Using Java 8 Stream API for Data Processing: Filters, Maps, Sorting, and Parallel Streams

Java 8 added the Stream API, offering a declarative way to process collections similar to SQL queries, where data flows through a pipeline of operations.

The tutorial demonstrates the API using a UserPo class with name and score fields.

filter : selects elements that satisfy a predicate.

long count = list.stream().filter(p -> null != p.getScore()).count();

map : transforms each element into another form.

List
scoreList = list.stream().map(p -> p.getScore()).collect(Collectors.toList());
String nameString = list.stream().map(p -> p.getName()).collect(Collectors.joining(","));

sorted : orders elements, optionally in reverse.

List
filterList = list.stream()
    .filter(p -> null != p.getScore())
    .sorted(Comparator.comparing(UserPo::getScore).reversed())
    .collect(Collectors.toList());

forEach : performs an action on each element, often mutating the original objects.

filterList.stream().forEach(p -> p.setScore(p.getScore() + 10));

collect : aggregates elements, e.g., grouping by a field.

Map
> groupByScoreMap = list.stream()
    .filter(p -> null != p.getScore())
    .collect(Collectors.groupingBy(UserPo::getScore));

statistics : computes summary statistics such as max, min, sum, and average.

DoubleSummaryStatistics statistics = filterList.stream()
    .mapToDouble(p -> p.getScore())
    .summaryStatistics();

parallelStream : enables parallel processing to improve performance, but requires careful evaluation.

long parallelCount = list.parallelStream()
    .filter(p -> null != p.getScore())
    .count();

Complete example code:

public class UserPo {
    private String name;
    private Double score;
    public UserPo(String name, Double score) { this.name = name; this.score = score; }
    public String getName() { return name; }
    public void setName(String name) { this.name = name; }
    public Double getScore() { return score; }
    public void setScore(Double score) { this.score = score; }
    @Override
    public String toString() {
        return "UserPo{name='" + name + "', score=" + score + "}";
    }
}

public class StreamTest {
    public static void main(String[] args) {
        List
list = new ArrayList<>();
        list.add(new UserPo("小一", 10.0d));
        list.add(new UserPo("小五", 50.0d));
        list.add(new UserPo("小六", 60.0d));
        list.add(new UserPo("小6", 60.0d));
        list.add(new UserPo("小空", null));
        list.add(new UserPo("小九", 90.0d));

        long count = list.stream().filter(p -> p.getScore() != null).count();
        System.out.println("参加考试的学生人数：" + count);

        List
filterList = list.stream()
            .filter(p -> p.getScore() != null)
            .collect(Collectors.toList());
        System.out.println("参加考试的学生信息：");
        filterList.forEach(System.out::println);

        List
scoreList = list.stream().map(UserPo::getScore).collect(Collectors.toList());
        System.out.println("所有学生的成绩集合：" + scoreList);

        String nameString = list.stream().map(UserPo::getName).collect(Collectors.joining(","));
        System.out.println("所有学生的姓名字符串：" + nameString);

        filterList = list.stream()
            .filter(p -> p.getScore() != null)
            .sorted(Comparator.comparing(UserPo::getScore).reversed())
            .collect(Collectors.toList());
        System.out.println("所有学生的成绩集合，逆序排序：");
        filterList.forEach(System.out::println);

        Map
> groupByScoreMap = list.stream()
            .filter(p -> p.getScore() != null)
            .collect(Collectors.groupingBy(UserPo::getScore));
        for (Map.Entry
> entry : groupByScoreMap.entrySet()) {
            System.out.println("成绩：" + entry.getKey() + " 人数：" + entry.getValue().size());
        }

        filterList.forEach(p -> p.setScore(p.getScore() + 10));
        System.out.println("及格人数太少，给每个人加10分");
        filterList.forEach(System.out::println);

        DoubleSummaryStatistics stats = filterList.stream()
            .mapToDouble(UserPo::getScore)
            .summaryStatistics();
        System.out.println("列表中最大的数 : " + stats.getMax());
        System.out.println("列表中最小的数 : " + stats.getMin());
        System.out.println("所有数之和 : " + stats.getSum());
        System.out.println("平均数 : " + stats.getAverage());

        long parallelCount = list.parallelStream()
            .filter(p -> p.getScore() != null)
            .count();
        System.out.println("并行流处理参加考试的学生人数：" + parallelCount);
    }
}

The article also includes promotional messages for a ChatGPT community and related services, but the core instructional content remains a practical guide to Java Stream operations.