Stream of new features of jdk8

The newly added Stream API in Java 8 really introduces the functional programming style into Java, which is somewhat similar to the syntax of Mongodb

That is, the data of the operation can be regarded as a stream, the stream is transmitted in the pipeline, processed on the node, filtered, sorted, aggregated and so on.

Data source: The source of the stream, which can be a collection (List, Set), array, I/O, generator, etc.

Aggregation operations: some operations similar to SQL, such as: filter, map, find, sorted, match, etc.

Two important features of Stream:

1.Pipeling 中间操作都会返回流对象本身。这样多个操作串联成一个管道，如同流式风格
2.内部迭代：以前集合遍历都是Iterator或者Foreach等方式进行的外部迭代，Stream流提供了内部迭代的方式，通过访问者模式实现。

Creation of Stream stream: There are two ways to create stream stream:

    1.stream()  为集合创建串行流
    2.parallelStream() 为集合创建并行流

forEach iterates:

List<String> list = Arrays.asList("mysql", "java8", "tomcat", "spring", "mybatis", "springBoot");

list.stream().forEach(System.out::println);

遍历list的内容并进行打印

filter filters the elements of the stream by the filter conditions

List<String> list = Arrays.asList("mysql", "java8", "tomcat", "spring", "mybatis", "springBoot");

List<String> list = list.stream().filter(string->string.endsWith("s")).collect(Collectors.toList());

过滤以s结尾的元素

int count = list.stream().filter(string->string.endsWith("s")).collect(Collectors.toList()).count();

获取以s结尾元素的数量

map is used to map to the result corresponding to each element

 List<Integer> intList = Arrays.asList(12, 11, 20, 20, 20, 20, 4, 9, 99, 35);

List<Integer> list = intList.stream().map(i -> i + 10).distinct().collect(Collectors.toList());

对集合中每个元素进行+10操作

limit specifies the number of returned results

List<Integer> intList = Arrays.asList(12, 11, 20, 20, 20, 20, 4, 9, 99, 35);

List<Integer> list = intList.stream()limit(5).map(i -> i + 10).distinct().collect(Collectors.toList());

对集合每个元素+10操作，返回5条

sorted sorts the processed elements

List<Integer> intList = Arrays.asList(120, 11, 20, 5, 20, 20, 4, 9, 99, 35);

List<Integer> list = intList.stream()limit(5).sorted().map(i -> i + 10).distinct().collect(Collectors.toList());

对集合每个元素+10操作，返回5条,并且返回的结果进行排序

parallelStream parallel processing

  List<String> list1 = Arrays.asList("2","","333","","","323","fa");

  long emptyCount = list1.parallelStream().filter(i->i.isEmpty()).count();
  
  获取空字符串的数量

Collectors implement many reduction operations, such as converting streams to sets or strings, etc.

List<String> list = list.stream().filter(string->string.endsWith("s")).collect(Collectors.toList());
将流转化为List

String result = list.stream().limit(5).sorted().filter(i -> i.length() > 5).collect(Collectors.joining(", "));

将流转化为字符串

Statistics are generally used for numerical types such as int double long float and other elements in the collection to perform statistics on them.

    List<Integer> intList = Arrays.asList(12, 11, 20, 20, 20, 20, 4, 9, 99, 35);

    IntSummaryStatistics sum = intList.stream().mapToInt((x) -> x).summaryStatistics();

    System.out.println(sum);
    System.out.println(sum.getMax());
    System.out.println(sum.getAverage());
    System.out.println(sum.getMin());
    System.out.println(sum.getCount());

Example of use:

        List<ChapterEntity> chapterList = chapterMapper.findList(subjectId);
                
        RelationKnowledge condition = new RelationKnowledge(knowledgeId, subjectId, knowledgeName);
        List<RelationKnowledge> list = relationMapper.getKnowledgeListBySubject(condition);

        chapterList.stream().forEach(chapter -> {
            List<RelationKnowledge> tempList = list.stream().filter(relationKnowledge ->
                    chapter.getId().equals(relationKnowledge.getChapterId())
            ).collect(Collectors.toList());

            chapter.setRelationKnowledgeList(tempList);
        });

Stream of new features of jdk8

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