diff options
author | Dongjoon Hyun <dongjoon@apache.org> | 2016-03-09 10:31:26 +0000 |
---|---|---|
committer | Sean Owen <sowen@cloudera.com> | 2016-03-09 10:31:26 +0000 |
commit | c3689bc24e03a9471cd6e8169da61963c4528252 (patch) | |
tree | 5d1ee90afa2087ede8e4dbc4dd666d699578c230 /examples/src/main/java | |
parent | cbff2803ef117d7cffe6f05fc1bbd395a1e9c587 (diff) | |
download | spark-c3689bc24e03a9471cd6e8169da61963c4528252.tar.gz spark-c3689bc24e03a9471cd6e8169da61963c4528252.tar.bz2 spark-c3689bc24e03a9471cd6e8169da61963c4528252.zip |
[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.
Diffstat (limited to 'examples/src/main/java')
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 |