JAVA后端面试|java 8 stream API 开发语言|spring|java

java 8 stream API

1 Collectors
- 1.1 groupingBy
- 1.2 joining
- 1.3 partitioningBy
- 1.4 toMap
- 1.5 averagingDouble
- 1.6 maxBy | minBy
- 1.7 summingLong
- 1.8 summarizingLong
2. 流操作
- 2.1 SummaryStatistics
- 2.2. reduce

【JAVA后端面试|java 8 stream API】
1 Collectors

操控的实体类

@Data @Builder @NoArgsConstructor @AllArgsConstructor private static class User{ private Long id; private String name; private Integer age; private Boolean isValid; @JSONField(format = "yyyy-MM-dd HH:mm:ss") private Date birth; }

已经存在的用户集合

User u1 = User.builder().id(1L).name("zs").age(18).isValid(true).birth(new Date()).build(); User u2 = User.builder().id(2L).name("zs xxx").age(18).isValid(true).birth(new Date()).build(); User u3 = User.builder().id(3L).name("xxx 001").age(20).isValid(true).birth(new Date()).build(); User u4 = User.builder().id(4L).name("zs xxx 001").age(24).isValid(true).birth(new Date()).build(); User u5 = User.builder().id(5L).name("001 zs xxx").age(24).isValid(true).birth(new Date()).build(); List userList = Arrays.asList(u1, u2, u3, u4, u5);

1.1 groupingBy

groupingBy 通过对象某个字段进行分组

Map> collect_1 = userList.stream().collect(Collectors.groupingBy(User::getAge));

1.2 joining

对结果集进行连接

String collect_2 = userList.stream().map(User::getName).collect(Collectors.joining(",","[","]"));

1.3 partitioningBy

通过自定义的一个条件进行分组

Map> collect_3 = userList.stream().collect(Collectors.partitioningBy(user -> user.getAge() != null && user.getAge() > 20));

1.4 toMap

List 转换成 Map

Map stringUserMap = userList.stream().collect(Collectors.toMap(User::getName, user -> user));

1.5 averagingDouble

对所有用户集合的age进行求平均值

Double collect1 = userList.stream().collect(Collectors.averagingDouble(value -> value.getAge().doubleValue()));

1.6 maxBy | minBy

根据某个字段求最大值的那个对象

User orElseGet = userList.stream().collect(Collectors.maxBy(Comparator.comparing(user -> user.getId()))) .orElseGet(null); User maxU = userList.stream().max(Comparator.comparing(user -> user.getId())) .orElseGet(null);

1.7 summingLong

求和Long：对long型数据求和

Long aLong = userList.stream().collect(Collectors.summingLong(user -> user.getId())); // 和下方产生的结果相同 Long collect3 = userList.stream().mapToLong(User::getId).sum();

1.8 summarizingLong

总结Long：包含max min count avg

LongSummaryStatistics summaryStatistics = userList.stream().collect(Collectors.summarizingLong(User::getId)); long count4 = summaryStatistics.getCount(); long max3 = summaryStatistics.getMax(); long min2 = summaryStatistics.getMin(); double average1 = summaryStatistics.getAverage();

2. 流操作 2.1 SummaryStatistics

汇总统计

// long类型汇总统计最大值最小值等 LongSummaryStatistics longSummaryStatistics = userList.stream().filter(i -> i.getAge() != null && i.getAge() > 20) .sorted((o1, o2) -> -o1.getId().compareTo(o2.getId())) .mapToLong(User::getId) .summaryStatistics(); long count3 = longSummaryStatistics.getCount(); long max2 = longSummaryStatistics.getMax(); long min1 = longSummaryStatistics.getMin();

2.2. reduce

// 使用 reduce 对 User对象结果集的年龄进行求和 Optional reduce = userList.stream().map(User::getAge).reduce(Integer::sum); Integer integer1 = reduce.orElse(0); // 使用reduce对bigdecimal进行求和 List decimalList = new ArrayList<>(); decimalList.add(new BigDecimal("0.020")); decimalList.add(new BigDecimal("0.0233")); decimalList.add(new BigDecimal("0.0244")); decimalList.add(new BigDecimal("0.02555")); Optional bigDecimal = decimalList.stream().reduce(BigDecimal::add); BigDecimal bigDecimal2 = decimalList.stream() .reduce(BigDecimal::add) .orElse(BigDecimal.ZERO) .setScale(4, BigDecimal.ROUND_HALF_UP);