从源代码深入Stream /

学习的时候,官方文档是最重要的.

及其重要的内容我们不仅要知道stream用,要知道为什么这么用,还要知道底层是怎么去实现的.

--个人注释:从此看出,虽然新的jdk版本对开发人员提供了很大的遍历,但是从底层角度来说,实现确实是非常复杂的.
--对外提供很简单的接口使用. (一定是框架给封装到底层了,所以你才用着简单.)

遇到问题,能够从底层深入解决问题.

学习一门技术的时候,先学会用,然后去挖掘深层次的内容(底层代码和运作方式).

引入:Example.

public class StudentTest1 {
    public static void main(String[] args) {
        Student student1 = new Student("zhangsan", 80);
        Student student2 = new Student("lisi", 90);
        Student student3 = new Student("wangwu", 100);
        Student student4 = new Student("zhaoliu", 90);

        List students = Arrays.asList(student1, student2, student3, student4);

        //collect()方法深入源码详解
        //op1:集合转换为stream, 然后stream转换为List
        List students1 = students.stream().collect(Collectors.toList());
        students1.forEach(System.out::println);

        System.out.println("----------");
        System.out.println("count: "+ students.stream().collect(counting()));//Collectors类提供的counting()方法
        System.out.println("count: "+ students.stream().count()); //stream提供的方法 , 底层实现 mapToLong()->sum

        //当jdk底层提供有通用的方法和具体的实现方法,越具体的越好.
    }
}

静态导入(直接导入指定Java类中实现的方法)

import static java.util.stream.Collectors.*;

• collect:收集器
• Collector是一个接口,是特别重要的接口.

Collector接口源码解读

题外话:虽然JDK提供了很多Collector的实现,但是很多人仅停留在使用阶段.

我们这次一行一行的读javadoc. 因为真的很重要.

/**
 * A mutable reduction operation 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.
 一个可变的汇聚操作.将输入元素累积到可变的结果容器当中.它会在所有元素都处理完毕后,将累积之后的结果转换成一个最终的表示(这是一个可选操作).汇聚操作支持串行和并行两种方式执行.
 
 --如 ArrayList:就是一个可变的容器.
 --支持并行操作:确保数据不会错,线程可以并发.很难.另外并不是说并行一定比串行要快,因为并行是有额外开销的.
 
 *
 * 
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.
 可变的reduction(汇聚)操作包括:将元素累积到集合当中,使用StringBuilder将字符串给拼在一起,计算关于元素的sum,min,max or average等,计算数据透视图计算:如根据销售商获取最大销售额等.这个Collectors类,提供了大量的可变汇聚的实现.
 
 -- Collectors本身实际上是一个工厂.
 
 *
 * 
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: 

 *     
creation of a new result container ({@link #supplier()})
 *     
incorporating a new data element into a result container ({@link #accumulator()})
 *     
combining two result containers into one ({@link #combiner()})
 *     
performing an optional final transform on the container ({@link #finisher()})
 * 
 一个Collector是由4个函数组成的,可以对结果进行一个最终的转化.
 4个方法分别是:
 1.创建一个新的接结果容器   new  
 2.将新的数据元素给合并到一个结果容器中.  add
 3.将两个结果容器合并成一个.  + 
 4.将中间的累积类型,转换成结果类型.   result
 每个方法都会返回一个函数式皆苦.
 --学习的时候,官方文档是最重要的.
 *
 * 
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.
 Collectors 还会返回这么一个集合 Characteristics#CONCURRENT. (也就是这个类中的枚举类)
 
 *
 * 
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.
 一个汇聚操作串行的实现,会创建一个唯一的一个结果容器.使用函数. 每一个输入元素都会调用累积函数(accumulator())一次. 
 
 一个并行的实现,将会对输入进行分区,分成多个区域,每一次分区都会创建一个结果容器,然后函数.累积每一个结果容器的内容区内形成一个,然后通过comtainer()给合并成一个.
 -- 解释:
 combiner函数,假如有4个线程同时去执行,那么就会生成4个部分结果.
 结果分别是:1.2.3.4
 可能是:
 1.2 -> 5 
 5.3 -> 6
 6.4 -> 7
 这5.6.7新创建的集合,就叫做 新的结果容器
 
 也可能是:
 1.2 -> 1+2 (新的一个)
 1.3 -> 1(新的一个)
 这种新的折叠后的,叫做折叠成一个参数容器.
 
 *
 * 
To ensure that sequential and parallel executions produce equivalent
 * results, the collector functions must satisfy an identity and an
 * associativity constraints.
 为了确保串行与并行获得等价的结果.  collector(收集器)的函数必须满足2个条件.
 1. identity: 同一性
 2.	Associativity :结合性
 
 *
 * 
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())}.
 同一性是说:针对于任何部分累积的结果来说,将他与一个空的容器融合,必须会生成一个等价的结果.等价于部分的累积结果.
 也就是说对于一个部分的累积结果a,对于任何一条线上的combiner invocations.
 a == combiner.apply(a, supplier.get())
 	supplier.get() ,获取一个空的结果容器.
 	然后将a与空的结果容器容器. 保证a == (融合等式) .
 	
 	这个特性就是:同一性.
 	
 --部分累积的结果:是在流程中产生的中间结果.
 --解释上述等式为什么成立:a是线程某一个分支得到的部分结果. 后面的是调用BiarnyOperator.apply()
 (List list1,List list2)->{list1.addAll(list2);return list1;}
 这个类似于之前说的: 将两个结果集折叠到同一个容器.然后返回来第一个结果的融合.
 
 *
 * 
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:
  结合性是说:分割执行的时候,也必须产生相同的结果.每一份处理完之后,也得到相应的结果.
 
 * 
{@code
 *     A a1 = supplier.get();//获取结果容器 a1.
 *     accumulator.accept(a1, t1); //a1:每一次累积的中间结果, t1:流中下一个待累积的元素.
 *     accumulator.accept(a1, t2); //t1->a1, a1已经有东西. 然后 t2->t1 = r1 (也就是下一步)
 *     R r1 = finisher.apply(a1);  // result without splitting
 *
 *     A a2 = supplier.get();  //另外一个线程
 *     accumulator.accept(a2, t1);   //两个结果集转换成中间结果.
 *     A a3 = supplier.get();  //第三个线程
 *     accumulator.accept(a3, t2); //两个中间结果转换成最终结果.
 *     R r2 = finisher.apply(combiner.apply(a2, a3));  // result with splitting
 * } 
所以要保证:无论是单线程,还是多线程(串行和并行)的结果都要是一样的. 
这就是所谓的:结合性.
 
 --个人注释:从此看出,虽然新的jdk版本对开发人员提供了很大的遍历,但是从底层角度来说,实现确实是非常复杂的.
 --对外提供很简单的接口使用.  (一定是框架给封装到底层了,所以你才用着简单.)
 
 *
 * 
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.)
对于一个不包含无序的收集器来说, a1 和 a2是等价的. 条件:finisher.apply(a1).equals(finisher.apply(a2)
对于无序的收集器来说:这种等价性就没有那么严格了,它会考虑到顺序上的区别所对应的不相等性.

 *
 * 
Libraries that implement reduction based on {@code Collector}, such as
 * {@link Stream#collect(Collector)}, must adhere to the following constraints:
 基于Collector 去实现汇聚(reduction)操作的这种库, 必须遵守如下的约定.
 
 - 注释:汇聚其实有多种实现.
 如Collectors中的reducting(). 
 如Stream接口中有三种reduce()重载的方法.
 
 这两个有很大的本质的差别: (注意单线程和多线程情况下的影响.)
 reduce:要求不可变性
 Collectors收集器方式:可变的结果容器.
 
 * 

 *     
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.
 			1. 传递给accumulate函数的参数,以及给Combiner的两个参数,以及finisher函数的参数,
 				他们必须是 这几个supplier, accumulator, or combiner 函数函数上一次调用的结果(泛型-T).
 
 *     
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.
 			2. 实现不应该对, 生成的  ---  结果 做任何的事情. 除了将他们再传给下一个函数.
 			(中间不要做任何的操作,否则肯定是紊乱的.)
 			
 *     
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.
 			3.如果一个结果被传递给combiner或者finisher函数,相同的对象并没有从函数里面返回,
 				那么他们再也不会被使用了.(表示已经被用完了.)
 				
 *     
Once a result is passed to the combiner or finisher function, it
 *     is never passed to the accumulator function again.
 			4.一个函数如果被执行给了combiner或者finisher函数之后,它再也不会被accumulate函数调用了.
 				(就是说,如果被结束函数执行完了. 就不会再被中间操作了.)
 
 *     
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.
 			5. 对于非并发的收集起来说.从supplier, accumulator, or combiner任何的结果返回一定是被限定在当前的线程了. 所以可以被用在并行的操作了.
 			reduction的操作必须被确保被正确的分析了,4个线程,被分为4个区,不会相互干扰,再都执行完毕之后,再讲中间容器进行融合.形成最终结果返回.
 
 *     
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.
  			6.对于并发的收集器,实现可以自由的选择. 和上面的5相对于.
  			在累积阶段不需要保持独立性.
 
 *     A concurrent reduction should only be applied if the collector has the
 *     {@link Characteristics#UNORDERED} characteristics or if the
 *     originating data is unordered.
 一个并发的,在这个时候一定会被使用; 无序的.
 	
--到此结束,重要的 概念基本上已经介绍完毕了.

 * 
 
 *
 * 
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:
 *
 * 
{@code
 *     Collector> intoSet =
 *         Collector.of(TreeSet::new, TreeSet::add,
 *                      (left, right) -> { left.addAll(right); return left; });
 * }

		使用.三个参数构造的 of 方法,()
		三个参数
		1.结果容器
		2.将数据元素累积添加到结果容器
		3.返回结果容器.(此处使用TreeSet)

 *
 * (This behavior is also implemented by the predefined collector.预定义的Collector.
 * {@link Collectors#toCollection(Supplier)}).
 *
 * @apiNote
 * Performing a reduction operation with a {@code Collector} should produce a
 * result equivalent to:
 * 
{@code
 *     R container = collector.supplier().get();
 *     for (T t : data)
 *         collector.accumulator().accept(container, t);
 *     return collector.finisher().apply(container);
 * }
 上述:汇聚容器的实现过程.
 1.创建一个容器
 2.累加到容器
 3.返回结果容器.
 
 *
 * 
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.)
 性能的好坏:取决于实际情况.
 (并行不一定比串行性能高.)
 
 *
 * 
Collectors are designed to be composed; 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:
 收集器本身被设计成可以组合的. 也就是说收集器本身的组合.例如下.
 
 *
 * 
{@code
 *     Collector summingSalaries
 *         = Collectors.summingInt(Employee::getSalary))
 * }
 Collector(),三个参数.  
 
 *
 * 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)}:
 如果想创建一个组合的容器.
 就是之前用的groupingBy()的分类函数.如下例子.
 
 *
 * 
{@code
 *     Collector> summingSalariesByDept
 *         = Collectors.groupingBy(Employee::getDepartment, summingSalaries);
 * }
 分组->求和
 分组->求和
 二级分组.
 
 *
 * @see Stream#collect(Collector)
 * @see Collectors
 *
 * @param  the type of input elements to the reduction operation
 * @param  the mutable accumulation type of the reduction operation (often
 *            hidden as an implementation detail)
 * @param  the result type of the reduction operation
 * @since 1.8
 */

 * @param  the type of input elements to the reduction operation
 * @param  the mutable accumulation type of the reduction operation (often
 *            hidden as an implementation detail)
 * @param  the result type of the reduction operatio

    /**
     * A function that creates and returns a new mutable result container.
     * 创建一个新的可变结果容器.返回 Supplier函数式接口.
     * @return a function which returns a new, mutable result container
     		泛型 - A : 可变容器的类型.
     */
    Supplier supplier();

/**
 * A function that folds a value into a mutable result container.
 * 将一个新的元素数据元素折叠(累加)到一个结果容器当中. 返回值为 BiConsumer函数式接口
 * @return a function which folds a value into a mutable result container
 泛型-A:返回的中间容器的类型(结果类型)
 泛型-T:流中待处理的下一个元素的类型.(源类型)
 */
BiConsumer accumulator();

/**
和并行流紧密相关.
 * 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.
 * 接收两个部分结果,然后给合并起来.将结果状态从一个参数转换成另一个参数,或者返回一个新的结果容器....*(有点难理解.) 返回一个组合的操作符函数接口类.
 
 -- 解释:
 combiner函数,假如有4个线程同时去执行,那么就会生成4个部分结果.
 结果分别是:1.2.3.4
 可能是:
 1.2 -> 5 
 5.3 -> 6
 6.4 -> 7
 这5.6.7新创建的集合,就叫做 新的结果容器
 
 也可能是:
 1.2 -> 1+2 (新的一个)
 1.3 -> 1(新的一个)
 这种新的折叠后的,叫做折叠成一个参数容器.
 
 所以:combiner 是 专门用在 并行流中的.
 
 * @return a function which combines two partial results into a combined
 * result
 泛型-A: (结果容器类型.中间结果容器的类型.) TTT 
 */
BinaryOperator combiner();

/**
 * Perform the final transformation from the intermediate accumulation type
 * {@code A} to the final result type {@code R}.
 *接收一个中间对象,返回另外一个结果.对象.
 * 
If the characteristic {@code IDENTITY_TRANSFORM} is
 * set, this function may be presumed to be an identity transform with an
 * unchecked cast from {@code A} to {@code R}.
 *如果这个特性被设置值了的话,.....  返回一个Function接口类型.
 * @return a function which transforms the intermediate result to the final
 * result
 泛型-A :结果容器类型
 泛型-R : 最终要使用的类型.(最终返回的结果的类型.)
 */
Function finisher();

		/**
     * Characteristics indicating properties of a {@code Collector}, which can
     * be used to optimize reduction implementations.
      这个类中显示的这些属性,被用作:优化汇聚的实现.
      
      --解释: 类的作用:告诉收集器,我可以对这个目标进行怎么样的执行动作.
     */
    enum Characteristics {
        /**
         * Indicates that this collector is concurrent, meaning that
         * the result container can support the accumulator function being
         * called concurrently with the same result container from multiple
         * threads.
         *
         * 
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,//表示可以支持并发.

        /**
         * 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
    }

static class CollectorImpl implements Collector {
        private final Supplier supplier;
        private final BiConsumer accumulator;
        private final BinaryOperator combiner;
        private final Function finisher;
        private final Set characteristics;

        CollectorImpl(Supplier supplier,
                      BiConsumer accumulator,
                      BinaryOperator combiner,
                      Function finisher,
                      Set characteristics) {
            this.supplier = supplier;
            this.accumulator = accumulator;
            this.combiner = combiner;
            this.finisher = finisher;
            this.characteristics = characteristics;
        }

        CollectorImpl(Supplier supplier,
                      BiConsumer accumulator,
                      BinaryOperator combiner,
                      Set characteristics) {
            this(supplier, accumulator, combiner, castingIdentity(), characteristics);
        }

        @Override
        public BiConsumer accumulator() {
            return accumulator;
        }

        @Override
        public Supplier supplier() {
            return supplier;
        }

        @Override
        public BinaryOperator combiner() {
            return combiner;
        }

        @Override
        public Function finisher() {
            return finisher;
        }

        @Override
        public Set characteristics() {
            return characteristics;
        }
    }

/**

		收集了常见的一些操作.
 * Implementations of {@link Collector} that implement various useful reduction
 * operations, such as accumulating elements into collections, summarizing
 * elements according to various criteria, etc.
 *
 * 
The following are examples of using the predefined collectors to perform
 * common mutable reduction tasks:
 	使用预定义的收集器,去执行课常见的收集任务.
 
 以下案例:
 *
 * 
{@code
 *     // Accumulate names into a List . 将name融合到LIst中.
 *     List list = people.stream().map(Person::getName).collect(Collectors.toList());
 *
 
 			融合进TreeSet
 *     // Accumulate names into a TreeSet . 
 *     Set set = people.stream().map(Person::getName).collect(Collectors.toCollection(TreeSet::new));
 *
 			转换成字符串,然后用","去分隔.
 *     // Convert elements to strings and concatenate them, separated by commas
 *     String joined = things.stream()
 *                           .map(Object::toString)
 *                           .collect(Collectors.joining(", "));
 *
 
 			计算员工的工资的总数.
 *     // Compute sum of salaries of employee
 *     int total = employees.stream()
 *                          .collect(Collectors.summingInt(Employee::getSalary)));
 
	 分组:
 
 			根据部门分组.  分类器 
 *     // Group employees by department
 *     Map> byDept
 *         = employees.stream()
 *                    .collect(Collectors.groupingBy(Employee::getDepartment));
 *
 
 			groupingBy的重载,处理完之后,再处理.
 *     // Compute sum of salaries by department
 *     Map totalByDept
 *         = employees.stream()
 *                    .collect(Collectors.groupingBy(Employee::getDepartment,
 *                                                   Collectors.summingInt(Employee::getSalary)));
 *
 
 		分区: partitioningBy()
 		
 *     // Partition students into passing and failing
 *     Map> passingFailing =
 *         students.stream()
 *                 .collect(Collectors.partitioningBy(s -> s.getGrade() >= PASS_THRESHOLD));
 *
 * }
 *
 * @since 1.8
 */

Student student1 = new Student("zhangsan", 80);
        Student student2 = new Student("lisi", 90);
        Student student3 = new Student("wangwu", 100);
        Student student4 = new Student("zhaoliu", 90);
        Student student5 = new Student("zhaoliu", 90);

        List students = Arrays.asList(student1, student2, student3, student4,student5);

        //collect()方法深入源码详解
        //op1:集合转换为stream, 然后stream转换为List
        List students1 = students.stream().collect(Collectors.toList());
        students1.forEach(System.out::println);

        System.out.println("----------");
        System.out.println("count: "+ students.stream().collect(counting()));//Collectors类提供的counting()方法
        System.out.println("count: "+ students.stream().count()); //stream提供的方法 , 底层实现 mapToLong()->sum

        //当jdk底层提供有通用的方法和具体的实现方法,越具体的越好.

        //函数使用.
        //分数最小值
        students.stream().collect(minBy(Comparator.comparingInt(Student::getScore))).ifPresent(System.out::println);
        //分数最大值
        students.stream().collect(maxBy(Comparator.comparingInt(Student::getScore))).ifPresent(System.out::println);
        //平均值
        Double collect4 = students.stream().collect(averagingInt(Student::getScore));
        //总和
        Integer collect5 = students.stream().collect(summingInt(Student::getScore));
        //摘要信息 (分数的汇总信息.)
        students.stream().collect(summarizingInt(Student::getScore));

        System.out.println("---------");

        //字符串拼接
        String collect1 = students.stream().map(Student::getName).collect(joining());
        String collect2 = students.stream().map(Student::getName).collect(joining(","));//带分隔符
        String collect3 = students.stream().map(Student::getName).collect(joining(",", "pre", "suf"));//带分隔符.前缀后缀

        //分组
        //二级分组. 先根据分数分组,再根据名字分组.
        Map>> collect = 
                students.stream().collect(groupingBy(Student::getScore, groupingBy(Student::getName)));
        System.out.println(collect);
        System.out.println("---------");

        //分区
        //根据分数分区
        Map> collect6 = students.stream().collect(partitioningBy(student -> student.getScore() > 80));
        System.out.println(collect6);
        System.out.println("---------");

        //先分区80, 再分区90
        Map>> collect7 = students.stream().collect(partitioningBy(student -> student.getScore() > 80, partitioningBy(student -> student.getScore() > 90)));
        System.out.println(collect7);
        System.out.println("---------");

        //可以看出,Collectors是可以聚合的.
        //先分区,再分组.... 先分区,再求和.... 先分组,再求平均值...  先分组,再进行各种计算...
        Map collect8 = students.stream().collect(partitioningBy(student -> student.getScore() > 80, counting()));
        System.out.println(collect8);
        System.out.println("---------");

        //collectingAndThen() 这个方法. 先求最小值,然后再get返回值,一定是有值的.
        Map collect9 =
                students.stream().collect(groupingBy(Student::getName,
                        collectingAndThen(minBy(Comparator.comparingInt(Student::getScore)), Optional::get)));
        System.out.println(collect9);