diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
index 919bde55a1b8c..716cfd9e103c8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
@@ -32,6 +32,7 @@ import org.apache.spark.mllib.impl.PeriodicGraphCheckpointer
 import org.apache.spark.mllib.linalg.{Vector, DenseVector, SparseVector, Matrices}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.Utils
+import org.apache.spark.mllib.rdd.RDDFunctions._
 
 
 /**
@@ -430,8 +431,7 @@ private[clustering] object LDA {
                             else if (D / 1000 < 4) 4
                             else D / 1000
     val batchNumber = (D/batchSize + 1).toInt
-    // todo: performance killer, need to be replaced
-    private val batches = documents.randomSplit(Array.fill[Double](batchNumber)(1.0))
+    private val batches = documents.sliding(batchNumber).collect()
 
     // Initialize the variational distribution q(beta|lambda)
     var _lambda = getGammaMatrix(k, vocabSize)              // K * V