Java 8 Steam 例子整理

2016.06.06 17:44* 字数 1860 阅读 3901评论 0喜欢 6

为什么需要Steam

Java 8 中的Steam是对集合 (Collection) 对象功能的增强, 他专注于对集合对象进行各种非常便利，高效的聚合操作(aggregate operation), 或者大批量数据操作 (bulk data operation).
Steam API借助于同样新出现的Lambda 表达式, 极大的提高编程效率和程序可读性. 同时他提供穿行和并行两种模式进行汇聚操作, 并发模式能够成分利用多核处理器的优势, 使用fork/join 并行法师来拆分任务和加速处理过程.
通常编写并行代码很难而且容易出错, 但使用Steam API无需编写一行多线程的代码, 就可以很方便地写出高性能的并发代码.
Java 8中首次出现的java.util.stream是一个函数式语言+多核时代综合影响的产物.

什么是聚合操作

TODO

(需求: 如果发现type为grocery的所有交易, 然后返回以交易值降序排序的交易ID集合)

public class Transaction {
    private final int id;
    private final Integer value;
    private final Type type;

    public Transaction(int id, Integer value, Type type) {
        this.id = id;
        this.value = value;
        this.type = type;
    }

    public enum Type {
        A, B, C, D, GEOCERY
    }
    
    public int getId() {return id;}
    public Integer getValue() {return value;}
    public Type getType() {return type;}
}

清单 1. Java 7的排序，取值实现

    public static void main(String[] args) {
        List<Transaction> transactions = new ArrayList<>();
        transactions.add(new Transaction(1, 10, Transaction.Type.GEOCERY));
        transactions.add(new Transaction(3, 30, Transaction.Type.GEOCERY));
        transactions.add(new Transaction(6, 60, Transaction.Type.GEOCERY));
        transactions.add(new Transaction(5, 50, Transaction.Type.GEOCERY));
        transactions.add(new Transaction(2, 20, Transaction.Type.A));
        transactions.add(new Transaction(4, 40, Transaction.Type.C));

        // JDK 7 发现type为grocery的所有交易
        List<Transaction> groceryTransactions = new ArrayList<>();
        for (Transaction t : transactions) {
            if (t.getType() == Transaction.Type.GEOCERY) {
                groceryTransactions.add(t);
            }
        }
        // 集合排序 交易值降序排序
        Collections.sort(groceryTransactions, new Comparator<Transaction>() {
            @Override
            public int compare(Transaction o1, Transaction o2) {
                return o2.getValue().compareTo(o1.getValue());
            }
        });
        // 交易ID 获取
        List<Integer> transactionIds = new ArrayList<>();
        for (Transaction t : groceryTransactions) {
            transactionIds.add(t.getId());
        }

        System.out.println(transactionIds);//[6, 5, 3, 1]
    }

清单 2. Java 8的排序，取值实现

        // JDK 8 如果发现type为grocery的所有交易, 然后返回以交易值降序排序的交易ID集合
        List<Integer> transactionsIds =
                transactions.parallelStream().filter(t -> t.getType() == Transaction.Type.GEOCERY)
                        .sorted(Comparator.comparing(Transaction::getValue).reversed())
                        .map(Transaction::getId)
                        .collect(Collectors.toList());
        System.out.println(transactionsIds);//[6, 5, 3, 1]

Steam 总览

流的操作类型分为两种:
Intermediate: 一个流可以后面跟随零个或者多个intermediate操作, 其目的主要是打开流，做出某种程度的数据映射/过滤，然后返回一个新的流，交给下一个操作使用。这类操作都是Lazy的，也就是说仅仅调用这类方法，并没有真正开始流的遍历.
Terminal: 一个流只能有一个terminal操作, 当这个操作执行后，流就被使用“光”了, 无法在被操作。所以这必定是流的最后一个操作。Terminal操作的执行，才会真正开始流的遍历，并且会生成一个结果，或者一个side effect.

清单 3. 一个流的操作示例

// JDK 8 
public class Widget {
    private final Color color;
    private final int weight;
    enum Color {RED, BLACK, BLUE}

    public Widget(Color color, int weight) {
        this.color = color;
        this.weight = weight;
    }

    public Color getColor() {return color;}
    public int getWeight() {return weight;}

    public static void main(String[] args) {
        List<Widget> widgets = new ArrayList<>();
        widgets.add(new Widget(Color.RED, 1));
        widgets.add(new Widget(Color.RED, 2));
        widgets.add(new Widget(Color.BLACK, 3));
        widgets.add(new Widget(Color.BLUE, 4));
        // stream() 获取当前的source, filter 和 mapToInt为intermediate操作, 进行数据筛选和转换, 
        // 最后一个sum为terminal操作，对符合条件的全部widget做重量求和
        int sum = widgets.stream()
                .filter(w -> w.getColor() == Color.RED)
                .mapToInt(w -> w.getWeight())
                .sum();
        System.out.println(sum);// 3
    }
}

清单 4. 构造流的几种常见方法

// JDK 8 
public class SteamConstruct {
    public static void main(String[] args) {
        // 1. Individual values 单独值
        Stream stream = Stream.of("a1", "b1", "c1");
        stream.forEach(System.out::print);//打印 a1b1c1

        // 2. Arrays 数组
        String[] strArray = new String[] {"a2", "b2", "c2"};
        stream = Stream.of(strArray);
        stream = Arrays.stream(strArray);
        System.out.println(stream.collect(Collectors.joining(",")).toString());//打印 a2,b2,c2

        // 3. Collections 集合
        List<String> list = Arrays.asList(strArray);
        stream = list.stream();
    }
}

清单 5. 数值流的构造(对于基本数值型，目前有三种对应的包装类型Stream: 1. IntStream 2. LongStream 3. DoubleStream )

// JDK 8 
public class BasicStream {
    // IntStream, LongStream, DoubleStream. 当然我们也可以用Stream<Integer>, Stream<Long>, Stream<Double>, 
    // 但是boxing 和 unboxing会很耗时, 所以特别为这三个基本数值型提供了对应的Stream
    public static void main(String[] args) {
        IntStream.of(new int[] {1, 2, 3}).forEach(System.out::print);// 123
        IntStream.range(1, 3).forEach(System.out::print);// [1,3) 12
        IntStream.rangeClosed(1, 3).forEach(System.out::print);// [1,3] 123
    }
}

清单 6. 流转换为其他数据结构 (一个Stream只可以使用一次，否则会报错)

public class StreamExchange {
    public static void main(String[] args) {
        Stream stream = Stream.of("a1", "b1", "c1");
        // 1. Array
        String[] strArray1 = (String[]) stream.toArray(String[]::new);
        for (String s : strArray1) { System.out.print(s); } //a1b1c1
        // 2.Collection list
        stream = Stream.of("a1", "b1", "c1");// stream has already been operated upon or closed
        List<String> list1 = (List<String>) stream.collect(Collectors.toList());
        for (String s : list1) { System.out.print(s); }//a1b1c1
        // 2.Collection list
        stream = Stream.of("a1", "b1", "c1");// stream has already been operated upon or closed
        List<String> list2 = (List<String>) stream.collect(Collectors.toCollection(ArrayList::new));
        for (String s : list2) { System.out.print(s); } //a1b1c1
        // 2.Collection set
        stream = Stream.of("a1", "b1", "c1");// stream has already been operated upon or closed
        Set<String> set = (Set<String>) stream.collect(Collectors.toSet());
        for (String s : set) { System.out.print(s); } //a1c1b1
        // 2.Collection stack
        stream = Stream.of("a1", "b1", "c1");// stream has already been operated upon or closed
        Stack<String> stack = (Stack<String>) stream.collect(Collectors.toCollection(Stack::new));
        for (String s : stack) { System.out.print(s); } //a1b1c1
        // 3. String
        stream = Stream.of("a1", "b1", "c1");// stream has already been operated upon or closed
        String str = stream.collect(Collectors.joining()).toString();
        System.out.print(str); // a1b1c1
    }
}

流的操作

Intermediate
map(mapToInt,flatMap等), filter, distinct, sorted, peek, limit, skip, parallel, sequential, unordered
Terminal
forEach, forEachOrdered, toArray, reduce, collect, min, max, count, anyMatch, allMatch, noneMatch, findFirst, findAny, iterator
Short-cricuiting
anyMatch, allMatch, noneMatch, findFirst, findAny, limit

Map/flatMap

清单 7. 转换大写【.map(String::toUpperCase)】和【map(s -> { return s.toUpperCase(); })】和【.map(s -> s.toUpperCase())】

public class ToUpperCase {
    public static void main(String[] args) {
        Stream<String> stream = Stream.of("hello", "world", "java8", "stream");
        List<String> wordList = stream.map(String::toUpperCase).collect(Collectors.toList());
        System.out.println(wordList.toString());// [HELLO, WORLD, JAVA8, STREAM]

        stream = Stream.of("hello", "world", "java8", "stream");
        wordList = stream.map(s -> { return s.toUpperCase(); }).collect(Collectors.toList());
        System.out.println(wordList.toString());// [HELLO, WORLD, JAVA8, STREAM]

        stream = Stream.of("hello", "world", "java8", "stream");
        wordList = stream.map(s -> s.toUpperCase()).collect(Collectors.toList());
        System.out.println(wordList.toString());// [HELLO, WORLD, JAVA8, STREAM]
    }
}

清单 8. 平方数 (map 生产的是个1:1的映射，每个输入元素，都按照规则转换成另一个元素)

public class ToSquare {
    public static void main(String[] args) {
        Stream<Integer> stream = Arrays.asList(1, 2, 3, 4).stream();
        List<Integer> squareList = stream.map(n -> n * n).collect(Collectors.toList());
        System.out.println(squareList.toString());// [1, 4, 9, 16]
    }
}

清单 9. 一对多 (flatMap把input stream中的层级结构扁平化，就是将底层元素抽出来放到一起，最终output的Stream里面已经没有List了，都是直接的数字)

public class ManyToOne {
    public static void main(String[] args) {
        Stream<List<Integer>> inputStream =
                Stream.of(Arrays.asList(1), Arrays.asList(2, 3), Arrays.asList(4, 5, 6));
        Stream<Integer> outputStream = inputStream.flatMap(childList -> childList.stream());
        System.out.print(outputStream.collect(Collectors.toList()).toString());// [1, 2, 3, 4, 5, 6]
    }
}

Filter

清单 10. 留下偶数

public class KeepEvenNumber {
    public static void main(String[] args) {
        Integer[] sixNums = {1, 2, 3, 4, 5, 6};
        Integer[] evens = Stream.of(sixNums).filter(n -> n % 2 == 0).toArray(Integer[]::new);
        System.out.println(Arrays.toString(evens));// [2, 4, 6]
    }
}

清单 11. 把单词挑出来 (首先把每行的单词用flatMap整理到新的Stream，然后保留长度不为0的，就是正品文章中的全部单词了)

public class PickAllWords {
    public static void main(String[] args) {
        Path path = Paths.get(System.getProperty("user.dir")
                + "/src/main/java/com/wdxxl/jdk8/ibm/stream/PickAllWords.java");

        // 1. Java 8 Read File + Stream
        try (Stream<String> stream = Files.lines(path)) {
            List<String> output = stream.flatMap(line -> Stream.of(line.split(" ")))
                    .filter(word -> word.length() > 0).collect(Collectors.toList());
            System.out.println(output);
        } catch (IOException e) {
            e.printStackTrace();
        }

        // 2. BufferedReader + Stream
        try (BufferedReader br = Files.newBufferedReader(path)) {
            List<String> output = br.lines().flatMap(line -> Stream.of(line.split(" ")))
                    .filter(word -> word.length() > 0).collect(Collectors.toList());
            System.out.println(output);
        } catch (IOException e) {
            e.printStackTrace();
        }
    }
}

ForEach

清单 12. 打印姓名 (forEach 和pre-java8的对比) 【forEach 不能修改自己包含的本地变量值，也不能用break/return 之类的关键字提前结束循环】

public class TestForEach {
    public static void main(String[] args) {
        List<Person> roster = new ArrayList<>();
        roster.add(new Person(Person.Sex.FEMALE, "Lisa"));
        roster.add(new Person(Person.Sex.MALE, "King"));
        roster.add(new Person(Person.Sex.MALE, "Jake"));
        // JDK 8
        roster.stream().filter(p -> p.gender == Person.Sex.MALE)
                       .forEach(p -> System.out.println(p.name));
        // JDK 7
        for (Person p : roster) {
            if(p.gender == Person.Sex.MALE){
                System.out.println(p.name);
            }
        }
    }
}
class Person {
    Sex gender;
    String name;
    public enum Sex { MALE, FEMALE }
    public Person(Sex gender, String name) {
        this.gender = gender;
        this.name = name;
    }
}

清单 13. peek 对每个元素执行操作并且返回一个新的Stream 【peek : 偷窥】注意执行顺序

public class Peek {
    public static void main(String[] args) {
        Stream.of("one", "two", "three", "four")
                .filter(p -> p.length() > 3)
                .peek(v -> System.out.println("Filtered Value:" + v))
                .map(String::toUpperCase)
                .peek(v -> System.out.println("Mapped Value:" + v))
                .collect(Collectors.toList());
        // 1. Filtered Value:three
        // 2. Mapped Value:THREE
        // 3. Filtered Value:four
        // 4. Mapped Value:FOUR
    }
}

清单 14. Optional的两个用例【使用Optional代码的可读性好，而且它提供的是编译时检查，能极大的降低NPE对程序的影响】

public class OptionalTest {
    public static void main(String[] args) {
        String strA = " abcd", strB = null;
        print(strA);
        print(" ");
        print(strB);
        System.out.println(getLength(strA));
        System.out.println(getLength(" "));
        System.out.println(getLength(strB));
    }
    public static void print(String text) {
        // JDK 8
        Optional.ofNullable(text).ifPresent(System.out::println);
        // Pre-JDK 8
        if (text != null) { System.out.println(text); }
    }
    public static int getLength(String text) {
        // JDK 8
        return Optional.ofNullable(text).map(String::length).orElse(-1);
        // Pre-JDK 8
        // return (text != null) ? text.length() : -1;
    }
}

reduce

清单 15. reduce的用例

public class ReduceTest {
    public static void main(String[] args) {
        // 1. 求和 SUM 10
        Integer sum = Stream.of(1, 2, 3, 4).reduce(0, (a, b) -> a + b);
        sum = Stream.of(1, 2, 3, 4).reduce(0, Integer::sum); //有起始值
        sum = Stream.of(1, 2, 3, 4).reduce(Integer::sum).get(); //无起始值
        // 2. 最小值 minValue = -3.0
        double minValue = Stream.of(-1.5, 1.0, -3.0, -2.0).reduce(Double.MAX_VALUE, Double::min);
        minValue = Stream.of(-1.5, 1.0, -3.0, -2.0).reduce(Double::min).get();
        // 2. 最大数值 maxValue = 1.0
        double maxValue = Stream.of(-1.5, 1.0, -3.0, -2.0).reduce(Double.MIN_VALUE, Double::max);
        maxValue = Stream.of(-1.5, 1.0, -3.0, -2.0).reduce(Double::max).get();
        // 3. 字符串连接 Concat "ABCD"
        String concat = Stream.of("A", "B", "C", "D").reduce("", String::concat);
        // 4. 过滤和字符串连接 Filter & Concat = "ace"
        concat = Stream.of("a", "B", "c", "D", "e", "F")
                .filter(x -> x.compareTo("Z") > 0)
                .reduce("", String::concat);
    }
}

limit/skip (limit 返回Stream的前面n个元素; skip 则是扔掉前n个元素; 它是由一个叫subStream的方法改名而来.)

清单 16. limit和skip对运行次数的影响

public class LimitSkipTest {
    public static void main(String[] args) {
        List<LimitSkipTest.User> users = new ArrayList<>();
        LimitSkipTest limitSkipTest = new LimitSkipTest();
        for (int i = 0; i < 100; i++) {
            users.add(limitSkipTest.new User(i, "name_" + i)); // 内部类构造
        }

        List<String> userList = users.stream()
                .map(User::getName) // name_0name_1name_2name_3name_4name_5name_6name_7name_8name_9
                .limit(10)
                .skip(3)
                .collect(Collectors.toList());

        System.out.println(userList);// [name_3, name_4, name_5, name_6, name_7, name_8, name_9]
    }
    // 内部类
    class User {
        public int no;
        private final String name;
        public User(int no, String name) { this.no = no; this.name = name; }
        public String getName() { System.out.print(name); return name; }
    }
}

清单 17. limit和skip对sorted后的运行次数无影响

public class LimitSkipTest2 {
    public static void main(String[] args) {
        List<LimitSkipTest2.User> users = new ArrayList<>();
        LimitSkipTest2 limitSkipTest2 = new LimitSkipTest2();
        for (int i = 0; i < 5; i++) {
            users.add(limitSkipTest2.new User(i, "name_" + i));
        }
        // 对users做了13次微调，首先对5个元素的Stream排序，然后进行limit操作
        List<String> userList = users.stream()
                .sorted((p1, p2) -> p1.getName().compareTo(p2.getName()))
                .map(User::getName) // name_1,name_0,name_2,name_1,name_3,name_2,name_4,name_3,name_0,name_1,
                .limit(2)
                .collect(Collectors.toList());

        System.out.println(userList);// [name_0, name_1]
    }
    // 内部类
    class User {
        public int no;
        private final String name;
        public User(int no, String name) { this.no = no; this.name = name; }
        public String getName() { System.out.print(name); return name; }
    }
}

sorted

清单 18. 排序前进行limit和skip (这种优化是有business logic上的局限性的: 既不需要排序后再取值)

 List<String> userList = users.stream()
                .limit(2)
                .sorted((p1, p2) -> p1.getName().compareTo(p2.getName()))
                .map(User::getName) // name_1,name_0,name_0,name_1,
                .collect(Collectors.toList());
System.out.println(userList);// [name_0, name_1]

min/max/distinct 【min和max的功能也可以通过对Stream元素先排序，再findFirst来实现，但前者的性能会更好，为O(n)，而sorted的成本是O(n log n)】

清单 19. 找出最长一行的长度

public class FindLongestLine {
    public static void main(String[] args) {
        Path path = Paths.get(System.getProperty("user.dir")
                + "/src/main/java/com/wdxxl/jdk8/ibm/stream/FindLongestLine.java");
        // 2. BufferedReader + Stream
        try (BufferedReader br = Files.newBufferedReader(path)) {
            int output = br.lines()
                    .mapToInt(String::length)
                    .max()
                    .getAsInt();
            System.out.println(output);// 83
        } catch (IOException e) {
            e.printStackTrace();
        }
    }
}

清单 20. 找出全文的单词，转小写，并且排序

public class OperateWords {
    public static void main(String[] args) {
        Path path = Paths.get(System.getProperty("user.dir")
                + "/src/main/java/com/wdxxl/jdk8/ibm/stream/OperateWords.java");
        // 2. BufferedReader + Stream
        try (BufferedReader br = Files.newBufferedReader(path)) {
            List<String> output = br.lines()
                    .flatMap(line -> Stream.of(line.split(" ")))
                    .map(String::toLowerCase)
                    .distinct()
                    .sorted()
                    .collect(Collectors.toList());
            System.out.println(output);
        } catch (IOException e) {
            e.printStackTrace();
        }
    }
}

Match

allMatch: Stream 中全部元素符合传入的predicate，返回true
anyMatch: Stream 中只要有一个元素符合传入的predicate，返回true
noneMatch: Stream 中没有一个元素符合传入的predicate，返回true

清单 21. 使用Match

public class MatchTest {
    public static void main(String[] args) {
        List<MatchTest.User> users = new ArrayList<>();
        MatchTest matchTest = new MatchTest();
        for (int i = 0; i < 5; i++) {
            users.add(matchTest.new User(i, "name_" + i, i * 5));
        }

        boolean isAllAdult = users.stream().allMatch(p -> {
            System.out.println(p.age); // 0 和 private final int age; ??
            return p.age > 18;
        });
        System.out.println("All are adult? " + isAllAdult); // All are adult? false
        boolean isAnyChild = users.stream().anyMatch(p -> p.age < 12);
        System.out.println("Any Child? " + isAnyChild); // Any Child? true
        boolean noneOldThan19 = users.stream().noneMatch(p -> p.age > 19);
        System.out.println("none Old Than 19? " + noneOldThan19);// none Old Than 19? false
        boolean noneOldThan50 = users.stream().noneMatch(p -> p.age > 50);
        System.out.println("none Old Than 50? " + noneOldThan50);// none Old Than 50? true
    }

    class User {
        public int no;
        public String name;
        private final int age;
        public User(int no, String name, int age) { this.no = no; this.name = name; this.age = age; }
    }
}

进阶: 自己生成流

Stream.generate
通过实现Supplier借口，你可以自己来控制流的生成。这种情形通常用于随机数，常量的Stream，或者需要前后元素建维持着某种状态信息的Stream。把Supplier示例传递给Stream.generate() 生成的Stream，默认是串行（相对parallel而言）但无序的（相对于ordered而言）。由于它是无限的，在管道中，必须利用limit之类的操作限制Stream大小。

清单 22. 生产10个随机整数

public class RandomTest {
    public static void main(String[] args) {
        Random seed = new Random();
        Supplier<Integer> random = seed::nextInt;
        Stream.generate(random)
                .limit(10)
                .forEach(System.out::println);
        // Another way
        IntStream.generate(() -> (int) (System.nanoTime() % 100))
                .limit(10)
                .forEach(System.out::println);
    }
}

清单 23. 自实现Supplier 【Stream.generate 还接受自己实现的Supplier。例如在构造海量测试数据的时候，用某种自动的规则给每一个变量赋值，或者依据公式计算Stream的每个元素之。这些都是维持状态信息的情形】

public class SupplierTest {
    public static void main(String[] args) {
        SupplierTest supplierTest = new SupplierTest();
        Stream.generate(supplierTest.new UserSupplier()).limit(10)
                .forEach(p -> System.out.println(p.name + ":" + p.age));
    }

    class UserSupplier implements Supplier<User> {
        private int index = 0;
        private final Random random = new Random();
        @Override
        public User get() {
            return new User(index++, "name_" + index, random.nextInt(100));
        }
    }

    class User {
        public int no;
        private final String name;
        private final int age;
        public User(int no, String name, int age) {  this.no = no;  this.name = name; this.age = age; }
    }
}

清单 24. 生产一个等差数列

public class Sequence {
    public static void main(String[] args) {
        Stream.iterate(0, n -> n + 3)
              .limit(10).forEach(x -> System.out.print(x + " "));// 0 3 6 9 12 15 18 21 24 27 
        Stream.iterate(4, n -> n + 3)
              .limit(10).forEach(x -> System.out.print(x + " "));// 4 7 10 13 16 19 22 25 28 31 
    }
}

进阶: 用Collectors来进行reduction操作

grouping/partitioningBy

清单 25. groupingBy 按照年龄归组

public class AdultGroup {
    public static void main(String[] args) {
        AdultGroup adultGroup = new AdultGroup();
        Map<Integer, List<User>> children = Stream.generate(adultGroup.new UserSupplier())
                .limit(100)
                .collect(Collectors.groupingByConcurrent(User::getAge));

        Iterator it = children.entrySet().iterator();
        while (it.hasNext()) {
            Map.Entry<Integer, List<User>> users = (Map.Entry) it.next();
            System.out.println("Age: " + users.getKey() + "=" + users.getValue().size());
        }
    }

    class UserSupplier implements Supplier<User> {
        private int index = 0;
        private final Random random = new Random();
        @Override
        public User get() {
            return new User(index++, "name_" + index, random.nextInt(100));
        }
    }
    class User {
        public int no;
        public String name;
        public int age;
        public User(int no, String name, int age) { this.no = no;  this.name = name;  this.age = age; }
        public int getAge() { return age; }
    }
}

清单 26. partitioningBy 按照未成年人和成年人归组

在使用条件“年龄小于18”进行分组后可以看到，不到18岁的未成年人是一组，成年人是另外一组。

public class AdultPartition {
    public static void main(String[] args) {
        AdultPartition adultPartition = new AdultPartition();
        Map<Boolean, List<User>> children = Stream.generate(adultPartition.new UserSupplier())
                .limit(100)
                .collect(Collectors.partitioningBy(p -> p.age > 18));
        System.out.println("Children number:" + children.get(false).size());
        System.out.println("Adult number:" + children.get(true).size());
    }

    class UserSupplier implements Supplier<User> {
        private int index = 0;
        private final Random random = new Random();
        @Override
        public User get() {
            return new User(index++, "name_" + index, random.nextInt(100));
        }
    }
    class User {
        public int no;
        public String name;
        public int age;
        public User(int no, String name, int age) { this.no = no;  this.name = name;  this.age = age; }
    }
}

结束语

总之，Stream 的特性可以归纳为：

不是数据结构
它没有内部存储，它只是用操作管道从 source（数据结构、数组、generator function、IO channel）抓取数据。
它也绝不修改自己所封装的底层数据结构的数据。例如 Stream 的 filter 操作会产生一个不包含被过滤元素的新 Stream，而不是从 source 删除那些元素。
所有 Stream 的操作必须以 lambda 表达式为参数
不支持索引访问
你可以请求第一个元素，但无法请求第二个，第三个，或最后一个。不过请参阅下一项。
很容易生成数组或者 List
惰性化
很多 Stream 操作是向后延迟的，一直到它弄清楚了最后需要多少数据才会开始。
Intermediate 操作永远是惰性化的。
并行能力
当一个 Stream 是并行化的，就不需要再写多线程代码，所有对它的操作会自动并行进行的。
可以是无限的
集合有固定大小，Stream 则不必。limit(n) 和 findFirst() 这类的 short-circuiting 操作可以对无限的 Stream 进行运算并很快完成。

posted on 2018-01-11 11:34 还有梦阅读(8627) 评论(0) 编辑收藏举报

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