java8中Collectors的方法：
- toCollection
- toList
- toSet
- toMap
- joining
- mapping/flatMapping
- filtering
- collectingAndThen
- counting
- minBy
- maxBy
- summingInt/summingLong/summingDouble
- averagingInt/averagingLong/averagingDouble
- groupingBy
- groupingByConcurrent
- partitioningBy
- BinaryOperator
- summarizingInt
toCollection
此函数返回一个收集器，它将输入元素累积到一个集合中。
List strList = Arrays.asList("a", "b", "c", "b", "a");
// toCollection()
Collection strCollection = strList.parallelStream().collect(Collectors.toCollection(HashSet::new));
System.out.println(strCollection); // [a, b, c]
Set strSet = strList.parallelStream().collect(Collectors.toCollection(HashSet::new));
System.out.println(strSet); // [a, b, c]
List strList1 = strList.parallelStream().sorted(String::compareToIgnoreCase)
.collect(Collectors.toCollection(ArrayList::new));
System.out.println(strList1); // [a, a, b, b, c]
toList()
返回一个收集器，它将输入元素累积到一个新的List中。
List strList = Arrays.asList("a", "b", "c", "b", "a");
List uppercaseList = strList.parallelStream().map(String::toUpperCase).collect(Collectors.toList());
System.out.println(uppercaseList); // [A, B, C, B, A]
List uppercaseUnmodifiableList = strList.parallelStream().map(String::toUpperCase)
.collect(Collectors.toUnmodifiableList());
System.out.println(uppercaseUnmodifiableList); // [A, B, C, B, A]
toSet()
List strList = Arrays.asList("a", "b", "c", "b", "a");
Set uppercaseSet = strList.parallelStream().map(String::toUpperCase).collect(Collectors.toSet());
System.out.println(uppercaseSet); // [A, B, C]
Set uppercaseUnmodifiableSet = strList.parallelStream().map(String::toUpperCase)
.collect(Collectors.toUnmodifiableSet());
System.out.println(uppercaseUnmodifiableSet); // [A, B, C]
toMap
映射生成map
Map map = Stream.of("a", "b", "c")
.collect(Collectors.toMap(Function.identity(), String::toUpperCase));
System.out.println(map); // {a=A, b=B, c=C}
// Duplicate Keys will throw: Exception in thread "main"
// java.lang.IllegalStateException: Duplicate key a (attempted merging values A
// and A)
Map mapD = Stream.of("a", "b", "c", "b", "a")
.collect(Collectors.toMap(Function.identity(), String::toUpperCase, String::concat));
System.out.println(mapD); // {a=AA, b=BB, c=C}
// above are HashMap, use below to create different types of Map
TreeMap mapTree = Stream.of("a", "b", "c", "b")
.collect(Collectors.toMap(Function.identity(), String::toUpperCase, String::concat, TreeMap::new));
System.out.println(mapTree); {a=A, b=BB, c=C}
joining
连接字符串
String concat = Stream.of("a", "b").collect(Collectors.joining());
System.out.println(concat); // ab
String csv = Stream.of("a", "b").collect(Collectors.joining(","));
System.out.println(csv); // a,b
String csv1 = Stream.of("a", "b").collect(Collectors.joining(",", "[", "]"));
System.out.println(csv1); // [a,b]
String csv2 = Stream.of("a", new StringBuilder("b"), new StringBuffer("c")).collect(Collectors.joining(","));
System.out.println(csv2); // a,b
mapping/flatMapping
它将Function应用于输入元素，然后将它们累积到给定的Collector
Set setStr = Stream.of("a", "a", "b")
.collect(Collectors.mapping(String::toUpperCase, Collectors.toSet()));
System.out.println(setStr); // [A, B]
Set setStr1 = Stream.of("a", "a", "b")
.collect(Collectors.flatMapping(s -> Stream.of(s.toUpperCase()), Collectors.toSet()));
System.out.println(setStr1); // [A, B]
filtering
设置过滤条件
List strList2 = Lists.newArrayList("1", "2", "10", "100", "20", "999");
Set set = strList2.parallelStream()
.collect(Collectors.filtering(s -> s.length() < 2, Collectors.toSet()));
System.out.println(set); // [1, 2]
collectingAndThen
返回一个收集器，该收集器将输入元素累积到给定的收集器中，然后执行其他完成功能
List strList2 = Lists.newArrayList("1", "2", "10", "100", "20", "999");
List unmodifiableList = strList2.parallelStream()
.collect(Collectors.collectingAndThen(Collectors.toList(), Collections::unmodifiableList));
System.out.println(unmodifiableList); // [1, 2, 10, 100, 20, 999]
counting
计数
Long evenCount = Stream.of(1, 2, 3, 4, 5).filter(x -> x % 2 == 0).collect(Collectors.counting());
System.out.println(evenCount); // 2
minBy
根据给定的比较器返回最小元素
Optional min = Stream.of(1, 2, 3, 4, 5).collect(Collectors.minBy((x, y) -> x - y));
System.out.println(min); // Optional[1]
maxBy
它根据给定的比较器返回最大元素
Optional max = Stream.of(1, 2, 3, 4, 5).collect(Collectors.maxBy((x, y) -> x - y));
System.out.println(max); // Optional[5]
summingInt/summingLong/summingDouble
求总和
List strList3 = Arrays.asList("1", "2", "3", "4", "5");
Integer sum = strList3.parallelStream().collect(Collectors.summingInt(Integer::parseInt));
System.out.println(sum); // 15
Long sumL = Stream.of("12", "23").collect(Collectors.summingLong(Long::parseLong));
System.out.println(sumL); // 35
Double sumD = Stream.of("1e2", "2e3").collect(Collectors.summingDouble(Double::parseDouble));
System.out.println(sumD); // 2100.0
averagingInt/averagingLong/averagingDouble
求平均值
List strList4 = Arrays.asList("1", "2", "3", "4", "5");
Double average = strList4.parallelStream().collect(Collectors.averagingInt(Integer::parseInt));
System.out.println(average); // 3.0
Double averageL = Stream.of("12", "23").collect(Collectors.averagingLong(Long::parseLong));
System.out.println(averageL); // 17.5
Double averageD = Stream.of("1e2", "2e3").collect(Collectors.averagingDouble(Double::parseDouble));
System.out.println(averageD); // 1050.0
groupingBy
分组
Map> mapGroupBy = Stream.of(1, 2, 3, 4, 5, 4, 3).collect(Collectors.groupingBy(x -> x * 10));
System.out.println(mapGroupBy); // {50=[5], 20=[2], 40=[4, 4], 10=[1], 30=[3, 3]}
groupingByConcurrent
分组，是并发和无序的
Map> mapGroupBy = Stream.of(1, 2, 3, 4, 5, 4, 3).collect(Collectors.groupingByConcurrent(x -> x * 10));
System.out.println(mapGroupBy); // {50=[5], 20=[2], 40=[4, 4], 10=[1], 30=[3, 3]}
partitioningBy
返回一个Collector，它根据Predicate对输入元素进行分区，并将它们组织成Map
Map> mapPartitionBy = Stream.of(1, 2, 3, 4, 5, 4, 3).collect(Collectors.partitioningBy(x -> x % 2 == 0));
System.out.println(mapPartitionBy); // {false=[1, 3, 5, 3], true=[2, 4, 4]}
BinaryOperator
返回一个收集器，它在指定的BinaryOperator下执行其输入元素的减少。这主要用于多级缩减，例如使用groupingBy（）和partitioningBy（）方法指定下游收集器
Map> reducing = Stream.of(1, 2, 3, 4, 5, 4, 3).collect(Collectors.partitioningBy(
x -> x % 2 == 0, Collectors.reducing(BinaryOperator.maxBy(Comparator.comparing(Integer::intValue)))));
System.out.println(reducing); // {false=Optional[5], true=Optional[4]}
summarizingInt
返回统计数据：min, max, average, count, sum
IntSummaryStatistics summarizingInt = Stream.of("12", "23", "35")
.collect(Collectors.summarizingInt(Integer::parseInt));
System.out.println(summarizingInt);
//IntSummaryStatistics{count=3, sum=70, min=12, average=23.333333, max=35}