Java8 Stream groupingBy对List进行分组 |
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提到Group By，首先想到的往往是sql中的group by操作，对搜索结果进行分组。其实Java8 Streams API中的Collector也支持流中的数据进行分组和分区操作，本片文章讲简单介绍一下，如何使用groupingBy 和 partitioningBy来对流中的元素进行分组和分区。 groupingBy首先看一下Java8之前如果想对一个List做分组操作，我们需要如下代码操作： @Test public void groupListBeforeJava8() { Map result = new HashMap(); for (Employee e : employees) { String city = e.getCity(); List empsInCity = result.get(city); if (empsInCity == null) { empsInCity = new ArrayList(); result.put(city, empsInCity); } empsInCity.add(e); } System.out.println(result); assertEquals(result.get("London").size(), 2); }而如果使用Java8中Stream的groupingBy分组器，就可以这样操作： /** * 使用java8 stream groupingBy操作,按城市分组list */ @Test public void groupingByTest() { Map employeesByCity = employees.stream().collect(Collectors.groupingBy(Employee::getCity)); System.out.println(employeesByCity); assertEquals(employeesByCity.get("London").size(), 2); }上面是groupingBy分组器最常见的一个用法，下面简单介绍一下其他用法： 统计每个分组的count /** * 使用java8 stream groupingBy操作,按城市分组list统计count */ @Test public void groupingByCountTest() { Map employeesByCity = employees.stream().collect(Collectors.groupingBy(Employee::getCity, Collectors.counting())); System.out.println(employeesByCity); assertEquals(employeesByCity.get("London").longValue(), 2L); } 统计分组平均值 /** * 使用java8 stream groupingBy操作,按城市分组list并计算分组销售平均值 */ @Test public void groupingByAverageTest() { Map employeesByCity = employees.stream().collect(Collectors.groupingBy(Employee::getCity, Collectors.averagingInt(Employee::getSales))); System.out.println(employeesByCity); assertEquals(employeesByCity.get("London").intValue(), 175); } 统计分组总值 /** * 使用java8 stream groupingBy操作,按城市分组list并计算分组销售总值 */ @Test public void groupingBySumTest() { Map employeesByCity = employees.stream().collect(Collectors.groupingBy(Employee::getCity, Collectors.summingLong(Employee::getSales))); //对Map按照分组销售总值逆序排序 Map finalMap = new LinkedHashMap(); employeesByCity.entrySet().stream() .sorted(Map.Entry.comparingByValue() .reversed()).forEachOrdered(e -> finalMap.put(e.getKey(), e.getValue())); System.out.println(finalMap); assertEquals(finalMap.get("London").longValue(), 350); } Join分组List /** * 通过type分组list，通过join操作连接分组list */ @Test public void groupingByConvertResultTest(){ List blogPostList = Lists.newArrayList(); blogPostList.add(new BlogPost("post1", "zhuoli", 1, 30)); blogPostList.add(new BlogPost("post2", "zhuoli", 1, 40)); blogPostList.add(new BlogPost("post3", "zhuoli", 2, 15)); blogPostList.add(new BlogPost("post4", "zhuoli", 3, 33)); blogPostList.add(new BlogPost("post5", "Alice", 1, 99)); blogPostList.add(new BlogPost("post6", "Michael", 3, 65)); Map postsPerType = blogPostList.stream() .collect(Collectors.groupingBy(BlogPost::getType, Collectors.mapping(BlogPost::getTitle, Collectors.joining(", ", "Post titles: [", "]")))); System.out.println(postsPerType); } 转换分组结果List -> List /** * 使用java8 stream groupingBy操作,按城市分组list,将List转化为name的List */ @Test public void groupingByCityMapList(){ Map namesByCity = employees.stream().collect(Collectors.groupingBy(Employee::getCity, Collectors.mapping(Employee::getName, Collectors.toList()))); System.out.println(namesByCity); assertThat(namesByCity.get("London"), contains("Alice", "Bob")); } 转换分组结果List -> Set /** * 使用java8 stream groupingBy操作,按城市分组list,将List转化为name的Set */ @Test public void groupingByCityMapListToSet(){ Map namesByCity = employees.stream().collect(Collectors.groupingBy(Employee::getCity, Collectors.mapping(Employee::getName, Collectors.toSet()))); System.out.println(namesByCity); assertThat(namesByCity.get("London"), containsInAnyOrder("Alice", "Bob")); } 使用对象分组List /** * 使用java8 stream groupingBy操作,通过Object对象的成员分组List */ @Test public void groupingByObjectTest(){ List blogPostList = Lists.newArrayList(); blogPostList.add(new BlogPost("post1", "zhuoli", 1, 30)); blogPostList.add(new BlogPost("post2", "zhuoli", 1, 40)); blogPostList.add(new BlogPost("post3", "zhuoli", 2, 15)); blogPostList.add(new BlogPost("post4", "zhuoli", 3, 33)); blogPostList.add(new BlogPost("post5", "Alice", 1, 99)); blogPostList.add(new BlogPost("post6", "Michael", 3, 65)); Map postsPerTypeAndAuthor = blogPostList.stream() .collect(Collectors.groupingBy(post -> new Tuple(post.getAuthor(), post.getType()))); System.out.println(postsPerTypeAndAuthor); } 使用两个成员分组List /** * 通过author和type分组list */ @Test public void groupingByMultiItemTest(){ List blogPostList = Lists.newArrayList(); blogPostList.add(new BlogPost("post1", "zhuoli", 1, 30)); blogPostList.add(new BlogPost("post2", "zhuoli", 1, 40)); blogPostList.add(new BlogPost("post3", "zhuoli", 2, 15)); blogPostList.add(new BlogPost("post4", "zhuoli", 3, 33)); blogPostList.add(new BlogPost("post5", "Alice", 1, 99)); blogPostList.add(new BlogPost("post6", "Michael", 3, 65)); Map map = blogPostList.stream() .collect(Collectors.groupingBy(BlogPost::getAuthor, Collectors.groupingBy(BlogPost::getType))); System.out.println(map); } 自定义DistinctBy对分组结果去重使用groupingBy源于工作的一个需求，存在如下数据结构： @Data @AllArgsConstructor public class TestData { private Integer scene; private Integer placement; private Long bid; }对TestData的List分组，统计每个sene已被占用的placement，我当时直接使用groupIngBy进行分组，得到了一个Map entry.getValue().stream().filter(distinctByKey(TestData::getPlacement)).map(TestData::getPlacement).collect(Collectors.toList()))); System.out.println(resultMap); } private static Predicate distinctByKey(Function keyExtractor) { Set seen = ConcurrentHashMap.newKeySet(); return t -> seen.add(keyExtractor.apply(t)); } } 其实Stream的distinct方法，也是filter的一个特别实现。上述示例中distinctByKey也是通过filter，实现的，不多说，看一下示例代码就可以看明白。 示例代码：卓立 – 码云 – groupingBy操作 参考链接： Java 8 Streams API：对Stream分组和分区Java 8 – Stream Collectors groupingBy examples |
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