JDK1.8之stream用法

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());

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 = ruleGroupList.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 = ruleGroupList.stream().sorted(Comparator.comparingLong(AttendanceRuleGroup::getSort).reversed()).collect(Collectors.groupingBy(AttendanceRuleGroup::getCategory, LinkedHashMap::new, Collectors.toList()));

分区

//List<Employee> list

//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)));

拼接

//将某个字段,按照某个字符串拼接: List<Map<String, Object>> deviceMapList

String sns = deviceMapList.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)

list转map

// (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 = hecmEnergyDevicesList.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));

map转list

//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());

去重

//去重之后进行拼接: List<String> deviceNodeList

Srting deviceNodeStr = deviceNodeList.stream().distinct().collect(Collectors.joining("','"));

//直接去重返回list

// List<String> deviceIdList

List<String> deviceIdList = deviceIdList.stream().distinct().collect(Collectors.toList());

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());

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;

}

//List<Employee> list

//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();

OptionalInt optionalMax = list.stream().mapToInt(Employee::getId).max();

optionalMax.getAsInt();

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());

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());

平均值

OptionalDouble average = list.stream().mapToInt(Employee::getId).average();

average.getAsDouble();

某个值的数量

//List<Employee> list

Map<BigDecimal, Long> collect = list.stream().collect(Collectors.groupingBy(i -> i.getSalary(),Collectors.counting()));

//List<Map<String,Object>> egyList

long count = egyList.stream()

.filter((Map m)->StringUtils.isNotEmpty(m.get(cols)+""))

.map((Map m)->new BigDecimal(m.get(cols)+""))

.count();

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}

全部转大写

List<String> list = Arrays.asList("a","vvv","ddd");

//中间操作：不会执行任何操作

Stream<String> stream = list.stream()

.map(x -> x.toUpperCase());

//终止操作：一次性执行全部内容，惰性求值

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

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();

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);

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);