使用Java 8中的Stream

添加时间:2015-7-12 11:15:11 编辑:罗建东 阅读:217


Stream是Java 8 提供的高效操作集合类(Collection)数据的API。

1. 从Iterator到Stream

有一个字符串的list,要统计其中长度大于7的字符串的数量,用迭代来实现:

List<String> wordList = Arrays.asList("regular", "expression", "specified", "as", "a", "string", "must");

int countByIterator = 0;
for (String word: wordList) {
    if (word.length() > 7) {
        countByIterator++;
    } }

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用Stream实现:

long countByStream = wordList.stream().filter(w -> w.length() > 7).count();

显然,用stream实现更简洁,不仅如此,stream很容易实现并发操作,比如:

long countByParallelStream = wordList.parallelStream().
    filter(w -> w.length() > 7).count();

stream遵循的原则是:告诉我做什么,不用管我怎么做。比如上例:告诉stream通过多线程统计字符串长度,至于以什么顺序、
在哪个线程中执行,由stream来负责;而在迭代实现中,由于计算的方式已确定,很难优化了。

Stream和Collection的区别主要有:

  1. stream本身并不存储数据,数据是存储在对应的collection里,或者在需要的时候才生成的;
  2. stream不会修改数据源,总是返回新的stream;
  3. stream的操作是懒执行(lazy)的:仅当最终的结果需要的时候才会执行,比如上面的例子中,结果仅需要前3个长度大于7
    的字符串,那么在找到前3个长度符合要求的字符串后,filter()将停止执行;

使用stream的步骤如下:

  1. 创建stream;
  2. 通过一个或多个中间操作(intermediate operations)将初始stream转换为另一个stream;
  3. 通过中止操作(terminal operation)获取结果;该操作触发之前的懒操作的执行,中止操作后,该stream关闭,不能再
    使用了;

在上面的例子中,wordList.stream()和wordList.parallelStream()是创建stream,filter()是中间操作,过
滤后生成一个新的stream,count()是中止操作,获取结果。

2. 创建Stream的方式

1) 从array或list创建stream:

Stream<Integer> integerStream = Stream.of(10, 20, 30, 40); String[] cityArr = {"Beijing", "Shanghai", "Chengdu"};
Stream<String> cityStream = Stream.of(cityArr);
Stream<String> nameStream = Arrays.asList("Daniel", "Peter", "Kevin").
    stream();
Stream<String> cityStream2 = Arrays.stream(cityArr, 0, 1);
Stream<String> emptyStream = Stream.empty();

2) 通过generate和iterate创建无穷stream:

Stream<String> echos = Stream.generate(() -> "echo");
Stream<Integer> integers = Stream.iterate(0, num -> num + 1);

3) 通过其它API创建stream:

Stream<String> lines = Files.lines(Paths.get("test.txt")) String content = "AXDBDGXC";
Stream<String> contentStream = Pattern.compile("[ABC]{1,3}").
    splitAsStream(content);

3. Stream转换

1)filter()用于过滤,即使原stream中满足条件的元素构成新的stream:

List<String> langList = Arrays.asList("Java", "Python", "Swift", "HTML");
Stream<String> filterStream = langList.stream().filter(lang -> lang.equalsIgnoreCase("java"));

2)map()用于映射,遍历原stream中的元素,转换后构成新的stream:

List<String> langList = Arrays.asList("Java", "Python", "Swift", "HTML");
Stream<String> mapStream = langList.stream().map(String::toUpperCase);

3)flatMap()用于将[["ABC", "DEF"], ["FGH", "IJK"]]的形式转换为
["ABC", "DEF", "FGH", "IJK"]:

Stream<String> cityStream = Stream.of("Beijing", "Shanghai", "Shenzhen");
// [['B', 'e', 'i', 'j', 'i', 'n', 'g'], ['S', 'h', 'a', 'n', 'g', 'h', 'a', 'i'], ...]
Stream<Stream<Character>> characterStream1 = cityStream.
    map(city -> characterStream(city));

Stream<String> cityStreamCopy = Stream.of("Beijing", "Shanghai", "Shenzhen");
// ['B', 'e', 'i', 'j', 'i', 'n', 'g', 'S', 'h', 'a', 'n', 'g', 'h', 'a', 'i', ...]
Stream<Character> characterStreamCopy = cityStreamCopy.
    flatMap(city -> characterStream(city));

其中,characterStream()返回有参数字符串的字符构成的Stream<Character>;

4)limit()表示限制stream中元素的数量,skip()表示跳过stream中前几个元素,concat表示将多个stream连接
起来,peek()主要用于debug时查看stream中元素的值:

Stream<Integer> limitStream = Stream.of(18, 20, 12, 35, 89).sorted().limit(3);
Stream<Integer> skipStream = Stream.of(18, 20, 12, 35, 89).sorted(Comparator.reverseOrder())
    .skip(1);
Stream<Integer> concatStream = Stream.concat(Stream.of(1, 2, 3), Stream.of(4, 5, 6));
concatStream.peek(i -> System.out.println(i)).count();

peek()是intermediate operation,所以后面需要一个terminal operation,如count()才能在输出中
看到结果;

5) 有状态的(stateful)转换,即元素之间有依赖关系,如distinct()返回由唯一元素构成的stream,sorted()返回排
序后的stream:

Stream<String> distinctStream = Stream.of("Beijing", "Tianjin", "Beijing").distinct();
Stream<String> sortedStream = Stream.of("Beijing", "Shanghai", "Chengdu")
    .sorted(Comparator.comparing(String::length).reversed());

4. Stream reduction

reduction就是从stream中取出结果,是terminal operation,因此经过reduction后的stream不能再使用了。

4.1 Optional

Optional<T>表示或者有一个T类型的对象,或者没有值;

1) 创建Optional对象:

直接通过Optional的类方法:of()/empty()/ofNullable():

Optional<Integer> intOpt = Optional.of(10); Optional<String> emptyOpt = Optional.empty(); Optional<Double> doubleOpt = Optional.ofNullable(5.5);

2) 使用Optional对象:

你当然可以这么使用:

if (intOpt.isPresent()) {
    intOpt.get();
}

但是,最好这么使用:

doubleOpt.orElse(0.0);
doubleOpt.orElseGet(() -> 1.0);
doubleOpt.orElseThrow(RuntimeException::new);
List<Double> doubleList = new ArrayList<>();
doubleOpt.ifPresent(doubleList::add);

map()方法与ifPresent()用法相同,就是多个返回值,flatMap()用于Optional的链式表达:

Optional<Boolean> addOk = doubleOpt.map(doubleList::add);
Optional.of(4.0).flatMap(num -> Optional.ofNullable(num * 100))
    .flatMap(num -> Optional.ofNullable(Math.sqrt(num)));

4.2 简单的reduction

主要包含以下操作:findFirst()/findAny()/allMatch/anyMatch()/noneMatch,比如:

Optional<String> firstWord = wordStream.filter(s -> s.startsWith("Y")).findFirst();
Optional<String> anyWord = wordStream.filter(s -> s.length() > 3).findAny();
wordStream.allMatch(s -> s.length() > 3);
wordStream.anyMatch(s -> s.length() > 3);
wordStream.noneMatch(s -> s.length() > 3);

4.3 reduce方法

1)reduce(accumulator):参数是一个执行双目运算的Functional Interface,假如这个参数表示的操作为op,
stream中的元素为x, y, z, ...,则reduce()执行的就是x op y op z ...,所以要求op这个操作具有结合性
(associative),即满足:(x op y) op z = x op (y op z),满足这个要求的操作主要有:求和、求积、求最大值、
求最小值、字符串连接、集合并集和交集等。另外,该函数的返回值是Optional的:

Optional<Integer> sum1 = numStream.reduce((x, y) -> x + y);

2)reduce(identity, accumulator):可以认为第一个参数为默认值,但需要满足identity op x = x,所以对于求
和操作,identity的值为0,对于求积操作,identity的值为1。返回值类型是stream元素的类型:

Integer sum2 = numStream.reduce(0, Integer::sum);

5. collect结果

1)collect()方法:

reduce()和collect()的区别是:

  • reduce()的结果是一个值;
  • collect()可以对stream中的元素进行各种处理后,得到stream中元素的值;

Collectors接口提供了很方便的创建Collector对象的工厂方法:

// collect to Collection
Stream.of("You", "may", "assume").collect(Collectors.toList());
Stream.of("You", "may", "assume").collect(Collectors.toSet());
Stream.of("You", "may", "assume").collect(Collectors.toCollection(TreeSet::new)); // join element
Stream.of("You", "may", "assume").collect(Collectors.joining());
Stream.of("You", "may", "assume").collect(Collectors.joining(", ")); // summarize element
IntSummaryStatistics summary = Stream.of("You", "may", "assume")
    .collect(Collectors.summarizingInt(String::length));
summary.getMax();

2)foreach()方法:

foreach()用于遍历stream中的元素,属于terminal operation;
forEachOrdered()是按照stream中元素的顺序遍历,也就无法利用并发的优势;

Stream.of("You", "may", "assume", "you", "can", "fly").parallel()
    .forEach(w -> System.out.println(w));
Stream.of("You", "may", "assume", "you", "can", "fly")
    .forEachOrdered(w -> System.out.println(w));

3)toArray()方法:

得到由stream中的元素得到的数组,默认是Object[],可以通过参数设置需要结果的类型:

Object[] words1 = Stream.of("You", "may", "assume").toArray(); String[] words2 = Stream.of("You", "may", "assume").toArray(String[]::new);

4)toMap()方法:

toMap: 将stream中的元素映射为<key, value>的形式,两个参数分别用于生成对应的key和value的值。比如有一个字符串
stream,将首字母作为key,字符串值作为value,得到一个map:

Stream<String> introStream = Stream. of("Get started with UICollectionView and the photo library".split(" ")); Map<String, String> introMap = 
    introStream.collect(Collectors.toMap(s -> s.substring(0, 1), s -> s));

如果一个key对应多个value,则会抛出异常,需要使用第三个参数设置如何处理冲突,比如仅使用原来的value、使用新的
value,或者合并:

Stream<String> introStream = Stream.of("Get started with UICollectionView and the photo library" .split(" ")); Map<Integer, String> introMap2 = introStream.collect(Collectors.toMap(s -> s.length(), 
    s -> s, (existingValue, newValue) -> existingValue));

如果value是一个集合,即将key对应的所有value放到一个集合中,则需要使用第三个参数,将多个value合并:

Stream<String> introStream3 = Stream.of("Get started with UICollectionView and the photo library" .split(" "));
Map<Integer, Set<String>> introMap3 = introStream3.collect(Collectors.toMap(s -> s.length(),
        s -> Collections.singleton(s), (existingValue, newValue) -> {
            HashSet<String> set = new HashSet<>(existingValue);
            set.addAll(newValue); return set;
        }
    ));
introMap3.forEach((k, v) -> System.out.println(k + ": " + v));

如果value是对象自身,则使用Function.identity(),如:

Map<Integer, Person> idToPerson = people.collect(Collectors
    .toMap(Person::getId, Function.identity()));

toMap()默认返回的是HashMap,如果需要其它类型的map,比如TreeMap,则可以在第四个参数指定构造方法:

Map<Integer, String> introMap2 = introStream.collect(
    Collectors.toMap(s -> s.length(), s -> s, (existingValue, newValue) -> existingValue, TreeMap::new));

6. Grouping和Partitioning

1)groupingBy()表示根据某一个字段或条件进行分组,返回一个Map,其中key为分组的字段或条件,value默认为
list,groupingByConcurrent()是其并发版本:

Map<String, List<Locale>> countryToLocaleList = Stream.of(Locale.getAvailableLocales())
    .collect(Collectors.groupingBy(l -> l.getDisplayCountry()));

2) 如果groupingBy()分组的依据是一个bool条件,则key的值为true/false,此时与partitioningBy()等价,且
partitioningBy()的效率更高:

// predicate Map<Boolean, List<Locale>> englishAndOtherLocales = Stream.of(Locale.getAvailableLocales())
    .collect(Collectors.groupingBy(l -> l.getDisplayLanguage().equalsIgnoreCase("English"))); // partitioningBy Map<Boolean, List<Locale>> englishAndOtherLocales2 = Stream.of(Locale.getAvailableLocales())
    .collect(Collectors.partitioningBy(l -> l.getDisplayLanguage().equalsIgnoreCase("English")));

3)groupingBy()提供第二个参数,表示downstream,即对分组后的value作进一步的处理:

返回set,而不是list:

Map<String, Set<Locale>> countryToLocaleSet = Stream.of(Locale.getAvailableLocales())
          .collect(Collectors.groupingBy(l -> l.getDisplayCountry(), Collectors.toSet()));

返回value集合中元素的数量:

Map<String, Long> countryToLocaleCounts = Stream.of(Locale.getAvailableLocales())
          .collect(Collectors.groupingBy(l -> l.getDisplayCountry(), Collectors.counting()));

对value集合中的元素求和:

Map<String, Integer> cityToPopulationSum = Stream.of(cities)
        .collect(Collectors.groupingBy(City::getName, Collectors.summingInt(City::getPopulation)));

对value的某一个字段求最大值,注意value是Optional的:

Map<String, Optional<City>> cityToPopulationMax = Stream.of(cities)
        .collect(Collectors.groupingBy(City::getName, 
            Collectors.maxBy(Comparator.comparing(City::getPopulation))));

使用mapping对value的字段进行map处理:

Map<String, Optional<String>> stateToNameMax = Stream.of(cities)
    .collect(Collectors.groupingBy(City::getState, Collectors.mapping(City::getName, 
        Collectors.maxBy(Comparator.comparing(String::length))))); Map<String, Set<String>> stateToNameSet = Stream.of(cities)
.collect(Collectors.groupingBy(City::getState, 
    Collectors.mapping(City::getName, Collectors.toSet())));

通过summarizingXXX获取统计结果:

Map<String, IntSummaryStatistics> stateToPopulationSummary = Stream.of(cities)
    .collect(Collectors.groupingBy(City::getState, Collectors.summarizingInt(City::getPopulation)));

reducing()可以对结果作更复杂的处理,但是reducing()却并不常用:

Map<String, String> stateToNameJoining = Stream.of(cities)
    .collect(Collectors.groupingBy(City::getState, Collectors.reducing("", City::getName, (s, t) -> s.length() == 0 ? t : s + ", " + t)));

比如上例可以通过mapping达到同样的效果:

Map<String, String> stateToNameJoining2 = Stream.of(cities)
        .collect(Collectors.groupingBy(City::getState, 
            Collectors.mapping(City::getName, Collectors.joining(", ")
        )));

7. Primitive Stream

Stream<Integer>对应的Primitive Stream就是IntStream,类似的还有DoubleStream和LongStream。

1) Primitive Stream的构造:of(),range(),rangeClosed(),Arrays.stream():

IntStream intStream = IntStream.of(10, 20, 30);
IntStream zeroToNintyNine = IntStream.range(0, 100);
IntStream zeroToHundred = IntStream.rangeClosed(0, 100); double[] nums = {10.0, 20.0, 30.0};
DoubleStream doubleStream = Arrays.stream(nums, 0, 3);

2) Object Stream与Primitive Stream之间的相互转换,通过mapToXXX()和boxed():

// map to Stream<String> cityStream = Stream.of("Beijing", "Tianjin", "Chengdu");
IntStream lengthStream = cityStream.mapToInt(String::length); // box Stream<Integer> oneToNine = IntStream.range(0, 10).boxed();

3) 与Object Stream相比,Primitive Stream的特点:

toArray()方法返回的是对应的Primitive类型:

int[] intArr = intStream.toArray();

自带统计类型的方法,如:max(),average(),summaryStatistics():

OptionalInt maxNum = intStream.max(); IntSummaryStatistics intSummary = intStream.summaryStatistics();

8. Parallel Stream

1) Stream支持并发操作,但需要满足以下几点:

构造一个paralle stream,默认构造的stream是顺序执行的,调用paralle()构造并行的stream:

IntStream scoreStream = IntStream.rangeClosed(10, 30).parallel();

要执行的操作必须是可并行执行的,即并行执行的结果和顺序执行的结果是一致的,而且必须保证stream中执行的操作是线程安
全的:

int[] wordLength = new int[12];
Stream.of("It", "is", "your", "responsibility").parallel().forEach(s -> { if (s.length() < 12) wordLength[s.length()]++;
});

这段程序的问题在于,多线程访问共享数组wordLength,是非线程安全的。解决的思路有:1)构造AtomicInteger数组;
2)使用groupingBy()根据length统计;

2) 可以通过并行提高效率的常见场景:

使stream无序:对于distinct()和limit()等方法,如果不关心顺序,则可以使用并行:

LongStream.rangeClosed(5, 10).unordered().parallel().limit(3);
IntStream.of(14, 15, 15, 14, 12, 81).unordered().parallel().distinct();

在groupingBy()的操作中,map的合并操作是比较重的,可以通过groupingByConcurrent()来并行处理,不过前提是
parallel stream:

Stream.of(cities).parallel().collect(Collectors.groupingByConcurrent(City::getState));

在执行stream操作时不能修改stream对应的collection;

stream本身是不存储数据的,数据保存在对应的collection中,所以在执行stream操作的同时修改对应的collection,结果
是未定义的:

// ok Stream<String> wordStream = wordList.stream();
wordList.add("number");
wordStream.distinct().count(); // ConcurrentModificationException Stream<String> wordStream = wordList.stream();
wordStream.forEach(s -> { if (s.length() >= 6) wordList.remove(s);});

9. Functional Interface

仅包含一个抽象方法的interface被成为Functional Interface,比如:Predicate,Function,Consumer等。
此时我们一般传入一个lambda表达式或Method Reference。

常见的Functional Interface有:

Functional Interface     Parameter     Return Type     Description Types
Supplier<T>             None T Supplies a value of type T Consumer<T> T void            Consumes a value of type T BiConsumer<T, U> T,U         void            Consumes values of types T and U
Predicate<T> T boolean            A Boolean-valued function ToIntFunction<T> T int                An int-, long-, or double-valued function ToLongFunction<T> T long
ToDoubleFunction<T> T double
IntFunction<R>             int         R                A function with argument of type int, long, or double
LongFunction<R>         long
DoubleFunction<R>         double
Function<T, R> T R                A function with argument of type T BiFunction<T, U, R> T,U         R                A function with arguments of types T and U
UnaryOperator<T> T T A unary operator on the type T BinaryOperator<T> T,T T A binary operator on the type T