[SPARK-15031][SPARK-15134][EXAMPLE][DOC] Use SparkSession and update indent in examples

## What changes were proposed in this pull request?
1, Use `SparkSession` according to [SPARK-15031](https://issues.apache.org/jira/browse/SPARK-15031)
2, Update indent for `SparkContext` according to [SPARK-15134](https://issues.apache.org/jira/browse/SPARK-15134)
3, BTW, remove some duplicate space and add missing '.'

## How was this patch tested?
manual tests

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #13050 from zhengruifeng/use_sparksession.

34 files changed, 192 insertions, 151 deletions

diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java
index 733bc4181c..bdb76f004f 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java
@@ -32,7 +32,9 @@ import org.apache.spark.sql.SparkSession;
 public class JavaDecisionTreeClassificationExample {
   public static void main(String[] args) {
     SparkSession spark = SparkSession
-      .builder().appName("JavaDecisionTreeClassificationExample").getOrCreate();
+      .builder()
+      .appName("JavaDecisionTreeClassificationExample")
+      .getOrCreate();
 
     // $example on$
     // Load the data stored in LIBSVM format as a DataFrame.
@@ -52,10 +54,10 @@ public class JavaDecisionTreeClassificationExample {
     VectorIndexerModel featureIndexer = new VectorIndexer()
       .setInputCol("features")
       .setOutputCol("indexedFeatures")
-      .setMaxCategories(4) // features with > 4 distinct values are treated as continuous
+      .setMaxCategories(4) // features with > 4 distinct values are treated as continuous.
       .fit(data);
 
-    // Split the data into training and test sets (30% held out for testing)
+    // Split the data into training and test sets (30% held out for testing).
     Dataset<Row>[] splits = data.randomSplit(new double[]{0.7, 0.3});
     Dataset<Row> trainingData = splits[0];
     Dataset<Row> testData = splits[1];
@@ -71,11 +73,11 @@ public class JavaDecisionTreeClassificationExample {
       .setOutputCol("predictedLabel")
       .setLabels(labelIndexer.labels());
 
-    // Chain indexers and tree in a Pipeline
+    // Chain indexers and tree in a Pipeline.
     Pipeline pipeline = new Pipeline()
       .setStages(new PipelineStage[]{labelIndexer, featureIndexer, dt, labelConverter});
 
-    // Train model.  This also runs the indexers.
+    // Train model. This also runs the indexers.
     PipelineModel model = pipeline.fit(trainingData);
 
     // Make predictions.
@@ -84,7 +86,7 @@ public class JavaDecisionTreeClassificationExample {
     // Select example rows to display.
     predictions.select("predictedLabel", "label", "features").show(5);
 
-    // Select (prediction, true label) and compute test error
+    // Select (prediction, true label) and compute test error.
     MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
       .setLabelCol("indexedLabel")
       .setPredictionCol("prediction")
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeRegressionExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeRegressionExample.java
index bd6dc3edd3..cffb7139ed 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeRegressionExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeRegressionExample.java
@@ -33,7 +33,9 @@ import org.apache.spark.sql.SparkSession;
 public class JavaDecisionTreeRegressionExample {
   public static void main(String[] args) {
     SparkSession spark = SparkSession
-      .builder().appName("JavaDecisionTreeRegressionExample").getOrCreate();
+      .builder()
+      .appName("JavaDecisionTreeRegressionExample")
+      .getOrCreate();
     // $example on$
     // Load the data stored in LIBSVM format as a DataFrame.
     Dataset<Row> data = spark.read().format("libsvm")
@@ -47,7 +49,7 @@ public class JavaDecisionTreeRegressionExample {
       .setMaxCategories(4)
       .fit(data);
 
-    // Split the data into training and test sets (30% held out for testing)
+    // Split the data into training and test sets (30% held out for testing).
     Dataset<Row>[] splits = data.randomSplit(new double[]{0.7, 0.3});
     Dataset<Row> trainingData = splits[0];
     Dataset<Row> testData = splits[1];
@@ -56,11 +58,11 @@ public class JavaDecisionTreeRegressionExample {
     DecisionTreeRegressor dt = new DecisionTreeRegressor()
       .setFeaturesCol("indexedFeatures");
 
-    // Chain indexer and tree in a Pipeline
+    // Chain indexer and tree in a Pipeline.
     Pipeline pipeline = new Pipeline()
       .setStages(new PipelineStage[]{featureIndexer, dt});
 
-    // Train model.  This also runs the indexer.
+    // Train model. This also runs the indexer.
     PipelineModel model = pipeline.fit(trainingData);
 
     // Make predictions.
@@ -69,7 +71,7 @@ public class JavaDecisionTreeRegressionExample {
     // Select example rows to display.
     predictions.select("label", "features").show(5);
 
-    // Select (prediction, true label) and compute test error
+    // Select (prediction, true label) and compute test error.
     RegressionEvaluator evaluator = new RegressionEvaluator()
       .setLabelCol("label")
       .setPredictionCol("prediction")
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
index 49bad0afc0..3265c4d7ec 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
@@ -62,7 +62,7 @@ public class JavaDeveloperApiExample {
         new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5)));
     Dataset<Row> training = spark.createDataFrame(localTraining, LabeledPoint.class);
 
-    // Create a LogisticRegression instance.  This instance is an Estimator.
+    // Create a LogisticRegression instance. This instance is an Estimator.
     MyJavaLogisticRegression lr = new MyJavaLogisticRegression();
     // Print out the parameters, documentation, and any default values.
     System.out.println("MyJavaLogisticRegression parameters:\n" + lr.explainParams() + "\n");
@@ -70,7 +70,7 @@ public class JavaDeveloperApiExample {
     // We may set parameters using setter methods.
     lr.setMaxIter(10);
 
-    // Learn a LogisticRegression model.  This uses the parameters stored in lr.
+    // Learn a LogisticRegression model. This uses the parameters stored in lr.
     MyJavaLogisticRegressionModel model = lr.fit(training);
 
     // Prepare test data.
@@ -214,7 +214,7 @@ class MyJavaLogisticRegressionModel
   }
 
   /**
-   * Number of classes the label can take.  2 indicates binary classification.
+   * Number of classes the label can take. 2 indicates binary classification.
    */
   public int numClasses() { return 2; }
 
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java
index 5ba8e6cf44..889f5785df 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java
@@ -38,7 +38,9 @@ import org.apache.spark.sql.SparkSession;
 public class JavaEstimatorTransformerParamExample {
   public static void main(String[] args) {
     SparkSession spark = SparkSession
-      .builder().appName("JavaEstimatorTransformerParamExample").getOrCreate();
+      .builder()
+      .appName("JavaEstimatorTransformerParamExample")
+      .getOrCreate();
 
     // $example on$
     // Prepare training data.
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java
index baacd796a0..5c2e03eda9 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java
@@ -75,11 +75,11 @@ public class JavaGradientBoostedTreeClassifierExample {
       .setOutputCol("predictedLabel")
       .setLabels(labelIndexer.labels());
 
-    // Chain indexers and GBT in a Pipeline
+    // Chain indexers and GBT in a Pipeline.
     Pipeline pipeline = new Pipeline()
       .setStages(new PipelineStage[] {labelIndexer, featureIndexer, gbt, labelConverter});
 
-    // Train model.  This also runs the indexers.
+    // Train model. This also runs the indexers.
     PipelineModel model = pipeline.fit(trainingData);
 
     // Make predictions.
@@ -88,7 +88,7 @@ public class JavaGradientBoostedTreeClassifierExample {
     // Select example rows to display.
     predictions.select("predictedLabel", "label", "features").show(5);
 
-    // Select (prediction, true label) and compute test error
+    // Select (prediction, true label) and compute test error.
     MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
       .setLabelCol("indexedLabel")
       .setPredictionCol("prediction")
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeRegressorExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeRegressorExample.java
index 6d3f21fdaf..769b5c3e85 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeRegressorExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeRegressorExample.java
@@ -34,7 +34,9 @@ import org.apache.spark.sql.SparkSession;
 public class JavaGradientBoostedTreeRegressorExample {
   public static void main(String[] args) {
     SparkSession spark = SparkSession
-      .builder().appName("JavaGradientBoostedTreeRegressorExample").getOrCreate();
+      .builder()
+      .appName("JavaGradientBoostedTreeRegressorExample")
+      .getOrCreate();
 
     // $example on$
     // Load and parse the data file, converting it to a DataFrame.
@@ -48,7 +50,7 @@ public class JavaGradientBoostedTreeRegressorExample {
       .setMaxCategories(4)
       .fit(data);
 
-    // Split the data into training and test sets (30% held out for testing)
+    // Split the data into training and test sets (30% held out for testing).
     Dataset<Row>[] splits = data.randomSplit(new double[] {0.7, 0.3});
     Dataset<Row> trainingData = splits[0];
     Dataset<Row> testData = splits[1];
@@ -59,10 +61,10 @@ public class JavaGradientBoostedTreeRegressorExample {
       .setFeaturesCol("indexedFeatures")
       .setMaxIter(10);
 
-    // Chain indexer and GBT in a Pipeline
+    // Chain indexer and GBT in a Pipeline.
     Pipeline pipeline = new Pipeline().setStages(new PipelineStage[] {featureIndexer, gbt});
 
-    // Train model.  This also runs the indexer.
+    // Train model. This also runs the indexer.
     PipelineModel model = pipeline.fit(trainingData);
 
     // Make predictions.
@@ -71,7 +73,7 @@ public class JavaGradientBoostedTreeRegressorExample {
     // Select example rows to display.
     predictions.select("prediction", "label", "features").show(5);
 
-    // Select (prediction, true label) and compute test error
+    // Select (prediction, true label) and compute test error.
     RegressionEvaluator evaluator = new RegressionEvaluator()
       .setLabelCol("label")
       .setPredictionCol("prediction")
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearRegressionWithElasticNetExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearRegressionWithElasticNetExample.java
index b6ea1fed25..dcd209e28e 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearRegressionWithElasticNetExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearRegressionWithElasticNetExample.java
@@ -30,10 +30,12 @@ import org.apache.spark.sql.SparkSession;
 public class JavaLinearRegressionWithElasticNetExample {
   public static void main(String[] args) {
     SparkSession spark = SparkSession
-      .builder().appName("JavaLinearRegressionWithElasticNetExample").getOrCreate();
+      .builder()
+      .appName("JavaLinearRegressionWithElasticNetExample")
+      .getOrCreate();
 
     // $example on$
-    // Load training data
+    // Load training data.
     Dataset<Row> training = spark.read().format("libsvm")
       .load("data/mllib/sample_linear_regression_data.txt");
 
@@ -42,14 +44,14 @@ public class JavaLinearRegressionWithElasticNetExample {
       .setRegParam(0.3)
       .setElasticNetParam(0.8);
 
-    // Fit the model
+    // Fit the model.
     LinearRegressionModel lrModel = lr.fit(training);
 
-    // Print the coefficients and intercept for linear regression
+    // Print the coefficients and intercept for linear regression.
     System.out.println("Coefficients: "
       + lrModel.coefficients() + " Intercept: " + lrModel.intercept());
 
-    // Summarize the model over the training set and print out some metrics
+    // Summarize the model over the training set and print out some metrics.
     LinearRegressionTrainingSummary trainingSummary = lrModel.summary();
     System.out.println("numIterations: " + trainingSummary.totalIterations());
     System.out.println("objectiveHistory: " + Vectors.dense(trainingSummary.objectiveHistory()));
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionSummaryExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionSummaryExample.java
index fd040aead4..dee56799d8 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionSummaryExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionSummaryExample.java
@@ -31,7 +31,9 @@ import org.apache.spark.sql.functions;
 public class JavaLogisticRegressionSummaryExample {
   public static void main(String[] args) {
     SparkSession spark = SparkSession
-      .builder().appName("JavaLogisticRegressionSummaryExample").getOrCreate();
+      .builder()
+      .appName("JavaLogisticRegressionSummaryExample")
+      .getOrCreate();
 
     // Load training data
     Dataset<Row> training = spark.read().format("libsvm")
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionWithElasticNetExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionWithElasticNetExample.java
index f00c7a05cd..6101c79fb0 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionWithElasticNetExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionWithElasticNetExample.java
@@ -28,7 +28,9 @@ import org.apache.spark.sql.SparkSession;
 public class JavaLogisticRegressionWithElasticNetExample {
   public static void main(String[] args) {
     SparkSession spark = SparkSession
-      .builder().appName("JavaLogisticRegressionWithElasticNetExample").getOrCreate();
+      .builder()
+      .appName("JavaLogisticRegressionWithElasticNetExample")
+      .getOrCreate();
 
     // $example on$
     // Load training data
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaCrossValidationExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaCrossValidationExample.java
index a4ec4f5815..975c65edc0 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaCrossValidationExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaCrossValidationExample.java
@@ -43,7 +43,9 @@ import org.apache.spark.sql.SparkSession;
 public class JavaModelSelectionViaCrossValidationExample {
   public static void main(String[] args) {
     SparkSession spark = SparkSession
-      .builder().appName("JavaModelSelectionViaCrossValidationExample").getOrCreate();
+      .builder()
+      .appName("JavaModelSelectionViaCrossValidationExample")
+      .getOrCreate();
 
     // $example on$
     // Prepare training documents, which are labeled.
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaTrainValidationSplitExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaTrainValidationSplitExample.java
index 63a0ad1cb8..0f96293f03 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaTrainValidationSplitExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaTrainValidationSplitExample.java
@@ -43,7 +43,9 @@ import org.apache.spark.sql.SparkSession;
 public class JavaModelSelectionViaTrainValidationSplitExample {
   public static void main(String[] args) {
     SparkSession spark = SparkSession
-      .builder().appName("JavaModelSelectionViaTrainValidationSplitExample").getOrCreate();
+      .builder()
+      .appName("JavaModelSelectionViaTrainValidationSplitExample")
+      .getOrCreate();
 
     // $example on$
     Dataset<Row> data = spark.read().format("libsvm")
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java
index d547a2a64b..c7d03d8593 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java
@@ -33,7 +33,9 @@ public class JavaMultilayerPerceptronClassifierExample {
 
   public static void main(String[] args) {
     SparkSession spark = SparkSession
-      .builder().appName("JavaMultilayerPerceptronClassifierExample").getOrCreate();
+      .builder()
+      .appName("JavaMultilayerPerceptronClassifierExample")
+      .getOrCreate();
 
     // $example on$
     // Load training data
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java
index 94e3fafcab..16f58a852d 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java
@@ -35,7 +35,9 @@ import org.apache.spark.sql.types.StructType;
 public class JavaQuantileDiscretizerExample {
   public static void main(String[] args) {
     SparkSession spark = SparkSession
-      .builder().appName("JavaQuantileDiscretizerExample").getOrCreate();
+      .builder()
+      .appName("JavaQuantileDiscretizerExample")
+      .getOrCreate();
 
     // $example on$
     List<Row> data = Arrays.asList(
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java
index 21e783a968..14af2fbbbb 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java
@@ -33,7 +33,9 @@ import org.apache.spark.sql.SparkSession;
 public class JavaRandomForestClassifierExample {
   public static void main(String[] args) {
     SparkSession spark = SparkSession
-      .builder().appName("JavaRandomForestClassifierExample").getOrCreate();
+      .builder()
+      .appName("JavaRandomForestClassifierExample")
+      .getOrCreate();
 
     // $example on$
     // Load and parse the data file, converting it to a DataFrame.
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestRegressorExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestRegressorExample.java
index ece184a878..a7078453de 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestRegressorExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestRegressorExample.java
@@ -34,7 +34,9 @@ import org.apache.spark.sql.SparkSession;
 public class JavaRandomForestRegressorExample {
   public static void main(String[] args) {
     SparkSession spark = SparkSession
-      .builder().appName("JavaRandomForestRegressorExample").getOrCreate();
+      .builder()
+      .appName("JavaRandomForestRegressorExample")
+      .getOrCreate();
 
     // $example on$
     // Load and parse the data file, converting it to a DataFrame.
@@ -62,7 +64,7 @@ public class JavaRandomForestRegressorExample {
     Pipeline pipeline = new Pipeline()
       .setStages(new PipelineStage[] {featureIndexer, rf});
 
-    // Train model.  This also runs the indexer.
+    // Train model. This also runs the indexer.
     PipelineModel model = pipeline.fit(trainingData);
 
     // Make predictions.
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java
index 0787079ba4..ff1eb07dc6 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java
@@ -46,7 +46,7 @@ public class JavaSimpleParamsExample {
       .getOrCreate();
 
     // Prepare training data.
-    // We use LabeledPoint, which is a JavaBean.  Spark SQL can convert RDDs of JavaBeans
+    // We use LabeledPoint, which is a JavaBean. Spark SQL can convert RDDs of JavaBeans
     // into DataFrames, where it uses the bean metadata to infer the schema.
     List<LabeledPoint> localTraining = Lists.newArrayList(
       new LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
@@ -56,7 +56,7 @@ public class JavaSimpleParamsExample {
     Dataset<Row> training =
       spark.createDataFrame(localTraining, LabeledPoint.class);
 
-    // Create a LogisticRegression instance.  This instance is an Estimator.
+    // Create a LogisticRegression instance. This instance is an Estimator.
     LogisticRegression lr = new LogisticRegression();
     // Print out the parameters, documentation, and any default values.
     System.out.println("LogisticRegression parameters:\n" + lr.explainParams() + "\n");
@@ -65,7 +65,7 @@ public class JavaSimpleParamsExample {
     lr.setMaxIter(10)
       .setRegParam(0.01);
 
-    // Learn a LogisticRegression model.  This uses the parameters stored in lr.
+    // Learn a LogisticRegression model. This uses the parameters stored in lr.
     LogisticRegressionModel model1 = lr.fit(training);
     // Since model1 is a Model (i.e., a Transformer produced by an Estimator),
     // we can view the parameters it used during fit().
@@ -82,7 +82,7 @@ public class JavaSimpleParamsExample {
 
     // One can also combine ParamMaps.
     ParamMap paramMap2 = new ParamMap();
-    paramMap2.put(lr.probabilityCol().w("myProbability")); // Change output column name
+    paramMap2.put(lr.probabilityCol().w("myProbability")); // Change output column name.
     ParamMap paramMapCombined = paramMap.$plus$plus(paramMap2);
 
     // Now learn a new model using the paramMapCombined parameters.
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java
index 9516ce1f4f..7c24c46d2e 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java
@@ -43,7 +43,9 @@ public class JavaSimpleTextClassificationPipeline {
 
   public static void main(String[] args) {
     SparkSession spark = SparkSession
-      .builder().appName("JavaSimpleTextClassificationPipeline").getOrCreate();
+      .builder()
+      .appName("JavaSimpleTextClassificationPipeline")
+      .getOrCreate();
 
     // Prepare training documents, which are labeled.
     List<LabeledDocument> localTraining = Lists.newArrayList(
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeClassificationExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeClassificationExample.scala
index 7f6c8de967..b3103ced91 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeClassificationExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeClassificationExample.scala
@@ -47,10 +47,10 @@ object DecisionTreeClassificationExample {
     val featureIndexer = new VectorIndexer()
       .setInputCol("features")
       .setOutputCol("indexedFeatures")
-      .setMaxCategories(4) // features with > 4 distinct values are treated as continuous
+      .setMaxCategories(4) // features with > 4 distinct values are treated as continuous.
       .fit(data)
 
-    // Split the data into training and test sets (30% held out for testing)
+    // Split the data into training and test sets (30% held out for testing).
     val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
 
     // Train a DecisionTree model.
@@ -64,11 +64,11 @@ object DecisionTreeClassificationExample {
       .setOutputCol("predictedLabel")
       .setLabels(labelIndexer.labels)
 
-    // Chain indexers and tree in a Pipeline
+    // Chain indexers and tree in a Pipeline.
     val pipeline = new Pipeline()
       .setStages(Array(labelIndexer, featureIndexer, dt, labelConverter))
 
-    // Train model.  This also runs the indexers.
+    // Train model. This also runs the indexers.
     val model = pipeline.fit(trainingData)
 
     // Make predictions.
@@ -77,7 +77,7 @@ object DecisionTreeClassificationExample {
     // Select example rows to display.
     predictions.select("predictedLabel", "label", "features").show(5)
 
-    // Select (prediction, true label) and compute test error
+    // Select (prediction, true label) and compute test error.
     val evaluator = new MulticlassClassificationEvaluator()
       .setLabelCol("indexedLabel")
       .setPredictionCol("prediction")
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala
index eadb02ab0d..310418008c 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala
@@ -23,7 +23,6 @@ import scala.language.reflectiveCalls
 
 import scopt.OptionParser
 
-import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.examples.mllib.AbstractParams
 import org.apache.spark.ml.{Pipeline, PipelineStage, Transformer}
 import org.apache.spark.ml.classification.{DecisionTreeClassificationModel, DecisionTreeClassifier}
@@ -40,7 +39,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession}
  * {{{
  * ./bin/run-example ml.DecisionTreeExample [options]
  * }}}
- * Note that Decision Trees can take a large amount of memory.  If the run-example command above
+ * Note that Decision Trees can take a large amount of memory. If the run-example command above
  * fails, try running via spark-submit and specifying the amount of memory as at least 1g.
  * For local mode, run
  * {{{
@@ -87,7 +86,7 @@ object DecisionTreeExample {
         .text(s"min info gain required to create a split, default: ${defaultParams.minInfoGain}")
         .action((x, c) => c.copy(minInfoGain = x))
       opt[Double]("fracTest")
-        .text(s"fraction of data to hold out for testing.  If given option testInput, " +
+        .text(s"fraction of data to hold out for testing. If given option testInput, " +
           s"this option is ignored. default: ${defaultParams.fracTest}")
         .action((x, c) => c.copy(fracTest = x))
       opt[Boolean]("cacheNodeIds")
@@ -106,7 +105,7 @@ object DecisionTreeExample {
          s"default: ${defaultParams.checkpointInterval}")
         .action((x, c) => c.copy(checkpointInterval = x))
       opt[String]("testInput")
-        .text(s"input path to test dataset.  If given, option fracTest is ignored." +
+        .text(s"input path to test dataset. If given, option fracTest is ignored." +
           s" default: ${defaultParams.testInput}")
         .action((x, c) => c.copy(testInput = x))
       opt[String]("dataFormat")
@@ -157,11 +156,10 @@ object DecisionTreeExample {
    * @param dataFormat  "libsvm" or "dense"
    * @param testInput  Path to test dataset.
    * @param algo  Classification or Regression
-   * @param fracTest  Fraction of input data to hold out for testing.  Ignored if testInput given.
+   * @param fracTest  Fraction of input data to hold out for testing. Ignored if testInput given.
    * @return  (training dataset, test dataset)
    */
   private[ml] def loadDatasets(
-      sc: SparkContext,
       input: String,
       dataFormat: String,
       testInput: String,
@@ -200,18 +198,21 @@ object DecisionTreeExample {
   }
 
   def run(params: Params) {
-    val conf = new SparkConf().setAppName(s"DecisionTreeExample with $params")
-    val sc = new SparkContext(conf)
-    params.checkpointDir.foreach(sc.setCheckpointDir)
+    val spark = SparkSession
+      .builder
+      .appName(s"DecisionTreeExample with $params")
+      .getOrCreate()
+
+    params.checkpointDir.foreach(spark.sparkContext.setCheckpointDir)
     val algo = params.algo.toLowerCase
 
     println(s"DecisionTreeExample with parameters:\n$params")
 
     // Load training and test data and cache it.
     val (training: DataFrame, test: DataFrame) =
-      loadDatasets(sc, params.input, params.dataFormat, params.testInput, algo, params.fracTest)
+      loadDatasets(params.input, params.dataFormat, params.testInput, algo, params.fracTest)
 
-    // Set up Pipeline
+    // Set up Pipeline.
     val stages = new mutable.ArrayBuffer[PipelineStage]()
     // (1) For classification, re-index classes.
     val labelColName = if (algo == "classification") "indexedLabel" else "label"
@@ -228,7 +229,7 @@ object DecisionTreeExample {
       .setOutputCol("indexedFeatures")
       .setMaxCategories(10)
     stages += featuresIndexer
-    // (3) Learn Decision Tree
+    // (3) Learn Decision Tree.
     val dt = algo match {
       case "classification" =>
         new DecisionTreeClassifier()
@@ -255,13 +256,13 @@ object DecisionTreeExample {
     stages += dt
     val pipeline = new Pipeline().setStages(stages.toArray)
 
-    // Fit the Pipeline
+    // Fit the Pipeline.
     val startTime = System.nanoTime()
     val pipelineModel = pipeline.fit(training)
     val elapsedTime = (System.nanoTime() - startTime) / 1e9
     println(s"Training time: $elapsedTime seconds")
 
-    // Get the trained Decision Tree from the fitted PipelineModel
+    // Get the trained Decision Tree from the fitted PipelineModel.
     algo match {
       case "classification" =>
         val treeModel = pipelineModel.stages.last.asInstanceOf[DecisionTreeClassificationModel]
@@ -280,7 +281,7 @@ object DecisionTreeExample {
       case _ => throw new IllegalArgumentException("Algo ${params.algo} not supported.")
     }
 
-    // Evaluate model on training, test data
+    // Evaluate model on training, test data.
     algo match {
       case "classification" =>
         println("Training data results:")
@@ -296,11 +297,11 @@ object DecisionTreeExample {
         throw new IllegalArgumentException("Algo ${params.algo} not supported.")
     }
 
-    sc.stop()
+    spark.stop()
   }
 
   /**
-   * Evaluate the given ClassificationModel on data.  Print the results.
+   * Evaluate the given ClassificationModel on data. Print the results.
    * @param model  Must fit ClassificationModel abstraction
    * @param data  DataFrame with "prediction" and labelColName columns
    * @param labelColName  Name of the labelCol parameter for the model
@@ -314,7 +315,7 @@ object DecisionTreeExample {
     val fullPredictions = model.transform(data).cache()
     val predictions = fullPredictions.select("prediction").rdd.map(_.getDouble(0))
     val labels = fullPredictions.select(labelColName).rdd.map(_.getDouble(0))
-    // Print number of classes for reference
+    // Print number of classes for reference.
     val numClasses = MetadataUtils.getNumClasses(fullPredictions.schema(labelColName)) match {
       case Some(n) => n
       case None => throw new RuntimeException(
@@ -325,7 +326,7 @@ object DecisionTreeExample {
   }
 
   /**
-   * Evaluate the given RegressionModel on data.  Print the results.
+   * Evaluate the given RegressionModel on data. Print the results.
    * @param model  Must fit RegressionModel abstraction
    * @param data  DataFrame with "prediction" and labelColName columns
    * @param labelColName  Name of the labelCol parameter for the model
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeRegressionExample.scala
index 799070ef47..ee61200ad1 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeRegressionExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeRegressionExample.scala
@@ -46,7 +46,7 @@ object DecisionTreeRegressionExample {
       .setMaxCategories(4)
       .fit(data)
 
-    // Split the data into training and test sets (30% held out for testing)
+    // Split the data into training and test sets (30% held out for testing).
     val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
 
     // Train a DecisionTree model.
@@ -54,11 +54,11 @@ object DecisionTreeRegressionExample {
       .setLabelCol("label")
       .setFeaturesCol("indexedFeatures")
 
-    // Chain indexer and tree in a Pipeline
+    // Chain indexer and tree in a Pipeline.
     val pipeline = new Pipeline()
       .setStages(Array(featureIndexer, dt))
 
-    // Train model.  This also runs the indexer.
+    // Train model. This also runs the indexer.
     val model = pipeline.fit(trainingData)
 
     // Make predictions.
@@ -67,7 +67,7 @@ object DecisionTreeRegressionExample {
     // Select example rows to display.
     predictions.select("prediction", "label", "features").show(5)
 
-    // Select (prediction, true label) and compute test error
+    // Select (prediction, true label) and compute test error.
     val evaluator = new RegressionEvaluator()
       .setLabelCol("label")
       .setPredictionCol("prediction")
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala
index a522d2127e..b8f47bf12b 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala
@@ -50,7 +50,7 @@ object DeveloperApiExample {
       LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
       LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))))
 
-    // Create a LogisticRegression instance.  This instance is an Estimator.
+    // Create a LogisticRegression instance. This instance is an Estimator.
     val lr = new MyLogisticRegression()
     // Print out the parameters, documentation, and any default values.
     println("MyLogisticRegression parameters:\n" + lr.explainParams() + "\n")
@@ -58,7 +58,7 @@ object DeveloperApiExample {
     // We may set parameters using setter methods.
     lr.setMaxIter(10)
 
-    // Learn a LogisticRegression model.  This uses the parameters stored in lr.
+    // Learn a LogisticRegression model. This uses the parameters stored in lr.
     val model = lr.fit(training.toDF())
 
     // Prepare test data.
@@ -84,7 +84,7 @@ object DeveloperApiExample {
 /**
  * Example of defining a parameter trait for a user-defined type of [[Classifier]].
  *
- * NOTE: This is private since it is an example.  In practice, you may not want it to be private.
+ * NOTE: This is private since it is an example. In practice, you may not want it to be private.
  */
 private trait MyLogisticRegressionParams extends ClassifierParams {
 
@@ -96,7 +96,7 @@ private trait MyLogisticRegressionParams extends ClassifierParams {
    *   - def getMyParamName
    *   - def setMyParamName
    * Here, we have a trait to be mixed in with the Estimator and Model (MyLogisticRegression
-   * and MyLogisticRegressionModel).  We place the setter (setMaxIter) method in the Estimator
+   * and MyLogisticRegressionModel). We place the setter (setMaxIter) method in the Estimator
    * class since the maxIter parameter is only used during training (not in the Model).
    */
   val maxIter: IntParam = new IntParam(this, "maxIter", "max number of iterations")
@@ -106,7 +106,7 @@ private trait MyLogisticRegressionParams extends ClassifierParams {
 /**
  * Example of defining a type of [[Classifier]].
  *
- * NOTE: This is private since it is an example.  In practice, you may not want it to be private.
+ * NOTE: This is private since it is an example. In practice, you may not want it to be private.
  */
 private class MyLogisticRegression(override val uid: String)
   extends Classifier[Vector, MyLogisticRegression, MyLogisticRegressionModel]
@@ -138,7 +138,7 @@ private class MyLogisticRegression(override val uid: String)
 /**
  * Example of defining a type of [[ClassificationModel]].
  *
- * NOTE: This is private since it is an example.  In practice, you may not want it to be private.
+ * NOTE: This is private since it is an example. In practice, you may not want it to be private.
  */
 private class MyLogisticRegressionModel(
     override val uid: String,
@@ -169,7 +169,7 @@ private class MyLogisticRegressionModel(
     Vectors.dense(-margin, margin)
   }
 
-  /** Number of classes the label can take.  2 indicates binary classification. */
+  /** Number of classes the label can take. 2 indicates binary classification. */
   override val numClasses: Int = 2
 
   /** Number of features the model was trained on. */
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/EstimatorTransformerParamExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/EstimatorTransformerParamExample.scala
index 972241e769..a2918d66ea 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/EstimatorTransformerParamExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/EstimatorTransformerParamExample.scala
@@ -43,7 +43,7 @@ object EstimatorTransformerParamExample {
       (1.0, Vectors.dense(0.0, 1.2, -0.5))
     )).toDF("label", "features")
 
-    // Create a LogisticRegression instance.  This instance is an Estimator.
+    // Create a LogisticRegression instance. This instance is an Estimator.
     val lr = new LogisticRegression()
     // Print out the parameters, documentation, and any default values.
     println("LogisticRegression parameters:\n" + lr.explainParams() + "\n")
@@ -52,7 +52,7 @@ object EstimatorTransformerParamExample {
     lr.setMaxIter(10)
       .setRegParam(0.01)
 
-    // Learn a LogisticRegression model.  This uses the parameters stored in lr.
+    // Learn a LogisticRegression model. This uses the parameters stored in lr.
     val model1 = lr.fit(training)
     // Since model1 is a Model (i.e., a Transformer produced by an Estimator),
     // we can view the parameters it used during fit().
@@ -63,11 +63,11 @@ object EstimatorTransformerParamExample {
     // We may alternatively specify parameters using a ParamMap,
     // which supports several methods for specifying parameters.
     val paramMap = ParamMap(lr.maxIter -> 20)
-      .put(lr.maxIter, 30)  // Specify 1 Param.  This overwrites the original maxIter.
+      .put(lr.maxIter, 30)  // Specify 1 Param. This overwrites the original maxIter.
       .put(lr.regParam -> 0.1, lr.threshold -> 0.55)  // Specify multiple Params.
 
     // One can also combine ParamMaps.
-    val paramMap2 = ParamMap(lr.probabilityCol -> "myProbability")  // Change output column name
+    val paramMap2 = ParamMap(lr.probabilityCol -> "myProbability")  // Change output column name.
     val paramMapCombined = paramMap ++ paramMap2
 
     // Now learn a new model using the paramMapCombined parameters.
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala
index 6b0be0f34e..a4274ae954 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala
@@ -23,13 +23,12 @@ import scala.language.reflectiveCalls
 
 import scopt.OptionParser
 
-import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.examples.mllib.AbstractParams
 import org.apache.spark.ml.{Pipeline, PipelineStage}
 import org.apache.spark.ml.classification.{GBTClassificationModel, GBTClassifier}
 import org.apache.spark.ml.feature.{StringIndexer, VectorIndexer}
 import org.apache.spark.ml.regression.{GBTRegressionModel, GBTRegressor}
-import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.{DataFrame, SparkSession}
 
 
 /**
@@ -37,7 +36,7 @@ import org.apache.spark.sql.DataFrame
  * {{{
  * ./bin/run-example ml.GBTExample [options]
  * }}}
- * Decision Trees and ensembles can take a large amount of memory.  If the run-example command
+ * Decision Trees and ensembles can take a large amount of memory. If the run-example command
  * above fails, try running via spark-submit and specifying the amount of memory as at least 1g.
  * For local mode, run
  * {{{
@@ -88,7 +87,7 @@ object GBTExample {
         .text(s"number of trees in ensemble, default: ${defaultParams.maxIter}")
         .action((x, c) => c.copy(maxIter = x))
       opt[Double]("fracTest")
-        .text(s"fraction of data to hold out for testing.  If given option testInput, " +
+        .text(s"fraction of data to hold out for testing. If given option testInput, " +
         s"this option is ignored. default: ${defaultParams.fracTest}")
         .action((x, c) => c.copy(fracTest = x))
       opt[Boolean]("cacheNodeIds")
@@ -109,7 +108,7 @@ object GBTExample {
         s"default: ${defaultParams.checkpointInterval}")
         .action((x, c) => c.copy(checkpointInterval = x))
       opt[String]("testInput")
-        .text(s"input path to test dataset.  If given, option fracTest is ignored." +
+        .text(s"input path to test dataset. If given, option fracTest is ignored." +
         s" default: ${defaultParams.testInput}")
         .action((x, c) => c.copy(testInput = x))
       opt[String]("dataFormat")
@@ -136,15 +135,18 @@ object GBTExample {
   }
 
   def run(params: Params) {
-    val conf = new SparkConf().setAppName(s"GBTExample with $params")
-    val sc = new SparkContext(conf)
-    params.checkpointDir.foreach(sc.setCheckpointDir)
+    val spark = SparkSession
+      .builder
+      .appName(s"GBTExample with $params")
+      .getOrCreate()
+
+    params.checkpointDir.foreach(spark.sparkContext.setCheckpointDir)
     val algo = params.algo.toLowerCase
 
     println(s"GBTExample with parameters:\n$params")
 
     // Load training and test data and cache it.
-    val (training: DataFrame, test: DataFrame) = DecisionTreeExample.loadDatasets(sc, params.input,
+    val (training: DataFrame, test: DataFrame) = DecisionTreeExample.loadDatasets(params.input,
       params.dataFormat, params.testInput, algo, params.fracTest)
 
     // Set up Pipeline
@@ -164,7 +166,7 @@ object GBTExample {
       .setOutputCol("indexedFeatures")
       .setMaxCategories(10)
     stages += featuresIndexer
-    // (3) Learn GBT
+    // (3) Learn GBT.
     val dt = algo match {
       case "classification" =>
         new GBTClassifier()
@@ -193,13 +195,13 @@ object GBTExample {
     stages += dt
     val pipeline = new Pipeline().setStages(stages.toArray)
 
-    // Fit the Pipeline
+    // Fit the Pipeline.
     val startTime = System.nanoTime()
     val pipelineModel = pipeline.fit(training)
     val elapsedTime = (System.nanoTime() - startTime) / 1e9
     println(s"Training time: $elapsedTime seconds")
 
-    // Get the trained GBT from the fitted PipelineModel
+    // Get the trained GBT from the fitted PipelineModel.
     algo match {
       case "classification" =>
         val rfModel = pipelineModel.stages.last.asInstanceOf[GBTClassificationModel]
@@ -218,7 +220,7 @@ object GBTExample {
       case _ => throw new IllegalArgumentException("Algo ${params.algo} not supported.")
     }
 
-    // Evaluate model on training, test data
+    // Evaluate model on training, test data.
     algo match {
       case "classification" =>
         println("Training data results:")
@@ -234,7 +236,7 @@ object GBTExample {
         throw new IllegalArgumentException("Algo ${params.algo} not supported.")
     }
 
-    sc.stop()
+    spark.stop()
   }
 }
 // scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala
index b6a8baba2d..0d1ffbe225 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala
@@ -51,7 +51,7 @@ object GradientBoostedTreeClassifierExample {
       .setMaxCategories(4)
       .fit(data)
 
-    // Split the data into training and test sets (30% held out for testing)
+    // Split the data into training and test sets (30% held out for testing).
     val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
 
     // Train a GBT model.
@@ -66,11 +66,11 @@ object GradientBoostedTreeClassifierExample {
       .setOutputCol("predictedLabel")
       .setLabels(labelIndexer.labels)
 
-    // Chain indexers and GBT in a Pipeline
+    // Chain indexers and GBT in a Pipeline.
     val pipeline = new Pipeline()
       .setStages(Array(labelIndexer, featureIndexer, gbt, labelConverter))
 
-    // Train model.  This also runs the indexers.
+    // Train model. This also runs the indexers.
     val model = pipeline.fit(trainingData)
 
     // Make predictions.
@@ -79,7 +79,7 @@ object GradientBoostedTreeClassifierExample {
     // Select example rows to display.
     predictions.select("predictedLabel", "label", "features").show(5)
 
-    // Select (prediction, true label) and compute test error
+    // Select (prediction, true label) and compute test error.
     val evaluator = new MulticlassClassificationEvaluator()
       .setLabelCol("indexedLabel")
       .setPredictionCol("prediction")
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeRegressorExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeRegressorExample.scala
index 62285b83cb..e53aab7f32 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeRegressorExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeRegressorExample.scala
@@ -45,7 +45,7 @@ object GradientBoostedTreeRegressorExample {
       .setMaxCategories(4)
       .fit(data)
 
-    // Split the data into training and test sets (30% held out for testing)
+    // Split the data into training and test sets (30% held out for testing).
     val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
 
     // Train a GBT model.
@@ -54,11 +54,11 @@ object GradientBoostedTreeRegressorExample {
       .setFeaturesCol("indexedFeatures")
       .setMaxIter(10)
 
-    // Chain indexer and GBT in a Pipeline
+    // Chain indexer and GBT in a Pipeline.
     val pipeline = new Pipeline()
       .setStages(Array(featureIndexer, gbt))
 
-    // Train model.  This also runs the indexer.
+    // Train model. This also runs the indexer.
     val model = pipeline.fit(trainingData)
 
     // Make predictions.
@@ -67,7 +67,7 @@ object GradientBoostedTreeRegressorExample {
     // Select example rows to display.
     predictions.select("prediction", "label", "features").show(5)
 
-    // Select (prediction, true label) and compute test error
+    // Select (prediction, true label) and compute test error.
     val evaluator = new RegressionEvaluator()
       .setLabelCol("label")
       .setPredictionCol("prediction")
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/LinearRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/LinearRegressionExample.scala
index 25be87811d..de96fb2979 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/LinearRegressionExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/LinearRegressionExample.scala
@@ -22,10 +22,9 @@ import scala.language.reflectiveCalls
 
 import scopt.OptionParser
 
-import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.examples.mllib.AbstractParams
 import org.apache.spark.ml.regression.LinearRegression
-import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.{DataFrame, SparkSession}
 
 /**
  * An example runner for linear regression with elastic-net (mixing L1/L2) regularization.
@@ -74,11 +73,11 @@ object LinearRegressionExample {
         s"to higher accuracy with the cost of more iterations, default: ${defaultParams.tol}")
         .action((x, c) => c.copy(tol = x))
       opt[Double]("fracTest")
-        .text(s"fraction of data to hold out for testing.  If given option testInput, " +
+        .text(s"fraction of data to hold out for testing. If given option testInput, " +
         s"this option is ignored. default: ${defaultParams.fracTest}")
         .action((x, c) => c.copy(fracTest = x))
       opt[String]("testInput")
-        .text(s"input path to test dataset.  If given, option fracTest is ignored." +
+        .text(s"input path to test dataset. If given, option fracTest is ignored." +
         s" default: ${defaultParams.testInput}")
         .action((x, c) => c.copy(testInput = x))
       opt[String]("dataFormat")
@@ -105,13 +104,15 @@ object LinearRegressionExample {
   }
 
   def run(params: Params) {
-    val conf = new SparkConf().setAppName(s"LinearRegressionExample with $params")
-    val sc = new SparkContext(conf)
+    val spark = SparkSession
+      .builder
+      .appName(s"LinearRegressionExample with $params")
+      .getOrCreate()
 
     println(s"LinearRegressionExample with parameters:\n$params")
 
     // Load training and test data and cache it.
-    val (training: DataFrame, test: DataFrame) = DecisionTreeExample.loadDatasets(sc, params.input,
+    val (training: DataFrame, test: DataFrame) = DecisionTreeExample.loadDatasets(params.input,
       params.dataFormat, params.testInput, "regression", params.fracTest)
 
     val lir = new LinearRegression()
@@ -136,7 +137,7 @@ object LinearRegressionExample {
     println("Test data results:")
     DecisionTreeExample.evaluateRegressionModel(lirModel, test, "label")
 
-    sc.stop()
+    spark.stop()
   }
 }
 // scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionExample.scala
index a380c90662..c2a87e1ddf 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionExample.scala
@@ -23,12 +23,11 @@ import scala.language.reflectiveCalls
 
 import scopt.OptionParser
 
-import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.examples.mllib.AbstractParams
 import org.apache.spark.ml.{Pipeline, PipelineStage}
 import org.apache.spark.ml.classification.{LogisticRegression, LogisticRegressionModel}
 import org.apache.spark.ml.feature.StringIndexer
-import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.{DataFrame, SparkSession}
 
 /**
  * An example runner for logistic regression with elastic-net (mixing L1/L2) regularization.
@@ -81,11 +80,11 @@ object LogisticRegressionExample {
         s"to higher accuracy with the cost of more iterations, default: ${defaultParams.tol}")
         .action((x, c) => c.copy(tol = x))
       opt[Double]("fracTest")
-        .text(s"fraction of data to hold out for testing.  If given option testInput, " +
+        .text(s"fraction of data to hold out for testing. If given option testInput, " +
         s"this option is ignored. default: ${defaultParams.fracTest}")
         .action((x, c) => c.copy(fracTest = x))
       opt[String]("testInput")
-        .text(s"input path to test dataset.  If given, option fracTest is ignored." +
+        .text(s"input path to test dataset. If given, option fracTest is ignored." +
         s" default: ${defaultParams.testInput}")
         .action((x, c) => c.copy(testInput = x))
       opt[String]("dataFormat")
@@ -112,16 +111,18 @@ object LogisticRegressionExample {
   }
 
   def run(params: Params) {
-    val conf = new SparkConf().setAppName(s"LogisticRegressionExample with $params")
-    val sc = new SparkContext(conf)
+    val spark = SparkSession
+      .builder
+      .appName(s"LogisticRegressionExample with $params")
+      .getOrCreate()
 
     println(s"LogisticRegressionExample with parameters:\n$params")
 
     // Load training and test data and cache it.
-    val (training: DataFrame, test: DataFrame) = DecisionTreeExample.loadDatasets(sc, params.input,
+    val (training: DataFrame, test: DataFrame) = DecisionTreeExample.loadDatasets(params.input,
       params.dataFormat, params.testInput, "classification", params.fracTest)
 
-    // Set up Pipeline
+    // Set up Pipeline.
     val stages = new mutable.ArrayBuffer[PipelineStage]()
 
     val labelIndexer = new StringIndexer()
@@ -141,7 +142,7 @@ object LogisticRegressionExample {
     stages += lor
     val pipeline = new Pipeline().setStages(stages.toArray)
 
-    // Fit the Pipeline
+    // Fit the Pipeline.
     val startTime = System.nanoTime()
     val pipelineModel = pipeline.fit(training)
     val elapsedTime = (System.nanoTime() - startTime) / 1e9
@@ -156,7 +157,7 @@ object LogisticRegressionExample {
     println("Test data results:")
     DecisionTreeExample.evaluateClassificationModel(pipelineModel, test, "indexedLabel")
 
-    sc.stop()
+    spark.stop()
   }
 }
 // scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionWithElasticNetExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionWithElasticNetExample.scala
index fcba813d5b..616263b8e9 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionWithElasticNetExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionWithElasticNetExample.scala
@@ -27,7 +27,9 @@ object LogisticRegressionWithElasticNetExample {
 
   def main(args: Array[String]): Unit = {
     val spark = SparkSession
-      .builder.appName("LogisticRegressionWithElasticNetExample").getOrCreate()
+      .builder
+      .appName("LogisticRegressionWithElasticNetExample")
+      .getOrCreate()
 
     // $example on$
     // Load training data
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/ModelSelectionViaCrossValidationExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/ModelSelectionViaCrossValidationExample.scala
index 5fb3536060..c29d36210a 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/ModelSelectionViaCrossValidationExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/ModelSelectionViaCrossValidationExample.scala
@@ -42,7 +42,9 @@ object ModelSelectionViaCrossValidationExample {
 
   def main(args: Array[String]): Unit = {
     val spark = SparkSession
-      .builder.appName("ModelSelectionViaCrossValidationExample").getOrCreate()
+      .builder
+      .appName("ModelSelectionViaCrossValidationExample")
+      .getOrCreate()
 
     // $example on$
     // Prepare training data from a list of (id, text, label) tuples.
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/ModelSelectionViaTrainValidationSplitExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/ModelSelectionViaTrainValidationSplitExample.scala
index 6bc082982c..75fef2922a 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/ModelSelectionViaTrainValidationSplitExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/ModelSelectionViaTrainValidationSplitExample.scala
@@ -36,7 +36,9 @@ object ModelSelectionViaTrainValidationSplitExample {
 
   def main(args: Array[String]): Unit = {
     val spark = SparkSession
-      .builder.appName("ModelSelectionViaTrainValidationSplitExample").getOrCreate()
+      .builder
+      .appName("ModelSelectionViaTrainValidationSplitExample")
+      .getOrCreate()
 
     // $example on$
     // Prepare training and test data.
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala
index ae0bd945d8..cccc4a6ea2 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala
@@ -51,7 +51,7 @@ object RandomForestClassifierExample {
       .setMaxCategories(4)
       .fit(data)
 
-    // Split the data into training and test sets (30% held out for testing)
+    // Split the data into training and test sets (30% held out for testing).
     val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
 
     // Train a RandomForest model.
@@ -66,11 +66,11 @@ object RandomForestClassifierExample {
       .setOutputCol("predictedLabel")
       .setLabels(labelIndexer.labels)
 
-    // Chain indexers and forest in a Pipeline
+    // Chain indexers and forest in a Pipeline.
     val pipeline = new Pipeline()
       .setStages(Array(labelIndexer, featureIndexer, rf, labelConverter))
 
-    // Train model.  This also runs the indexers.
+    // Train model. This also runs the indexers.
     val model = pipeline.fit(trainingData)
 
     // Make predictions.
@@ -79,7 +79,7 @@ object RandomForestClassifierExample {
     // Select example rows to display.
     predictions.select("predictedLabel", "label", "features").show(5)
 
-    // Select (prediction, true label) and compute test error
+    // Select (prediction, true label) and compute test error.
     val evaluator = new MulticlassClassificationEvaluator()
       .setLabelCol("indexedLabel")
       .setPredictionCol("prediction")
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala
index 7a00d99dfe..2419dc49cd 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala
@@ -23,13 +23,12 @@ import scala.language.reflectiveCalls
 
 import scopt.OptionParser
 
-import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.examples.mllib.AbstractParams
 import org.apache.spark.ml.{Pipeline, PipelineStage}
 import org.apache.spark.ml.classification.{RandomForestClassificationModel, RandomForestClassifier}
 import org.apache.spark.ml.feature.{StringIndexer, VectorIndexer}
 import org.apache.spark.ml.regression.{RandomForestRegressionModel, RandomForestRegressor}
-import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.{DataFrame, SparkSession}
 
 
 /**
@@ -37,7 +36,7 @@ import org.apache.spark.sql.DataFrame
  * {{{
  * ./bin/run-example ml.RandomForestExample [options]
  * }}}
- * Decision Trees and ensembles can take a large amount of memory.  If the run-example command
+ * Decision Trees and ensembles can take a large amount of memory. If the run-example command
  * above fails, try running via spark-submit and specifying the amount of memory as at least 1g.
  * For local mode, run
  * {{{
@@ -94,7 +93,7 @@ object RandomForestExample {
         s" default: ${defaultParams.numTrees}")
         .action((x, c) => c.copy(featureSubsetStrategy = x))
       opt[Double]("fracTest")
-        .text(s"fraction of data to hold out for testing.  If given option testInput, " +
+        .text(s"fraction of data to hold out for testing. If given option testInput, " +
         s"this option is ignored. default: ${defaultParams.fracTest}")
         .action((x, c) => c.copy(fracTest = x))
       opt[Boolean]("cacheNodeIds")
@@ -115,7 +114,7 @@ object RandomForestExample {
         s"default: ${defaultParams.checkpointInterval}")
         .action((x, c) => c.copy(checkpointInterval = x))
       opt[String]("testInput")
-        .text(s"input path to test dataset.  If given, option fracTest is ignored." +
+        .text(s"input path to test dataset. If given, option fracTest is ignored." +
         s" default: ${defaultParams.testInput}")
         .action((x, c) => c.copy(testInput = x))
       opt[String]("dataFormat")
@@ -142,18 +141,21 @@ object RandomForestExample {
   }
 
   def run(params: Params) {
-    val conf = new SparkConf().setAppName(s"RandomForestExample with $params")
-    val sc = new SparkContext(conf)
-    params.checkpointDir.foreach(sc.setCheckpointDir)
+    val spark = SparkSession
+      .builder
+      .appName(s"RandomForestExample with $params")
+      .getOrCreate()
+
+    params.checkpointDir.foreach(spark.sparkContext.setCheckpointDir)
     val algo = params.algo.toLowerCase
 
     println(s"RandomForestExample with parameters:\n$params")
 
     // Load training and test data and cache it.
-    val (training: DataFrame, test: DataFrame) = DecisionTreeExample.loadDatasets(sc, params.input,
+    val (training: DataFrame, test: DataFrame) = DecisionTreeExample.loadDatasets(params.input,
       params.dataFormat, params.testInput, algo, params.fracTest)
 
-    // Set up Pipeline
+    // Set up Pipeline.
     val stages = new mutable.ArrayBuffer[PipelineStage]()
     // (1) For classification, re-index classes.
     val labelColName = if (algo == "classification") "indexedLabel" else "label"
@@ -170,7 +172,7 @@ object RandomForestExample {
       .setOutputCol("indexedFeatures")
       .setMaxCategories(10)
     stages += featuresIndexer
-    // (3) Learn Random Forest
+    // (3) Learn Random Forest.
     val dt = algo match {
       case "classification" =>
         new RandomForestClassifier()
@@ -201,13 +203,13 @@ object RandomForestExample {
     stages += dt
     val pipeline = new Pipeline().setStages(stages.toArray)
 
-    // Fit the Pipeline
+    // Fit the Pipeline.
     val startTime = System.nanoTime()
     val pipelineModel = pipeline.fit(training)
     val elapsedTime = (System.nanoTime() - startTime) / 1e9
     println(s"Training time: $elapsedTime seconds")
 
-    // Get the trained Random Forest from the fitted PipelineModel
+    // Get the trained Random Forest from the fitted PipelineModel.
     algo match {
       case "classification" =>
         val rfModel = pipelineModel.stages.last.asInstanceOf[RandomForestClassificationModel]
@@ -226,7 +228,7 @@ object RandomForestExample {
       case _ => throw new IllegalArgumentException("Algo ${params.algo} not supported.")
     }
 
-    // Evaluate model on training, test data
+    // Evaluate model on training, test data.
     algo match {
       case "classification" =>
         println("Training data results:")
@@ -242,7 +244,7 @@ object RandomForestExample {
         throw new IllegalArgumentException("Algo ${params.algo} not supported.")
     }
 
-    sc.stop()
+    spark.stop()
   }
 }
 // scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestRegressorExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestRegressorExample.scala
index 96dc2f05be..9a0a001c26 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestRegressorExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestRegressorExample.scala
@@ -45,7 +45,7 @@ object RandomForestRegressorExample {
       .setMaxCategories(4)
       .fit(data)
 
-    // Split the data into training and test sets (30% held out for testing)
+    // Split the data into training and test sets (30% held out for testing).
     val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
 
     // Train a RandomForest model.
@@ -53,11 +53,11 @@ object RandomForestRegressorExample {
       .setLabelCol("label")
       .setFeaturesCol("indexedFeatures")
 
-    // Chain indexer and forest in a Pipeline
+    // Chain indexer and forest in a Pipeline.
     val pipeline = new Pipeline()
       .setStages(Array(featureIndexer, rf))
 
-    // Train model.  This also runs the indexer.
+    // Train model. This also runs the indexer.
     val model = pipeline.fit(trainingData)
 
     // Make predictions.
@@ -66,7 +66,7 @@ object RandomForestRegressorExample {
     // Select example rows to display.
     predictions.select("prediction", "label", "features").show(5)
 
-    // Select (prediction, true label) and compute test error
+    // Select (prediction, true label) and compute test error.
     val evaluator = new RegressionEvaluator()
       .setLabelCol("label")
       .setPredictionCol("prediction")
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala
index 3547dd95bd..83bab5c557 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala
@@ -41,7 +41,7 @@ object SimpleParamsExample {
     import spark.implicits._
 
     // Prepare training data.
-    // We use LabeledPoint, which is a case class.  Spark SQL can convert RDDs of case classes
+    // We use LabeledPoint, which is a case class. Spark SQL can convert RDDs of case classes
     // into DataFrames, where it uses the case class metadata to infer the schema.
     val training = spark.createDataFrame(Seq(
       LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
@@ -49,7 +49,7 @@ object SimpleParamsExample {
       LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
       LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))))
 
-    // Create a LogisticRegression instance.  This instance is an Estimator.
+    // Create a LogisticRegression instance. This instance is an Estimator.
     val lr = new LogisticRegression()
     // Print out the parameters, documentation, and any default values.
     println("LogisticRegression parameters:\n" + lr.explainParams() + "\n")
@@ -58,7 +58,7 @@ object SimpleParamsExample {
     lr.setMaxIter(10)
       .setRegParam(0.01)
 
-    // Learn a LogisticRegression model.  This uses the parameters stored in lr.
+    // Learn a LogisticRegression model. This uses the parameters stored in lr.
     val model1 = lr.fit(training)
     // Since model1 is a Model (i.e., a Transformer produced by an Estimator),
     // we can view the parameters it used during fit().
@@ -69,7 +69,7 @@ object SimpleParamsExample {
     // We may alternatively specify parameters using a ParamMap,
     // which supports several methods for specifying parameters.
     val paramMap = ParamMap(lr.maxIter -> 20)
-    paramMap.put(lr.maxIter, 30) // Specify 1 Param.  This overwrites the original maxIter.
+    paramMap.put(lr.maxIter, 30) // Specify 1 Param. This overwrites the original maxIter.
     paramMap.put(lr.regParam -> 0.1, lr.thresholds -> Array(0.45, 0.55)) // Specify multiple Params.
 
     // One can also combine ParamMaps.