[SPARK-13702][CORE][SQL][MLLIB] Use diamond operator for generic instance creation in Java code.

## What changes were proposed in this pull request?

In order to make `docs/examples` (and other related code) more simple/readable/user-friendly, this PR replaces existing codes like the followings by using `diamond` operator.

```
-    final ArrayList<Product2<Object, Object>> dataToWrite =
-      new ArrayList<Product2<Object, Object>>();
+    final ArrayList<Product2<Object, Object>> dataToWrite = new ArrayList<>();
```

Java 7 or higher supports **diamond** operator which replaces the type arguments required to invoke the constructor of a generic class with an empty set of type parameters (<>). Currently, Spark Java code use mixed usage of this.

## How was this patch tested?

Manual.
Pass the existing tests.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #11541 from dongjoon-hyun/SPARK-13702.

25 files changed, 40 insertions, 42 deletions

diff --git a/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java b/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java
index 1a6caa8cf8..8abc03e73d 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java
@@ -108,7 +108,7 @@ public final class JavaLogQuery {
     JavaPairRDD<Tuple3<String, String, String>, Stats> extracted = dataSet.mapToPair(new PairFunction<String, Tuple3<String, String, String>, Stats>() {
       @Override
       public Tuple2<Tuple3<String, String, String>, Stats> call(String s) {
-        return new Tuple2<Tuple3<String, String, String>, Stats>(extractKey(s), extractStats(s));
+        return new Tuple2<>(extractKey(s), extractStats(s));
       }
     });
 
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java b/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java
index 635fb6a373..c3ef93c5b6 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java
@@ -88,7 +88,7 @@ public final class JavaPageRank {
       @Override
       public Tuple2<String, String> call(String s) {
         String[] parts = SPACES.split(s);
-        return new Tuple2<String, String>(parts[0], parts[1]);
+        return new Tuple2<>(parts[0], parts[1]);
       }
     }).distinct().groupByKey().cache();
 
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java b/examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java
index af87488744..04a57a6bfb 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java
@@ -38,7 +38,7 @@ public final class JavaSparkPi {
 
     int slices = (args.length == 1) ? Integer.parseInt(args[0]) : 2;
     int n = 100000 * slices;
-    List<Integer> l = new ArrayList<Integer>(n);
+    List<Integer> l = new ArrayList<>(n);
     for (int i = 0; i < n; i++) {
       l.add(i);
     }
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaTC.java b/examples/src/main/java/org/apache/spark/examples/JavaTC.java
index 2563fcdd23..ca10384212 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaTC.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaTC.java
@@ -41,16 +41,16 @@ public final class JavaTC {
   private static final Random rand = new Random(42);
 
   static List<Tuple2<Integer, Integer>> generateGraph() {
-    Set<Tuple2<Integer, Integer>> edges = new HashSet<Tuple2<Integer, Integer>>(numEdges);
+    Set<Tuple2<Integer, Integer>> edges = new HashSet<>(numEdges);
     while (edges.size() < numEdges) {
       int from = rand.nextInt(numVertices);
       int to = rand.nextInt(numVertices);
-      Tuple2<Integer, Integer> e = new Tuple2<Integer, Integer>(from, to);
+      Tuple2<Integer, Integer> e = new Tuple2<>(from, to);
       if (from != to) {
         edges.add(e);
       }
     }
-    return new ArrayList<Tuple2<Integer, Integer>>(edges);
+    return new ArrayList<>(edges);
   }
 
   static class ProjectFn implements PairFunction<Tuple2<Integer, Tuple2<Integer, Integer>>,
@@ -59,7 +59,7 @@ public final class JavaTC {
 
     @Override
     public Tuple2<Integer, Integer> call(Tuple2<Integer, Tuple2<Integer, Integer>> triple) {
-      return new Tuple2<Integer, Integer>(triple._2()._2(), triple._2()._1());
+      return new Tuple2<>(triple._2()._2(), triple._2()._1());
     }
   }
 
@@ -79,7 +79,7 @@ public final class JavaTC {
       new PairFunction<Tuple2<Integer, Integer>, Integer, Integer>() {
         @Override
         public Tuple2<Integer, Integer> call(Tuple2<Integer, Integer> e) {
-          return new Tuple2<Integer, Integer>(e._2(), e._1());
+          return new Tuple2<>(e._2(), e._1());
         }
     });
 
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java b/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java
index d746a3d2b6..84dbea5caa 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java
@@ -55,7 +55,7 @@ public final class JavaWordCount {
     JavaPairRDD<String, Integer> ones = words.mapToPair(new PairFunction<String, String, Integer>() {
       @Override
       public Tuple2<String, Integer> call(String s) {
-        return new Tuple2<String, Integer>(s, 1);
+        return new Tuple2<>(s, 1);
       }
     });
 
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaElementwiseProductExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaElementwiseProductExample.java
index 2898accec6..c1f00dde0e 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaElementwiseProductExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaElementwiseProductExample.java
@@ -52,7 +52,7 @@ public class JavaElementwiseProductExample {
       RowFactory.create("b", Vectors.dense(4.0, 5.0, 6.0))
     ));
 
-    List<StructField> fields = new ArrayList<StructField>(2);
+    List<StructField> fields = new ArrayList<>(2);
     fields.add(DataTypes.createStructField("id", DataTypes.StringType, false));
     fields.add(DataTypes.createStructField("vector", new VectorUDT(), false));
 
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeClassificationExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeClassificationExample.java
index 5839b0cf8a..66387b9df5 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeClassificationExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeClassificationExample.java
@@ -54,7 +54,7 @@ class JavaDecisionTreeClassificationExample {
     // Set parameters.
     //  Empty categoricalFeaturesInfo indicates all features are continuous.
     Integer numClasses = 2;
-    Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
+    Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<>();
     String impurity = "gini";
     Integer maxDepth = 5;
     Integer maxBins = 32;
@@ -68,7 +68,7 @@ class JavaDecisionTreeClassificationExample {
       testData.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
         @Override
         public Tuple2<Double, Double> call(LabeledPoint p) {
-          return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
+          return new Tuple2<>(model.predict(p.features()), p.label());
         }
       });
     Double testErr =
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeRegressionExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeRegressionExample.java
index ccde578249..904e7f7e95 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeRegressionExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeRegressionExample.java
@@ -54,7 +54,7 @@ class JavaDecisionTreeRegressionExample {
 
     // Set parameters.
     // Empty categoricalFeaturesInfo indicates all features are continuous.
-    Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
+    Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<>();
     String impurity = "variance";
     Integer maxDepth = 5;
     Integer maxBins = 32;
@@ -68,7 +68,7 @@ class JavaDecisionTreeRegressionExample {
       testData.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
       @Override
       public Tuple2<Double, Double> call(LabeledPoint p) {
-        return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
+        return new Tuple2<>(model.predict(p.features()), p.label());
       }
     });
     Double testMSE =
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaGradientBoostingClassificationExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaGradientBoostingClassificationExample.java
index 0c2e4c928b..213949e525 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaGradientBoostingClassificationExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaGradientBoostingClassificationExample.java
@@ -58,7 +58,7 @@ public class JavaGradientBoostingClassificationExample {
     boostingStrategy.getTreeStrategy().setNumClasses(2);
     boostingStrategy.getTreeStrategy().setMaxDepth(5);
     // Empty categoricalFeaturesInfo indicates all features are continuous.
-    Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
+    Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<>();
     boostingStrategy.treeStrategy().setCategoricalFeaturesInfo(categoricalFeaturesInfo);
 
     final GradientBoostedTreesModel model =
@@ -69,7 +69,7 @@ public class JavaGradientBoostingClassificationExample {
       testData.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
         @Override
         public Tuple2<Double, Double> call(LabeledPoint p) {
-          return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
+          return new Tuple2<>(model.predict(p.features()), p.label());
         }
       });
     Double testErr =
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaGradientBoostingRegressionExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaGradientBoostingRegressionExample.java
index c1bc2098dc..78db442dbc 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaGradientBoostingRegressionExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaGradientBoostingRegressionExample.java
@@ -57,7 +57,7 @@ public class JavaGradientBoostingRegressionExample {
     boostingStrategy.setNumIterations(3); // Note: Use more iterations in practice.
     boostingStrategy.getTreeStrategy().setMaxDepth(5);
     // Empty categoricalFeaturesInfo indicates all features are continuous.
-    Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
+    Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<>();
     boostingStrategy.treeStrategy().setCategoricalFeaturesInfo(categoricalFeaturesInfo);
 
     final GradientBoostedTreesModel model =
@@ -68,7 +68,7 @@ public class JavaGradientBoostingRegressionExample {
       testData.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
         @Override
         public Tuple2<Double, Double> call(LabeledPoint p) {
-          return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
+          return new Tuple2<>(model.predict(p.features()), p.label());
         }
       });
     Double testMSE =
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaIsotonicRegressionExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaIsotonicRegressionExample.java
index e632e35110..0e15f75508 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaIsotonicRegressionExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaIsotonicRegressionExample.java
@@ -62,7 +62,7 @@ public class JavaIsotonicRegressionExample {
         @Override
         public Tuple2<Double, Double> call(Tuple3<Double, Double, Double> point) {
           Double predictedLabel = model.predict(point._2());
-          return new Tuple2<Double, Double>(predictedLabel, point._1());
+          return new Tuple2<>(predictedLabel, point._1());
         }
       }
     );
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLinearRegressionWithSGDExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLinearRegressionWithSGDExample.java
index 3e50118c0d..9ca9a7847c 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLinearRegressionWithSGDExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLinearRegressionWithSGDExample.java
@@ -70,7 +70,7 @@ public class JavaLinearRegressionWithSGDExample {
       new Function<LabeledPoint, Tuple2<Double, Double>>() {
         public Tuple2<Double, Double> call(LabeledPoint point) {
           double prediction = model.predict(point.features());
-          return new Tuple2<Double, Double>(prediction, point.label());
+          return new Tuple2<>(prediction, point.label());
         }
       }
     );
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java
index 478e615123..2b17dbb963 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java
@@ -46,7 +46,7 @@ public class JavaNaiveBayesExample {
       test.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
         @Override
         public Tuple2<Double, Double> call(LabeledPoint p) {
-          return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
+          return new Tuple2<>(model.predict(p.features()), p.label());
         }
       });
     double accuracy = predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaPCAExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaPCAExample.java
index faf76a9540..a42c29f52f 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaPCAExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaPCAExample.java
@@ -42,7 +42,7 @@ public class JavaPCAExample {
 
     // $example on$
     double[][] array = {{1.12, 2.05, 3.12}, {5.56, 6.28, 8.94}, {10.2, 8.0, 20.5}};
-    LinkedList<Vector> rowsList = new LinkedList<Vector>();
+    LinkedList<Vector> rowsList = new LinkedList<>();
     for (int i = 0; i < array.length; i++) {
       Vector currentRow = Vectors.dense(array[i]);
       rowsList.add(currentRow);
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaRandomForestClassificationExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaRandomForestClassificationExample.java
index f4c9d8a35d..24af5d0180 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaRandomForestClassificationExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaRandomForestClassificationExample.java
@@ -50,7 +50,7 @@ public class JavaRandomForestClassificationExample {
     // Train a RandomForest model.
     // Empty categoricalFeaturesInfo indicates all features are continuous.
     Integer numClasses = 2;
-    HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
+    HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<>();
     Integer numTrees = 3; // Use more in practice.
     String featureSubsetStrategy = "auto"; // Let the algorithm choose.
     String impurity = "gini";
@@ -67,7 +67,7 @@ public class JavaRandomForestClassificationExample {
       testData.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
         @Override
         public Tuple2<Double, Double> call(LabeledPoint p) {
-          return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
+          return new Tuple2<>(model.predict(p.features()), p.label());
         }
       });
     Double testErr =
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaRandomForestRegressionExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaRandomForestRegressionExample.java
index c71125ce41..afa9045878 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaRandomForestRegressionExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaRandomForestRegressionExample.java
@@ -51,7 +51,7 @@ public class JavaRandomForestRegressionExample {
 
     // Set parameters.
     // Empty categoricalFeaturesInfo indicates all features are continuous.
-    Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
+    Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<>();
     Integer numTrees = 3; // Use more in practice.
     String featureSubsetStrategy = "auto"; // Let the algorithm choose.
     String impurity = "variance";
@@ -67,7 +67,7 @@ public class JavaRandomForestRegressionExample {
       testData.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
         @Override
         public Tuple2<Double, Double> call(LabeledPoint p) {
-          return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
+          return new Tuple2<>(model.predict(p.features()), p.label());
         }
       });
     Double testMSE =
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaRecommendationExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaRecommendationExample.java
index 5e643420ad..f69aa4b75a 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaRecommendationExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaRecommendationExample.java
@@ -64,8 +64,7 @@ public class JavaRecommendationExample {
       model.predict(JavaRDD.toRDD(userProducts)).toJavaRDD().map(
         new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {
           public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r){
-            return new Tuple2<Tuple2<Integer, Integer>, Double>(
-              new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating());
+            return new Tuple2<>(new Tuple2<>(r.user(), r.product()), r.rating());
           }
         }
       ));
@@ -73,8 +72,7 @@ public class JavaRecommendationExample {
       JavaPairRDD.fromJavaRDD(ratings.map(
         new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {
           public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r){
-            return new Tuple2<Tuple2<Integer, Integer>, Double>(
-              new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating());
+            return new Tuple2<>(new Tuple2<>(r.user(), r.product()), r.rating());
           }
         }
       )).join(predictions).values();
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaSVDExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaSVDExample.java
index b417da8f85..3730e60f68 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaSVDExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaSVDExample.java
@@ -44,7 +44,7 @@ public class JavaSVDExample {
 
     // $example on$
     double[][] array = {{1.12, 2.05, 3.12}, {5.56, 6.28, 8.94}, {10.2, 8.0, 20.5}};
-    LinkedList<Vector> rowsList = new LinkedList<Vector>();
+    LinkedList<Vector> rowsList = new LinkedList<>();
     for (int i = 0; i < array.length; i++) {
       Vector currentRow = Vectors.dense(array[i]);
       rowsList.add(currentRow);
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaActorWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaActorWordCount.java
index 7bb70d0c06..7884b8cdff 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaActorWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaActorWordCount.java
@@ -129,7 +129,7 @@ public class JavaActorWordCount {
     }).mapToPair(new PairFunction<String, String, Integer>() {
       @Override
       public Tuple2<String, Integer> call(String s) {
-        return new Tuple2<String, Integer>(s, 1);
+        return new Tuple2<>(s, 1);
       }
     }).reduceByKey(new Function2<Integer, Integer, Integer>() {
       @Override
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java
index 3d668adcf8..5de56340c6 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java
@@ -82,7 +82,7 @@ public class JavaCustomReceiver extends Receiver<String> {
     JavaPairDStream<String, Integer> wordCounts = words.mapToPair(
       new PairFunction<String, String, Integer>() {
         @Override public Tuple2<String, Integer> call(String s) {
-          return new Tuple2<String, Integer>(s, 1);
+          return new Tuple2<>(s, 1);
         }
       }).reduceByKey(new Function2<Integer, Integer, Integer>() {
         @Override
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java
index 5107500a12..bfbad91e4f 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java
@@ -63,8 +63,8 @@ public final class JavaDirectKafkaWordCount {
     SparkConf sparkConf = new SparkConf().setAppName("JavaDirectKafkaWordCount");
     JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, Durations.seconds(2));
 
-    HashSet<String> topicsSet = new HashSet<String>(Arrays.asList(topics.split(",")));
-    HashMap<String, String> kafkaParams = new HashMap<String, String>();
+    HashSet<String> topicsSet = new HashSet<>(Arrays.asList(topics.split(",")));
+    HashMap<String, String> kafkaParams = new HashMap<>();
     kafkaParams.put("metadata.broker.list", brokers);
 
     // Create direct kafka stream with brokers and topics
@@ -95,7 +95,7 @@ public final class JavaDirectKafkaWordCount {
       new PairFunction<String, String, Integer>() {
         @Override
         public Tuple2<String, Integer> call(String s) {
-          return new Tuple2<String, Integer>(s, 1);
+          return new Tuple2<>(s, 1);
         }
       }).reduceByKey(
         new Function2<Integer, Integer, Integer>() {
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java
index 0df4cb40a9..655da6840c 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java
@@ -69,7 +69,7 @@ public final class JavaKafkaWordCount {
     JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new Duration(2000));
 
     int numThreads = Integer.parseInt(args[3]);
-    Map<String, Integer> topicMap = new HashMap<String, Integer>();
+    Map<String, Integer> topicMap = new HashMap<>();
     String[] topics = args[2].split(",");
     for (String topic: topics) {
       topicMap.put(topic, numThreads);
@@ -96,7 +96,7 @@ public final class JavaKafkaWordCount {
       new PairFunction<String, String, Integer>() {
         @Override
         public Tuple2<String, Integer> call(String s) {
-          return new Tuple2<String, Integer>(s, 1);
+          return new Tuple2<>(s, 1);
         }
       }).reduceByKey(new Function2<Integer, Integer, Integer>() {
         @Override
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java
index b82b319acb..5761da684b 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java
@@ -76,7 +76,7 @@ public final class JavaNetworkWordCount {
       new PairFunction<String, String, Integer>() {
         @Override
         public Tuple2<String, Integer> call(String s) {
-          return new Tuple2<String, Integer>(s, 1);
+          return new Tuple2<>(s, 1);
         }
       }).reduceByKey(new Function2<Integer, Integer, Integer>() {
         @Override
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaQueueStream.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaQueueStream.java
index 4ce8437f82..426eaa5f0a 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaQueueStream.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaQueueStream.java
@@ -50,7 +50,7 @@ public final class JavaQueueStream {
 
     // Create the queue through which RDDs can be pushed to
     // a QueueInputDStream
-    Queue<JavaRDD<Integer>> rddQueue = new LinkedList<JavaRDD<Integer>>();
+    Queue<JavaRDD<Integer>> rddQueue = new LinkedList<>();
 
     // Create and push some RDDs into the queue
     List<Integer> list = Lists.newArrayList();
@@ -68,7 +68,7 @@ public final class JavaQueueStream {
         new PairFunction<Integer, Integer, Integer>() {
           @Override
           public Tuple2<Integer, Integer> call(Integer i) {
-            return new Tuple2<Integer, Integer>(i % 10, 1);
+            return new Tuple2<>(i % 10, 1);
           }
         });
     JavaPairDStream<Integer, Integer> reducedStream = mappedStream.reduceByKey(
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaRecoverableNetworkWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaRecoverableNetworkWordCount.java
index f9929fc86d..a597ecbc5b 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaRecoverableNetworkWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaRecoverableNetworkWordCount.java
@@ -142,7 +142,7 @@ public final class JavaRecoverableNetworkWordCount {
       new PairFunction<String, String, Integer>() {
         @Override
         public Tuple2<String, Integer> call(String s) {
-          return new Tuple2<String, Integer>(s, 1);
+          return new Tuple2<>(s, 1);
         }
       }).reduceByKey(new Function2<Integer, Integer, Integer>() {
         @Override