JAVA8学习——深入Comparator&Collector（学习过程）

深入Comparator&Collector

从源码深入Comparator

Comparator从Java1.2就出来了,但是在1.8的时候,又添加了大量的默认方法.

compare()
equals()
reversed() //倒序
thenComparing(Comparator<? super T> other) //然后,再去比较.
thenComparing( Function<? super T, ? extends U> keyExtractor,
            Comparator<? super U> keyComparator) //先通过第一个比较器,再执行第二个比较器...串联
thenComparing()
thenComparingInt()
thenComparingLong()
thenComparingDouble()

reverseOrder()
naturalOrder()
nullsFirst()
nullsLast()
comparing () //静态方法
comparing()
comparingInt()
comparingLong()
comparingDouble()

从Demo代码看Comparator

package com.dawa.jdk8.stream2;

import java.util.Arrays;
import java.util.Collections;
import java.util.Comparator;
import java.util.List;

//关于比较器comparator,案例详解.
public class MyComparator {
    public static void main(String[] args) {
        List<String> list = Arrays.asList("hello", "world", "welcome", "nihao");

        //按照字母排序
        Collections.sort(list);
        System.out.println(list);

        //按照字符串的长度.
        Collections.sort(list, (item1, item2) -> item1.length() - item2.length());
        System.out.println(list);

        //按照字符串的长度降序排序.
        Collections.sort(list, (item1, item2) -> item2.length() - item1.length());

        //使用方法引用
        //长度排序
        Collections.sort(list, Comparator.comparingInt(String::length));
        System.out.println(list);
        //长度倒叙排序
        Collections.sort(list, Comparator.comparingInt(String::length).reversed());
        System.out.println(list);

        //使用lambda表达式实现上述两个方法
//        Collections.sort(list,Comparator.comparingInt(item->item.length()).reversed());
        //这里,reversed()方法,参数要的是Object类型.
        //参数的类型推断.
        Collections.sort(list,Comparator.comparingInt((String item)->item.length()).reversed());
        //这样写就行了.

        //问题:之前为什么会成功? 因为是从Stream<T> 类型开始推断的,可以获取到原属性的元素.
        //问题:为什么上述的类型推断失败了/? 看sort方法的 Comparator类的泛型<T>,T是传入参数的泛型- <? super T>.
        //      String上的类型.你没指定,编译器也没办法帮你指定.
        //    public static <T> void sort(List<T> list, Comparator<? super T> c) {
        //        list.sort(c);
        //    }
        //如:        Collections.sort(list,Comparator.comparingInt((Boolean item)->1).reversed());
        //这样不会被兼容.因为Boolean 不是 String的上类型.
        //如:        Collections.sort(list,Comparator.comparingInt((Object item)->1).reversed());
        //这样就是可以的.
        //如:        Collections.sort(list,Comparator.comparingInt(item->item.length());
        //这样也是可以的.
    }
}

    @SuppressWarnings({"unchecked", "rawtypes"})
    public static <T> void sort(List<T> list, Comparator<? super T> c) {
        list.sort(c);
    }

关于: <? super T> 泛型的使用.需要注意.

语义更宽泛,但是从实际结果类型,实际就是T类型本身.这个需要仔细思考一下.

Comparator比较器的串联使用

//通过两层比较,1:排序(升序) ,2:字母顺序排序. 使用thenComparing()
        Collections.sort(list,Comparator.comparingInt(String::length).thenComparing(String.CASE_INSENSITIVE_ORDER));

thenComparing()方法源码如下

		/**
     * Returns a lexicographic-order comparator with another comparator.
     * If this {@code Comparator} considers two elements equal, i.e.
     * {@code compare(a, b) == 0}, {@code other} is used to determine the order.
     *
     * <p>The returned comparator is serializable if the specified comparator
     * is also serializable.
     *
     * @apiNote
     * For example, to sort a collection of {@code String} based on the length
     * and then case-insensitive natural ordering, the comparator can be
     * composed using following code,
     *
     不区分大小写,的实现. 技术上述案例.
     * <pre>{@code
     *     Comparator<String> cmp = Comparator.comparingInt(String::length)
     *             .thenComparing(String.CASE_INSENSITIVE_ORDER);
     * }</pre>
     *
     * @param  other the other comparator to be used when this comparator
     *         compares two objects that are equal.
     * @return a lexicographic-order comparator composed of this and then the
     *         other comparator
     * @throws NullPointerException if the argument is null.
     * @since 1.8
     */
    default Comparator<T> thenComparing(Comparator<? super T> other) {
        Objects.requireNonNull(other);
        return (Comparator<T> & Serializable) (c1, c2) -> {
            int res = compare(c1, c2);
            return (res != 0) ? res : other.compare(c1, c2);
        };
    }

前面比较器的结果等于0,这个thenComparing()才会被调用. 就如三个长度相同的那三个数,才会被二次排序.

也就是说如果第一个比较器,能够排序,就用第一个,第一个排序不成再用第二个.

另一种实现

Collections.
  sort(list,Comparator.comparingInt(String::length).
       thenComparing((item1,item2)->item1.toLowerCase().compareTo(item2)));

另一种实现

Collections.sort(list,Comparator.comparingInt(String::length).thenComparing(Comparator.comparing(String::toUpperCase)));

另一种实现

Collections.sort(list,Comparator.comparingInt(String::length).thenComparing(Comparator.comparing(String::toLowerCase,Comparator.reverseOrder())));

上述几个案例,主要就是对于 thenComparing()方法的不同使用实现.

那么,下面这个方法的输出结果是什么?

Collections.sort(list,Comparator.comparingInt(String::length).thenComparing(Comparator.comparing(String::toLowerCase,Comparator.reverseOrder())));

再次重复一下:前面比较器的结果等于0,这个thenComparing()才会被调用. 就如三个长度相同的那三个数,才会被二次排序.也就是说如果第一个比较器,能够排序,就用第一个,第一个排序不成再用第二个.

多级排序

 Collections.sort(list,Comparator.comparingInt(String::length).reversed()
                .thenComparing(Comparator.comparing(String::toLowerCase, Comparator.reverseOrder()))
                .thenComparing(Comparator.reverseOrder()));

JDK1.8之前,Collections里面提供的方法是很少的,从JDK1.8之后,新增了大量的实现方法和具体的特化的实现.

避免了装箱和拆箱操作.这也可能会影响性能.

---

自定义Collector实现类

实现Collector接口

public interface Collector<T, A, R> {

    Supplier<A> supplier();

    BiConsumer<A, T> accumulator();

    BinaryOperator<A> combiner();

    Function<A, R> finisher();

    Set<Characteristics> characteristics();

    public static<T, R> Collector<T, R, R> of(Supplier<R> supplier,
                                              BiConsumer<R, T> accumulator,
                                              BinaryOperator<R> combiner,
                                              Characteristics... characteristics) {
        Objects.requireNonNull(supplier);
        Objects.requireNonNull(accumulator);
        Objects.requireNonNull(combiner);
        Objects.requireNonNull(characteristics);
        Set<Characteristics> cs = (characteristics.length == 0)
                                  ? Collectors.CH_ID
                                  : Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.IDENTITY_FINISH,
                                                                           characteristics));
        return new Collectors.CollectorImpl<>(supplier, accumulator, combiner, cs);
    }

    public static<T, A, R> Collector<T, A, R> of(Supplier<A> supplier,
                                                 BiConsumer<A, T> accumulator,
                                                 BinaryOperator<A> combiner,
                                                 Function<A, R> finisher,
                                                 Characteristics... characteristics) {
        Objects.requireNonNull(supplier);
        Objects.requireNonNull(accumulator);
        Objects.requireNonNull(combiner);
        Objects.requireNonNull(finisher);
        Objects.requireNonNull(characteristics);
        Set<Characteristics> cs = Collectors.CH_NOID;
        if (characteristics.length > 0) {
            cs = EnumSet.noneOf(Characteristics.class);
            Collections.addAll(cs, characteristics);
            cs = Collections.unmodifiableSet(cs);
        }
        return new Collectors.CollectorImpl<>(supplier, accumulator, combiner, finisher, cs);
    }

 Characteristics {

        CONCURRENT,

        UNORDERED,

        IDENTITY_FINISH
    }
}

自定义的收集器

package com.dawa.jdk8.stream2;

import java.util.*;
import java.util.function.BiConsumer;
import java.util.function.BinaryOperator;
import java.util.function.Function;
import java.util.function.Supplier;
import java.util.stream.Collector;

import static java.util.stream.Collector.Characteristics.IDENTITY_FINISH;

public class MySetCollector<T> implements Collector<T,Set<T>,Set<T>> {

    @Override
    public Supplier<Set<T>> supplier() {
        System.out.println("supplier invoked");
        return HashSet<T>::new;// 返回一个HasHSet容器.
    }

    @Override
    public BiConsumer<Set<T>, T> accumulator() {
        System.out.println("accumalator invoked");//累加器

        return Set<T>::add;
//        return HashSet<T>::add; //不行,没有静态方法支持. 应该是 Supplier返回值的父类接口. 不能使用具体类型的set.
    }

    @Override
    public BinaryOperator<Set<T>> combiner() {
        System.out.println("combiner invoked");//并行流的时候,合并中间结果

        return (set1,set2)->{
            set1.addAll(set2);return set1;
        };
    }

    @Override
    public Function<Set<T>, Set<T>> finisher() {//合并结果类型.结果容器
        System.out.println("finisher invoked");

//        return ts -> ts;
        return Function.identity(); //底层是一样的. 同一性.
    }

    @Override
    public Set<Characteristics> characteristics() {
        System.out.println("charcteristics  invoked ");

        return Collections.unmodifiableSet(EnumSet.of(IDENTITY_FINISH));
    }

    public static void main(String[] args) {
        List<String> list = Arrays.asList("hello", "world", "welcome");
        Set<String> collect = list.stream().collect(new MySetCollector<>());
        System.out.println(collect);
    }
}

从源码深入Collector

第一步:代码中调用collect()

    public static void main(String[] args) {
        List<String> list = Arrays.asList("hello", "world", "welcome");
        Set<String> collect = list.stream().collect(new MySetCollector<>());
        System.out.println(collect);
    }

第二步:collect()方法的实现类

    @Override
    @SuppressWarnings("unchecked")
    public final <R, A> R collect(Collector<? super P_OUT, A, R> collector) {
        A container;
        if (isParallel()
                && (collector.characteristics().contains(Collector.Characteristics.CONCURRENT))
                && (!isOrdered() || collector.characteristics().contains(Collector.Characteristics.UNORDERED))) {
            container = collector.supplier().get();
            BiConsumer<A, ? super P_OUT> accumulator = collector.accumulator();
            forEach(u -> accumulator.accept(container, u));
        }
        else {
            container = evaluate(ReduceOps.makeRef(collector));
        }
        return collector.characteristics().contains(Collector.Characteristics.IDENTITY_FINISH)
               ? (R) container
               : collector.finisher().apply(container);
    }

IDENTITY_FINISH的字段特别重要,在这里使用

第三步: makeRef(), 逐步调用者三个函数式接口对象

public static <T, I> TerminalOp<T, I>
    makeRef(Collector<? super T, I, ?> collector) {
        Supplier<I> supplier = Objects.requireNonNull(collector).supplier();
        BiConsumer<I, ? super T> accumulator = collector.accumulator();
        BinaryOperator<I> combiner = collector.combiner();
        class ReducingSink extends Box<I>
                implements AccumulatingSink<T, I, ReducingSink> {
            @Override
            public void begin(long size) {
                state = supplier.get();
            }

            @Override
            public void accept(T t) {
                accumulator.accept(state, t);
            }

            @Override
            public void combine(ReducingSink other) {
                state = combiner.apply(state, other.state);
            }
        }
        return new ReduceOp<T, I, ReducingSink>(StreamShape.REFERENCE) {
            @Override
            public ReducingSink makeSink() {
                return new ReducingSink();
            }

            @Override
            public int getOpFlags() {
                return collector.characteristics().contains(Collector.Characteristics.UNORDERED)
                       ? StreamOpFlag.NOT_ORDERED
                       : 0;
            }
        };
    }

Collector的一些"坑"

使用这个案例去理解运作过程.

把一个set集合进行收集,我们对结果做一个增强.(原来是直接放在set当中了.)我们现在放在Map当中.

声明一个Collector类,要求.

1. 输入:Set
2. 输出:Map<String,String>

示例输入:["hello","world","hello world"]

示例输出:[{hello,hello},{world,world},{hello world,hello world}

泛型:<T,T,T>

彻底理解Characteristics.IDENTITY_FINISH属性

package com.dawa.jdk8.stream2;

import java.util.*;
import java.util.function.BiConsumer;
import java.util.function.BinaryOperator;
import java.util.function.Function;
import java.util.function.Supplier;
import java.util.stream.Collector;

public class MySetCollector2<T> implements Collector<T, Set<T>, Map<T,T>> {

    @Override
    public Supplier<Set<T>> supplier() {
        System.out.println("supplier invoked");
        return HashSet<T>::new;
    }

    @Override
    public BiConsumer<Set<T>, T> accumulator() {
        System.out.println("accumulator invoked");
        return Set::add;
    }

    @Override
    public BinaryOperator<Set<T>> combiner() {
        System.out.println("combiner invoked");

        return (set1, set2) -> {
            set1.addAll(set2);
            return set1;
        };

    }

    @Override
    public Function<Set<T>, Map<T, T>> finisher() { //这里一定会被调用.因为结果类型和最终类型不同
        //示例输入:["hello","world","hello world"]
        //示例输出:[{hello,hello},{world,world},{hello world,hello world}
        System.out.println("finisher invoked");

        return set ->{
            Map<T, T> map = new HashMap<>();
            set.stream().forEach(item -> map.put(item, item));
            return map;
        };
    }

    @Override
    public Set<Characteristics> characteristics() {
        System.out.println("characteristics invoked");
        return Collections.unmodifiableSet(EnumSet.of(Characteristics.UNORDERED));
    }

    public static void main(String[] args) {
        List<String> list = Arrays.asList("hello", "world", "hello", "welocome", "a", "b", "c", "d", "e");
        HashSet<String> set = new HashSet<>(list);
        System.out.println("set:"+list);

        Map<String, String> collect = set.stream().collect(new MySetCollector2<>());
        System.out.println(collect);
    }

}

如果多一个参数:

return Collections.unmodifiableSet(EnumSet.of(Characteristics.UNORDERED,Characteristics.IDENTITY_FINISH));

则会出现类型转换异常.

        /**
         * Indicates that the finisher function is the identity function and
         * can be elided.  If set, it must be the case that an unchecked cast
         * from A to R will succeed.
         */
        IDENTITY_FINISH

如果定义这个属性,则代表 indentity和 finish 是同一个类型的,要执行强制类型转换.所以会出现上述异常.

收集器是什么特性的,都是由这个Characteristics类来由你定义的.

所以你必须要理解你写的程序的类型.才能正确的使用这个枚举定义类.

彻底理解Characteristics.CONCURRENT属性

分支合并框架ForkJoinPoll(并行流)

对程序进行一定的改造,打印出相应的线程名称

    @Override
    public BiConsumer<Set<T>, T> accumulator() {
        System.out.println("accumulator invoked");
        return (set,item)->{
            System.out.println("accumulator:"+ Thread.currentThread().getName());
            set.add(item);
        };
    }

• 串行情况下:

Map<String, String> collect = set.Stream().collect(new MySetCollector2<>());

运行结果如下:

• 并行情况下

Map<String, String> collect = set.parallelStream().collect(new MySetCollector2<>());

运行结果如下.

如果加上 Characteristics.CONCURRENT.

    @Override
    public Set<Characteristics> characteristics() {
        System.out.println("characteristics invoked");
        return Collections.unmodifiableSet(EnumSet.of(Characteristics.UNORDERED,Characteristics.CONCURRENT));
    }

则可能会出来一个异常

Caused by: java.util.ConcurrentModificationException

如果不加 ,则不会出现异常

多执行几次,会有一定的发现.

查看属性的源码.

        /**
         * Indicates that this collector is <em>concurrent</em>, meaning that
         * the result container can support the accumulator function being
         * called concurrently with the same result container from multiple
         * threads.
         *
         * <p>If a {@code CONCURRENT} collector is not also {@code UNORDERED},
         * then it should only be evaluated concurrently if applied to an
         * unordered data source.
         */
        CONCURRENT,

出现问题的原因:是在打印了set集合.

/**
 * This exception may be thrown by methods that have detected concurrent
 * modification of an object when such modification is not permissible.
 * <p>
 * For example, it is not generally permissible for one thread to modify a Collection
 * while another thread is iterating over it.  In general, the results of the
 * iteration are undefined under these circumstances.  Some Iterator
 * implementations (including those of all the general purpose collection implementations
 * provided by the JRE) may choose to throw this exception if this behavior is
 * detected.  Iterators that do this are known as <i>fail-fast</i> iterators,
 * as they fail quickly and cleanly, rather that risking arbitrary,
 * non-deterministic behavior at an undetermined time in the future.
 * <p>
 * Note that this exception does not always indicate that an object has
 * been concurrently modified by a <i>different</i> thread.  If a single
 * thread issues a sequence of method invocations that violates the
 * contract of an object, the object may throw this exception.  For
 * example, if a thread modifies a collection directly while it is
 * iterating over the collection with a fail-fast iterator, the iterator
 * will throw this exception.
 *
 * <p>Note that fail-fast behavior cannot be guaranteed as it is, generally
 * speaking, impossible to make any hard guarantees in the presence of
 * unsynchronized concurrent modification.  Fail-fast operations
 * throw {@code ConcurrentModificationException} on a best-effort basis.
 * Therefore, it would be wrong to write a program that depended on this
 * exception for its correctness: <i>{@code ConcurrentModificationException}
 * should be used only to detect bugs.</i>
 *
 * @author  Josh Bloch
 * @see     Collection
 * @see     Iterator
 * @see     Spliterator
 * @see     ListIterator
 * @see     Vector
 * @see     LinkedList
 * @see     HashSet
 * @see     Hashtable
 * @see     TreeMap
 * @see     AbstractList
 * @since   1.2
 */
public class ConcurrentModificationException extends RuntimeException {
}

并发修改异常.

因为如果加上这个属性,那么这个就有一个结果集

并行的时候,会对set进行操作,但是你同时又在遍历打印, 两个赶到一起了.然后就会抛出这个异常.

这就是抛出这个异常的根本原因.

注意:如果是并行的话,千万要避免 打印遍历 你要操作的对象.

如果不加这个属性,那么combiner()方法的中间结果集就会被调用,所以就不会出现抢占资源的现象.

扩展: sequential() && parallerl()方法的调用.

Set<String> collect = list.stream().parallel().sequential().sequential().parallel().collect(new MySetCollector<>());

只有最后一个会生效.

sequential()

    /**
     * Returns an equivalent stream that is sequential.  May return
     * itself, either because the stream was already sequential, or because
     * the underlying stream state was modified to be sequential.
     *
     * <p>This is an <a href="package-summary.html#StreamOps">intermediate
     * operation</a>.
     *
     * @return a sequential stream
     */
    S sequential();

parallerl()

    /**
     * Returns an equivalent stream that is parallel.  May return
     * itself, either because the stream was already parallel, or because
     * the underlying stream state was modified to be parallel.
     *
     * <p>This is an <a href="package-summary.html#StreamOps">intermediate
     * operation</a>.
     *
     * @return a parallel stream
     */
    S parallel();

关于Supplier()容器的定义.

修改代码.查看 串行 和并行的 区别.

    @Override
    public Supplier<Set<T>> supplier() {
        System.out.println("supplier invoked");
//        return HashSet<T>::new;// 返回一个HasHSet容器.
        System.out.println("-----");
        return HashSet::new;
    }

结论:串行的时候,会生成单个初始容器 / 并行的时候,会生成多个初始容器.

关于串行和并行的效率问题

并不是说串行的效率就一定比并行的效率低.这都是要看实际情况的.

最多会生成系统最大CPU核心

超线程技术

Collectors类方法详解

题外话:当你具备一些底层基础知识之后,你看一些东西会觉得是理所当然的.

如果你不具备这些知识的话,是看不懂的.云里雾里的.

关注一下JDK提供的方法是怎么实现的.对于Collectors静态工厂类来说,其实现一共分为两种方式.

1. 通过CollectorImpl来实现
2. 通过reducing来实现 (reducing本身又是通过CollectorImpl来实现)

所以,所有的方法都是通过CollectorImpl来实现的.

1. 4个变量

static final Set<Collector.Characteristics> CH_CONCURRENT_ID
            = Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.CONCURRENT,
                                                     Collector.Characteristics.UNORDERED,
                                                     Collector.Characteristics.IDENTITY_FINISH));
    static final Set<Collector.Characteristics> CH_CONCURRENT_NOID
            = Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.CONCURRENT,
                                                     Collector.Characteristics.UNORDERED));
    static final Set<Collector.Characteristics> CH_ID
            = Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.IDENTITY_FINISH));
    static final Set<Collector.Characteristics> CH_UNORDERED_ID
            = Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.UNORDERED,
                                                     Collector.Characteristics.IDENTITY_FINISH));
    static final Set<Collector.Characteristics> CH_NOID = Collections.emptySet();

1. toCollection()方法

    public static <T, C extends Collection<T>>
    Collector<T, ?, C> toCollection(Supplier<C> collectionFactory) {
        return new CollectorImpl<>(collectionFactory, Collection<T>::add,
                                   (r1, r2) -> { r1.addAll(r2); return r1; },
                                   CH_ID);
    }

1. toList()方法.是toCollection的一种特例.

public static <T>
    Collector<T, ?, List<T>> toList() {
        return new CollectorImpl<>((Supplier<List<T>>) ArrayList::new, List::add,
                                   (left, right) -> { left.addAll(right); return left; },
                                   CH_ID);
    }

1. toSet()方法.是toCollection的一种特例.

    public static <T>
    Collector<T, ?, Set<T>> toSet() {
        return new CollectorImpl<>((Supplier<Set<T>>) HashSet::new, Set::add,
                                   (left, right) -> { left.addAll(right); return left; },
                                   CH_UNORDERED_ID);
    }

1. joining(): 融合成一个字符串. 此外,还有两个重载的.单参数的和多参数的.

    public static Collector<CharSequence, ?, String> joining() {
        return new CollectorImpl<CharSequence, StringBuilder, String>(
                StringBuilder::new, StringBuilder::append,
                (r1, r2) -> { r1.append(r2); return r1; },
                StringBuilder::toString, CH_NOID);
    }

1. mapping() 映射函数

    public static <T, U, A, R>
    Collector<T, ?, R> mapping(Function<? super T, ? extends U> mapper,
                               Collector<? super U, A, R> downstream) {
        BiConsumer<A, ? super U> downstreamAccumulator = downstream.accumulator();
        return new CollectorImpl<>(downstream.supplier(),
                                   (r, t) -> downstreamAccumulator.accept(r, mapper.apply(t)),
                                   downstream.combiner(), downstream.finisher(),
                                   downstream.characteristics());
    }

1. collectingAndThen() 收集,并且做处理

   原理:把IDENTITY_FINISH标识符给去掉.

   为什么要去掉:不去掉的话,表示不会执行 finisher()方法.

public static<T,A,R,RR> Collector<T,A,RR> collectingAndThen(Collector<T,A,R> downstream,
                                                                Function<R,RR> finisher) {
        Set<Collector.Characteristics> characteristics = downstream.characteristics();
        if (characteristics.contains(Collector.Characteristics.IDENTITY_FINISH)) {
            if (characteristics.size() == 1)
                characteristics = Collectors.CH_NOID;
            else {
                characteristics = EnumSet.copyOf(characteristics);
                characteristics.remove(Collector.Characteristics.IDENTITY_FINISH);
                characteristics = Collections.unmodifiableSet(characteristics);
            }
        }
        return new CollectorImpl<>(downstream.supplier(),
                                   downstream.accumulator(),
                                   downstream.combiner(),
                                   downstream.finisher().andThen(finisher),
                                   characteristics);
    }

1. counting() 计算.

public static <T> Collector<T, ?, Long>
    counting() {
        return reducing(0L, e -> 1L, Long::sum);
    }

1. minBy()

public static <T> Collector<T, ?, Optional<T>>
    minBy(Comparator<? super T> comparator) {
        return reducing(BinaryOperator.minBy(comparator));
    }

1. maxBy()

public static <T> Collector<T, ?, Optional<T>>
    maxBy(Comparator<? super T> comparator) {
        return reducing(BinaryOperator.maxBy(comparator));
    }

1. summingInt(),Long(),Double

   为什么要用一个 int[1]? 最后还要返回一个数组中的单个数组呢?直接用一个数组行不行.

   因为:不行,因为直接用数字,数字是不能被传递的. 数组本身是一个引用.是可以改变的.数组本身就是一个容器.

    public static <T> Collector<T, ?, Integer>
    summingInt(ToIntFunction<? super T> mapper) {
        return new CollectorImpl<>(
                () -> new int[1],
                (a, t) -> { a[0] += mapper.applyAsInt(t); },
                (a, b) -> { a[0] += b[0]; return a; },
                a -> a[0], CH_NOID);
    }

1. averagingInt(),Long(),Double

    public static <T> Collector<T, ?, Double>
    averagingInt(ToIntFunction<? super T> mapper) {
        return new CollectorImpl<>(
                () -> new long[2],
                (a, t) -> { a[0] += mapper.applyAsInt(t); a[1]++; },
                (a, b) -> { a[0] += b[0]; a[1] += b[1]; return a; },
                a -> (a[1] == 0) ? 0.0d : (double) a[0] / a[1], CH_NOID);
    }

1. reducing() 重点函数.

    public static <T> Collector<T, ?, T>
    reducing(T identity, BinaryOperator<T> op) {
        return new CollectorImpl<>(
                boxSupplier(identity),
                (a, t) -> { a[0] = op.apply(a[0], t); },
                (a, b) -> { a[0] = op.apply(a[0], b[0]); return a; },
                a -> a[0],
                CH_NOID);
    }

1. groupingBy()方法的实现.(不支持并发)

public static <T, K> Collector<T, ?, Map<K, List<T>>>
    groupingBy(Function<? super T, ? extends K> classifier) {
        return groupingBy(classifier, toList());//调用下面2个参数的重载和toList()方法
    }

    public static <T, K, A, D>
    Collector<T, ?, Map<K, D>> groupingBy(Function<? super T, ? extends K> classifier,
                                          Collector<? super T, A, D> downstream) {
        return groupingBy(classifier, HashMap::new, downstream);//调用下面的三个参数的重载
    }

downstream下游. (接受一个,返回一个. 返回的就叫下游)

T:分类器函数,输入参数的类型.

K:分类器函数,返回的结果的类型.

D:返回的值的结果的类型.

HashMap::new :就是返回给客户的Map/

好处:为了给用户更好的使用.直接返回HashMap

坏处:局限了只能返回HashMap类型.

//groupBy函数的最底层实现.

    /**
     * Returns a {@code Collector} implementing a cascaded "group by" operation
     * on input elements of type {@code T}, grouping elements according to a
     * classification function, and then performing a reduction operation on
     * the values associated with a given key using the specified downstream
     * {@code Collector}.  The {@code Map} produced by the Collector is created
     * with the supplied factory function.
     *
     * <p>The classification function maps elements to some key type {@code K}.
     * The downstream collector operates on elements of type {@code T} and
     * produces a result of type {@code D}. The resulting collector produces a
     * {@code Map<K, D>}.
     *
     * <p>For example, to compute the set of last names of people in each city,
     * where the city names are sorted:
     * <pre>{@code
     *     Map<City, Set<String>> namesByCity
     *         = people.stream().collect(groupingBy(Person::getCity, TreeMap::new,
     *                                              mapping(Person::getLastName, toSet())));
     * }</pre>
     *
     * @implNote
     * The returned {@code Collector} is not concurrent.  For parallel stream
     * pipelines, the {@code combiner} function operates by merging the keys
     * from one map into another, which can be an expensive operation.  If
     * preservation of the order in which elements are presented to the downstream
     * collector is not required, using {@link #groupingByConcurrent(Function, Supplier, Collector)}
     * may offer better parallel performance.
     *
     * @param <T> the type of the input elements
     * @param <K> the type of the keys
     * @param <A> the intermediate accumulation type of the downstream collector
     * @param <D> the result type of the downstream reduction
     * @param <M> the type of the resulting {@code Map}
     * @param classifier a classifier function mapping input elements to keys
     * @param downstream a {@code Collector} implementing the downstream reduction
     * @param mapFactory a function which, when called, produces a new empty
     *                   {@code Map} of the desired type
     * @return a {@code Collector} implementing the cascaded group-by operation
     *
     * @see #groupingBy(Function, Collector)
     * @see #groupingBy(Function)
     * @see #groupingByConcurrent(Function, Supplier, Collector)
     */
public static <T, K, D, A, M extends Map<K, D>>
    Collector<T, ?, M> groupingBy(Function<? super T, ? extends K> classifier,
                                  Supplier<M> mapFactory,
                                  Collector<? super T, A, D> downstream) {
        Supplier<A> downstreamSupplier = downstream.supplier();
        BiConsumer<A, ? super T> downstreamAccumulator = downstream.accumulator();
        BiConsumer<Map<K, A>, T> accumulator = (m, t) -> {
            K key = Objects.requireNonNull(classifier.apply(t), "element cannot be mapped to a null key");
            A container = m.computeIfAbsent(key, k -> downstreamSupplier.get());
            downstreamAccumulator.accept(container, t);
        };
        BinaryOperator<Map<K, A>> merger = Collectors.<K, A, Map<K, A>>mapMerger(downstream.combiner());
        @SuppressWarnings("unchecked")
        Supplier<Map<K, A>> mangledFactory = (Supplier<Map<K, A>>) mapFactory;

        if (downstream.characteristics().contains(Collector.Characteristics.IDENTITY_FINISH)) {
            return new CollectorImpl<>(mangledFactory, accumulator, merger, CH_ID);
        }
        else {
            @SuppressWarnings("unchecked")
            Function<A, A> downstreamFinisher = (Function<A, A>) downstream.finisher();
            Function<Map<K, A>, M> finisher = intermediate -> {
                intermediate.replaceAll((k, v) -> downstreamFinisher.apply(v));
                @SuppressWarnings("unchecked")
                M castResult = (M) intermediate;
                return castResult;
            };
            return new CollectorImpl<>(mangledFactory, accumulator, merger, finisher, CH_NOID);
        }
    }

参数分析:

1.分类器: 输入T类型,返回K类型 返回的Map的键,是K类型.

2.容器:HashMap

3.下游收集器: D为下游收集器的返回的类型.

方法逻辑分析.

1. groupingByConcurrent() :(支持并发) (前提是你需要对顺序没有要求.)

public static <T, K>
    Collector<T, ?, ConcurrentMap<K, List<T>>>
    groupingByConcurrent(Function<? super T, ? extends K> classifier) {
        return groupingByConcurrent(classifier, ConcurrentHashMap::new, toList());
    }
//ConcurrentHashMap 实现起来支持并发.

public static <T, K, A, D, M extends ConcurrentMap<K, D>>
    Collector<T, ?, M> groupingByConcurrent(Function<? super T, ? extends K> classifier,
                                            Supplier<M> mapFactory,
                                            Collector<? super T, A, D> downstream) {
        Supplier<A> downstreamSupplier = downstream.supplier();
        BiConsumer<A, ? super T> downstreamAccumulator = downstream.accumulator();
        BinaryOperator<ConcurrentMap<K, A>> merger = Collectors.<K, A, ConcurrentMap<K, A>>mapMerger(downstream.combiner());
        @SuppressWarnings("unchecked")
        Supplier<ConcurrentMap<K, A>> mangledFactory = (Supplier<ConcurrentMap<K, A>>) mapFactory;
        BiConsumer<ConcurrentMap<K, A>, T> accumulator;
  //支持并发的同步的源码:
        if (downstream.characteristics().contains(Collector.Characteristics.CONCURRENT)) {
            accumulator = (m, t) -> {
                K key = Objects.requireNonNull(classifier.apply(t), "element cannot be mapped to a null key");
                A resultContainer = m.computeIfAbsent(key, k -> downstreamSupplier.get());
                downstreamAccumulator.accept(resultContainer, t);
            };
        }
        else {
            accumulator = (m, t) -> {
                K key = Objects.requireNonNull(classifier.apply(t), "element cannot be mapped to a null key");
                A resultContainer = m.computeIfAbsent(key, k -> downstreamSupplier.get());
                synchronized (resultContainer) {//同步锁.
                    downstreamAccumulator.accept(resultContainer, t);
                }
            };
        }

        if (downstream.characteristics().contains(Collector.Characteristics.IDENTITY_FINISH)) {
            return new CollectorImpl<>(mangledFactory, accumulator, merger, CH_CONCURRENT_ID);
        }
        else {
            @SuppressWarnings("unchecked")
            Function<A, A> downstreamFinisher = (Function<A, A>) downstream.finisher();
            Function<ConcurrentMap<K, A>, M> finisher = intermediate -> {
                intermediate.replaceAll((k, v) -> downstreamFinisher.apply(v));
                @SuppressWarnings("unchecked")
                M castResult = (M) intermediate;
                return castResult;
            };
            return new CollectorImpl<>(mangledFactory, accumulator, merger, finisher, CH_CONCURRENT_NOID);
        }
    }

1. partitioningBy() 分区方法.()

    public static <T>
    Collector<T, ?, Map<Boolean, List<T>>> partitioningBy(Predicate<? super T> predicate) {
        return partitioningBy(predicate, toList());//调用完全的重载方法.
    }

public static <T, D, A>
    Collector<T, ?, Map<Boolean, D>> partitioningBy(Predicate<? super T> predicate,
                                                    Collector<? super T, A, D> downstream) {
        BiConsumer<A, ? super T> downstreamAccumulator = downstream.accumulator();
        BiConsumer<Partition<A>, T> accumulator = (result, t) ->
                downstreamAccumulator.accept(predicate.test(t) ? result.forTrue : result.forFalse, t);
        BinaryOperator<A> op = downstream.combiner();
        BinaryOperator<Partition<A>> merger = (left, right) ->
                new Partition<>(op.apply(left.forTrue, right.forTrue),
                                op.apply(left.forFalse, right.forFalse));
        Supplier<Partition<A>> supplier = () ->
                new Partition<>(downstream.supplier().get(),
                                downstream.supplier().get());
        if (downstream.characteristics().contains(Collector.Characteristics.IDENTITY_FINISH)) {
            return new CollectorImpl<>(supplier, accumulator, merger, CH_ID);
        }
        else {
            Function<Partition<A>, Map<Boolean, D>> finisher = par ->
                    new Partition<>(downstream.finisher().apply(par.forTrue),
                                    downstream.finisher().apply(par.forFalse));
            return new CollectorImpl<>(supplier, accumulator, merger, finisher, CH_NOID);
        }
    }

自己提供的内部静态类:

/**
     * Implementation class used by partitioningBy.
     */
    private static final class Partition<T>
            extends AbstractMap<Boolean, T>
            implements Map<Boolean, T> {
        final T forTrue;
        final T forFalse;

        Partition(T forTrue, T forFalse) {
            this.forTrue = forTrue;
            this.forFalse = forFalse;
        }

        @Override
        public Set<Map.Entry<Boolean, T>> entrySet() {
            return new AbstractSet<Map.Entry<Boolean, T>>() {
                @Override
                public Iterator<Map.Entry<Boolean, T>> iterator() {
                    Map.Entry<Boolean, T> falseEntry = new SimpleImmutableEntry<>(false, forFalse);
                    Map.Entry<Boolean, T> trueEntry = new SimpleImmutableEntry<>(true, forTrue);
                    return Arrays.asList(falseEntry, trueEntry).iterator();
                }

                @Override
                public int size() {
                    return 2;
                }
            };
        }
    }

...

Stream类

public interface Stream<T> extends BaseStream<T, Stream<T>> {}

BaseStream类

package java.util.stream;

import java.nio.charset.Charset;
import java.nio.file.Files;
import java.nio.file.Path;
import java.util.Collection;
import java.util.Iterator;
import java.util.Spliterator;
import java.util.concurrent.ConcurrentHashMap;
import java.util.function.IntConsumer;
import java.util.function.Predicate;

/**
 * Base interface for streams, which are sequences of elements supporting
 * sequential and parallel aggregate operations.  The following example
 * illustrates an aggregate operation using the stream types {@link Stream}
 * and {@link IntStream}, computing the sum of the weights of the red widgets:
 *
 * <pre>{@code
 *     int sum = widgets.stream()
 *                      .filter(w -> w.getColor() == RED)
 *                      .mapToInt(w -> w.getWeight())
 *                      .sum();
 * }</pre>
 *
 * See the class documentation for {@link Stream} and the package documentation
 * for <a href="package-summary.html">java.util.stream</a> for additional
 * specification of streams, stream operations, stream pipelines, and
 * parallelism, which governs the behavior of all stream types.
 *
 * @param <T> the type of the stream elements
 * @param <S> the type of the stream implementing {@code BaseStream}
 * @since 1.8
 * @see Stream
 * @see IntStream
 * @see LongStream
 * @see DoubleStream
 * @see <a href="package-summary.html">java.util.stream</a>
 */
public interface BaseStream<T, S extends BaseStream<T, S>>
        extends AutoCloseable {
    /**
     * Returns an iterator for the elements of this stream.
     *
     * <p>This is a <a href="package-summary.html#StreamOps">terminal
     * operation</a>.
     *
     * @return the element iterator for this stream
     */
    Iterator<T> iterator();

    /**
     * Returns a spliterator for the elements of this stream.
     *
     * <p>This is a <a href="package-summary.html#StreamOps">terminal
     * operation</a>.
     *
     * @return the element spliterator for this stream
     */
    Spliterator<T> spliterator();

    /**
     * Returns whether this stream, if a terminal operation were to be executed,
     * would execute in parallel.  Calling this method after invoking an
     * terminal stream operation method may yield unpredictable results.
     *
     * @return {@code true} if this stream would execute in parallel if executed
     */
    boolean isParallel();

    /**
     * Returns an equivalent stream that is sequential.  May return
     * itself, either because the stream was already sequential, or because
     * the underlying stream state was modified to be sequential.
     *
     * <p>This is an <a href="package-summary.html#StreamOps">intermediate
     * operation</a>.
     *
     * @return a sequential stream
     */
    S sequential();

    /**
     * Returns an equivalent stream that is parallel.  May return
     * itself, either because the stream was already parallel, or because
     * the underlying stream state was modified to be parallel.
     *
     * <p>This is an <a href="package-summary.html#StreamOps">intermediate
     * operation</a>.
     *
     * @return a parallel stream
     */
    S parallel();

    /**
     * Returns an equivalent stream that is
     * <a href="package-summary.html#Ordering">unordered</a>.  May return
     * itself, either because the stream was already unordered, or because
     * the underlying stream state was modified to be unordered.
     *
     * <p>This is an <a href="package-summary.html#StreamOps">intermediate
     * operation</a>.
     *
     * @return an unordered stream
     */
    S unordered();

    /**
     * Returns an equivalent stream with an additional close handler.  Close
     * handlers are run when the {@link #close()} method
     * is called on the stream, and are executed in the order they were
     * added.  All close handlers are run, even if earlier close handlers throw
     * exceptions.  If any close handler throws an exception, the first
     * exception thrown will be relayed to the caller of {@code close()}, with
     * any remaining exceptions added to that exception as suppressed exceptions
     * (unless one of the remaining exceptions is the same exception as the
     * first exception, since an exception cannot suppress itself.)  May
     * return itself.
     *
     * <p>This is an <a href="package-summary.html#StreamOps">intermediate
     * operation</a>.
     *
     * @param closeHandler A task to execute when the stream is closed
     * @return a stream with a handler that is run if the stream is closed
     */
    S onClose(Runnable closeHandler);

    /**
     * Closes this stream, causing all close handlers for this stream pipeline
     * to be called.
     *
     * @see AutoCloseable#close()
     */
    @Override
    void close();
}

扩展:AutoCloseable接口

package java.lang;

/**
 * An object that may hold resources (such as file or socket handles)
 * until it is closed. The {@link #close()} method of an {@code AutoCloseable}
 * object is called automatically when exiting a {@code
 * try}-with-resources block for which the object has been declared in
 * the resource specification header. This construction ensures prompt
 * release, avoiding resource exhaustion exceptions and errors that
 * may otherwise occur.
 一个对象在关闭之前,会持有一些资源. 句柄之类的.
 在退出块的时候,会自动调用close()
 避免资源被耗尽等异常.
 *
 * @apiNote
 * <p>It is possible, and in fact common, for a base class to
 * implement AutoCloseable even though not all of its subclasses or
 * instances will hold releasable resources.  For code that must operate
 * in complete generality, or when it is known that the {@code AutoCloseable}
 * instance requires resource release, it is recommended to use {@code
 * try}-with-resources constructions. However, when using facilities such as
 * {@link java.util.stream.Stream} that support both I/O-based and
 * non-I/O-based forms, {@code try}-with-resources blocks are in
 * general unnecessary when using non-I/O-based forms.
 *
 * @author Josh Bloch
 * @since 1.7
 */
public interface AutoCloseable {
    /**
     * Closes this resource, relinquishing any underlying resources.
     * This method is invoked automatically on objects managed by the
     * {@code try}-with-resources statement.
     *
     * <p>While this interface method is declared to throw {@code
     * Exception}, implementers are <em>strongly</em> encouraged to
     * declare concrete implementations of the {@code close} method to
     * throw more specific exceptions, or to throw no exception at all
     * if the close operation cannot fail.
     *
     * <p> Cases where the close operation may fail require careful
     * attention by implementers. It is strongly advised to relinquish
     * the underlying resources and to internally <em>mark</em> the
     * resource as closed, prior to throwing the exception. The {@code
     * close} method is unlikely to be invoked more than once and so
     * this ensures that the resources are released in a timely manner.
     * Furthermore it reduces problems that could arise when the resource
     * wraps, or is wrapped, by another resource.
     *
     * <p><em>Implementers of this interface are also strongly advised
     * to not have the {@code close} method throw {@link
     * InterruptedException}.</em>
     *
     * This exception interacts with a thread's interrupted status,
     * and runtime misbehavior is likely to occur if an {@code
     * InterruptedException} is {@linkplain Throwable#addSuppressed
     * suppressed}.
     *
     * More generally, if it would cause problems for an
     * exception to be suppressed, the {@code AutoCloseable.close}
     * method should not throw it.
     *
     * <p>Note that unlike the {@link java.io.Closeable#close close}
     * method of {@link java.io.Closeable}, this {@code close} method
     * is <em>not</em> required to be idempotent.  In other words,
     * calling this {@code close} method more than once may have some
     * visible side effect, unlike {@code Closeable.close} which is
     * required to have no effect if called more than once.
     *
     * However, implementers of this interface are strongly encouraged
     * to make their {@code close} methods idempotent.
     *
     * @throws Exception if this resource cannot be closed
     */
    void close() throws Exception;
}

使用Example去理解这个接口

public class AutoCloseableTest implements AutoCloseable {
    public static void main(String[] args) {
        try(AutoCloseableTest autoCloseableTest = new AutoCloseableTest()) {
            autoCloseableTest.doSomething();
        } catch (Exception e) {
            e.printStackTrace();
        } //这种写法.try with source.
    }

    @Override
    public void close() throws Exception {
        System.out.println("close invoked");
    }

    public void doSomething(){
        System.out.println("doSomething invoked");
    }
}

运行结果: (实现了这个接口的类,会自动执行 close()方法.)

总结:

1. JDK内置的函数式接口在这里得以体现.

看底层的原因:

不是因为要让你开发过程中去

看了源码之后,你使用的时候的信心就非常足.

在遇到问题的时候,你能快速的将问题fix掉.

学习方法

1.看优秀的代码

2.去学习别人的东西

3.用的多了就会变成自己的东西.

附加一个小插曲

​