背景
java8.0中新推出了stream方法，增強了對集合的操作。代碼更優雅，更具有可讀性
基本概念
串行流：逐個計算
並行流：將流切成多份（fork），分別計算，然後組裝(join) 慎重使用，有可能有副作用
注：可通過parallel() -> 並行流、sequential() -> 串行流方便的在兩種類型中變化
流執行都是懶加載模式，只有在使用的時候纔會執行。最好的方式是先減少數量，在進行操作。
案例：某班有50個學生，語文成績分別爲50~99
基本語法
過濾：filter
假如我們需要找到成績在60分以下的學生
studentList.stream().filter({it.getAt("grade")<=60}).collect(Collectors.toList())
result ： [[grade:50, id:0], [grade:51, id:1], [grade:52, id:2], [grade:53, id:3], [grade:54, id:4], [grade:55, id:5], [grade:56, id:6], [grade:57, id:7], [grade:58, id:8], [grade:59, id:9], [grade:60, id:10]]
映射：map
假如我們需要將60分以下的成績不顯示設置爲不及格
studentList.stream().map({if(it.getAt("grade")<=60){it.putAt("grade", "不及格");};return it;}).collect(Collectors.toList())
result ： [[grade:不及格, id:0], [grade:不及格, id:1], [grade:不及格, id:2], [grade:不及格, id:3], [grade:不及格, id:4], [grade:不及格, id:5], [grade:不及格, id:6], [grade:不及格, id:7], [grade:不及格, id:8], [grade:不及格, id:9], [grade:不及格, id:10], [grade:61, id:11], [grade:62, id:12], [grade:63, id:13], [grade:64, id:14], [grade:65, id:15], [grade:66, id:16], [grade:67, id:17], [grade:68, id:18], [grade:69, id:19], [grade:70, id:20], [grade:71, id:21], [grade:72, id:22], [grade:73, id:23], [grade:74, id:24], [grade:75, id:25], [grade:76, id:26], [grade:77, id:27], [grade:78, id:28], [grade:79, id:29], [grade:80, id:30], [grade:81, id:31], [grade:82, id:32], [grade:83, id:33], [grade:84, id:34], [grade:85, id:35], [grade:86, id:36], [grade:87, id:37], [grade:88, id:38], [grade:89, id:39], [grade:90, id:40], [grade:91, id:41], [grade:92, id:42], [grade:93, id:43], [grade:94, id:44], [grade:95, id:45], [grade:96, id:46], [grade:97, id:47], [grade:98, id:48], [grade:99, id:49]]
限制大小：limit|skip
取出前一個元素
studentList.stream().limit(1).collect(Collectors.toList())
result ： [[grade:50, id:0]]
扔掉前五個元素
studentList.stream().skip(5).collect(Collectors.toList())
result ： [[grade:55, id:5], [grade:56, id:6], [grade:57, id:7], [grade:58, id:8], [grade:59, id:9], [grade:60, id:10], [grade:61, id:11], [grade:62, id:12], [grade:63, id:13], [grade:64, id:14], [grade:65, id:15], [grade:66, id:16], [grade:67, id:17], [grade:68, id:18], [grade:69, id:19], [grade:70, id:20], [grade:71, id:21], [grade:72, id:22], [grade:73, id:23], [grade:74, id:24], [grade:75, id:25], [grade:76, id:26], [grade:77, id:27], [grade:78, id:28], [grade:79, id:29], [grade:80, id:30], [grade:81, id:31], [grade:82, id:32], [grade:83, id:33], [grade:84, id:34], [grade:85, id:35], [grade:86, id:36], [grade:87, id:37], [grade:88, id:38], [grade:89, id:39], [grade:90, id:40], [grade:91, id:41], [grade:92, id:42], [grade:93, id:43], [grade:94, id:44], [grade:95, id:45], [grade:96, id:46], [grade:97, id:47], [grade:98, id:48], [grade:99, id:49]]
排序：sorted
按成績排序
studentList.stream().sorted({ s1, s2 -> return s2.getAt("grade").compareTo(s1.getAt("grade"));}).collect(Collectors.toList())
result ： [[grade:99, id:49], [grade:98, id:48], [grade:97, id:47], [grade:96, id:46], [grade:95, id:45], [grade:94, id:44], [grade:93, id:43], [grade:92, id:42], [grade:91, id:41], [grade:90, id:40], [grade:89, id:39], [grade:88, id:38], [grade:87, id:37], [grade:86, id:36], [grade:85, id:35], [grade:84, id:34], [grade:83, id:33], [grade:82, id:32], [grade:81, id:31], [grade:80, id:30], [grade:79, id:29], [grade:78, id:28], [grade:77, id:27], [grade:76, id:26], [grade:75, id:25], [grade:74, id:24], [grade:73, id:23], [grade:72, id:22], [grade:71, id:21], [grade:70, id:20], [grade:69, id:19], [grade:68, id:18], [grade:67, id:17], [grade:66, id:16], [grade:65, id:15], [grade:64, id:14], [grade:63, id:13], [grade:62, id:12], [grade:61, id:11], [grade:60, id:10], [grade:59, id:9], [grade:58, id:8], [grade:57, id:7], [grade:56, id:6], [grade:55, id:5], [grade:54, id:4], [grade:53, id:3], [grade:52, id:2], [grade:51, id:1], [grade:50, id:0]]
去重：distinct
取出成績分佈數量
Map student = new HashMap();
student.put("id", 50);
student.put("grade", 50);
studentList.add(student);
studentList.stream().map({it.getAt("grade")}).collect(Collectors.toList()).size() result ： 51
studentList.stream().map({it.getAt("grade")}).distinct().collect(Collectors.toList()).size() result ： 50
注：使用distinct需要注意實現hashCode和equals.
完全匹配：allMatch
學生成績是否都在60分以上
studentList.stream().map({it.getAt("grade")}).allMatch({it>=60}) result ： false
學生成績是否都在50分以上
studentList.stream().map({it.getAt("grade")}).allMatch({it>=50}) result ： true
存在匹配：anyMatch
學生成績是否存在90分以上
studentList.stream().map({it.getAt("grade")}).anyMatch({it>=90}) result ：true
學生成績是否存在100分
studentList.stream().map({it.getAt("grade")}).anyMatch({it==100}) result ：false
無匹配：noneMatch
學生成績是否都在90分以下
studentList.stream().map({it.getAt("grade")}).noneMatch({it>=90}) result ：false
學生成績是否都沒有達到100分
studentList.stream().map({it.getAt("grade")}).noneMatch({it==100}) result ：true
找到第一個：findFirst
studentList.stream().findFirst().get() result：[grade:50, id:0]
找到任何一個：findAny
studentList.stream().parallel().findAny().get() result：[grade:81, id:31]
生成stream：iterate
手動生成一個1~50的數組
Stream.iterate(1,{i -> i+1}).limit(50).collect(Collectors.toList())
最值：min|max
取出成績最高的那個
studentList.stream().max({s1,s2 -> return s1.getAt("grade")-s2.getAt("grade")}).get() result：[grade:99, id:49]
取出成績最差的那個
studentList.stream().min({s1,s2 -> return s1.getAt("grade")-s2.getAt("grade")}).get() result：[grade:50, id:0]
注：min() max()返回的對象是Optional類型