[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.

2 files changed, 4 insertions, 4 deletions

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index c4d277f9bf..89fe873851 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -760,7 +760,7 @@ JavaRDD<String> people = sc.textFile("examples/src/main/resources/people.txt");
 String schemaString = "name age";
 
 // Generate the schema based on the string of schema
-List<StructField> fields = new ArrayList<StructField>();
+List<StructField> fields = new ArrayList<>();
 for (String fieldName: schemaString.split(" ")) {
   fields.add(DataTypes.createStructField(fieldName, DataTypes.StringType, true));
 }
@@ -1935,7 +1935,7 @@ val jdbcDF = sqlContext.read.format("jdbc").options(
 
 {% highlight java %}
 
-Map<String, String> options = new HashMap<String, String>();
+Map<String, String> options = new HashMap<>();
 options.put("url", "jdbc:postgresql:dbserver");
 options.put("dbtable", "schema.tablename");
 
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index e92b01aa77..998644f2e2 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -186,7 +186,7 @@ Next, we want to count these words.
 JavaPairDStream<String, Integer> pairs = 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);
     }
   });
 JavaPairDStream<String, Integer> wordCounts = pairs.reduceByKey(
@@ -2095,7 +2095,7 @@ unifiedStream.print()
 <div data-lang="java" markdown="1">
 {% highlight java %}
 int numStreams = 5;
-List<JavaPairDStream<String, String>> kafkaStreams = new ArrayList<JavaPairDStream<String, String>>(numStreams);
+List<JavaPairDStream<String, String>> kafkaStreams = new ArrayList<>(numStreams);
 for (int i = 0; i < numStreams; i++) {
   kafkaStreams.add(KafkaUtils.createStream(...));
 }