diff --git a/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala b/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala
index dec118330aec..fef788f78359 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala
@@ -76,12 +76,12 @@ private[libsvm] class LibSVMFileFormat
 
   override def toString: String = "LibSVM"
 
-  private def verifySchema(dataSchema: StructType): Unit = {
+  private def verifySchema(dataSchema: StructType, forWriting: Boolean): Unit = {
     if (
       dataSchema.size != 2 ||
         !dataSchema(0).dataType.sameType(DataTypes.DoubleType) ||
         !dataSchema(1).dataType.sameType(new VectorUDT()) ||
-        !(dataSchema(1).metadata.getLong(LibSVMOptions.NUM_FEATURES).toInt > 0)
+        !(forWriting || dataSchema(1).metadata.getLong(LibSVMOptions.NUM_FEATURES).toInt > 0)
     ) {
       throw new IOException(s"Illegal schema for libsvm data, schema=$dataSchema")
     }
@@ -119,7 +119,7 @@ private[libsvm] class LibSVMFileFormat
       job: Job,
       options: Map[String, String],
       dataSchema: StructType): OutputWriterFactory = {
-    verifySchema(dataSchema)
+    verifySchema(dataSchema, true)
     new OutputWriterFactory {
       override def newInstance(
           path: String,
@@ -142,7 +142,7 @@ private[libsvm] class LibSVMFileFormat
       filters: Seq[Filter],
       options: Map[String, String],
       hadoopConf: Configuration): (PartitionedFile) => Iterator[InternalRow] = {
-    verifySchema(dataSchema)
+    verifySchema(dataSchema, false)
     val numFeatures = dataSchema("features").metadata.getLong(LibSVMOptions.NUM_FEATURES).toInt
     assert(numFeatures > 0)
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/source/libsvm/LibSVMRelationSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/source/libsvm/LibSVMRelationSuite.scala
index a67e49d54e14..3eabff434e8d 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/source/libsvm/LibSVMRelationSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/source/libsvm/LibSVMRelationSuite.scala
@@ -19,13 +19,16 @@ package org.apache.spark.ml.source.libsvm
 
 import java.io.{File, IOException}
 import java.nio.charset.StandardCharsets
+import java.util.List
 
 import com.google.common.io.Files
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
+import org.apache.spark.ml.linalg.SQLDataTypes.VectorType
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.{Row, SaveMode}
+import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
 import org.apache.spark.util.Utils
 
 
@@ -44,14 +47,14 @@ class LibSVMRelationSuite extends SparkFunSuite with MLlibTestSparkContext {
       """
         |0 2:4.0 4:5.0 6:6.0
       """.stripMargin
-    val dir = Utils.createDirectory(tempDir.getCanonicalPath, "data")
+    val dir = Utils.createTempDir()
     val succ = new File(dir, "_SUCCESS")
     val file0 = new File(dir, "part-00000")
     val file1 = new File(dir, "part-00001")
     Files.write("", succ, StandardCharsets.UTF_8)
     Files.write(lines0, file0, StandardCharsets.UTF_8)
     Files.write(lines1, file1, StandardCharsets.UTF_8)
-    path = dir.toURI.toString
+    path = dir.getPath
   }
 
   override def afterAll(): Unit = {
@@ -108,12 +111,12 @@ class LibSVMRelationSuite extends SparkFunSuite with MLlibTestSparkContext {
 
   test("write libsvm data and read it again") {
     val df = spark.read.format("libsvm").load(path)
-    val tempDir2 = new File(tempDir, "read_write_test")
-    val writepath = tempDir2.toURI.toString
+    val writePath = Utils.createTempDir().getPath
+
     // TODO: Remove requirement to coalesce by supporting multiple reads.
-    df.coalesce(1).write.format("libsvm").mode(SaveMode.Overwrite).save(writepath)
+    df.coalesce(1).write.format("libsvm").mode(SaveMode.Overwrite).save(writePath)
 
-    val df2 = spark.read.format("libsvm").load(writepath)
+    val df2 = spark.read.format("libsvm").load(writePath)
     val row1 = df2.first()
     val v = row1.getAs[SparseVector](1)
     assert(v == Vectors.sparse(6, Seq((0, 1.0), (2, 2.0), (4, 3.0))))
@@ -126,6 +129,27 @@ class LibSVMRelationSuite extends SparkFunSuite with MLlibTestSparkContext {
     }
   }
 
+  test("write libsvm data from scratch and read it again") {
+    val rawData = new java.util.ArrayList[Row]()
+    rawData.add(Row(1.0, Vectors.sparse(3, Seq((0, 2.0), (1, 3.0)))))
+    rawData.add(Row(4.0, Vectors.sparse(3, Seq((0, 5.0), (2, 6.0)))))
+
+    val struct = StructType(
+      StructField("labelFoo", DoubleType, false) ::
+      StructField("featuresBar", VectorType, false) :: Nil
+    )
+    val df = spark.sqlContext.createDataFrame(rawData, struct)
+
+    val writePath = Utils.createTempDir().getPath
+
+    df.coalesce(1).write.format("libsvm").mode(SaveMode.Overwrite).save(writePath)
+
+    val df2 = spark.read.format("libsvm").load(writePath)
+    val row1 = df2.first()
+    val v = row1.getAs[SparseVector](1)
+    assert(v == Vectors.sparse(3, Seq((0, 2.0), (1, 3.0))))
+  }
+
   test("select features from libsvm relation") {
     val df = spark.read.format("libsvm").load(path)
     df.select("features").rdd.map { case Row(d: Vector) => d }.first