IBM: Java 8 中的 Streams API 详解

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

1. Intermediate
   map(mapToInt,flatMap等), filter, distinct, sorted, peek, limit, skip, parallel, sequential, unordered
2. Terminal
   forEach, forEachOrdered, toArray, reduce, collect, min, max, count, anyMatch, allMatch, noneMatch, findFirst, findAny, iterator
3. 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

1. allMatch: Stream 中全部元素符合传入的predicate，返回true
2. anyMatch: Stream 中只要有一个元素符合传入的predicate，返回true
3. 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：

1. 不是数据结构
2. 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。
3. 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。
4. 所有 Stream 的操作必须以 lambda 表达式为参数
5. 不支持索引访问
6. 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。
7. 很容易生成数组或者 List
8. 惰性化
9. 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。
10. Intermediate 操作永远是惰性化的。
11. 并行能力
12. 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。
13. 可以是无限的
14. 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。