Java Stream 流常用方法 lambda 表达式实现交集、并集、差集、去重复并集等

古城微笑少年丶 2024-04-06 13:15 26阅读 0赞

### 一般的javaList 交、[并集][Link 1]采用简单的 removeAll retainAll 等操作，不过这也破坏了原始的javaList对象，采用java8 lambda表达式流操作则可以不影响原始list对象而得到两个javaList对象的 交、并、差集。 ###

### 1、分组 ###

// 按照sn分组:  List<Map<String, Object>> dataList
    Map<String, List<Map<String, Object>>> dataMap = dataList.stream().collect(Collectors.groupingBy(e -> e.get("sn") + ""));	
    
    //按照职员部分分组: List<Employee> list
    Map<String, List<Employee>> collect = list.stream().collect(Collectors.groupingBy(i -> i.getUnitName()));
    
    //多条件分组
    Map<String, Map<String,List<Employee>>> collect =list.stream().collect(Collectors.groupingBy(i -> i.getUnitName(),Collectors.groupingBy(i -> i.getWorkType())));
    
    //按年龄分组，年龄相同的是一组
    Map<Integer, List<Person>> 分组 = list.stream().collect(Collectors.groupingBy(Person::getAge));
    
    //按年龄分组后按工资分组，多级分组
    Map<Integer, Map<String, List<Person>>> 多级分组 = list.stream().collect(Collectors.groupingBy(Person::getAge, Collectors.groupingBy(x -> {
    	return x.getSalary() > 3000 ? "高" : "低";
    })));
     
    // 分组排序 ，拿已经排好序的过来分组
    LinkedHashMap<String, List<AttendanceRuleGroup>> groupingByruleGroupList = list.stream().collect(Collectors.groupingBy(AttendanceRuleGroup::getCategory, LinkedHashMap::new, Collectors.toList()));
    
    // 分组排序，集合没排序，我们自己按我们想要的排序
    LinkedHashMap<String, List<AttendanceRuleGroup>> groupingByruleGroupList = list.stream().sorted(Comparator.comparingLong(AttendanceRuleGroup::getSort).reversed()).collect(Collectors.groupingBy(AttendanceRuleGroup::getCategory, LinkedHashMap::new, Collectors.toList()));

### 2、过滤 ###

//根据指定sn,过滤出符合的数据: List<Map<String, Object>> deviceDataList
    List<Map<String, Object>> tempDeviceDataList = deviceDataList.stream().filter(map -> map.get("sn").toString().equals(sn)).collect(Collectors.toList());
    
    //筛选出工资大于10000的职员
    List<Employee> newList = list.stream().filter(item -> {
    			return item.getSalary().compareTo(new BigDecimal(10000)) > 0 && !item.getWorkType().equals("项目经理");
    		}).collect(Collectors.toList());

### 3、List map互转 ###

// (k1,k2)->k2 避免键重复 k1-取第一个数据；k2-取最后一条数据
    //key和value,都可以根据传入的值返回不同的Map
    Map<String, String> deviceMap = list.stream().collect(Collectors.toMap(i -> i.getDeviceNum(), j -> j.getDeviceName(), (k1, k2) -> k1));
    
    Map<String, Object> map = list.stream().collect(Collectors.toMap(i -> i.getEmpName() + i.getUnitName(), j -> j, (k1, k2) -> k1));
    
    //在.map里面构造数据 return什么数据就转成什么类型的list
    List<Employee> collect = map.entrySet().stream().map(item -> {
    			Employee employee = new Employee();
    			employee.setId(item.getKey());
    			employee.setEmpName(item.getValue());
    			return employee;
    		}).collect(Collectors.toList());

### 4、求和/极值 ###

//在egyList里面求cols的和
    public static BigDecimal getSumBig(List<Map<String,Object>> egyList, String cols){
            BigDecimal consuBig = egyList.stream()
                    .filter((Map m)->StringUtils.isNotEmpty(m.get(cols)+"") && !"null".equals(String.valueOf(m.get(cols)))
                            && !"-".equals(String.valueOf(m.get(cols))))
                    .map((Map m)->new BigDecimal(m.get(cols)+""))
                    .reduce(BigDecimal.ZERO,BigDecimal::add);
            return consuBig;
    }
    
    //Bigdecimal求和/极值: 
    BigDecimal sum = list.stream().map(Employee::getSalary).reduce(BigDecimal.ZERO,BigDecimal::add);
    BigDecimal max = list.stream().map(Employee::getSalary).reduce(BigDecimal.ZERO,BigDecimal::max);
    
    //基本数据类型求和/极值:
    Integer sum = list.stream().mapToInt(Employee::getId).sum();
    Long sum = list.stream().mapToLong(Employee::getId).sum();
    Double sum = list.stream().mapToDouble(Employee::getId).sum();
    
    OptionalInt optionalMax = list.stream().mapToInt(Employee::getId).max();
    optionalMax.getAsInt();

### 5、求最大/最小值的对象 ###

Optional<Employee> optional = list.stream().collect(Collectors.maxBy(Comparator.comparing(Employee::getId)));
     if (optional.isPresent()) { // 判断是否有值
     		Employee user = optional.get();
     }
    return optional.orElse(new Employee());

### 6、去重 ###

List<String> list1 = new ArrayList();
    list1.add("1111");
    list1.add("2222");
    list1.add("3333");
    
    List<String> list2 = new ArrayList();
    list2.add("3333");
    list2.add("4444");
    list2.add("5555");
    
    // 交集
    List<String> intersection = list1.stream().filter(item -> list2.contains(item)).collect(toList());
    System.out.println("---得到交集 intersection---");
    intersection.parallelStream().forEach(System.out :: println);
    
    // 差集 (list1 - list2)
    List<String> reduce1 = list1.stream().filter(item -> !list2.contains(item)).collect(toList());
    System.out.println("---得到差集 reduce1 (list1 - list2)---");
    reduce1.parallelStream().forEach(System.out :: println);
    
    // 差集 (list2 - list1)
    List<String> reduce2 = list2.stream().filter(item -> !list1.contains(item)).collect(toList());
    System.out.println("---得到差集 reduce2 (list2 - list1)---");
    reduce2.parallelStream().forEach(System.out :: println);
    
    // 并集
    List<String> listAll = list1.parallelStream().collect(toList());
    List<String> listAll2 = list2.parallelStream().collect(toList());
    listAll.addAll(listAll2);
    System.out.println("---得到并集 listAll---");
    listAll.parallelStream().forEach(System.out :: println);
    
    // 去重并集
    List<String> listAllDistinct = listAll.stream().distinct().collect(toList());
    System.out.println("---得到去重并集 listAllDistinct---");
    listAllDistinct.parallelStream().forEach(System.out :: println);
    
    System.out.println("---原来的List1---");
    list1.parallelStream().forEach(System.out :: println);
    System.out.println("---原来的List2---");
    list2.parallelStream().forEach(System.out :: println);
    
    //去重之后进行拼接
    Srting deviceNodeStr = list.stream().distinct().collect(Collectors.joining("','"));
    //直接去重返回list
    List<String> deviceIdList = list.stream().distinct().collect(Collectors.toList());

### 7、排序 ###

//按照时间排序 1升 -1降
    Collections.sort(listFast, (p1, p2) -> {
         return String.valueOf(p1.get("time")).compareTo(p2.get("time") + "");
    });
    
    // s1-s2 升序   s2-s1降序
    Collections.sort(list,(s1,s2) -> s1.getSalary().compareTo(s2.getSalary()));
    
    //多条件排序: List<Employee> list, s1-s2 升序   s2-s1降序
    list.sort(Comparator.comparing(Employee::getSalary).reversed().thenComparing(Employee::getId).reversed());

### 8、拼接 ###

//将某个字段,按照某个字符串拼接
    String sns = mapList.stream()
         	.map((m)->m.get("sn")+"").collect(Collectors.joining(","));
    //使用场景很多,在sql里面用于组织in的值.比如:
    SELECT sn,time,value FROM electric_real_time WHERE FIND_IN_SET(sn,?)
    List<Map<String, Object>> dataList = JdbcUtil.getJdbcTemplate().queryForList(dataSql, sns)
    
    List<String> strs = Arrays.asList("a","b","cd");
    
    //连接所有内容
    String str = strs.stream().collect(Collectors.joining());
    System.out.println(str);
    //输出：abcd
    
    //连接所有内容,中间加一个逗号隔开
    String str1 = strs.stream().collect(Collectors.joining(","));
    System.out.println(str1);
    //输出：a,b,cd
    
    //连接所有内容,中间加一个逗号隔开，两边加上括号
    String str2 = strs.stream().collect(Collectors.joining(",","(",")"));
    System.out.println(str2);
    //输出：(a,b,cd)

### 9、统计 ###

//统计：和、数量、最大值、最小值、平均值: List<Employee> list
    IntSummaryStatistics collect = list.stream().collect(Collectors.summarizingInt(Employee::getId));
    System.out.println("和：" + collect.getSum());
    System.out.println("数量：" + collect.getCount());
    System.out.println("最大值：" + collect.getMax());
    System.out.println("最小值：" + collect.getMin());
    System.out.println("平均值：" + collect.getAverage());
    
    //平均值
    OptionalDouble average = list.stream().mapToInt(Employee::getId).average();
    average.getAsDouble();
    
    //分组后求每个分组数量
    Map<Integer, Long> collect = list.stream().collect(Collectors.groupingBy(Student::getId, Collectors.counting()));
    
    //分组后求某个字段值的和
    Map<Integer, Integer> collect = list.stream().collect(Collectors.groupingBy(Student::getId, Collectors.summingInt(Student::getScore)));
    
    // 某个值的数量
    Map<BigDecimal, Long> collect = list.stream().collect(Collectors.groupingBy(i -> i.getSalary(),Collectors.counting()));
    long count = mapList.stream()
         .filter((Map m)->StringUtils.isNotEmpty(m.get(cols)+""))
         .map((Map m)->new BigDecimal(m.get(cols)+""))
         .count();

## 10、分区 ##

//单层分区
    Map<Boolean, List<Employee>> collect = list.stream().collect(Collectors.partitioningBy(i -> i.getId() == 1));
    
    //分区 满足条件的一个区，不满足条件的一个区
    Map<Boolean, List<Person>> collect1 = list.stream().collect(Collectors.partitioningBy(e -> e.getSalary() < 2000));
    
    //多层分区
    Map<Boolean, Map<Boolean,List<Employee>>> collect = list.stream().collect(Collectors.partitioningBy(i -> i.getId() == 1,Collectors.partitioningBy(i -> i.getSalary().compareTo(new BigDecimal(20000)) == 0)));

## 11、截断 ##

List<Integer> list = Arrays.asList(1,2,3,4,5,6,7,8);
    
    //中间操作：不会执行任何操作
    Stream<Integer> stream = list.stream()
    		.filter(e -> {
    			System.out.println("过滤 中间操作");
    			return e>3;
    		})
    		.limit(2);
    
    //终止操作：一次性执行全部内容，惰性求值
    stream.forEach(System.out::println);

## 12、跳过 ##

List<Integer> list = Arrays.asList(1,2,3,4,5,6,7,8);
    
    //中间操作：不会执行任何操作
    Stream<Integer> stream = list.stream().skip(5);
    
    //终止操作：一次性执行全部内容，惰性求值
    stream.forEach(System.out::println);

## 13、查找与匹配 ##

List<Person> list = Arrays.asList(
    		new Person(18,3939),
    		new Person(38,9999),
    		new Person(17,9999),
    		new Person(19,9988),
    		new Person(38,99)
    );
    
    //是否匹配所有元素 此处返回false
    boolean b = list.stream().allMatch(e -> e.getAge() == 18);
    System.out.println(b);
    
    //至少匹配一个元素，此处返回true
    boolean b1 = list.stream().anyMatch(e -> e.getAge() == 19);
    System.out.println(b1);
    
    //流中是否没有匹配元素，此处返回false
    boolean b2 = list.stream().noneMatch(e -> e.getAge() == 19);
    System.out.println(b2);
    
    //排序后获取第一个元素
    Optional<Person> first = list.stream().sorted((x, y) -> x.getAge().compareTo(y.getAge())).findFirst();
    System.out.println(first);
    
    //获取流中任意一个元素
    list.stream().findAny();
    
    //返回流中元素的总个数
    list.stream().count();
    
    //返回流中最大值 此处根据年龄比较
    Optional<Person> max = list.stream().max((x, y) -> x.getAge().compareTo(y.getAge()));
    System.out.println(max.get());
    
    //返回流中最小值 此处根据年龄比较
    Optional<Person> min = list.stream().min((x, y) -> x.getAge().compareTo(y.getAge()));
    System.out.println(min.get());
    
    //获取最小的年龄
    Optional<Integer> age = list.stream().map(Person::getAge).min(Integer::compareTo);
    System.out.println(age.get());
    
    
    //获取一个并行流，并行流会使用多个线程操作流，stream()获取的是串行流，单个线程操作流
    list.parallelStream();
    
    //查找第一个元素
    Optional<Dish> collect = menu.stream().filter(dish -> dish.getCalories() > 1000).findFrist();

## 14、收集 ##

//取出所有年龄放到list集合中
    List<Integer> toList = list.stream().map(Person::getAge).collect(Collectors.toList());
    
    //取出所有年龄放到set集合中
    Set<Integer> toSet = list.stream().map(Person::getAge).collect(Collectors.toSet());
    
    //取出所有年龄放到hashSet集合中
    HashSet<Integer> toHashSet = list.stream().map(Person::getAge).collect(Collectors.toCollection(HashSet::new));
    
    //获取集合中元素总和
    Long count = list.stream().collect(Collectors.counting());
    
    //获取年龄平均值
    Double avg = list.stream().collect(Collectors.averagingInt(Person::getAge));
    
    //获取工资总和
    Double sum = list.stream().collect(Collectors.summingDouble(Person::getSalary));
    
    //获取工资最大值的人
    Optional<Person> max = list.stream().collect(Collectors.maxBy((p1, p2) -> Double.compare(p1.getSalary(), p2.getSalary())));
    System.out.println(max.get());
    
    //获取工资最小值的人
    Optional<Person> min = list.stream().collect(Collectors.minBy((p1, p2) -> Double.compare(p1.getSalary(), p2.getSalary())));
    System.out.println(min.get());
    
    //获取元素个数、总和、最小值、平均值、最大值
    DoubleSummaryStatistics collect = list.stream().collect(Collectors.summarizingDouble(Person::getSalary));
    System.out.println(collect);
    //输出结果:DoubleSummaryStatistics{count=5, sum=34024.000000, min=99.000000, average=6804.800000, max=9999.000000}

[Link 1]: https://so.csdn.net/so/search?q=%E5%B9%B6%E9%9B%86&spm=1001.2101.3001.7020