Java8新特性 Collectors的方法（四）

r囧r小猫 2022-12-19 06:06 154阅读 0赞

// Collectors的方法
    public class Lambda2 {
    
    
        public static List<Integer> createListByInteger() {
            List<Integer> list = Lists.newArrayList(1, 2, 32, 3, 4, 5, 6, 7, 3, 7, 10, 11, 4);
            return list;
        }
    
        public static List<String> createListByString() {
            List<String> list = Lists.newArrayList("1", "2", "32", "3", "4", "5", "6", "7", "8", "9", "10", "11", "0");
            return list;
        }
    
        public static List<String> createListByCharacter() {
            List<String> list = Lists.newArrayList("a", "d", "a", "c", "f", "e", "t", "l");
            return list;
        }
    
        public static List<Product> createProduct() {
            List<Product> list = new ArrayList<Product>();
            String str = "[\n" +
                    "    {\n" +
                    "        \"goods_status\":\"正常\",\n" +
                    "        \"goods_name\":\"鲜淮山炒黑木耳\",\n" +
                    "        \"goods_cover\":\"https://ss0.bdstatic.com/70cFuHSh_Q1YnxGkpoWK1HF6hhy/it/u=1405304736,1966318522&fm=26&gp=0.jpg\",\n" +
                    "        \"classify_status\":\"正常\",\n" +
                    "        \"goods_price\":28,\n" +
                    "        \"classify_name\":\"每日抢购\",\n" +
                    "        \"classify_id\":\"1323454502753538048\",\n" +
                    "        \"goods_id\":\"1317807825745088512\",\n" +
                    "        \"goods_discount\":0\n" +
                    "    },\n" +
                    "    {\n" +
                    "        \"goods_status\":\"正常\",\n" +
                    "        \"goods_name\":\"玉米松子\",\n" +
                    "        \"goods_cover\":\"https://ss0.bdstatic.com/70cFuHSh_Q1YnxGkpoWK1HF6hhy/it/u=1405304736,1966318522&fm=26&gp=0.jpg\",\n" +
                    "        \"classify_status\":\"正常\",\n" +
                    "        \"goods_price\":35,\n" +
                    "        \"classify_name\":\"每日抢购\",\n" +
                    "        \"classify_id\":\"1323454502753538048\",\n" +
                    "        \"goods_id\":\"1317807947237298176\",\n" +
                    "        \"goods_discount\":0\n" +
                    "    },\n" +
                    "    {\n" +
                    "        \"goods_status\":\"正常\",\n" +
                    "        \"goods_name\":\"腊八豆蒸排骨\",\n" +
                    "        \"goods_cover\":\"https://ss0.bdstatic.com/70cFuHSh_Q1YnxGkpoWK1HF6hhy/it/u=1405304736,1966318522&fm=26&gp=0.jpg\",\n" +
                    "        \"classify_status\":\"正常\",\n" +
                    "        \"goods_price\":18,\n" +
                    "        \"classify_name\":\"每日抢购\",\n" +
                    "        \"classify_id\":\"1323454502753538048\",\n" +
                    "        \"goods_id\":\"1317807639778037760\",\n" +
                    "        \"goods_discount\":0\n" +
                    "    },\n" +
                    "    {\n" +
                    "        \"goods_status\":\"正常\",\n" +
                    "        \"goods_name\":\"蒜蓉粉丝蒸娃娃菜\",\n" +
                    "        \"goods_cover\":\"https://ss0.bdstatic.com/70cFuHSh_Q1YnxGkpoWK1HF6hhy/it/u=1405304736,1966318522&fm=26&gp=0.jpg\",\n" +
                    "        \"classify_status\":\"正常\",\n" +
                    "        \"goods_price\":28,\n" +
                    "        \"classify_name\":\"优先新品\",\n" +
                    "        \"classify_id\":\"1323454357907443712\",\n" +
                    "        \"goods_id\":\"1317807566960726016\",\n" +
                    "        \"goods_discount\":0\n" +
                    "    },\n" +
                    "    {\n" +
                    "        \"goods_status\":\"正常\",\n" +
                    "        \"goods_name\":\"湘水肉沫蒸蛋\",\n" +
                    "        \"goods_cover\":\"https://ss0.bdstatic.com/70cFuHSh_Q1YnxGkpoWK1HF6hhy/it/u=1405304736,1966318522&fm=26&gp=0.jpg\",\n" +
                    "        \"classify_status\":\"正常\",\n" +
                    "        \"goods_price\":33,\n" +
                    "        \"classify_name\":\"优先新品\",\n" +
                    "        \"classify_id\":\"1323454357907443712\",\n" +
                    "        \"goods_id\":\"1317807434039037952\",\n" +
                    "        \"goods_discount\":0\n" +
                    "    },\n" +
                    "    {\n" +
                    "        \"goods_status\":\"正常\",\n" +
                    "        \"goods_name\":\"紫苏土豆牛肉碎\",\n" +
                    "        \"goods_cover\":\"https://ss0.bdstatic.com/70cFuHSh_Q1YnxGkpoWK1HF6hhy/it/u=1405304736,1966318522&fm=26&gp=0.jpg\",\n" +
                    "        \"classify_status\":\"正常\",\n" +
                    "        \"goods_price\":70,\n" +
                    "        \"classify_name\":\"优先新品\",\n" +
                    "        \"classify_id\":\"1323454357907443712\",\n" +
                    "        \"goods_id\":\"1317807129075388416\",\n" +
                    "        \"goods_discount\":0\n" +
                    "    },\n" +
                    "    {\n" +
                    "        \"goods_status\":\"正常\",\n" +
                    "        \"goods_name\":\"青豆炒肉泥\",\n" +
                    "        \"goods_cover\":\"https://ss0.bdstatic.com/70cFuHSh_Q1YnxGkpoWK1HF6hhy/it/u=1405304736,1966318522&fm=26&gp=0.jpg\",\n" +
                    "        \"classify_status\":\"正常\",\n" +
                    "        \"goods_price\":58,\n" +
                    "        \"classify_name\":\"优先新品\",\n" +
                    "        \"classify_id\":\"1323454357907443712\",\n" +
                    "        \"goods_id\":\"1317806865106866176\",\n" +
                    "        \"goods_discount\":0\n" +
                    "    },\n" +
                    "    {\n" +
                    "        \"goods_status\":\"正常\",\n" +
                    "        \"goods_name\":\"吮指青尾鱼\",\n" +
                    "        \"goods_cover\":\"https://ss0.bdstatic.com/70cFuHSh_Q1YnxGkpoWK1HF6hhy/it/u=1405304736,1966318522&fm=26&gp=0.jpg\",\n" +
                    "        \"classify_status\":\"正常\",\n" +
                    "        \"goods_price\":128,\n" +
                    "        \"classify_name\":\"热销商品\",\n" +
                    "        \"classify_id\":\"1323454099097915392\",\n" +
                    "        \"goods_id\":\"1317805910818820096\",\n" +
                    "        \"goods_discount\":0\n" +
                    "    },\n" +
                    "    {\n" +
                    "        \"goods_status\":\"正常\",\n" +
                    "        \"goods_name\":\"香爆酱牛肉\",\n" +
                    "        \"goods_cover\":\"https://ss0.bdstatic.com/70cFuHSh_Q1YnxGkpoWK1HF6hhy/it/u=1405304736,1966318522&fm=26&gp=0.jpg\",\n" +
                    "        \"classify_status\":\"正常\",\n" +
                    "        \"goods_price\":88,\n" +
                    "        \"classify_name\":\"热销商品\",\n" +
                    "        \"classify_id\":\"1323454099097915392\",\n" +
                    "        \"goods_id\":\"1317805307065536512\",\n" +
                    "        \"goods_discount\":0\n" +
                    "    },\n" +
                    "    {\n" +
                    "        \"goods_status\":\"正常\",\n" +
                    "        \"goods_name\":\"清远鸡\",\n" +
                    "        \"goods_cover\":\"https://ss0.bdstatic.com/70cFuHSh_Q1YnxGkpoWK1HF6hhy/it/u=1405304736,1966318522&fm=26&gp=0.jpg\",\n" +
                    "        \"classify_status\":\"正常\",\n" +
                    "        \"goods_price\":98,\n" +
                    "        \"classify_name\":\"热销商品\",\n" +
                    "        \"classify_id\":\"1323454099097915392\",\n" +
                    "        \"goods_id\":\"1317804058198937600\",\n" +
                    "        \"goods_discount\":0\n" +
                    "    }\n" +
                    "]";
    
            JSONArray productArray = JSONArray.parseArray(str);
            for (int i = 0; i < productArray.size(); i++) {
                Product product = new Product();
                JSONObject productJson = productArray.getJSONObject(i);
                product = JSON.parseObject(productJson.toJSONString(), Product.class);
    
                list.add(product);
            }
            return list;
        }
    
        public static void collectors() {
            // toCollection 方法返回一个收集器 输入元素到一个新的集合中
            List<Integer> list = createListByInteger();
            Collection<Integer> collection = list.parallelStream().collect(Collectors.toCollection(ArrayList::new));
            System.out.println(collection);
    
            System.out.println("###########################################");
    
            Collection<Integer> collection1 = list.parallelStream().collect(Collectors.toCollection(HashSet::new));
            System.out.println(collection1);
    
            System.out.println("*******************************************");
    
            // toList()  返回一个收集器把数据收集到一个新的list 中
            List<String> list1 = createListByCharacter();
            List<String> list2 = list1.parallelStream().map(String::toUpperCase).collect(Collectors.toList());
            System.out.println(list2);
    
            System.out.println("###########################################");
    
            // toSet()
            Set<String> list3 = list1.parallelStream().map(String::toUpperCase).collect(Collectors.toSet());
            System.out.println(list3);
    
            System.out.println("*******************************************");
    
            // toMap()  Function.identity() 类似于 item -> item
            Map<String, String> map = list1.parallelStream().collect(Collectors.toMap(Function.identity(), String::toUpperCase, (k1, k2) -> k1));
            Map<String, String> map1 = list1.parallelStream().collect(Collectors.toMap(item -> item.toUpperCase(), String::toUpperCase, (k1, k2) -> k1));
            System.out.println(map);
    
            System.out.println("###########################################");
    
            System.out.println(map1);
    
            System.out.println("*******************************************");
    
            // joining  连接字符串
            String concat = Stream.of("a", "b").collect(Collectors.joining());
            System.out.println(concat);
    
            System.out.println("###########################################");
    
            String concat1 = Stream.of("a", "b").collect(Collectors.joining(","));
            System.out.println(concat1);
    
            System.out.println("###########################################");
    
            String concat2 = Stream.of("a", "b").collect(Collectors.joining(",", "[", "]"));
            System.out.println(concat2);
    
            System.out.println("###########################################");
    
            String concat3 = Stream.of("a", new StringBuilder("b"), new StringBuilder("c")).collect(Collectors.joining(","));
            System.out.println(concat3);
    
            System.out.println("*******************************************");
    
            // mapping/flatMapping  它将Function应用于输入元素，然后将它们累积到给定的Collector
            List<String> list4 = list1.parallelStream().collect(Collectors.mapping(String::toUpperCase, Collectors.toList()));
            System.out.println(list4.toString());
    
            System.out.println("###########################################");
    
            Set<String> set = list1.parallelStream().collect(Collectors.mapping(Function.identity(), Collectors.toSet()));
            System.out.println(set);
    
            System.out.println("*******************************************");
    
            // filter 设置过滤条件
            List<String> list5 = list1.parallelStream().filter(item -> item.toUpperCase().contains("A")).collect((Collectors.toList()));
            System.out.println(list5);
    
            System.out.println("*******************************************");
    
            // collectingAndThen
    
            // count 计数
            long count = list1.parallelStream().filter(item -> item.toUpperCase().contains("A")).count();
            System.out.println(count);
    
            System.out.println("*******************************************");
    
            // minBy 根据给定的比较器返回最小的元素(Interger)
            Optional<Integer> min = list.stream().collect(Collectors.minBy((x, y) -> x - y));
            System.out.println(min);
    
            System.out.println("*******************************************");
    
            // maxBy 它根据给定的比较器返回最大元素
            Optional<Integer> max = list.stream().collect(Collectors.maxBy((x, y) -> x - y));
            System.out.println(max);
    
            System.out.println("*******************************************");
    
            // 求数据的总和
            Integer sum1 = list.stream().collect(Collectors.summingInt(Integer::intValue));
            Integer sum2 = list.stream().collect(Collectors.summingInt(item -> item));
            System.out.println(sum1);
            System.out.println(sum2);
    
            System.out.println("*******************************************");
    
            // 求数据的平均数
            Double avg = list.stream().collect(Collectors.averagingDouble(Integer::intValue));
            System.out.println(avg);
    
            System.out.println("*******************************************");
    
            // 分组 groupingBy
            List<Product> productList = createProduct();
            Map<String, List<Product>> productMap = productList.parallelStream().collect(Collectors.groupingBy(Product::getClassify_name));
            System.out.println(productMap);
    
            System.out.println("###########################################");
    
            // groupingByConcurrent 分组，是并发和无序的
            Map<String, List<Product>> productMap1 = productList.parallelStream().collect(Collectors.groupingByConcurrent(Product::getClassify_name));
            System.out.println(productMap);
    
            System.out.println("*******************************************");
    
            // partitioningBy    返回一个Collector，它根据Predicate对输入元素进行分区，并将它们组织成Map <Boolean，List>
            Map<Boolean, List<Integer>> map2 = list.stream().collect(Collectors.partitioningBy(item -> item % 2 == 0));
            System.out.println(map2);
    
            System.out.println("*******************************************");
    
            // BinaryOperator 返回一个收集器，它在指定的BinaryOperator下执行其输入元素的减少。 类似于having操作
            // 这主要用于多级缩减，例如使用groupingBy（）和partitioningBy（）方法指定下游收集器
            // Collectors.reducing 定制归一化收集器（reducing）得到自定义结果集
            Map<Boolean, Optional<Integer>> map3 = list.stream().collect(Collectors.partitioningBy(item -> item % 2 == 0, Collectors.reducing(BinaryOperator.maxBy(Comparator.comparing(Integer::intValue)))));
            System.out.println(map3);
    
            System.out.println("*******************************************");
    
            // summarizingInt 返回统计数据：min, max, average, count, sum
            IntSummaryStatistics intSummaryStatistics = list.stream().collect(Collectors.summarizingInt(Integer::intValue));
            System.out.println(intSummaryStatistics);
        }
    
        public static void main(String[] args) {
            collectors();
        }
    }

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[20201214163531501.jpg]: /images/20221120/fc172d85b3c34ff4b696c2c2ea4b57eb.png