一般的javaList 交、并集采用简单的 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> dataList
Map>> dataMap = dataList.stream().collect(Collectors.groupingBy(e -> e.get("sn") + ""));
//按照职员部分分组: List list
Map> collect = list.stream().collect(Collectors.groupingBy(i -> i.getUnitName()));
//多条件分组
Map>> 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> 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>> 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> 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> 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> deviceDataList
List> 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 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 deviceMap = list.stream().collect(Collectors.toMap(i -> i.getDeviceNum(), j -> j.getDeviceName(), (k1, k2) -> k1));
Map 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 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> 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 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 list1 = new ArrayList();
list1.add("1111");
list1.add("2222");
list1.add("3333");
List list2 = new ArrayList();
list2.add("3333");
list2.add("4444");
list2.add("5555");
// 交集
List 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 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 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 listAll = list1.parallelStream().collect(toList());
List 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 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 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 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> dataList = JdbcUtil.getJdbcTemplate().queryForList(dataSql, sns)
List 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 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 collect = list.stream().collect(Collectors.groupingBy(Student::getId, Collectors.counting()));
//分组后求某个字段值的和
Map collect = list.stream().collect(Collectors.groupingBy(Student::getId, Collectors.summingInt(Student::getScore)));
// 某个值的数量
Map 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> 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> collect1 = list.stream().collect(Collectors.partitioningBy(e -> e.getSalary() < 2000));
//多层分区
Map>> collect = list.stream().collect(Collectors.partitioningBy(i -> i.getId() == 1,Collectors.partitioningBy(i -> i.getSalary().compareTo(new BigDecimal(20000)) == 0)));
11、截断
List list = Arrays.asList(1,2,3,4,5,6,7,8);
//中间操作：不会执行任何操作
Stream 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 list = Arrays.asList(1,2,3,4,5,6,7,8);
//中间操作：不会执行任何操作
Stream 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 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 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 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 min = list.stream().min((x, y) -> x.getAge().compareTo(y.getAge()));
System.out.println(min.get());
//获取最小的年龄
Optional 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 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 toList = list.stream().map(Person::getAge).collect(Collectors.toList());
//取出所有年龄放到set集合中
Set toSet = list.stream().map(Person::getAge).collect(Collectors.toSet());
//取出所有年龄放到hashSet集合中
HashSet 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 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 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}
