优雅的将一个对象的集合转化成另一个对象的集合
List<OrderDetail> orderDetailList = orderDetailService.listOrderDetails();
List<CartDTO> cartDTOList = orderDetailList.stream()
.map(e -> new CartDTO(e.getProductId(), e.getProductQuantity()))
.collect(Collectors.toList());
交集 (list1 + list2)
List<T> intersect = list1.stream()
.filter(list2::contains)
.collect(Collectors.toList());
差集
//(list1 - list2)
List<String> reduce1 = list1.stream().filter(item -> !list2.contains(item)).collect(toList());
//(list2 - list1)
List<String> reduce2 = list2.stream().filter(item -> !list1.contains(item)).collect(toList());
并集
//使用并行流
List<String> listAll = list1.parallelStream().collect(toList());
List<String> listAll2 = list2.parallelStream().collect(toList());
listAll.addAll(listAll2);
去重并集
List<String> listAllDistinct = listAll.stream()
.distinct().collect(toList());
从List中过滤出一个元素
User match = users.stream()
.filter((user) -> user.getId() == 1).findAny().get();
Map集合转 List
List<Person> list = map.entrySet().stream().sorted(Comparator.comparing(e -> e.getKey()))
.map(e -> new Person(e.getKey(), e.getValue())).collect(Collectors.toList());
List<Person> list = map.entrySet().stream().sorted(Comparator.comparing(Map.Entry::getValue)).map(e -> new Person(e.getKey(), e.getValue())).collect(Collectors.toList());
List<Person> list = map.entrySet().stream().sorted(Map.Entry.comparingByKey()).map(e -> new Person(e.getKey(), e.getValue())).collect(Collectors.toList());
Collectors toList
streamArr.collect(Collectors.toList());
List<Integer> collectList = Stream.of(1, 2, 3, 4)
.collect(Collectors.toList());
System.out.println("collectList: " + collectList);
// 打印结果 collectList: [1, 2, 3, 4]
Collectors toMap
map value 为对象 student
Map<Integer, Student> map = list.stream().collect(Collectors.toMap(Student::getId, student -> student));
// 遍历打印结果
map.forEach((key, value) -> {
System.out.println("key: " + key + " value: " + value);
});
map value 为对象中的属性
Map<Integer, String> map = list.stream().collect(Collectors.toMap(Student::getId, Student::getName));
map.forEach((key, value) -> {
System.out.println("key: " + key + " value: " + value);
});
List集合转 Map
/*使用Collectors.toMap形式*/
Map result = peopleList.stream().collect(Collectors.toMap(p -> p.name, p -> p.age, (k1, k2) -> k1));
//其中Collectors.toMap方法的第三个参数为键值重复处理策略，如果不传入第三个参数，当有相同的键时，会抛出一个IlleageStateException。
//或者
Map<Integer, String> result1 = list.stream().collect(Collectors.toMap(Hosting::getId, Hosting::getName));
//List<People> -> Map<String,Object>
List<People> peopleList = new ArrayList<>();
peopleList.add(new People("test1", "111"));
peopleList.add(new People("test2", "222"));
Map result = peopleList.stream().collect(HashMap::new,(map,p)->map.put(p.name,p.age),Map::putAll);
List 转 Map<Integer,Apple>
/** * List<Apple> -> Map<Integer,Apple> * 需要注意的是： * toMap 如果集合对象有重复的key，会报错Duplicate key .... * apple1,apple12的id都为1。 * 可以用 (k1,k2)->k1 来设置，如果有重复的key,则保留key1,舍弃key2 */
Map<Integer, Apple> appleMap = appleList.stream().collect(Collectors.toMap(Apple::getId, a -> a,(k1, k2) -> k1));
List 转 List<Map<String,Object>>
List<Map<String,Object>> personToMap = peopleList.stream().map((p) -> {
Map<String, Object> map = new HashMap<>();
map.put("name", p.name);
map.put("age", p.age);
return map;
}).collect(Collectors.toList());
//或者
List<Map<String,Object>> personToMap = peopleList.stream().collect(ArrayList::new, (list, p) -> {
Map<String, Object> map = new HashMap<>();
map.put("name", p.name);
map.put("age", p.age);
list.add(map);
}, List::addAll);
字典查询和数据转换 toMap时，如果value为null,会报空指针异常
解决办法一：
Map<String, List<Dict>> resultMaps = Arrays.stream(dictTypes)
.collect(Collectors.toMap(i -> i, i -> Optional.ofNullable(dictMap.get(i)).orElse(new ArrayList<>()), (k1, k2) -> k2));
解决办法二：
Map<String, List<Dict>> resultMaps = Arrays.stream(dictTypes)
.filter(i -> dictMap.get(i) != null).collect(Collectors.toMap(i -> i, dictMap::get, (k1, k2) -> k2));
解决办法三：
Map<String, String> memberMap = list.stream().collect(HashMap::new, (m,v)->
m.put(v.getId(), v.getImgPath()),HashMap::putAll);
System.out.println(memberMap);
解决办法四：
Map<String, String> memberMap = new HashMap<>();
list.forEach((answer) -> memberMap.put(answer.getId(), answer.getImgPath()));
System.out.println(memberMap);
Map<String, String> memberMap = new HashMap<>();
for (Member member : list) {
memberMap.put(member.getId(), member.getImgPath());
}
假设有一个User实体类，有方法getId(),getName(),getAge()等方法，现在想要将User类型的流收集到一个Map中，示例如下：
Stream<User> userStream = Stream.of(new User(0, "张三", 18), new User(1, "张四", 19), new User(2, "张五", 19), new User(3, "老张", 50));
Map<Integer, User> userMap = userSteam.collect(Collectors.toMap(User::getId, item -> item));
假设要得到按年龄分组的Map<Integer,List>,可以按这样写：
Map<Integer, List<User>> ageMap = userStream.collect(Collectors.toMap(User::getAge, Collections::singletonList, (a, b) -> {
List<User> resultList = new ArrayList<>(a);
resultList.addAll(b);
return resultList;
}));
Map<Integer, String> map = persons
.stream()
.collect(Collectors.toMap(
p -> p.age,
p -> p.name,
(name1, name2) -> name1 + ";" + name2));
System.out.println(map);
// {18=Max, 23=Peter;Pamela, 12=David}
Map 转 另一个Map
//示例1 Map<String, List<String>> 转 Map<String,User>
Map<String,List<String>> map = new HashMap<>();
map.put("java", Arrays.asList("1.7", "1.8"));
map.entrySet().stream();
@Getter
@Setter
@AllArgsConstructor
public static class User{
private List<String> versions;
}
Map<String, User> collect = map.entrySet().stream()
.collect(Collectors.toMap(
item -> item.getKey(),
item -> new User(item.getValue())));
//示例2 Map<String,Integer> 转 Map<String,Double>
Map<String, Integer> pointsByName = new HashMap<>();
Map<String, Integer> maxPointsByName = new HashMap<>();
Map<String, Double> gradesByName = pointsByName.entrySet().stream()
.map(entry -> new AbstractMap.SimpleImmutableEntry<>(
entry.getKey(), ((double) entry.getValue() /
maxPointsByName.get(entry.getKey())) * 100d))
.collect(Collectors.toMap(Map.Entry::getKey, Map.Entry::getValue));
List<String> 转String
//java8 String.join 方式
List<String> webs = Arrays.asList("voidcc.com", "voidmvn.com", "voidtool.com");
//webs 必须是List<String>
String allwebs = String.join(",", webs);
System.out.println(allwebs);
//stream
List<String> webs = Arrays.asList("voidcc.com", "voidmvn.com", "voidtool.com");
String allwebs = webs.stream().collect(Collectors.joining(","));
System.out.println(allwebs);
Collectors toSet
Set<String> result = Stream.of("aa", "bb", "cc", "aa").collect(HashSet::new, HashSet::add, HashSet::addAll);
//Collectors类中已经预定义好了toList，toSet，toMap，toCollection等方便使用的方法，所以以上代码还可以简化如下：
Set<String> result2 = Stream.of("aa", "bb", "cc", "aa").collect(Collectors.toSet());
Set<Integer> collectSet = Stream.of(1, 2, 3, 4).collect(Collectors.toSet());
System.out.println("collectSet: " + collectSet);
// 打印结果 collectSet: [1, 2, 3, 4]
Stack stack1 = stream.collect(Collectors.toCollection(Stack::new));
// collect toString
String str = stream.collect(Collectors.joining()).toString();
排序
//按照自然顺序进行排序 如果要自定义排序sorted 传入自定义的 Comparator
list.stream()
.sorted()
.filter((s) -> s.startsWith("a"))
.forEach(System.out::println);
//对象排序比较 请重写对象的equals()和hashCode()方法
list.sorted((a, b) -> b.compareTo(a))
Collections.sort(names, (a, b) -> b.compareTo(a));
比较
Comparator<Person> comparator = (p1, p2) -> p1.firstName.compareTo(p2.firstName);
Person p1 = new Person("John", "Doe");
Person p2 = new Person("Alice", "Wonderland");
comparator.compare(p1, p2); // > 0
comparator.reversed().compare(p1, p2); // < 0
Collectors groupingBy 分组
Map<Integer, List<User>> ageMap2 = userStream
.collect(Collectors.groupingBy(User::getAge));
对集合按照多个属性分组
将多个字段拼接成一个新字段，然后再使用groupBy分组
Map<String, List<EntryDeliveryDetailywk>> detailmap = details.stream()
.collect(Collectors.groupingBy(this::fetchGroupKey));
private String fetchGroupKey(EntryDeliveryDetailywk detail){
return detail.getSkuId().toString()
+ detail.getItemsName()
+ detail.getWarehouseId().toString()
+ detail.getSupplierId().toString();
}
groupingBy 分组后操作
//Collectors中还提供了一些对分组后的元素进行downStream处理的方法：
//counting方法返回所收集元素的总数；
//summing方法会对元素求和；
//maxBy和minBy会接受一个比较器，求最大值，最小值；
//mapping函数会应用到downstream结果上，并需要和其他函数配合使用；
Map<Integer, Long> sexCount = userStream.collect(Collectors.groupingBy(User::getSex,Collectors.counting()));
Map<Integer, Integer> ageCount = userStream.collect(Collectors.groupingBy(User::getSex,Collectors.summingInt(User::getAge)));
Map<Integer, Optional<User>> ageMax = userStream.collect(Collectors.groupingBy(User::getSex,Collectors.maxBy(Comparator.comparing(User::getAge))));
Map<Integer, List<String>> nameMap = userStream.collect(Collectors.groupingBy(User::getSex,Collectors.mapping(User::getName,Collectors.toList())));
groupingBy 根据年龄来分组：
Map<Integer, List> peopleByAge = peoples.stream()
.filter(p -> p.age > 12).collect(Collectors.groupingBy(p -> p.age, Collectors.toList()));
groupingBy 根据年龄分组，年龄对应的键值List存储的为Person的姓名:
Map<Integer, List> peopleByAge = people.stream()
.collect(Collectors.groupingBy(p -> p.age, Collectors.mapping((Person p) -> p.name, Collectors.toList())));
//mapping即为对各组进行投影操作，和Stream的map方法基本一致。
groupingBy 根据姓名分组，获取每个姓名下人的年龄总和:
Map sumAgeByName = peoples.stream().collect(Collectors.groupingBy(p -> p.name, Collectors.reducing(0, (Person p) -> p.age, Integer::sum)));
/* 或者使用summingInt方法 */
sumAgeByName = peoples.stream().collect(Collectors.groupingBy(p -> p.name, Collectors.summingInt((Person p) -> p.age)));
groupingBy Boolean分组:
Map<Boolean, List<Integer>> collectGroup = Stream.of(1, 2, 3, 4)
.collect(Collectors.groupingBy(it -> it > 3));
System.out.println("collectGroup : " + collectGroup);
// 打印结果
// collectGroup : {false=[1, 2, 3], true=[4]}
groupingBy 按年龄分组
Map<Integer, List<Person>> personsByAge = persons.stream().collect(Collectors.groupingBy(p -> p.age));
personsByAge.forEach((age, p) -> System.out.format("age %s: %s\n", age, p));
// age 18: [Max]
// age 23: [Peter, Pamela]
// age 12: [David]
Map.merge() 类似于分组之后sum
Map<String, Integer> studentScoreMap2 = new HashMap<>();
studentScoreList.forEach(studentScore -> studentScoreMap2.merge(
studentScore.getStuName(),
studentScore.getScore(),
Integer::sum));
Collectors partitioningBy
Collectors中还提供了partitioningBy方法，接受一个Predicate函数，该函数返回boolean值，用于将内容分为两组。假设User实体中包含性别信息getSex()，可以按如下写法将userStream按性别分组：
Map<Boolean, List<User>> sexMap = userStream
.collect(Collectors.partitioningBy(item -> item.getSex() > 0));
可以看到Java8的分组功能相当强大，当然你还可以完成更复杂的功能。另外Collectors中还存在一个类似groupingBy的方法：partitioningBy，它们的区别是partitioningBy为键值为Boolean类型的groupingBy，这种情况下它比groupingBy更有效率。
partitioningBy 将数字的Stream分解成奇数集合和偶数集合。
Map<Boolean, List<Integer>> collectParti = Stream.of(1, 2, 3, 4)
.collect(Collectors.partitioningBy(it -> it % 2 == 0));
System.out.println("collectParti : " + collectParti);
// 打印结果
// collectParti : {false=[1, 3], true=[2, 4]}
Collectors joining
Collectors.joining 收集Stream中的值，该方法可以方便地将Stream得到一个字符串。joining函数接受三个参数，分别表示允（用以分隔元素）、前缀和后缀:
String names = peoples.stream().map(p->p.name).collect(Collectors.joining(","))
String strJoin = Stream.of("1", "2", "3", "4")
.collect(Collectors.joining(",", "[", "]"));
System.out.println("strJoin: " + strJoin);
// 打印结果
// strJoin: [1,2,3,4]
//字符串连接
String phrase = persons
.stream()
.filter(p -> p.age >= 18)
.map(p -> p.name)
.collect(Collectors.joining(" and ", "In Germany ", " are of legal age."));
System.out.println(phrase);
// In Germany Max and Peter and Pamela are of legal age.
组合 Collectors:
Map<Boolean, Long> partiCount = Stream.of(1, 2, 3, 4)
.collect(Collectors.partitioningBy(it -> it.intValue() % 2 == 0,
Collectors.counting()));
System.out.println("partiCount: " + partiCount);
// 打印结果
// partiCount: {false=2, true=2}
Collectors分别提供了求平均值averaging、总数couting、最小值minBy、最大值maxBy、求和suming等操作。但是假如你希望将流中结果聚合为一个总和、平均值、最大值、最小值，那么Collectors.summarizing(Int/Long/Double)就是为你准备的，它可以一次行获取前面的所有结果，其返回值为(Int/Long/Double)SummaryStatistics。
DoubleSummaryStatistics dss = people.collect(Collectors.summarizingDouble((Person p)->p.age));
double average=dss.getAverage();
double max=dss.getMax();
double min=dss.getMin();
double sum=dss.getSum();
double count=dss.getCount();
IntSummaryStatistics ageSummary = persons
.stream()
.collect(Collectors.summarizingInt(p -> p.age));
System.out.println(ageSummary);
// IntSummaryStatistics{count=4, sum=76, min=12, average=19.000000, max=23}
使用collect可以将Stream转换成值。maxBy和minBy允许用户按照某个特定的顺序生成一个值。
averagingDouble:求平均值，Stream的元素类型为double
averagingInt:求平均值，Stream的元素类型为int
averagingLong:求平均值，Stream的元素类型为long
counting:Stream的元素个数
maxBy:在指定条件下的，Stream的最大元素
minBy:在指定条件下的，Stream的最小元素
reducing: reduce操作
summarizingDouble:统计Stream的数据(double)状态，其中包括count，min，max，sum和平均。
summarizingInt:统计Stream的数据(int)状态，其中包括count，min，max，sum和平均。
summarizingLong:统计Stream的数据(long)状态，其中包括count，min，max，sum和平均。
summingDouble:求和，Stream的元素类型为double
summingInt:求和，Stream的元素类型为int
summingLong:求和，Stream的元素类型为long
Optional<Integer> collectMaxBy = Stream.of(1, 2, 3, 4)
.collect(Collectors.maxBy(Comparator.comparingInt(o -> o)));
System.out.println("collectMaxBy:" + collectMaxBy.get());
// 打印结果
// collectMaxBy:4
Collectors averagingInt
计算集合的平均年龄
Double averageAge = persons
.stream()
.collect(Collectors.averagingInt(p -> p.age));
System.out.println(averageAge); // 19.0
自定义 Collector
Collector<Person, StringJoiner, String> personNameCollector =
Collector.of(
() -> new StringJoiner(" | "), // supplier
(j, p) -> j.add(p.name.toUpperCase()), // accumulator
(j1, j2) -> j1.merge(j2), // combiner
StringJoiner::toString); // finisher
String names = persons
.stream()
.collect(personNameCollector);
System.out.println(names); // MAX | PETER | PAMELA | DAVID
本站声明:网站内容来源于网络,如有侵权,请联系我们https://www.qiquanji.com,我们将及时处理。
微信扫码关注
更新实时通知