JAVA8学习——从源码角度深入Stream流(学习过程)
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<Student> 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<Student> 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 <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.
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.
*
* <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.
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.
*
* <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>
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 <supplier()> new
2.将新的数据元素给合并到一个结果容器中.<accumulator()> add
3.将两个结果容器合并成一个.<combiner()> +
4.将中间的累积类型,转换成结果类型. <finisher()> 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.
*
* <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.
Collectors 还会返回这么一个集合 Characteristics#CONCURRENT. (也就是这个类中的枚举类)
*
* <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.
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<Supplier>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.
*
* <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.
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
*
* <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())}.
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<String> list1,List<String> 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.
*
* <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:
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.
* <pre>{@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
* } </pre>
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.)
*
* <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.)
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.
*
* <p>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.
* <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>
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).
* <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>
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.)
* <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>
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.)
* <li>Once a result is passed to the combiner or finisher function, it
* is never passed to the accumulator function again.</li>
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.)
* <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>
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.
* <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.
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.</li>
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.
* </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>
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:
* <pre>{@code
* R container = collector.supplier().get();
* for (T t : data)
* collector.accumulator().accept(container, t);
* return collector.finisher().apply(container);
* }</pre>
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.
*
* <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.)
性能的好坏:取决于实际情况.
(并行不一定比串行性能高.)
*
* <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:
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.
*
* <pre>{@code
* Collector<Employee, ?, Integer> summingSalaries
* = Collectors.summingInt(Employee::getSalary))
* }</pre>
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.
*
* <pre>{@code
* Collector<Employee, ?, Map<Department, Integer>> summingSalariesByDept
* = Collectors.groupingBy(Employee::getDepartment, summingSalaries);
* }</pre>
分组->求和
分组->求和
二级分组.
*
* @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
*/
理解到这里,受益匪浅.
Collector接口详解
Collector的三个泛型<T,A,R>详解
* @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 operatio
- T:需要被融合操作的输入参数的类型 (也就是流中的每一个元素的类型)
- A:reduction操作的可变的累积的类型.(累积的集合的类型.)(中间结果容器的类型.)(返回结果容器的类型)
- R:汇聚操作的结果类型.
supplier()
/**
* 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<A> supplier();
accumulator()
/**
* 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<A, T> accumulator();
combiner()
/**
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<A> combiner();
finisher()
/**
* 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.
* <p>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<A, R> finisher();
枚举类 Characteristics
/**
* 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 <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,//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
}
静态内部类 CollectorImpl
<此静态类在Collectors类中.>
static class CollectorImpl<T, A, R> implements Collector<T, A, R> {
private final Supplier<A> supplier;
private final BiConsumer<A, T> accumulator;
private final BinaryOperator<A> combiner;
private final Function<A, R> finisher;
private final Set<Characteristics> characteristics;
CollectorImpl(Supplier<A> supplier,
BiConsumer<A, T> accumulator,
BinaryOperator<A> combiner,
Function<A,R> finisher,
Set<Characteristics> characteristics) {
this.supplier = supplier;
this.accumulator = accumulator;
this.combiner = combiner;
this.finisher = finisher;
this.characteristics = characteristics;
}
CollectorImpl(Supplier<A> supplier,
BiConsumer<A, T> accumulator,
BinaryOperator<A> combiner,
Set<Characteristics> characteristics) {
this(supplier, accumulator, combiner, castingIdentity(), characteristics);
}
@Override
public BiConsumer<A, T> accumulator() {
return accumulator;
}
@Override
public Supplier<A> supplier() {
return supplier;
}
@Override
public BinaryOperator<A> combiner() {
return combiner;
}
@Override
public Function<A, R> finisher() {
return finisher;
}
@Override
public Set<Characteristics> characteristics() {
return characteristics;
}
}
为什么会定义一个这么一个静态内部类?
-
因为,Collectors是一个工厂,向开发者提供非常常见的那些收集器,如counting() , grouping by()....
-
绝大多数方法都是静态方法.
-
Collectors和CollectorImpl紧密相关,结合性非常密切.从设计角度,直接放在一个类里面.
函数式编程的最大特点:表示做什么,而不是如何做.如:toList(), counting()...
Collectors收集器注释:
/**
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.
*
* <p>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:
*
* <pre>{@code
* // Accumulate names into a List . 将name融合到LIst中.
* List<String> list = people.stream().map(Person::getName).collect(Collectors.toList());
*
融合进TreeSet
* // Accumulate names into a TreeSet .
* Set<String> 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<Department, List<Employee>> 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<Department, Integer> totalByDept
* = employees.stream()
* .collect(Collectors.groupingBy(Employee::getDepartment,
* Collectors.summingInt(Employee::getSalary)));
*
分区: partitioningBy()
* // Partition students into passing and failing
* Map<Boolean, List<Student>> passingFailing =
* students.stream()
* .collect(Collectors.partitioningBy(s -> s.getGrade() >= PASS_THRESHOLD));
*
* }</pre>
*
* @since 1.8
*/
收集器Collectors的Demo
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<Student> 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<Student> 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<Integer, Map<String, List<Student>>> 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<Boolean, List<Student>> 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<Boolean, Map<Boolean, List<Student>>> 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<Boolean, Long> 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<String, Student> collect9 =
students.stream().collect(groupingBy(Student::getName,
collectingAndThen(minBy(Comparator.comparingInt(Student::getScore)), Optional::get)));
System.out.println(collect9);
什么时候开始都不晚——沃尔舅·硕德
