[SPARK-15562][ML] Delete temp directory after program exit in DataFrameExample

## What changes were proposed in this pull request?
Temp directory used to save records is not deleted after program exit in DataFrameExample. Although it called deleteOnExit, it doesn't work as the directory is not empty. Similar things happend in ContextCleanerSuite. Update the code to make sure temp directory is deleted after program exit.

## How was this patch tested?

unit tests and local build.

Author: dding3 <ding.ding@intel.com>

Closes #13328 from dding3/master.

2 files changed, 4 insertions, 4 deletions

diff --git a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
index 69ff6c7c28..6724af9525 100644
--- a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
@@ -32,6 +32,7 @@ import org.apache.spark.internal.Logging
 import org.apache.spark.rdd.{RDD, ReliableRDDCheckpointData}
 import org.apache.spark.shuffle.sort.SortShuffleManager
 import org.apache.spark.storage._
+import org.apache.spark.util.Utils
 
 /**
  * An abstract base class for context cleaner tests, which sets up a context with a config
@@ -206,8 +207,7 @@ class ContextCleanerSuite extends ContextCleanerSuiteBase {
   }
 
   test("automatically cleanup normal checkpoint") {
-    val checkpointDir = java.io.File.createTempFile("temp", "")
-    checkpointDir.deleteOnExit()
+    val checkpointDir = Utils.createTempDir()
     checkpointDir.delete()
     var rdd = newPairRDD()
     sc.setCheckpointDir(checkpointDir.toString)
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DataFrameExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DataFrameExample.scala
index c69027babb..11faa6192b 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/DataFrameExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/DataFrameExample.scala
@@ -28,6 +28,7 @@ import org.apache.spark.ml.linalg.Vector
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.sql.{DataFrame, Row, SparkSession}
+import org.apache.spark.util.Utils
 
 /**
  * An example of how to use [[org.apache.spark.sql.DataFrame]] for ML. Run with
@@ -86,8 +87,7 @@ object DataFrameExample {
     println(s"Selected features column with average values:\n ${featureSummary.mean.toString}")
 
     // Save the records in a parquet file.
-    val tmpDir = Files.createTempDir()
-    tmpDir.deleteOnExit()
+    val tmpDir = Utils.createTempDir()
     val outputDir = new File(tmpDir, "dataframe").toString
     println(s"Saving to $outputDir as Parquet file.")
     df.write.parquet(outputDir)