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// 1.比较器链。如先按重量递减排序，两个苹果一样重时，再按国家排序
inventory.sort(comparing(Apple::getWeoght))
    .reversed()
    .thenComparing(Apple::getCountry)
// 2.谓词复合。and和or是按照在表达式链中的位置从左向右确定优先级的。a.or(b).and(c)可以看做(a || b) && c
Predicate<Apple> redAndHeavyAppleOrGreen = redApple.and(a -> a.getWeight() > 150)
											.or(a -> "green".equals(a.getColor()));
// 3.函数复合。
Function<Integer, Integer> f = x -> x + 1;
Function<Integer, Integer> g = x -> x * 2;
Function<Integer, Integer> h = f.andThen(g); //数学上写作g(f(x))
Function<Integer, Integer> h = f.compose(g); //数学上写作f(g(x))
int result = h.apply(1);

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//由值创建流
Stream<String> stream = Stream.of("Java 8 ", "Lambdas ", "In ", "Action");
Stream<String> emptyStream = Stream.empty();
//由数组创建流
int[] numbers = {2, 3, 5, 7, 11, 13};
int sum = Arrays.stream(numbers).sum();
//由函数生成流
Stream.iterate(new int[]{0, 1},t -> new int[]{t[1],t[0] + t[1]})
.limit(10)
.map(t -> t[0])
.forEach(System.out::println);
// ===相比于iterate，generate是有状态的
IntSupplier fib = new IntSupplier(){
private int previous = 0;
private int current = 1;
public int getAsInt(){
int oldPrevious = this.previous;
int nextValue = this.previous + this.current;
this.previous = this.current;
this.current = nextValue;
return oldPrevious;
}
};
IntStream.generate(fib).limit(10).forEach(System.out::println);
//=====

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package java.util.stream;

import java.util.Collections;
import java.util.EnumSet;
import java.util.Objects;
import java.util.Set;
import java.util.function.BiConsumer;
import java.util.function.BinaryOperator;
import java.util.function.Function;
import java.util.function.Supplier;

// A compilation test for the code snippets in this class-level javadoc can be found at:
// test/jdk/java/util/stream/test/org/openjdk/tests/java/util/stream/CollectorExample.java
// The test needs to be updated if the examples in this javadoc change or new examples are added.

/**
 * A <a href="package-summary.html#Reduction">mutable reduction operation</a> that
 * accumulates input elements into a mutable result container, optionally transforming
 * the accumulated result into a final representation after all input elements
 * have been processed.  Reduction operations can be performed either sequentially
 * or in parallel.
 *
 * <p>Examples of mutable reduction operations include:
 * accumulating elements into a {@code Collection}; concatenating
 * strings using a {@code StringBuilder}; computing summary information about
 * elements such as sum, min, max, or average; computing "pivot table" summaries
 * such as "maximum valued transaction by seller", etc.  The class {@link Collectors}
 * provides implementations of many common mutable reductions.
 *
 * <p>A {@code Collector} is specified by four functions that work together to
 * accumulate entries into a mutable result container, and optionally perform
 * a final transform on the result.  They are: <ul>
 *     <li>creation of a new result container ({@link #supplier()})</li>
 *     <li>incorporating a new data element into a result container ({@link #accumulator()})</li>
 *     <li>combining two result containers into one ({@link #combiner()})</li>
 *     <li>performing an optional final transform on the container ({@link #finisher()})</li>
 * </ul>
 *
 * <p>Collectors also have a set of characteristics, such as
 * {@link Characteristics#CONCURRENT}, that provide hints that can be used by a
 * reduction implementation to provide better performance.
 *
 * <p>A sequential implementation of a reduction using a collector would
 * create a single result container using the supplier function, and invoke the
 * accumulator function once for each input element.  A parallel implementation
 * would partition the input, create a result container for each partition,
 * accumulate the contents of each partition into a subresult for that partition,
 * and then use the combiner function to merge the subresults into a combined
 * result.
 *
 * <p>To ensure that sequential and parallel executions produce equivalent
 * results, the collector functions must satisfy an <em>identity</em> and an
 * <a href="package-summary.html#Associativity">associativity</a> constraints.
 *
 * <p>The identity constraint says that for any partially accumulated result,
 * combining it with an empty result container must produce an equivalent
 * result.  That is, for a partially accumulated result {@code a} that is the
 * result of any series of accumulator and combiner invocations, {@code a} must
 * be equivalent to {@code combiner.apply(a, supplier.get())}.
 *
 * <p>The associativity constraint says that splitting the computation must
 * produce an equivalent result.  That is, for any input elements {@code t1}
 * and {@code t2}, the results {@code r1} and {@code r2} in the computation
 * below must be equivalent:
 * <pre>{@code
 *     A a1 = supplier.get();
 *     accumulator.accept(a1, t1);
 *     accumulator.accept(a1, t2);
 *     R r1 = finisher.apply(a1);  // result without splitting
 *
 *     A a2 = supplier.get();
 *     accumulator.accept(a2, t1);
 *     A a3 = supplier.get();
 *     accumulator.accept(a3, t2);
 *     R r2 = finisher.apply(combiner.apply(a2, a3));  // result with splitting
 * } </pre>
 *
 * <p>For collectors that do not have the {@code UNORDERED} characteristic,
 * two accumulated results {@code a1} and {@code a2} are equivalent if
 * {@code finisher.apply(a1).equals(finisher.apply(a2))}.  For unordered
 * collectors, equivalence is relaxed to allow for non-equality related to
 * differences in order.  (For example, an unordered collector that accumulated
 * elements to a {@code List} would consider two lists equivalent if they
 * contained the same elements, ignoring order.)
 *
 * <p>Libraries that implement reduction based on {@code Collector}, such as
 * {@link Stream#collect(Collector)}, must adhere to the following constraints:
 * <ul>
 *     <li>The first argument passed to the accumulator function, both
 *     arguments passed to the combiner function, and the argument passed to the
 *     finisher function must be the result of a previous invocation of the
 *     result supplier, accumulator, or combiner functions.</li>
 *     <li>The implementation should not do anything with the result of any of
 *     the result supplier, accumulator, or combiner functions other than to
 *     pass them again to the accumulator, combiner, or finisher functions,
 *     or return them to the caller of the reduction operation.</li>
 *     <li>If a result is passed to the combiner or finisher
 *     function, and the same object is not returned from that function, it is
 *     never used again.</li>
 *     <li>Once a result is passed to the combiner or finisher function, it
 *     is never passed to the accumulator function again.</li>
 *     <li>For non-concurrent collectors, any result returned from the result
 *     supplier, accumulator, or combiner functions must be serially
 *     thread-confined.  This enables collection to occur in parallel without
 *     the {@code Collector} needing to implement any additional synchronization.
 *     The reduction implementation must manage that the input is properly
 *     partitioned, that partitions are processed in isolation, and combining
 *     happens only after accumulation is complete.</li>
 *     <li>For concurrent collectors, an implementation is free to (but not
 *     required to) implement reduction concurrently.  A concurrent reduction
 *     is one where the accumulator function is called concurrently from
 *     multiple threads, using the same concurrently-modifiable result container,
 *     rather than keeping the result isolated during accumulation.
 *     A concurrent reduction should only be applied if the collector has the
 *     {@link Characteristics#UNORDERED} characteristics or if the
 *     originating data is unordered.</li>
 * </ul>
 *
 * <p>In addition to the predefined implementations in {@link Collectors}, the
 * static factory methods {@link #of(Supplier, BiConsumer, BinaryOperator, Characteristics...)}
 * can be used to construct collectors.  For example, you could create a collector
 * that accumulates widgets into a {@code TreeSet} with:
 *
 * <pre>{@code
 *     Collector<Widget, ?, TreeSet<Widget>> intoSet =
 *         Collector.of(TreeSet::new, TreeSet::add,
 *                      (left, right) -> { left.addAll(right); return left; });
 * }</pre>
 *
 * (This behavior is also implemented by the predefined collector
 * {@link Collectors#toCollection(Supplier)}).
 *
 * @apiNote
 * Performing a reduction operation with a {@code Collector} should produce a
 * result equivalent to:
 * <pre>{@code
 *     A container = collector.supplier().get();
 *     for (T t : data)
 *         collector.accumulator().accept(container, t);
 *     return collector.finisher().apply(container);
 * }</pre>
 *
 * <p>However, the library is free to partition the input, perform the reduction
 * on the partitions, and then use the combiner function to combine the partial
 * results to achieve a parallel reduction.  (Depending on the specific reduction
 * operation, this may perform better or worse, depending on the relative cost
 * of the accumulator and combiner functions.)
 *
 * <p>Collectors are designed to be <em>composed</em>; many of the methods
 * in {@link Collectors} are functions that take a collector and produce
 * a new collector.  For example, given the following collector that computes
 * the sum of the salaries of a stream of employees:
 *
 * <pre>{@code
 *     Collector<Employee, ?, Integer> summingSalaries
 *         = Collectors.summingInt(Employee::getSalary))
 * }</pre>
 *
 * If we wanted to create a collector to tabulate the sum of salaries by
 * department, we could reuse the "sum of salaries" logic using
 * {@link Collectors#groupingBy(Function, Collector)}:
 *
 * <pre>{@code
 *     Collector<Employee, ?, Map<Department, Integer>> summingSalariesByDept
 *         = Collectors.groupingBy(Employee::getDepartment, summingSalaries);
 * }</pre>
 *
 * @see Stream#collect(Collector)
 * @see Collectors
 *
 * @param <T> the type of input elements to the reduction operation
 * @param <A> the mutable accumulation type of the reduction operation (often
 *            hidden as an implementation detail)
 * @param <R> the result type of the reduction operation
 * @since 1.8
 */
public interface Collector<T, A, R> {
    /**
     * 创建并返回新的可变结果容器的函数。
     * 返回值：返回新的可变结果容器的函数
     */
    Supplier<A> supplier();

    /**
     * A function that folds a value into a mutable result container.
     *
     * @return a function which folds a value into a mutable result container
     */
    BiConsumer<A, T> accumulator();

    /**
     * A function that accepts two partial results and merges them.  The
     * combiner function may fold state from one argument into the other and
     * return that, or may return a new result container.
     *
     * @return a function which combines two partial results into a combined
     * result
     */
    BinaryOperator<A> combiner();

    /**
     * Perform the final transformation from the intermediate accumulation type
     * {@code A} to the final result type {@code R}.
     *
     * <p>If the characteristic {@code IDENTITY_FINISH} is
     * set, this function may be presumed to be an identity transform with an
     * unchecked cast from {@code A} to {@code R}.
     *
     * @return a function which transforms the intermediate result to the final
     * result
     */
    Function<A, R> finisher();

    /**
     * Returns a {@code Set} of {@code Collector.Characteristics} indicating
     * the characteristics of this Collector.  This set should be immutable.
     *
     * @return an immutable set of collector characteristics
     */
    Set<Characteristics> characteristics();

    /**
     * Returns a new {@code Collector} described by the given {@code supplier},
     * {@code accumulator}, and {@code combiner} functions.  The resulting
     * {@code Collector} has the {@code Collector.Characteristics.IDENTITY_FINISH}
     * characteristic.
     *
     * @param supplier The supplier function for the new collector
     * @param accumulator The accumulator function for the new collector
     * @param combiner The combiner function for the new collector
     * @param characteristics The collector characteristics for the new
     *                        collector
     * @param <T> The type of input elements for the new collector
     * @param <R> The type of intermediate accumulation result, and final result,
     *           for the new collector
     * @throws NullPointerException if any argument is null
     * @return the new {@code Collector}
     */
    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);
    }

    /**
     * Returns a new {@code Collector} described by the given {@code supplier},
     * {@code accumulator}, {@code combiner}, and {@code finisher} functions.
     *
     * @param supplier The supplier function for the new collector
     * @param accumulator The accumulator function for the new collector
     * @param combiner The combiner function for the new collector
     * @param finisher The finisher function for the new collector
     * @param characteristics The collector characteristics for the new
     *                        collector
     * @param <T> The type of input elements for the new collector
     * @param <A> The intermediate accumulation type of the new collector
     * @param <R> The final result type of the new collector
     * @throws NullPointerException if any argument is null
     * @return the new {@code Collector}
     */
    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 indicating properties of a {@code Collector}, which can
     * be used to optimize reduction implementations.
     */
    enum Characteristics {
        /**
         * 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,

        /**
         * Indicates that the collection operation does not commit to preserving
         * the encounter order of input elements.  (This might be true if the
         * result container has no intrinsic order, such as a {@link Set}.)
         */
        UNORDERED,

        /**
         * 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
    }
}

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import java.util.stream.*;

public class ParallelStreams {

    public static long iterativeSum(long n) {
        long result = 0;
        for (long i = 0; i <= n; i++) {
            result += i;
        }
        return result;
    }

    public static long sequentialSum(long n) {
        return Stream.iterate(1L, i -> i + 1).limit(n).reduce(Long::sum).get();
    }

    public static long parallelSum(long n) {
        return Stream.iterate(1L, i -> i + 1).limit(n).parallel().reduce(Long::sum).get();
    }

    public static long rangedSum(long n) {
        return LongStream.rangeClosed(1, n).reduce(Long::sum).getAsLong();
    }

    public static long parallelRangedSum(long n) {
        return LongStream.rangeClosed(1, n).parallel().reduce(Long::sum).getAsLong();
    }

    public static long sideEffectSum(long n) {
        Accumulator accumulator = new Accumulator();
        LongStream.rangeClosed(1, n).forEach(accumulator::add);
        return accumulator.total;
    }

    public static long sideEffectParallelSum(long n) {
        Accumulator accumulator = new Accumulator();
        LongStream.rangeClosed(1, n).parallel().forEach(accumulator::add);
        return accumulator.total;
    }

    public static class Accumulator {
        private long total = 0;

        public void add(long value) {
            total += value;
        }
    }
}

package chl;

//===============================================================================================

import java.util.function.Function;

public class MyParallelStreams {
    public static long measureSumPerf(Function<Long, Long> adder, long n) {
        long fastest = Long.MAX_VALUE;
        for (int i = 0; i < 10; i++) {
            long start = System.nanoTime();
            long sum = adder.apply(n);
            long duration = (System.nanoTime() - start) / 1000000;
            System.out.println("Result:" + sum);
            if (duration < fastest) fastest = duration;
        }
        return fastest;
    }

    public static void main(String[] args) {
        System.out.println("耗时:" + measureSumPerf(ParallelStreams::sideEffectParallelSum, 10000000));
    }
}

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public abstract class RecursiveTask<V> extends ForkJoinTask<V> {
    private static final long serialVersionUID = 5232453952276485270L;

    @SuppressWarnings("serial") 
    V result;

    protected abstract V compute();

    public final V getRawResult() {
        return result;
    }

    protected final void setRawResult(V value) {
        result = value;
    }

    protected final boolean exec() {
        result = compute();
        return true;
    }

}

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public interace Spliterator<T> {
    /**
     * 按序遍历，如果没有元素则返回false，否则对元素执行给定的操作并返回true
     */
    boolean tryAdvance(Consumer<? super T> action);
    
    /**
     * 把元素划出去分给第二个Spliterator,让他们并行处理，直到返回null
     */
    Spliterator<T> trySplit();
    
     /**
     * 返回forEachRemaining遍历将遇到的估计值，如果无限或无法计算，则返回Long.MAX_VALUE
     */
    long estimateSize();
    
    int characteristics();
}

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LocalDate date = LocalDate.of(2014, 3, 18);
String s1 = date.format(DateTimeFormatter.BASIC_ISO_DATE); // 20140318
String s2 = date.format(DateTimeFormatter.ISO_LOCAL_DATE); // 2014-03-18

LocalDate date1 = LocalDate.parse("20140318",DateTimeFormatter.BASIC_ISO_DATE);
LocalDate date2 = LocalDate.parse("2014-03-18",DateTimeFormatter.ISO_LOCAL_DATE);

DateTimeFormatter formatter = DateTimeFormatter.ofPattern("dd/MM/yyyy");
LocalDate date1 = LocalDate.of(2014, 3, 18);
String formattedDate = date1.format(formatter);
LocalDate date2 = LocalDate.parse(formattedDate, formatter);