java 32 lines · 7 steps

Flattening nested data with Java streams

Four stream pipelines show how flatMap collapses nested collections before transforming and aggregating them.

Explained by highlit

Intermediate streams flatmap collectors functional aggregation

1public List<String> collectTagsFromArticles(List<Article> articles) {
2    return articles.stream()
3        .flatMap(article -> article.getTags().stream())
4        .map(String::trim)
5        .filter(tag -> !tag.isEmpty())
6        .map(String::toLowerCase)
7        .distinct()
8        .sorted()
9        .collect(Collectors.toList());
10}
11 
12public Map<String, Long> countLineItemsByCategory(List<Order> orders) {
13    return orders.stream()
14        .flatMap(order -> order.getLineItems().stream())
15        .collect(Collectors.groupingBy(
16            item -> item.getProduct().getCategory(),
17            Collectors.counting()
18        ));
19}
20 
21public List<Coordinate> flattenGrid(List<List<Coordinate>> rows) {
22    return rows.stream()
23        .flatMap(List::stream)
24        .collect(Collectors.toList());
25}
26 
27public OptionalDouble averageScoreAcrossExams(List<Student> students) {
28    return students.stream()
29        .flatMapToInt(student -> student.getExams().stream()
30            .mapToInt(Exam::getScore))
31        .average();
32}

01 / 01

STEP 01

Walkthrough

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Three takeaways

1flatMap turns a stream of collections into one flat stream you can process uniformly.
2Collectors like groupingBy and counting fold a stream into a summarized data structure in one pass.
3Primitive stream variants such as flatMapToInt unlock numeric operations like average without boxing.

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