConcentration(termSmoothing)
+      .setDocConcentration(docConcentration)
+      .setTopicConcentration(topicConcentration)
       .setMaxIterations(5)
       .setSeed(12345)
     val corpus = sc.parallelize(tinyCorpus, 2)