java8的stream流处理使用例子

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java8的stream流处理使用例子

java8的stream流常用例子

数据准备

		//准备map数据List<Map<String,String>> mapDatas = new ArrayList<>();for (int i = 0; i < 10; i++) {Map<String,String> map = new HashMap<>();if (i % 2 == 0){map.put("province","北京");map.put("listcount",""+i*Math.round(15f));map.put("index","count"+i);}else {map.put("province","上海");map.put("listcount",""+i*Math.round(15f));map.put("index","count"+i);}mapDatas.add(map);}//准备entity数据List<Student> studentList = new ArrayList<>();studentList.add(new Student("1231","典韦","男","2011-09-08","12379","刀"));studentList.add(new Student("1233","孙尚香","女","2012-09-08","12379","枪"));studentList.add(new Student("1232","李白","男","2010-09-08","12379","剑"));studentList.add(new Student("1234","王昭君","女","2010-09-08","12379","棍子"));

1. stream().forEach

		//1 stream().forEach的使用(对象list跟maplist是一样使用的)mapDatas.stream().forEach(map -> {//获取当前map的索引int curIndex = mapDatas.indexOf(map);System.err.println(map);});        

2. stream().filter

		//2 stream().filter 的用法 过滤出北京的数据List<Map<String, String>> filterDatas = mapDatas.stream().filter(map -> {return map.get("province").equals("北京");}).collect(Collectors.toList());System.err.println(filterDatas);结果:[{listcount=0, province=北京, index=count0}, {listcount=30, province=北京, index=count2}, {listcount=60, province=北京, index=count4}, {listcount=90, province=北京, index=count6}, {listcount=120, province=北京, index=count8}]

3. stream().map

		//3 stream().map 提取list对象的某个属性为list,也可以提取map的某个keyList<String> snames = studentList.stream().map(student -> {return student.getSname();}).collect(Collectors.toList());System.err.println(snames);结果:[典韦, 孙尚香, 李白, 王昭君]

4. stream().sorted

		//4 stream().sorted 根据某个属性排序// 升序// entity排序List<Student> sortStudents = studentList.stream().sorted(Comparatorparing(Student::getSno)).collect(Collectors.toList());System.err.println(sortStudents);System.err.println("-------------");// map 排序List<Map<String, String>> mapSortDatas = mapDatas.stream().sorted(Comparatorparing(map -> (map.get("index")))).collect(Collectors.toList());System.err.println(mapSortDatas);System.err.println("------------------");//降序// entity排序List<Student> sortStudents2 = studentList.stream().sorted((student1, student2) -> student2.getSno()pareTo(student1.getSno())).collect(Collectors.toList());System.err.println(sortStudents2);System.err.println("-------------");// map 排序List<Map<String, String>> mapSortDatas2 = mapDatas.stream().sorted((map1, map2) -> map2.get("index")pareTo(map1.get("index"))).collect(Collectors.toList());System.err.println(mapSortDatas2);结果:[Student{sno='1231', sname='典韦', sex='男', birthday='2011-09-08', phone='12379', dorm='刀'}, Student{sno='1232', sname='李白', sex='男', birthday='2010-09-08', phone='12379', dorm='剑'}, Student{sno='1233', sname='孙尚香', sex='女', birthday='2012-09-08', phone='12379', dorm='枪'}, Student{sno='1234', sname='王昭君', sex='女', birthday='2010-09-08', phone='12379', dorm='棍子'}]-------------[{listcount=0, province=北京, index=count0}, {listcount=15, province=上海, index=count1}, {listcount=30, province=北京, index=count2}, {listcount=45, province=上海, index=count3}, {listcount=60, province=北京, index=count4}, {listcount=75, province=上海, index=count5}, {listcount=90, province=北京, index=count6}, {listcount=105, province=上海, index=count7}, {listcount=120, province=北京, index=count8}, {listcount=135, province=上海, index=count9}]------------------[Student{sno='1234', sname='王昭君', sex='女', birthday='2010-09-08', phone='12379', dorm='棍子'}, Student{sno='1233', sname='孙尚香', sex='女', birthday='2012-09-08', phone='12379', dorm='枪'}, Student{sno='1232', sname='李白', sex='男', birthday='2010-09-08', phone='12379', dorm='剑'}, Student{sno='1231', sname='典韦', sex='男', birthday='2011-09-08', phone='12379', dorm='刀'}]-------------[{listcount=135, province=上海, index=count9}, {listcount=120, province=北京, index=count8}, {listcount=105, province=上海, index=count7}, {listcount=90, province=北京, index=count6}, {listcount=75, province=上海, index=count5}, {listcount=60, province=北京, index=count4}, {listcount=45, province=上海, index=count3}, {listcount=30, province=北京, index=count2}, {listcount=15, province=上海, index=count1}, {listcount=0, province=北京, index=count0}]

5. stream().collect 利用Collectors分组
list entity分组

list map 分组

分组过后排序(根据某一字段排序)

	list entity 分组排序

		Map<String, List<Student>> sortGroupStudent = studentList.stream().sorted(Comparatorparing(Student::getSno)).collect(Collectors.groupingBy(Student::getSex));System.err.println(sortGroupStudent);结果:{女=[Student{sno='1233', sname='孙尚香', sex='女', birthday='2012-09-08', phone='12379', dorm='枪'}, Student{sno='1234', sname='王昭君', sex='女', birthday='2010-09-08', phone='12379', dorm='棍子'}], 男=[Student{sno='1231', sname='典韦', sex='男', birthday='2011-09-08', phone='12379', dorm='刀'}, Student{sno='1232', sname='李白', sex='男', birthday='2010-09-08', phone='12379', dorm='剑'}]}

	list map 分组排序

		Map<String, List<Map<String, String>>> sortGroupMap = mapDatas.stream().sorted(Comparatorparing(map -> map.get("index"))).collect(Collectors.groupingBy(map -> (map.get("province"))));System.err.println(sortGroupMap);结果:{上海=[{listcount=15, province=上海, index=count1}, {listcount=45, province=上海, index=count3}, {listcount=75, province=上海, index=count5}, {listcount=105, province=上海, index=count7}, {listcount=135, province=上海, index=count9}], 北京=[{listcount=0, province=北京, index=count0}, {listcount=30, province=北京, index=count2}, {listcount=60, province=北京, index=count4}, {listcount=90, province=北京, index=count6}, {listcount=120, province=北京, index=count8}]}

分组过后根据key排序

entity分组根据key排序

       Map<String, List<GroundwaterPointEntity>> groupDatas = groundwaterPointEntities.stream().sorted(Comparatorparing(GroundwaterPointEntity::getPointtype)).collect(Collectors.toList()).stream().collect(Collectors.groupingBy(GroundwaterPointEntity::getPointtype,LinkedHashMap::new,Collectors.toList()));

	结果:		

groupingBy解析
类似于Collectors.groupingBy(GWYearMonitorPointDto::getPointtypename)这种
默认进来是这个方法

继续点到下一层

可以看到groupingBy默认给了一个hashmap返回数据结构
当用对象entity进行分组的时候可以用这种分组构造方法,传入LinkedHashMap进行排序

map分组根据key排序

LinkedHashMap<Serializable, List<Map<String, Serializable>>> pointtype = allGWLevelDataList.stream().collect(Collectors.groupingBy(temp -> temp.get("pointtype"))).entrySet().stream().sorted((o1, o2) -> {return String.valueOf(o1)pareTo(String.valueOf(o2));}).collect(Collectors.toMap(Map.Entry::getKey, Map.Entry::getValue, (oldvalue, newvalue) -> oldvalue, LinkedHashMap::new));

先将数据分组,然后再把分组后的数据进行排序利用Collectors.toMap
返回一个排序后的新map