Using Java 8 Stream API for Collection Processing with Practical Examples

Java 8 added the Stream API, a high‑level abstraction that lets developers process collections in a declarative, SQL‑like style.

The stream model treats a collection as a flow of elements that passes through a pipeline of operations such as filtering, mapping, sorting, and aggregation.

Below is a simple PO class used in the examples:

public class UserPo {
private String name;
private Double score;
// constructors, getters, setters omitted for brevity
}

filter : removes elements that do not satisfy a predicate. Example – count students whose score is not null.

// Count students with a non‑null score
long count = list.stream()
    .filter(p -> p.getScore() != null)
    .count();

map : transforms each element into another form. Example – extract all scores into a list and concatenate all names.

// List of scores
List<Double> scoreList = list.stream()
    .map(UserPo::getScore)
    .collect(Collectors.toList());
// Comma‑separated names
String nameString = list.stream()
    .map(UserPo::getName)
    .collect(Collectors.joining(","));

sorted : orders the stream according to a comparator. Example – sort by score in descending order.

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

forEach : performs an action on each element. Example – give every student an extra 10 points.

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

collect : gathers the stream into a collection or other result. Example – group students by score.

Map<Double, List<UserPo>> groupByScore = list.stream()
    .filter(p -> p.getScore() != null)
    .collect(Collectors.groupingBy(UserPo::getScore));

statistics : obtains summary statistics for numeric streams.

DoubleSummaryStatistics stats = list.stream()
    .filter(p -> p.getScore() != null)
    .mapToDouble(UserPo::getScore)
    .summaryStatistics();
System.out.println("Max: " + stats.getMax());
System.out.println("Min: " + stats.getMin());
System.out.println("Sum: " + stats.getSum());
System.out.println("Avg: " + stats.getAverage());

parallelStream : processes the stream in parallel, leveraging multiple threads for better performance when appropriate.

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

Full example combining the above operations:

public class StreamTest {
public static void main(String[] args) {
List
list = new ArrayList<>();
list.add(new UserPo("Alice", 10.0));
list.add(new UserPo("Bob", 50.0));
// ... more data ...
// Demonstrate filter, map, sorted, forEach, collect, statistics, parallelStream as shown earlier
}
}

In addition to the technical tutorial, the original article contains promotional messages for a ChatGPT‑related community, paid courses, and related offers.