Java 8 Stream API: Practical Guide for PO Processing with Filter, Map, Sorted, Collect, and ParallelStream

Java 8 added the Stream API, a high‑level abstraction that lets developers process collections in a declarative, SQL‑like manner, treating the data as a flow through a pipeline of operations.

The style is analogous to Linux pipelines and is especially useful when handling PO/VO objects rather than simple strings.

Stream Pipeline Overview

+--------------------+ +------+ +------+ +---+ +-------+</code><code>| stream of elements +-----> |filter+-> |sorted+-> |map+-> |collect|</code><code>+--------------------+ +------+ +------+ +---+ +-------+

Filter

Filters elements based on a predicate; only those matching the condition pass through.

// Count students whose score is not null</code><code>long count = list.stream().filter(p -> p.getScore() != null).count();

Map

Transforms each element into another form, e.g., extracting a list of scores or concatenating names.

// Extract all scores</code><code>List<Double> scoreList = list.stream().map(p -> p.getScore()).collect(Collectors.toList());</code><code>// Join all names with commas</code><code>String nameString = list.stream().map(p -> p.getName()).collect(Collectors.joining(","));

Sorted

Orders the stream; the example sorts by score in descending order.

// Sort by score descending</code><code>List<UserPo> sortedList = list.stream()</code><code>    .filter(p -> p.getScore() != null)</code><code>    .sorted(Comparator.comparing(UserPo::getScore).reversed())</code><code>    .collect(Collectors.toList());

forEach

Applies an action to each element; here it adds 10 points to every student's score.

// Add 10 points to each student</code><code>sortedList.stream().forEach(p -> p.setScore(p.getScore() + 10));

Note: Only operations like map, filter, sorted do not modify the original collection; forEach can change the elements.

Collect

Aggregates results, useful for grouping, converting to lists, or joining strings.

// Group by score</code><code>Map<Double, List<UserPo>> groupByScore = list.stream()</code><code>    .filter(p -> p.getScore() != null)</code><code>    .collect(Collectors.groupingBy(UserPo::getScore));</code><code>// Convert to list</code><code>List<Double> scores = list.stream().map(UserPo::getScore).collect(Collectors.toList());

Statistics

Provides summary statistics such as max, min, sum, and average.

DoubleSummaryStatistics stats = filteredList.stream()</code><code>    .mapToDouble(UserPo::getScore)</code><code>    .summaryStatistics();</code><code>System.out.println("Max: " + stats.getMax());</code><code>System.out.println("Min: " + stats.getMin());</code><code>System.out.println("Sum: " + stats.getSum());</code><code>System.out.println("Avg: " + stats.getAverage());

ParallelStream

Enables parallel processing of streams to leverage multiple CPU cores, but requires careful evaluation of thread‑safety.

// Parallel count of students with a score</code><code>long parallelCount = list.parallelStream()</code><code>    .filter(p -> p.getScore() != null)</code><code>    .count();

Complete Example Code

public class UserPo {</code><code>    private String name;</code><code>    private Double score;</code><code>    // Constructors, getters, setters omitted for brevity</code><code>    @Override</code><code>    public String toString() {</code><code>        return "UserPo{" + "name='" + name + '\'' + ", score=" + score + '}';</code><code>    }</code><code>}</code><code></code><code>public class StreamTest {</code><code>    public static void main(String[] args) {</code><code>        List<UserPo> list = new ArrayList<>();</code><code>        list.add(new UserPo("小一", 10.0));</code><code>        list.add(new UserPo("小五", 50.0));</code><code>        list.add(new UserPo("小六", 60.0));</code><code>        list.add(new UserPo("小6", 60.0));</code><code>        list.add(new UserPo("小空", null));</code><code>        list.add(new UserPo("小九", 90.0));</code><code></code><code>        long count = list.stream().filter(p -> p.getScore() != null).count();</code><code>        System.out.println("参加考试的学生人数：" + count);</code><code></code><code>        List<UserPo> filtered = list.stream()</code><code>            .filter(p -> p.getScore() != null)</code><code>            .collect(Collectors.toList());</code><code>        System.out.println("参加考试的学生信息：");</code><code>        filtered.forEach(System.out::println);</code><code></code><code>        List<Double> scores = list.stream().map(UserPo::getScore).collect(Collectors.toList());</code><code>        System.out.println("所有学生的成绩集合：" + scores);</code><code></code><code>        String names = list.stream().map(UserPo::getName).collect(Collectors.joining(","));</code><code>        System.out.println("所有学生的姓名字符串：" + names);</code><code></code><code>        List<UserPo> sorted = list.stream()</code><code>            .filter(p -> p.getScore() != null)</code><code>            .sorted(Comparator.comparing(UserPo::getScore).reversed())</code><code>            .collect(Collectors.toList());</code><code>        System.out.println("所有学生的成绩集合，逆序排序：");</code><code>        sorted.forEach(System.out::println);</code><code></code><code>        Map<Double, List<UserPo>> groupByScore = list.stream()</code><code>            .filter(p -> p.getScore() != null)</code><code>            .collect(Collectors.groupingBy(UserPo::getScore));</code><code>        groupByScore.forEach((score, users) -></code><code>            System.out.println("成绩：" + score + " 人数：" + users.size()));</code><code></code><code>        sorted.forEach(p -> p.setScore(p.getScore() + 10));</code><code>        System.out.println("及格人数太少，给每个人加10分");</code><code>        sorted.forEach(System.out::println);</code><code></code><code>        long passCount = sorted.stream().filter(p -> p.getScore() >= 60).count();</code><code>        System.out.println("最后及格人数" + passCount);</code><code></code><code>        DoubleSummaryStatistics stats = sorted.stream()</code><code>            .mapToDouble(UserPo::getScore)</code><code>            .summaryStatistics();</code><code>        System.out.println("列表中最大的数 : " + stats.getMax());</code><code>        System.out.println("列表中最小的数 : " + stats.getMin());</code><code>        System.out.println("所有数之和 : " + stats.getSum());</code><code>        System.out.println("平均数 : " + stats.getAverage());</code><code></code><code>        long parallelCount = list.parallelStream()</code><code>            .filter(p -> p.getScore() != null)</code><code>            .count();</code><code>        System.out.println("并行流处理参加考试的学生人数：" + parallelCount);</code><code>    }</code><code>}

The tutorial demonstrates how to replace verbose for‑loops with concise Stream operations, improving readability and maintainability when processing collections of PO objects.